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Journal articles on the topic 'Breast parenchymal patterns'

Author: Grafiati

Published: 19 February 2023

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1

Bayar, S., S. Tükel, S. Koçak, S. Aydintugˇ, and S. Dizbay Sak. "Mammographic parenchymal, patterns and breast histology." European Journal of Cancer 34 (September 1998): S79. http://dx.doi.org/10.1016/s0959-8049(98)80329-8.

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2

SAFTLAS, AUDREY F., and MOYSES SZKLO. "MAMMOGRAPHIC PARENCHYMAL PATTERNS AND BREAST CANCER RISK1." Epidemiologic Reviews 9, no. 1 (1987): 146–74. http://dx.doi.org/10.1093/oxfordjournals.epirev.a036300.

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3

Koo, Bong Sig, Jong Wha Lee, Young Jun Lee, Jun Bae Lee, Byung Soo Kim, and Yang Sook Kim. "Xeromammographic breast parenchymal patterns and their relationship to breast cancer." Journal of the Korean Radiological Society 27, no. 2 (1991): 297. http://dx.doi.org/10.3348/jkrs.1991.27.2.297.

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4

Sickles, Edward A. "Wolfe Mammographic Parenchymal Patterns and Breast Cancer Risk." American Journal of Roentgenology 188, no. 2 (February 2007): 301–3. http://dx.doi.org/10.2214/ajr.06.0635.

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5

Pertuz, Said, Antti Sassi, Mirva Karivaara-Mäkelä, Kirsi Holli-Helenius, Anna-Leena Lääperi, Irina Rinta-Kiikka, Otso Arponen, and Joni-Kristian Kämäräinen. "Micro-parenchymal patterns for breast cancer risk assessment." Biomedical Physics & Engineering Express 5, no. 6 (September 23, 2019): 065008. http://dx.doi.org/10.1088/2057-1976/ab42f4.

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6

Whitehouse, G. H., and S. J. Leinster. "The Variation of Breast Parenchymal Patterns with Age." British Journal of Radiology 58, no. 688 (April 1985): 315–18. http://dx.doi.org/10.1259/0007-1285-58-688-315.

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7

van Gils, C. H., J. D. M. Otten, A. L. M. Verbeek, and J. Hendriks. "Breast parenchymal patterns and their changes with age." British Journal of Radiology 68, no. 814 (October 1995): 1133–35. http://dx.doi.org/10.1259/0007-1285-68-814-1133.

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8

Hall, FM. "Mammographic parenchymal patterns and estrogen receptors in breast cancer." American Journal of Roentgenology 145, no. 6 (December 1985): 1316–17. http://dx.doi.org/10.2214/ajr.145.6.1316.

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9

Oza, Amit M., and Norman F. Boyd. "Mammographic Parenchymal Patterns: A Marker of Breast Cancer Risk." Epidemiologic Reviews 15, no. 1 (1993): 196–208. http://dx.doi.org/10.1093/oxfordjournals.epirev.a036105.

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10

SAFTLAS, AUDREY F., JOHN N. WOLFE, ROBERT N. HOOVER, LOUISE A. BRINTON, CATHERINE SCHAIRER, MARTINE SALANE, and MOYSES SZKLO. "MAMMOGRAPHIC PARENCHYMAL PATTERNS AS INDICATORS OF BREAST CANCER RISK." American Journal of Epidemiology 129, no. 3 (March 1989): 518–26. http://dx.doi.org/10.1093/oxfordjournals.aje.a115163.

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11

Breuer, Brenda, Daniel G. Miller, Martine Salane, John N. Wolf, Audrey F. Saftlas, David R. Olson, Robert N. Hoover, Louise A. Brinton, and Moyses Szklo. "Mammographic parenchymal history of breast cancer patterns and family." Cancer 69, no. 2 (January 15, 1992): 602–3. http://dx.doi.org/10.1002/1097-0142(19920115)69:2<602::aid-cncr2820690255>3.0.co;2-8.

