Статті в журналах: "Breast region of interest"

1

Yap, Moi Hoon, Manu Goyal, Fatima Osman, Robert Martí, Erika Denton, Arne Juette, and Reyer Zwiggelaar. "Breast ultrasound region of interest detection and lesion localisation." Artificial Intelligence in Medicine 107 (July 2020): 101880. http://dx.doi.org/10.1016/j.artmed.2020.101880.

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Kok Swee, Sim, Chia Fu Keong, Chong Sze Siang, Tso Chih Peng, Siti Fathimah Abbas, and Sarimah Omar. "Projection Based Region of Interest Segmentation in Breast MRI Images." International Journal on Advanced Science, Engineering and Information Technology 1, no. 1 (2011): 113. http://dx.doi.org/10.18517/ijaseit.1.1.26.

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Forbes, Florence, Nathalie Peyrard, Chris Fraley, Dianne Georgian-Smith, David M. Goldhaber, and Adrian E. Raftery. "Model-based Region-of-interest Selection in Dynamic Breast MRI." Journal of Computer Assisted Tomography 30, no. 4 (July 2006): 675–87. http://dx.doi.org/10.1097/00004728-200607000-00020.

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4

Sánchez-Ruiz, Daniel, Ivan Olmos-Pineda, and J. Arturo Olvera-López. "Automatic region of interest segmentation for breast thermogram image classification." Pattern Recognition Letters 135 (July 2020): 72–81. http://dx.doi.org/10.1016/j.patrec.2020.03.025.

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Nagarkar, Dilip B., Ezgi Mercan, Donald L. Weaver, Tad T. Brunyé, Patricia A. Carney, Mara H. Rendi, Andrew H. Beck, Paul D. Frederick, Linda G. Shapiro, and Joann G. Elmore. "Region of interest identification and diagnostic agreement in breast pathology." Modern Pathology 29, no. 9 (May 20, 2016): 1004–11. http://dx.doi.org/10.1038/modpathol.2016.85.

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Saadoon Abdoon, Rabab. "Utilizing image processing techniques for detecting breast abnormalities in thermography images." International Journal of Engineering & Technology 7, no. 4 (October 6, 2018): 2810. http://dx.doi.org/10.14419/ijet.v7i4.18312.

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Thermal Infrared (TIR) imaging of breasts involves a non-invasive, non-ionized, passive, safe and painless scan of the breasts. It is a graphing of the changes in breasts skin temperature using thermography. Thermograms are temperature distribution patterns with different colors to indicate temperature of the different regions within the tested breast, each color refers to a certain temperature range. In this work, three breast thermography images: one for normal case and two for cancerous cases, were employed to test the performance of the proposed segmentation methods: Region growing; clustering (K-means and FCM) algorithms and Histogram based enhancement technique to segment, detect and isolate the suspicious abnormal regions. These techniques were performed with the aid of suitable morphological operations to get the refined regions of interest. The results proved the efficiency of the proposed techniques to extract the abnormal (of high temperature) regions.

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ÇETİNEL, Gökçen, Fuldem MUTLU, and Sevda GÜL. "Detection of Breast Region of Interest via Breast MR Scan on an Axial Slice." International Journal of Applied Mathematics Electronics and Computers 8, no. 2 (June 30, 2020): 39–44. http://dx.doi.org/10.18100/ijamec.679142.

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Muhammad, Muhammad, Diyar Zeebaree, Adnan Mohsin Abdulazeez Brifcani, Jwan Saeed, and Dilovan Asaad Zebari. "Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images: A Review." Journal of Applied Science and Technology Trends 1, no. 3 (June 24, 2020): 78–91. http://dx.doi.org/10.38094/jastt20201328.

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The most prevalent cancer amongst women is woman breast cancer. Ultrasound imaging is a widely employed method for identifying and diagnosing breast abnormalities. Computer-aided diagnosis technologies have lately been developed with ultrasound images to help radiologists enhance the accuracy of the diagnosis. This paper presents several ultrasound image segmentation techniques, mainly focus on eight clustering methods over the last 10 years, and it shows the advantages and disadvantages of these approaches. Breast ultrasound image segmentation is, therefore, still an accessible and challenging issue due to numerous ultrasound artifacts introduced in the imaging process, including high speckle noise, poor contrast, blurry edges, weak signal-to-noise ratio, and intensity inhomogeneity.

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Divyashree, BV, Amarnath R, and Naveen M. "Novel approach to locate region of interest in mammograms for Breast cancer." International Journal of Intelligent Systems and Applications in Engineering 3, no. 6 (September 29, 2018): 185–90. http://dx.doi.org/10.18201/ijisae.2018644775.

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10

Muhammad, Muhammad, Diyar Zeebaree, Adnan Mohsin Abdulazeez Brifcani, Jwan Saeed, and Dilovan Asaad Zebari. "A Review on Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images." Journal of Applied Science and Technology Trends 1, no. 3 (June 24, 2020): 78–91. http://dx.doi.org/10.38094/2020jastt1328.

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The most prevalent cancer amongst women is woman breast cancer. Ultrasound imaging is a widely employed method for identifying and diagnosing breast abnormalities. Computer-aided diagnosis technologies have lately been developed with ultrasound images to help radiologists enhance the accuracy of the diagnosis. This paper presents several ultrasound image segmentation techniques, mainly focus on eight clustering methods over the last 10 years, and it shows the advantages and disadvantages of these approaches. Breast ultrasound image segmentation is, therefore, still an accessible and challenging issue due to numerous ultrasound artifacts introduced in the imaging process, including high speckle noise, poor contrast, blurry edges, weak signal-to-noise ratio, and intensity inhomogeneity.

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11

Muhammad, Muhammad, Diyar Zeebaree, Adnan Mohsin Abdulazeez Brifcani, Jwan Saeed, and Dilovan Asaad Zebari. "A Review on Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images." Journal of Applied Science and Technology Trends 1, no. 3 (June 24, 2020): 78–91. http://dx.doi.org/10.38094/jastt1328.

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Анотація:

The most prevalent cancer amongst women is woman breast cancer. Ultrasound imaging is a widely employed method for identifying and diagnosing breast abnormalities. Computer-aided diagnosis technologies have lately been developed with ultrasound images to help radiologists enhance the accuracy of the diagnosis. This paper presents several ultrasound image segmentation techniques, mainly focus on eight clustering methods over the last 10 years, and it shows the advantages and disadvantages of these approaches. Breast ultrasound image segmentation is, therefore, still an accessible and challenging issue due to numerous ultrasound artifacts introduced in the imaging process, including high speckle noise, poor contrast, blurry edges, weak signal-to-noise ratio, and intensity inhomogeneity.

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12

Lavazza, Luigi, and Sandro Morasca. "Considerations on the region of interest in the ROC space." Statistical Methods in Medical Research 31, no. 3 (December 20, 2021): 419–37. http://dx.doi.org/10.1177/09622802211060515.