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12

Blend, R., D. F. Rideout, L. Kaizer, P. Shannon, B. Tudor-Roberts, and N. F. Boyd. "Parenchymal patterns of the breast defined by real time ultrasound." European Journal of Cancer Prevention 4, no. 4 (August 1995): 293–98. http://dx.doi.org/10.1097/00008469-199508000-00004.

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13

FAJARDO, LAURIE L., BRUCE J. HILLMAN, and CLAUDE FREY. "Correlation Between Breast Parenchymal Patterns and Mammographersʼ Certainty of Diagnosis." Investigative Radiology 23, no. 7 (July 1988): 505–8. http://dx.doi.org/10.1097/00004424-198807000-00004.

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14

Lee, Joo Mi, Jeong Hyun Yoo, and Chung Sik Rhee. "The Study of Relation between Breast parenchymal Patterns and Breast Cancer by Mammography." Ewha Medical Journal 17, no. 1 (1994): 59. http://dx.doi.org/10.12771/emj.1994.17.1.59.

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15

Ikedo, Yuji, Takako Morita, Daisuke Fukuoka, Takeshi Hara, Gobert Lee, Hiroshi Fujita, Etsuo Takada, and Tokiko Endo. "Automated analysis of breast parenchymal patterns in whole breast ultrasound images: preliminary experience." International Journal of Computer Assisted Radiology and Surgery 4, no. 3 (March 14, 2009): 299–306. http://dx.doi.org/10.1007/s11548-009-0295-0.

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16

Garcia, Eloy, Yago Diez, Oliver Diaz, Xavier Llado, Albert Gubern-Merida, Robert Marti, Joan Marti, and Arnau Oliver. "Multimodal Breast Parenchymal Patterns Correlation Using a Patient-Specific Biomechanical Model." IEEE Transactions on Medical Imaging 37, no. 3 (March 2018): 712–23. http://dx.doi.org/10.1109/tmi.2017.2749685.

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17

Li, Hui, Maryellen L. Giger, Olufunmilayo I. Olopade, and Li Lan. "Fractal Analysis of Mammographic Parenchymal Patterns in Breast Cancer Risk Assessment." Academic Radiology 14, no. 5 (May 2007): 513–21. http://dx.doi.org/10.1016/j.acra.2007.02.003.

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18

Salminen, Tiina, Matti Hakama, Minna Heikkilä, and Irma Saarenmaa. "Favorable change in mammographic parenchymal patterns and breast cancer risk factors." International Journal of Cancer 78, no. 4 (November 9, 1998): 410–14. http://dx.doi.org/10.1002/(sici)1097-0215(19981109)78:4<410::aid-ijc3>3.0.co;2-x.

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19

McCormack, V. A. "Breast Density and Parenchymal Patterns as Markers of Breast Cancer Risk: A Meta-analysis." Cancer Epidemiology Biomarkers & Prevention 15, no. 6 (June 1, 2006): 1159–69. http://dx.doi.org/10.1158/1055-9965.epi-06-0034.

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20

Kaizer, L., EK Fishell, and JW Hunt. "Ultrasonographically defined parenchymal patterns of the breast: Relationship to mammographic patterns and other risk factors for breast cancer." Maturitas 10, no. 3 (October 1988): 244. http://dx.doi.org/10.1016/0378-5122(88)90031-x.

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21

Kaizer, Leonard, Eve K. Fishell, John W. Hunt, F. Stuart Foster, and Norman F. Boyd. "Ultrasonographically defined parenchymal patterns of the breast: relationship to mammographic patterns and other risk factors for breast cancer." British Journal of Radiology 61, no. 722 (February 1988): 118–24. http://dx.doi.org/10.1259/0007-1285-61-722-118.

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22

Evis Sala, Ruth Warren, Jenny McCan. "Mammographic Parenchymal Patterns and Breast Cancer Natural History?A Case-Control Study." Acta Oncologica 40, no. 4 (January 2001): 461–65. http://dx.doi.org/10.1080/02841860119894.

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23

Sala, Evis, Ruth Warren, Jenny McCann, Stephen Duffy, Robert Luben, and Nicholas Day. "Mammographic Parenchymal Patterns and Breast Cancer Natural History—A Case-Control Study." Acta Oncologica 40, no. 4 (June 1, 2001): 461–65. http://dx.doi.org/10.1080/028418601750288172.

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24

Salminen, Tiina M., Irma E. Saarenmaa, Minna M. Heikkilä, and Matti Hakama. "Risk of Breast Cancer and Changes in Mammographic Parenchymal Patterns Over Time." Acta Oncologica 37, no. 6 (January 1998): 547–51. http://dx.doi.org/10.1080/028418698430241.

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25

Gravelle, I. H., J. C. Bulstrode, R. D. Bulbrook, D. Y. Wang, D. Allen, and J. L. Hayward. "A prospective study of mammographic parenchymal patterns and risk of breast cancer." British Journal of Radiology 59, no. 701 (May 1986): 487–91. http://dx.doi.org/10.1259/0007-1285-59-701-487.

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26

Jansen, Sanaz A., Vicky C. Lin, Maryellen L. Giger, Hui Li, Gregory S. Karczmar, and Gillian M. Newstead. "Normal parenchymal enhancement patterns in women undergoing MR screening of the breast." European Radiology 21, no. 7 (February 17, 2011): 1374–82. http://dx.doi.org/10.1007/s00330-011-2080-z.

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27

Li, Hui, Maryellen L. Giger, Olufunmilayo I. Olopade, and Michael R. Chinander. "Power Spectral Analysis of Mammographic Parenchymal Patterns for Breast Cancer Risk Assessment." Journal of Digital Imaging 21, no. 2 (January 3, 2008): 145–52. http://dx.doi.org/10.1007/s10278-007-9093-9.

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28

Cohen, Eric A., Omid Haji Maghsoudi, Raymond Acciavatti, Lauren Pantalone, Walter Mankowski, Alex A. Nguyen, Christopher G. Scott, et al. "Abstract P070: Volumetric parenchymal pattern analysis for breast cancer risk estimation." Cancer Prevention Research 16, no. 1_Supplement (January 1, 2023): P070. http://dx.doi.org/10.1158/1940-6215.precprev22-p070.

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Abstract Introduction: Mammographic breast density is among the strongest risk factors for breast cancer. However, breast density is typically assessed subjectively by the radiologist according to the Breast Imaging Reporting and Data System (BI-RADS) based on 2 dimensional (2D) digital mammography (DM) images. Digital breast tomosynthesis (DBT) is quickly replacing DM and allows more detailed volumetric imaging of the breast. Advances in radiomics, the high-throughput extraction of radiologic features, has enabled characterization of breast parenchymal complexity beyond breast density alone. The purpose of this study was to compare the performance of volumetric parenchymal pattern analysis from DBT and DM with conventional breast density measurement with respect to breast cancer risk estimation. Methods: We performed a case control study among women with concurrent DM and DBT screening (Selenia Dimensions, Hologic Inc.) at our institution between 3/2011-12/2014. Cases were diagnosed with breast cancer within 1 year of screening; controls were confirmed negative or benign at 1 year follow-up, matched on race (Black, White, other/unknown) and age (5-year bins). After exclusions for imaging artifacts, craniocaudal (CC) and mediolateral oblique (MLO) views for 187 cases and 737 controls, in six image formats were assessed: 1) raw (“FOR PROCESSING”) DM; 2) processed (“FOR PRESENTATION”) DM; 3) raw DBT central projection; 4) processed DBT central projection; 5) DBT central reconstructed slice; and 6) DBT reconstructed stack. For cases, we analyzed the breast contralateral to cancer diagnosis; for controls the same breast as the matched case. We extracted radiomic features using a lattice-based approach with the publicly available CaPTk software, averaging features for each breast over CC and MLO views. We examined 3 lattice window sizes (6.4, 12.8, and 25.6 mm) and 23 resolutions for image resampling (0.075 - 2mm). We performed PCA on the resulting 487 features for each combination of window size and resolution and built conditional logistic regression models to assess the association of the first 7 principal components with breast cancer, with models including age, BMI, and BI-RADS density. For each image type we calculated the model C-statistic at all window sizes and resolutions, for a total of 2304 experimental conditions. Results: Features from reconstructed DBT scans had on average higher C-statistics across all experimental conditions. A model using only age, BMI, and BI-RADS density had a C-statistic of 0.61. Models using radiomic features plus age, BMI, and BI-RADS density had mean C-statistic of 0.68 (IQR 0.68, 0.69) for reconstructed DBT scans; for all other image types, the mean C-statistic ranged from 0.64 to 0.66. Conclusions: Incorporating volumetric breast parenchymal patterns from DBT improves breast cancer risk estimation beyond markers derived from DM and beyond conventional BI-RADS density. Citation Format: Eric A. Cohen, Omid Haji Maghsoudi, Raymond Acciavatti, Lauren Pantalone, Walter Mankowski, Alex A Nguyen, Christopher G. Scott, Stacey Winham, Andrew D. Maidment, Anne Marie McCarthy, Celine M Vachon, Emily F Conant, Despina Kontos. Volumetric parenchymal pattern analysis for breast cancer risk estimation. [abstract]. In: Proceedings of the AACR Special Conference: Precision Prevention, Early Detection, and Interception of Cancer; 2022 Nov 17-19; Austin, TX. Philadelphia (PA): AACR; Can Prev Res 2023;16(1 Suppl): Abstract nr P070.