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Receiver Operating Characteristic curves have been widely used to represent the performance of diagnostic tests. The corresponding area under the curve, widely used to evaluate their performance quantitatively, has been criticized in several respects. Several proposals have been introduced to improve area under the curve by taking into account only specific regions of the Receiver Operating Characteristic space, that is, the plane to which Receiver Operating Characteristic curves belong. For instance, a region of interest can be delimited by setting specific thresholds for the true positive rate or the false positive rate. Different ways of setting the borders of the region of interest may result in completely different, even opposing, evaluations. In this paper, we present a method to define a region of interest in a rigorous and objective way, and compute a partial area under the curve that can be used to evaluate the performance of diagnostic tests. The method was originally conceived in the Software Engineering domain to evaluate the performance of methods that estimate the defectiveness of software modules. We compare this method with previous proposals. Our method allows the definition of regions of interest by setting acceptability thresholds on any kind of performance metric, and not just false positive rate and true positive rate: for instance, the region of interest can be determined by imposing that [Formula: see text] (also known as the Matthews Correlation Coefficient) is above a given threshold. We also show how to delimit the region of interest corresponding to acceptable costs, whenever the individual cost of false positives and false negatives is known. Finally, we demonstrate the effectiveness of the method by applying it to the Wisconsin Breast Cancer Data. We provide Python and R packages supporting the presented method.

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13

Kebede, Samuel Rahimeto, Taye Girma Debelee, Friedhelm Schwenker, and Dereje Yohannes. "Classifier Based Breast Cancer Segmentation." Journal of Biomimetics, Biomaterials and Biomedical Engineering 47 (November 2020): 41–61. http://dx.doi.org/10.4028/www.scientific.net/jbbbe.47.41.

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Breast cancer occurs as a result of erratic growth and proliferation cells that originate in the breast. In this paper, the classifiers were used to identify the abnormalities on mammograms to get the region of interest (ROI). Before classifier based segmentation, noise, pectoral muscles, and tags were removed for a successful segmentation process. Then the proposed approach extracted the brightest regions using modified k-means. From the extracted brightest regions, shape and texture features were extracted and given to classifiers (KNN and SVM) and marked as ROI only those non-overlapping abnormal regions. The ROIs obtained using the proposed classifier-based segmentation algorithm was compared with the ground truth annotated by the radiologists. The datasets used to evaluate the performance of the proposed algorithm was public (MIAS) and local datasets (BGH and DADC).

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LIN, GUO-SHIANG, SIN-KUO CHAI, WEI-CHENG YEH, and YI-CHANG LIN. "SUSPICIOUS REGION DETECTION AND IDENTIFICATION BASED ON INTRA-/INTER-FRAME ANALYSES AND FUZZY CLASSIFIER FOR BREAST MAGNETIC RESONANCE IMAGING." International Journal of Pattern Recognition and Artificial Intelligence 28, no. 03 (May 2014): 1450007. http://dx.doi.org/10.1142/s0218001414500074.

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Breast cancer is one of the leading causes of death from cancer in Taiwan. In this paper, we propose a feature-based scheme composed of preprocessing, feature extraction and a fuzzy classifier for suspicious region detection and identification. In the preprocessing stage, we first extract regions of interest and then coarsely determine suspicious regions via candidate screening. Some features are extracted based on intra-slice, texture and inter-slice analysis techniques for suspicious region identification. Intra-slice analysis evaluates the intensity and size of suspicious regions. To find a precise region, we propose a region growing algorithm based on ellipse-based approximation. In texture analysis, some texture cues are extracted from spatial and wavelet domains and integrated as a combined texture feature by using a neural network. Inter-slice analysis is based on the continuity characteristic and consistency of a suspicious region's size; the objective is to verify the static behavior of suspicious regions. Several magnetic resonance imaging (MRI) cases are utilized to evaluate the performance of the proposed scheme. Experimental results demonstrate that our scheme can not only extract regions of interest but also identify tumors well from magnetic resonance images.

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Fanning, S. R., S. Short, K. Coleman, S. Andresen, G. T. Budd, H. Moore, A. Rim, J. Crowe, and D. E. Weng. "Correlation of dynamic infrared imaging with radiologic and pathologic response for patients treated with primary systemic therapy for locally advanced breast cancer." Journal of Clinical Oncology 24, no. 18_suppl (June 20, 2006): 10696. http://dx.doi.org/10.1200/jco.2006.24.18_suppl.10696.

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10696 Background: Assessment of response to therapy for locally advanced breast cancer includes serial assessments of physical exam, radiologic imaging, or repeated biopsies. Expense, subjective assessment, and patient risk make these methods impractical. Dynamic infrared imaging (DIRI) utilizes a quantum well infrared photon (QWIP) sensor with software to analyze the emission patterns over time. DIRI can detect biological temperature gradients with sensitivity of 0.009ºC. Tumor-induced local tissue nitric oxide production can increase local capillary blood flow. Anti-tumor therapies have been shown to result in decreased peri-tumoral capillary blood flow. These changes in temperature, detected by serial DIRI imaging, may provide a low cost, non-invasive, easily reproducible objective tool for real-time clinical assessment. Methods: In this prospective pilot study, we are evaluating patients with locally advanced breast cancer using serial DIRI. Primary endpoints include: sensitivity, specificity, PPV, and NPV of DIRI in comparison to pathologic response, concordance of DIRI to physical exam, and concordance of DIRI to standard radiographic evaluation at initial diagnosis and prior to surgery. DIRI results are reported as quantification of changes in the 0.2Hz modulation of temperature over the breast during the course of treatment and measurement of area of average temperature in a region of interest compared between breasts. One hundred patients will be enrolled in this trial. Results: Sixteen patients have been enrolled. Six have proceeded to surgery. All but one patient exhibited evidence of tumor response by physical exam. These findings correlated with response when comparing initial to pre-surgical MRI. In all responding patients, DIRI results revealed a decrease in the number of regions over the breast in which the 0.2Hz frequency dominated. Similarly, DIRI evaluation according to area of average temperature in the region of interest compared between breasts was concordant in patients with response to therapy. Conclusions: Assessment of response by physical exam, MRI, and DIRI were consistent. Preliminary data reveals that serial DIRI imaging can be an effective adjunctive tool. No significant financial relationships to disclose.

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Dong, Min, Zhe Wang, Chenghui Dong, Xiaomin Mu, and Yide Ma. "Classification of Region of Interest in Mammograms Using Dual Contourlet Transform and Improved KNN." Journal of Sensors 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/3213680.

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Goal. Breast cancer is becoming one of the most common cancers among women. Early detection can help increase the survival rates. Feature extraction directly affects diagnosis result. In this work, a novel feature extraction method based on Dual Contourlet Transform (Dual-CT) is presented, and improved K nearest neighbor (KNN) is employed to improve the classification performance. Method. This presented method includes three main sections: firstly, the Region of Interest (ROI) is cropped manually according to gold standard from Mammographic Image Analysis Society (MIAS) database; secondly, the ROIs are decomposed into different resolution levels using Dual-CT, contourlet, and wavelet; a set of texture features are extracted. Then improved KNN and traditional KNN are implemented for classification. Experiments are performed on 324 ROIs which include 206 normal cases and 118 abnormal cases; the abnormal cases are composed of 66 benign cases and 52 malignant cases. Results. Experimental results prove the validity and superiority of Dual-CT-based feature and improved KNN. In particular, 94.14% and 95.76% classification accuracy is achieved based on Dual-CT domain. Moreover, the proposed method is comparable with state-of-the-art methods in terms of accuracy. Contribution. Dual-CT-based feature is used for analyzing mammogram and can help improve breast cancer diagnosis accuracy.

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Mussurakis, Stavros, David L. Buckley, and Anthony Horsman. "Dynamic MRI of Invasive Breast Cancer: Assessment of Three Region-of-Interest Analysis Methods." Journal of Computer Assisted Tomography 21, no. 3 (May 1997): 431–38. http://dx.doi.org/10.1097/00004728-199705000-00017.

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Pandey, Dinesh, Xiaoxia Yin, Hua Wang, Min-Ying Su, Jeon-Hor Chen, Jianlin Wu, and Yanchun Zhang. "Automatic and fast segmentation of breast region-of-interest (ROI) and density in MRIs." Heliyon 4, no. 12 (December 2018): e01042. http://dx.doi.org/10.1016/j.heliyon.2018.e01042.

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Milankovic, Ivan L., Nikola V. Mijailovic, Nenad D. Filipovic, and Aleksandar S. Peulic. "Acceleration of Image Segmentation Algorithm for (Breast) Mammogram Images Using High-Performance Reconfigurable Dataflow Computers." Computational and Mathematical Methods in Medicine 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/7909282.

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Image segmentation is one of the most common procedures in medical imaging applications. It is also a very important task in breast cancer detection. Breast cancer detection procedure based on mammography can be divided into several stages. The first stage is the extraction of the region of interest from a breast image, followed by the identification of suspicious mass regions, their classification, and comparison with the existing image database. It is often the case that already existing image databases have large sets of data whose processing requires a lot of time, and thus the acceleration of each of the processing stages in breast cancer detection is a very important issue. In this paper, the implementation of the already existing algorithm for region-of-interest based image segmentation for mammogram images on High-Performance Reconfigurable Dataflow Computers (HPRDCs) is proposed. As a dataflow engine (DFE) of such HPRDC, Maxeler’s acceleration card is used. The experiments for examining the acceleration of that algorithm on the Reconfigurable Dataflow Computers (RDCs) are performed with two types of mammogram images with different resolutions. There were, also, several DFE configurations and each of them gave a different acceleration value of algorithm execution. Those acceleration values are presented and experimental results showed good acceleration.

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Bajaj, Punam, Chiara Iacconi, David D. Dershaw, and Elizabeth A. Morris. "Diffusion-Weighted MRI of the Breast in Women with a History of Mantle Radiation: Does Radiation Alter Apparent Diffusion Coefficient?" Journal of Breast Imaging 1, no. 3 (August 29, 2019): 212–16. http://dx.doi.org/10.1093/jbi/wbz035.

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Abstract Objective Fibrosis from chest irradiation could lower the apparent diffusion coefficient (ADC) of breast tissue. ADC values of normal breast tissue in high-risk women who underwent mantle radiation before age 30 years were compared with a screening control group matched for breast fibroglandular tissue (FGT). Methods In this retrospective study, we reviewed 21 women with a history of mantle radiation who underwent breast MRI examinations between 2008 and 2013, and 20 nonirradiated patients (control group) imaged during the same period with matching FGT and similar age. The women were dichotomized into low FGT (10/20, 50%) and high-FGT (10/20, 50%) groups, based on BI-RADS descriptors. All MRI examinations included diffusion-weighted imaging (DWI) (b = 0, 1000); ADC maps were generated and evaluated on PACS workstations by two radiologists in agreement. Region of interest markers were placed on ADC maps in visualized breast tissue in the retroareolar region of each breast. The ADC value was averaged for the right and left breast in each patient included in the study. The Wilcoxon signed-rank test was used to compare the ADC values in the irradiated patients and the matched control patients. Results The median breast ADC was lower in the irradiated group (1.32 × 10-3mm2/sec) than in the control group (1.62 × 10-3mm2/sec; P = 0.0089). Low FGT in the irradiated group had a lower median ADC (1.25 × 10-3mm2/sec) than it did in the control group (1.53 × 10-3mm2/sec). Irradiated high-FGT breasts had a median ADC (1.52 × 10-3mm2/sec), as compared with nonirradiated control patients with high FGT (1.82 × 10-3mm2/sec). Conclusion Previously irradiated breasts have lower ADC values than do nonirradiated breasts.

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Utreja, Bhawna, Reecha Sharma, and Amit Wason. "A Survey on Segmentation Techniques for Breast Cancer Detection." ECS Transactions 107, no. 1 (April 24, 2022): 6703–9. http://dx.doi.org/10.1149/10701.6703ecst.

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Breast cancer is second most rank disease after lung cancer between ladies around the world. Early detection declines the cancer death rate among women. Computer aided detection (CAD) system have been emerged which help the radiologists by specifying tumor region and reducing error mistake. Segmentation plays an important role in finding tumor area, i.e. Region of Interest (ROI). This paper investigates various segmentation techniques for breast cancer detection. Also, two segmentation techniques, Fuzzy C-Mean (FCM) and K-means, have been applied on mammogram images taken from MIAS database. Results shows that K-means is capable of estimating tumor region boundary as compared to FCM.

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Ramadan, Saleem Z., and Mahmoud El-Banna. "Breast Cancer Diagnosis in Digital Mammography Images Using Automatic Detection for the Region of Interest." Current Medical Imaging Formerly Current Medical Imaging Reviews 16, no. 7 (September 9, 2020): 902–12. http://dx.doi.org/10.2174/1573405615666190717112820.

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Background: One of the early screening methods of breast cancer that is still used today is mammogram due to its low cost. Unfortunately, this low cost accompanied with low performance rate also. Methods: The low performance rate in mammograms is associated with low capability in determining the best region from which the features are extracted. Therefore, we offer an automatic method to detect the Region of Interest in the mammograms based on maximizing the area under receiver operating characteristic curve utilizing Genetic Algorithms. : The proposed method had been applied to the MIAS mammographic database, which is widely used in literature. Its performance had been evaluated using four different classifiers; Support Vector Machine, Naïve Bayes, K-Nearest Neighbor and Logistic Regression classifiers. Results & Conclusion: The results showed good classification performances for all the classifiers used due to the rich information contained in the features extracted from the automatically selected Region of Interest.

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Moon, Jin Hee, Ji-Young Hwang, Jeong Seon Park, Sung Hye Koh, and Sun-Young Park. "Impact of region of interest (ROI) size on the diagnostic performance of shear wave elastography in differentiating solid breast lesions." Acta Radiologica 59, no. 6 (September 12, 2017): 657–63. http://dx.doi.org/10.1177/0284185117732097.

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Background Shear wave elastography (SWE) using a region of interest (ROI) can demonstrate the quantitative elasticity of breast lesions. Purpose To prospectively evaluate the impact of two different ROI sizes on the diagnostic performance of SWE for differentiating benign and malignant breast lesions. Material and Methods A total of 154 breast lesions were included. Two types of ROIs were investigated: one involving an approximately 2-mm diameter, small round ROIs placed over the stiffest area of the lesion, as determined by SWE (ROI-S); and another ROI drawn along the margin of the lesion using a touch pen or track ball to encompass the entire lesion (ROI-M). Maximum elasticity (Emax), mean elasticity (Emean), minimum elasticity (Emin), and standard deviation (SD) were measured for the two ROIs. The area under the receiver operating characteristic curve (AUC) as well as the sensitivity and specificity of each elasticity value were determined. Results The AUCs for ROI-S were higher than those for ROI-M when differentiating benign and malignant breast solid lesions. The Emax, Emean, Emin, and SD of the elasticity values for ROI-S were 0.865, 0.857, 0.816, and 0.849, respectively, and for ROI-M were 0.820, 0.780, 0.724, and 0.837, respectively. However, only Emax ( P = 0.0024) and Emean ( P = 0.0015) showed statistically significant differences. For ROI-S, the sensitivity and specificity of Emax were 78.8% and 84.3%, respectively, and those for Emean were 80.8% and 81.4%, respectively. Conclusion Using ROI-S with Emax and Emean has better diagnostic performance than ROI-M for differentiating between benign and malignant breast lesions.

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Youk, Ji Hyun, Eun Ju Son, Kyunghwa Han, Hye Mi Gweon, and Jeong-Ah Kim. "Performance of shear-wave elastography for breast masses using different region-of-interest (ROI) settings." Acta Radiologica 59, no. 7 (October 23, 2017): 789–97. http://dx.doi.org/10.1177/0284185117735562.

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Background Various size and shape of region of interest (ROI) can be applied for shear-wave elastography (SWE). Purpose To investigate the diagnostic performance of SWE according to ROI settings for breast masses. Material and Methods To measure elasticity for 142 lesions, ROIs were set as follows: circular ROIs 1 mm (ROI-1), 2 mm (ROI-2), and 3 mm (ROI-3) in diameter placed over the stiffest part of the mass; freehand ROIs drawn by tracing the border of mass (ROI-M) and the area of peritumoral increased stiffness (ROI-MR); and circular ROIs placed within the mass (ROI-C) and to encompass the area of peritumoral increased stiffness (ROI-CR). Mean (Emean), maximum (Emax), and standard deviation (ESD) of elasticity values and their areas under the receiver operating characteristic (ROC) curve (AUCs) for diagnostic performance were compared. Results Means of Emean and ESD significantly differed between ROI-1, ROI-2, and ROI-3 ( P < 0.0001), whereas means of Emax did not ( P = 0.50). For ESD, ROI-1 (0.874) showed a lower AUC than ROI-2 (0.964) and ROI-3 (0.975) ( P < 0.002). The mean ESD was significantly different between ROI-M and ROI-MR and between ROI-C and ROI-CR ( P < 0.0001). The AUCs of ESD in ROI-M and ROI-C were significantly lower than in ROI-MR ( P = 0.041 and 0.015) and ROI-CR ( P = 0.007 and 0.004). Conclusion Shear-wave elasticity values and their diagnostic performance vary based on ROI settings and elasticity indices. Emax is recommended for the ROIs over the stiffest part of mass and an ROI encompassing the peritumoral area of increased stiffness is recommended for elastic heterogeneity of mass.

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Huang, Chieh-Ling. "BREAST MASS SEGMENTATION ON BREAST MRI USING THE SHAPE-BASED LEVEL SET METHOD." Biomedical Engineering: Applications, Basis and Communications 26, no. 04 (August 2014): 1440006. http://dx.doi.org/10.4015/s1016237214400067.

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Breast cancer is the most common threat to the health of women. Breast masses are usually important signs of breast cancer. Therefore, a level set method (LSM) with a shape model is proposed to segment breast masses in magnetic resonance imaging (MRI) images in this paper. Since the SM proposed by Chan and Vese does not work well on breast mass segmentation, this paper adds shape knowledge into the segmentation method. We first apply the Chan–Vese LSM to obtain a pre-segmented breast mass and then the position and size of the pre-segmented breast mass are calculated to establish the initial shape model. This paper uses dilation processing to calculate the distance to the shape model contour since it takes into consideration the need to update the level set function. Finally, the proposed method is applied to segment the breast mass in the MRI image of the breast. In order to eliminate noise interference in other regions of the breast, we also address the concept of region of interest (ROI). In the experiment, the proposed method is compared with the Chan–Vese method to prove that the proposed method can achieve better performance. The experimental results show that the breast mass can be correctly segmented by the above mechanism.

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Shanthi, S., and V. Murali Bhaskaran. "A Novel Approach for Detecting and Classifying Breast Cancer in Mammogram Images." International Journal of Intelligent Information Technologies 9, no. 1 (January 2013): 21–39. http://dx.doi.org/10.4018/jiit.2013010102.

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This study uses data mining techniques for computer-aided diagnosis that involves the feature extraction for cancer detection, so as to help doctors towards making optimal decisions quickly and accurately. Features play an important role in detecting the cancer in the digital mammogram and feature extraction stage is the most vital and difficult stage. In this paper, an enhanced feature extraction method named Multiscale Surrounding Region Dependence Method (MSRDM) is proposed to be effective in classifying the mammogram images into normal or benign or malignant. This proposed system is based on a four-step procedure: Regions of Interest specification, two dimensional discrete wavelet transformation, and multiscale surrounding region dependence matrix computation and feature extraction. The performance of the proposed feature set is compared with the conventional texture-analysis methods such as gray level cooccurence matrix features and surrounding region dependence method features. Experiments have been conducted on both real and benchmark data and the results have been proved to be progressive.

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Venkatachalam, Nirmala, Leninisha Shanmugam, Genitha C. Heltin, G. Govindarajan, and P. Sasipriya. "Enhanced Segmentation of Inflamed ROI to Improve the Accuracy of Identifying Benign and Malignant Cases in Breast Thermogram." Journal of Oncology 2021 (April 21, 2021): 1–17. http://dx.doi.org/10.1155/2021/5566853.

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Effective analysis of breast thermography needs an accurate segmentation of the inflamed region in Infrared Breast Thermal Images (IBTI) which helps in the diagnosis of breast cancer. However, IBTI suffers from intensity inhomogeneity, overlapping regions of interest, poor contrast, and low signal-to-noise ratio (SNR) due to the imperfect image acquisition process. To mitigate this, this work proposes an enhanced segmentation of the inflamed Region of Interest (ROI) using an active contour method driven by the multiscale local and global fitted image (MLGFI) model. The first phase proposes a bilateral histogram difference-based thresholding (BHDT) method for locating the inflamed ROI. This is then used for automatic initialization of active contours driven by MLGFI to segment the inflamed ROI from IBTI effectively. To prove the effectiveness of this segmentation method, its performance is compared with ground truth image and its accuracy is also evaluated with the state-of-the-art methods (Fuzzy C Means (FCM), Chan-Vese (CV-ACM), and K-means). From the analysis, it is found that the proposed method not only increases the precision and the segmentation accuracy but also reduces the oversegmentation and undersegmentation rate significantly. In the second phase, area-based feature (AF) and average intensity-based feature (AIF) along with the GLCM (gray level cooccurrence matrix) based second-order statistical features are extracted from the inflamed ROI. Based on these features, a system is developed to effectively classify the benign and malignant breast conditions. From the results, it is observed that the proposed model exhibits an improved accuracy of 91.5%, sensitivity of 91%, and specificity of 92% compared to the whole breast thermogram. Hence, it is concluded that the proposed method will improve the efficacy of thermal imaging in the diagnosis of breast cancer.

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KHALED A. ALY, M.D., HANAA A. ABDELHAMEED, M. D. ;., and NOHA H. SAKR, M.Sc. "Breast Lesion Elastography Region of Interest Selection and Quantitative Heterogeneity: A Systematic Review and Meta-Analysis." Medical Journal of Cairo University 88, no. 9 (September 1, 2020): 1933–42. http://dx.doi.org/10.21608/mjcu.2020.118551.

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Blank, Molly A. B., and James F. Antaki. "Breast Lesion Elastography Region of Interest Selection and Quantitative Heterogeneity: A Systematic Review and Meta-Analysis." Ultrasound in Medicine & Biology 43, no. 2 (February 2017): 387–97. http://dx.doi.org/10.1016/j.ultrasmedbio.2016.09.002.

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Buller, Danuta, Andrzej Buller, Peter R. Innocent, and Waldemar Pawlak. "Determining and classifying the region of interest in ultrasonic images of the breast using neural networks." Artificial Intelligence in Medicine 8, no. 1 (February 1996): 53–66. http://dx.doi.org/10.1016/0933-3657(95)00020-8.

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George, Minu, and Reyer Zwiggelaar. "Comparative Study on Local Binary Patterns for Mammographic Density and Risk Scoring." Journal of Imaging 5, no. 2 (February 1, 2019): 24. http://dx.doi.org/10.3390/jimaging5020024.

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Breast density is considered to be one of the major risk factors in developing breast cancer. High breast density can also affect the accuracy of mammographic abnormality detection due to the breast tissue characteristics and patterns. We reviewed variants of local binary pattern descriptors to classify breast tissue which are widely used as texture descriptors for local feature extraction. In our study, we compared the classification results for the variants of local binary patterns such as classic LBP (Local Binary Pattern), ELBP (Elliptical Local Binary Pattern), Uniform ELBP, LDP (Local Directional Pattern) and M-ELBP (Mean-ELBP). A wider comparison with alternative texture analysis techniques was studied to investigate the potential of LBP variants in density classification. In addition, we investigated the effect on classification when using descriptors for the fibroglandular disk region and the whole breast region. We also studied the effect of the Region-of-Interest (ROI) size and location, the descriptor size, and the choice of classifier. The classification results were evaluated based on the MIAS database using a ten-run ten-fold cross validation approach. The experimental results showed that the Elliptical Local Binary Pattern descriptors and Local Directional Patterns extracted most relevant features for mammographic tissue classification indicating the relevance of directional filters. Similarly, the study showed that classification of features from ROIs of the fibroglandular disk region performed better than classification based on the whole breast region.

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Fulton, Lawrence, Alex McLeod, Diane Dolezel, Nathaniel Bastian, and Christopher P. Fulton. "Deep Vision for Breast Cancer Classification and Segmentation." Cancers 13, no. 21 (October 27, 2021): 5384. http://dx.doi.org/10.3390/cancers13215384.

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(1) Background: Female breast cancer diagnoses odds have increased from 11:1 in 1975 to 8:1 today. Mammography false positive rates (FPR) are associated with overdiagnoses and overtreatment, while false negative rates (FNR) increase morbidity and mortality. (2) Methods: Deep vision supervised learning classifies 299 × 299 pixel de-noised mammography images as negative or non-negative using models built on 55,890 pre-processed training images and applied to 15,364 unseen test images. A small image representation from the fitted training model is returned to evaluate the portion of the loss function gradient with respect to the image that maximizes the classification probability. This gradient is then re-mapped back to the original images, highlighting the areas of the original image that are most influential for classification (perhaps masses or boundary areas). (3) Results: initial classification results were 97% accurate, 99% specific, and 83% sensitive. Gradient techniques for unsupervised region of interest mapping identified areas most associated with the classification results clearly on positive mammograms and might be used to support clinician analysis. (4) Conclusions: deep vision techniques hold promise for addressing the overdiagnoses and treatment, underdiagnoses, and automated region of interest identification on mammography.

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Haisan, Anca, Radu Rogojanu, Camelia Croitoru, Daniela Jitaru, Cristina Tarniceriu, Mihai Danciu, and Eugen Carasevici. "Digital Microscopy Assessment of Angiogenesis in Different Breast Cancer Compartments." BioMed Research International 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/286902.

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Background/Aim. Tumour angiogenesis defined by microvessel density (MVD) is generally accepted as a prognostic factor in breast cancer. However, due to variability of measurement systems and cutoffs, it is questionable to date whether it contributes to predictive outline. Our study aims to grade vascular heterogeneity by comparing clear-cut compartments: tumour associated stroma (TAS), tumour parenchyma, and tumour invasive front.Material and Methods. Computerized vessel area measurement was performed using a tissue cytometry system (TissueFAXS) on slides originated from 50 patients with breast cancer. Vessels were marked using immunohistochemistry with CD34. Regions of interest were manually defined for each tumour compartment.Results. Tumour invasive front vascular endothelia area was 2.15 times higher than that in tumour parenchyma and 4.61 times higher than that in TAS (P<0.002). Worth to mention that the lymph node negative subgroup of patients show a slight but constant increase of vessel index in all examined compartments of breast tumour.Conclusion. Whole slide digital examination and region of interest (ROI) analysis are a valuable tool in scoring angiogenesis markers and disclosing their prognostic capacity. Our study reveals compartments’ variability of vessel density inside the tumour and highlights the propensity of invasive front to associate an active process of angiogenesis with potential implications in adjuvant therapy.

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Bulum, Antonio, Gordana Ivanac, Eugen Divjak, Iva Biondić Špoljar, Martina Džoić Dominković, Kristina Bojanić, Marko Lucijanić, and Boris Brkljačić. "Elastic Modulus and Elasticity Ratio of Malignant Breast Lesions with Shear Wave Ultrasound Elastography: Variations with Different Region of Interest and Lesion Size." Diagnostics 11, no. 6 (June 1, 2021): 1015. http://dx.doi.org/10.3390/diagnostics11061015.

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Shear wave elastography (SWE) is a type of ultrasound elastography with which the elastic properties of breast tissues can be quantitatively assessed. The purpose of this study was to determine the impact of different regions of interest (ROI) and lesion size on the performance of SWE in differentiating malignant breast lesions. The study included 150 female patients with histopathologically confirmed malignant breast lesions. Minimal (Emin), mean (Emean), maximal (Emax) elastic modulus and elasticity ratio (e-ratio) values were measured using a circular ROI size of 2, 4 and 6 mm diameters and the lesions were divided into large (diameter ≥ 15 mm) and small (diameter < 15 mm). Highest Emin, Emean and e-ratio values and lowest variability were observed when using the 2 mm ROI. Emax values did not differ between different ROI sizes. Larger lesions had significantly higher Emean and Emax values, but there was no difference in e-ratio values between lesions of different sizes. In conclusion, when measuring the Emin, Emean and e-ratio of malignant breast lesions using SWE the smallest possible ROI size should be used regardless of lesion size. ROI size has no impact on Emax values while lesion size has no impact on e-ratio values.

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Mohideen, A. Kaja, and K. Thangavel. "Removal of Pectoral Muscle Region in Digital Mammograms using Binary Thresholding." International Journal of Computer Vision and Image Processing 2, no. 3 (July 2012): 21–29. http://dx.doi.org/10.4018/ijcvip.2012070102.

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The pectoral muscle represents a predominant density region in Medio-Lateral Oblique (MLO) views of mammograms, which appears at approximately the same density as the dense tissues of interest in the image and can affect the results of image analysis methods. Therefore, segmentation of pectoral muscle is important in order to limit the search for the breast abnormalities only to the breast region. In this paper, a simple and effective approach is proposed to exclude the pectoral muscle based on binary operation. The performance is analyzed by the Hausdorff Distance Measure (HDM) and also the Mean of Absolute Error Distance Measure (MAEDM) based on differences between the results received from the radiologists and by the proposed method. The digital mammogram images are taken from MIAS dataset which contains 322 images in total, out of which the proposed algorithm able to detect and remove the pectoral region from 291 images successfully.

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Bai, Xue, Ze Liu, Jie Zhang, Shengye Wang, Qing Hou, Guoping Shan, Ming Chen, and Binbing Wang. "Comparing of two dimensional and three dimensional fully convolutional networks for radiotherapy dose prediction in left-sided breast cancer." Science Progress 104, no. 3 (July 2021): 003685042110381. http://dx.doi.org/10.1177/00368504211038162.

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Fully convolutional networks were developed for predicting optimal dose distributions for patients with left-sided breast cancer and compared the prediction accuracy between two-dimensional and three-dimensional networks. Sixty cases treated with volumetric modulated arc radiotherapy were analyzed. Among them, 50 cases were randomly chosen to conform the training set, and the remaining 10 were to construct the test set. Two U-Net fully convolutional networks predicted the dose distributions, with two-dimensional and three-dimensional convolution kernels, respectively. Computed tomography images, delineated regions of interest, or their combination were considered as input data. The accuracy of predicted results was evaluated against the clinical dose. Most types of input data retrieved a similar dose to the ground truth for organs at risk ( p > 0.05). Overall, the two-dimensional model had higher performance than the three-dimensional model ( p < 0.05). Moreover, the two-dimensional region of interest input provided the best prediction results regarding the planning target volume mean percentage difference (2.40 ± 0.18%), heart mean percentage difference (4.28 ± 2.02%), and the gamma index at 80% of the prescription dose are with tolerances of 3 mm and 3% (0.85 ± 0.03), whereas the two-dimensional combined input provided the best prediction regarding ipsilateral lung mean percentage difference (4.16 ± 1.48%), lung mean percentage difference (2.41 ± 0.95%), spinal cord mean percentage difference (0.67 ± 0.40%), and 80% Dice similarity coefficient (0.94 ± 0.01). Statistically, the two-dimensional combined inputs achieved higher prediction accuracy regarding 80% Dice similarity coefficient than the two-dimensional region of interest input (0.94 ± 0.01 vs 0.92 ± 0.01, p < 0.05). The two-dimensional data model retrieves higher performance than its three-dimensional counterpart for dose prediction, especially when using region of interest and combined inputs.

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37

Preciado, María Victoria, Paola Andrea Chabay, Elena Noemí De Matteo, Pedro Gonzalez, Saúl Grinstein, Andrea Actis, and Hugo Daniel Gass. "Epstein-Barr Virus in Breast Carcinoma in Argentina." Archives of Pathology & Laboratory Medicine 129, no. 3 (March 1, 2005): 377–81. http://dx.doi.org/10.5858/2005-129-377-evibci.

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Abstract Context.—Because the etiology and progression of breast carcinoma remain unclear, novel mechanisms of disease pathogenesis need to be considered. Recent interest has focused on Epstein-Barr virus (EBV), an oncogenic ubiquitous herpesvirus. Investigations of this association could not only broaden understanding of breast cancer etiology but also have implications regarding early detection, treatment, and prevention. Objective.—To assess EBV presence in breast carcinoma in an Argentine series. Design.—Breast biopsy specimens of 69 women with breast carcinoma and fresh tumor tissue of 39 of these women were collected. As controls, 17 biopsy specimens of fibroadenomas, 9 of benign epithelial proliferation, 4 of atypical ductal hyperplasia, and 10 of usual ductal hyperplasia and 8 normal breast tissues from women were studied. The EBV-infected cells were identified by means of immunohistochemical analysis, using a monoclonal antibody against Epstein-Barr virus–encoded nuclear antigen 1 (EBNA-1). Polymerase chain reaction (PCR) was used to amplify EBV DNA, with primers that cover the EBV encoded RNA (EBER) and BamHIW regions. Results.—Nuclear expression of EBNA-1 was observed in tumor epithelial cells in 24 (35%) of the 69 cases. We confirmed both positive and negative immunohistochemical results by PCR in those cases where good quality DNA was also available, detecting amplification fragments of 108 base pairs (bp) from the EBER region and 122 bp from the BamHIW region. Neither immunohistochemical analysis nor PCR detected any positive EBV results in the control samples. Conclusions.—Our results demonstrate the presence and expression of EBV restricted to epithelial tumor cells in a subset of breast carcinomas studied. However, no significant association was observed between EBV expression and worse clinical and pathologic patient characteristics.

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R. Thamizhvani, T., Bincy Babu, A. Josephin Arockia Dhivya, R. J. Hemalatha, Josline Elsa Joseph, and A. Keerthana. "A quality enhanced preprocessing method for mammogram ROI extraction." International Journal of Engineering & Technology 7, no. 2.25 (May 3, 2018): 133. http://dx.doi.org/10.14419/ijet.v7i2.25.16575.

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Early detection of breast cancer is necessary because it is considered as one of the most common reason of cancer death among women. Nowadays, the basic screening test for detection of breast cancer is Mammography which con-sists of various artifacts. These artifacts leads to wrong results in detection of breast cancer. Therefore, Computer Aided Diagnosis (CAD) system mainly focus in removal of artifacts and mammogram quality enhancement. By this procedure, exact Region of Interest (ROI) can be obtained. This is a challenging procedure because detection of pecto-ral muscle and cancer region is difficult. Here a comparative study of different preprocessing and enhancement tech-niques are done by testing proposed system on mammogram mini-MIAS database. Result obtained shows that sug-gested system is efficient for CAD system.

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Yoon, Jung Hyun, Mi Kyung Song, and Eun-Kyung Kim. "Semi-Quantitative Strain Ratio in the Differential Diagnosis of Breast Masses: Measurements Using One Region-of-Interest." Ultrasound in Medicine & Biology 42, no. 8 (August 2016): 1800–1806. http://dx.doi.org/10.1016/j.ultrasmedbio.2016.03.030.

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Kettunen, Tiia, Hidemi Okuma, Päivi Auvinen, Mazen Sudah, Satu Tiainen, Anna Sutela, Amro Masarwah, et al. "Peritumoral ADC values in breast cancer: region of interest selection, associations with hyaluronan intensity, and prognostic significance." European Radiology 30, no. 1 (July 29, 2019): 38–46. http://dx.doi.org/10.1007/s00330-019-06361-y.

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Christgen, Matthias, Sabrina von Ahsen, Henriette Christgen, Florian Länger, and Hans Kreipe. "The region-of-interest size impacts on Ki67 quantification by computer-assisted image analysis in breast cancer." Human Pathology 46, no. 9 (September 2015): 1341–49. http://dx.doi.org/10.1016/j.humpath.2015.05.016.

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Sun, Lilei, Huijie Sun, Junqian Wang, Shuai Wu, Yong Zhao, and Yong Xu. "Breast Mass Detection in Mammography Based on Image Template Matching and CNN." Sensors 21, no. 8 (April 18, 2021): 2855. http://dx.doi.org/10.3390/s21082855.

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In recent years, computer vision technology has been widely used in the field of medical image processing. However, there is still a big gap between the existing breast mass detection methods and the real-world application due to the limited detection accuracy. It is known that humans locate the regions of interest quickly and further identify whether these regions are the targets we found. In breast cancer diagnosis, we locate all the potential regions of breast mass by glancing at the mammographic image from top to bottom and from left to right, then further identify whether these regions are a breast mass. Inspired by the process of human detection of breast mass, we proposed a novel breast mass detection method to detect breast mass on a mammographic image by stimulating the process of human detection. The proposed method preprocesses the mammographic image via the mathematical morphology method and locates the suspected regions of breast mass by the image template matching method. Then, it obtains the regions of breast mass by classifying these suspected regions into breast mass and background categories using a convolutional neural network (CNN). The bounding box of breast mass obtained by the mathematical morphology method and image template matching method are roughly due to the mathematical morphology method, which transforms all of the brighter regions into approximate circular areas. For regression of a breast mass bounding box, the optimal solution should be searched in the feasible region and the Particle Swarm Optimization (PSO) is suitable for solving the problem of searching the optimal solution within a certain range. Therefore, we refine the bounding box of breast mass by the PSO algorithm. The proposed breast mass detection method and the compared detection methods were evaluated on the open database Digital Database for Screening Mammography (DDSM). The experimental results demonstrate that the proposed method is superior to all of the compared detection methods in detection performance.

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Mann, Suman, Amit Kumar Bindal, Archana Balyan, Vijay Shukla, Zatin Gupta, Vivek Tomar, and Shahajan Miah. "Multiresolution-Based Singular Value Decomposition Approach for Breast Cancer Image Classification." BioMed Research International 2022 (August 11, 2022): 1–11. http://dx.doi.org/10.1155/2022/6392206.

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Breast cancer is the most prevalent form of cancer that can strike at any age; the higher the age, the greater the risk. The presence of malignant tissue has become more frequent in women. Although medical therapy has improved breast cancer diagnostic and treatment methods, still the death rate remains high due to failure of diagnosing breast cancer in its early stages. A classification approach for mammography images based on nonsubsampled contourlet transform (NSCT) is proposed in order to investigate it. The proposed method uses multiresolution NSCT decomposition to the region of interest (ROI) of mammography images and then uses Z-moments for extracting features from the NSCT-decomposed images. The matrix is formed by the components that are extracted from the region of interest and are then subjected to singular value decomposition (SVD) in order to remove the essential features that can generalize globally. The method employs a support vector machine (SVM) classification algorithm to categorize mammography pictures into normal, benign, and malignant and to identify and classify the breast lesions. The accuracy of the proposed model is 96.76 percent, and the training time is greatly decreased, as evident from the experiments performed. The paper also focuses on conducting the feature extraction experiments using morphological spectroscopy. The experiment combines 16 different algorithms with 4 classification methods for achieving exceptional accuracy and time efficiency outcomes as compared to other existing state-of-the-art approaches.

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44

Saxena, Eshika. "Deep Learning for Personalized Preoperative Planning of Microsurgical Free Tissue Transfers." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (June 28, 2022): 13140–41. http://dx.doi.org/10.1609/aaai.v36i11.21706.

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Breast reconstruction surgery requires extensive planning, usually with a CT scan that helps surgeons identify which vessels are suitable for harvest. Currently, there is no quantitative method for preoperative planning. In this work, we successfully develop a Deep Learning algorithm to segment the vessels within the region of interest for breast reconstruction. Ultimately, this information will be used to determine the optimal reconstructive method (choice of vessels, extent of the free flap/harvested tissue) to reduce intra- and postoperative complication rates.

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45

Tomakova, R. A., I. S. Egorov, N. A. Lyubitsky, and D. S. Kondrashov. "Intelligent system for processing and analysis of mammograms." Journal of Physics: Conference Series 2060, no. 1 (October 1, 2021): 012030. http://dx.doi.org/10.1088/1742-6596/2060/1/012030.

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Abstract The article discusses the possibilities for the development of automated processing of mammographic images based on the principles of a systematic approach. A structural diagram of an intelligent system for processing and analyzing mammographic images is proposed, which contains three main modules: a module for forming cascading windows, a module for combining cascading windows, and a module for classification and decision making. The software was developed in MATLAB 2018b environment. The possibilities of functioning of each module are considered. Experimental testing of the software of the intelligent system for the classification of breast radiographs according to the classes “no area of interest” or “area of interest” was carried out. A criterion for evaluating the results of classification of mammographic images is selected. Experiments on control samples showed diagnostic efficiency for the classes of radiographs “no region of interest” - “region of interest” not less than 90%.

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Foldi, Julia, Emily Reisenbichler, Liuliu Pan, Krsitina Sorg, Sarah E. Church, and Lajos Pusztai. "Abstract P1-05-02: Intratumor molecular tumor heterogeneity in low ER-expressing primary breast tumors." Cancer Research 82, no. 4_Supplement (February 15, 2022): P1–05–02—P1–05–02. http://dx.doi.org/10.1158/1538-7445.sabcs21-p1-05-02.

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Abstract Background: Change in estrogen receptor (ER) status between primary breast cancer and distant recurrence are seen in the clinic in up to 15-20% of patients. This is difficult to reconcile with the current understanding of breast cancer molecular subtypes, the large-scale molecular differences between ER-positive (ER+) and ER-negative (ER-) cancers suggest different cellular origins, luminal versus basal breast epithelium, respectively. A potential explanation for ER status switch is the presence of mixed molecular subtypes at diagnosis. ER+ cancers with less than 100% positivity may be composed by both luminal and basal-like cancer cells, and a recurrence might arise from one subtype that survived adjuvant therapy. The goal of this study was to test if molecular subtype heterogeneity exists at diagnosis in less than 100% ER+ breast cancer using the NanoString GeoMx™ platform. Methods: GeoMx™ is a highly multiplexed assay that quantifies RNA expression from spatially discrete regions of interest (ROIs) within formalin fixed paraffin embedded (FFPE) tissue sections. We identified 4 cancers with 30-40% ER-positivity on routine immunohistochemistry (IHC) staining (clone SP1). Eight ROIs per case were selected to represent both ER-high and -low regions of the section using ER IHC (clone 1D5). Invasive tumor cells were defined as PanCK positive cells. 1,825 mRNA species were measured separately in each ROI. mRNA expression results (unique molecular index [UMI] counts) were quantile normalized and differential expression analyses for all genes, ESR1, ER-regulated gene set, and PAM50 gene set were performed between ROIs within the same cancer. We also assessed PAM50 subtypes of each cases on bulk RNA using NanoString BC360 panel. Results: Three of the 4 cases had heterogenous ER IHC (1D5) staining; the fourth case was uniformly ER-negative during ROI selection. For this last case, ROIs were selected based on PanCK staining from different locations in the cancer. Bulk PAM50 subtyping indicated 1 basal-like, 2 HER2-enriched (HER2-E), and 1 luminal (lum)A cancer. We also attempted ROI-level subtyping; however, Pearson correlations to the nearest centroid were low (<0.5) for all ROIs, possibly due to low expression of PAM50 genes or to the exploratory methodology used. There was substantial within cancer, between ROI, expression heterogeneity for many genes including ESR1 and sensitivity to endocrine therapy (SET) index genes. Conclusions: ER- and ER+ cells are intermixed which makes defining low and high ER regions challenging. We observed region to region variation in ESR1 and SET index gene expression. ROI-level PAM50 subtyping was unstable. The novel NanoString GeoMx™ DSP technology can be used to study intratumor molecular heterogeneity in regions of interest on FFPE breast cancer tissue sections. We found substantial region-to-region gene expression differences within tissue sections. Case ID1234ER IHC SP130%40%40%30%ER IHC 1D50%40%40%30%Bulk PAM50 SubtypeBasalHER2-ELumAHER2-EESR1 UMI average (range across ROIs)28.45 (22.4-34.6)64.9 (18.8-173)179.9 (114-245)34.8 (25.1-45.4)SET Index (genes positively correlated with ESR1) UMI average (range across ROIs)41.5 (39.1-44.8)120.1 (86.9-182.9)94.9 (72.9-118.4)143.5 (124.9-150.3)SET Index (genes negatively correlated with ESR1) UMI average (range across ROIs)57.6 (45-75.3)43.4 (38.8-52.6)33.7 (31.8-36.7)44.8 (41.4-50.6) Citation Format: Julia Foldi, Emily Reisenbichler, Liuliu Pan, Krsitina Sorg, Sarah E. Church, Lajos Pusztai. Intratumor molecular tumor heterogeneity in low ER-expressing primary breast tumors [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P1-05-02.

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47

Park, Hee Jeong, Sun Mi Kim, Bo La Yun, Mijung Jang, Bohyoung Kim, Soo Hyun Lee, and Hye Shin Ahn. "Comparison of One- and Two-Region of Interest Strain Elastography Measurements in the Differential Diagnosis of Breast Masses." Korean Journal of Radiology 21, no. 4 (2020): 431. http://dx.doi.org/10.3348/kjr.2019.0479.

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48

Nogueira, Luísa, Sofia Brandão, Eduarda Matos, Rita Gouveia Nunes, Hugo Alexandre Ferreira, Joana Loureiro, and Isabel Ramos. "Region of interest demarcation for quantification of the apparent diffusion coefficient in breast lesions and its interobserver variability." Diagnostic and Interventional Radiology 21, no. 2 (March 9, 2015): 123–27. http://dx.doi.org/10.5152/dir.2014.14217.

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49

Preda, Anda, Karl Turetschek, Heike Daldrup, Eugenia Floyd, Viktor Novikov, David M. Shames, Timothy P. L. Roberts, Wayne O. Carter, and Robert C. Brasch. "The Choice of Region of Interest Measures in Contrast-Enhanced Magnetic Resonance Image Characterization of Experimental Breast Tumors." Investigative Radiology 40, no. 6 (June 2005): 349–54. http://dx.doi.org/10.1097/01.rli.0000163740.40474.48.

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Ohmae, Etsuko, Nobuko Yoshizawa, Kenji Yoshimoto, Maho Hayashi, Hiroko Wada, Tetsuya Mimura, Yuko Asano, et al. "Comparison of Lipid and Water Contents by Time-domain Diffuse Optical Spectroscopy and Dual-energy Computed Tomography in Breast Cancer Patients." Applied Sciences 9, no. 7 (April 9, 2019): 1482. http://dx.doi.org/10.3390/app9071482.

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We previously compared time-domain diffuse optical spectroscopy (TD-DOS) with magnetic resonance imaging (MRI) using various water/lipid phantoms. However, it is difficult to conduct similar comparisons in the breast, because of measurement differences due to modality-dependent differences in posture. Dual-energy computed tomography (DECT) examination is performed in the same supine position as a TD-DOS measurement. Therefore, we first verified the accuracy of the measured fat fraction of fibroglandular tissue in the normal breast on DECT by comparing it with MRI in breast cancer patients (n = 28). Then, we compared lipid and water signals obtained in TD-DOS and DECT from normal and tumor-tissue regions (n = 16). The TD-DOS breast measurements were carried out using reflectance geometry with a source–detector separation of 3 cm. A semicircular region of interest (ROI), with a transverse diameter of 3 cm and a depth of 2 cm that included the breast surface, was set on the DECT image. Although the measurement area differed between the modalities, the correlation coefficients of lipid and water signals between TD-DOS and DECT were rs = 0.58 (p < 0.01) and rs = 0.90 (p < 0.01), respectively. These results indicate that TD-DOS captures the characteristics of the lipid and water contents of the breast.

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