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29

Boné, B., M. Kristoffersen Wiberg, C. Parrado, U. Falkmer, P. Aspelin, and A. Gad. "Mechanism of contrast enhancement in breast lesions at MR imaging." Acta Radiologica 39, no. 5 (September 1998): 494–500. http://dx.doi.org/10.1080/02841859809172214.

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Purpose: the aim of the study was to explain why breast lesions are enhanced by contrast medium at MR imaging and to elucidate the histopathological basis for the overlap in the enhancement patterns of benign and malignant breast lesions Material and Methods: Ten invasive breast carcinomas and 10 benign breast lesions were selected for the study. of the 10 carcinomas, 5 showed a strong and early contrast enhancement, and 5 did not. of the 10 benign lesions, 5 showed a strong and early contrast enhancement, and 5 showed no enhancement. the following morphometric variables were evaluated: proliferation cell index of neoplastic parenchymal cells, S-phase fraction, nuclear ploidy analysed by image DNA-cytometry, microvessel density, and the percentage proportion of the interstitial area Results: Contrast enhancement was related to the proliferating activity of the hyperplastic or neoplastic parenchymal cells and was inversely correlated with the interstitial area in carcinomas as well as in benign tumours and non-neo-plastic lesions of the breast Conclusion: Morphometric variables play an important role in the general mechanism of MR contrast enhancement in examinations of the breast and explain the histopathological basis for the overlap in the enhancement patterns of benign and malignant breast lesions

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30

He, Wenda, Arne Juette, Erika R. E. Denton, Arnau Oliver, Robert Martí, and Reyer Zwiggelaar. "A Review on Automatic Mammographic Density and Parenchymal Segmentation." International Journal of Breast Cancer 2015 (2015): 1–31. http://dx.doi.org/10.1155/2015/276217.

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Breast cancer is the most frequently diagnosed cancer in women. However, the exact cause(s) of breast cancer still remains unknown. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective way to tackle breast cancer. There are more than 70 common genetic susceptibility factors included in the current non-image-based risk prediction models (e.g., the Gail and the Tyrer-Cuzick models). Image-based risk factors, such as mammographic densities and parenchymal patterns, have been established as biomarkers but have not been fully incorporated in the risk prediction models used for risk stratification in screening and/or measuring responsiveness to preventive approaches. Within computer aided mammography, automatic mammographic tissue segmentation methods have been developed for estimation of breast tissue composition to facilitate mammographic risk assessment. This paper presents a comprehensive review of automatic mammographic tissue segmentation methodologies developed over the past two decades and the evidence for risk assessment/density classification using segmentation. The aim of this review is to analyse how engineering advances have progressed and the impact automatic mammographic tissue segmentation has in a clinical environment, as well as to understand the current research gaps with respect to the incorporation of image-based risk factors in non-image-based risk prediction models.

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31

Sala, E., R. Warren, J. McCann, S. Duffy, N. Day, and Robert Luben. "Mammographic parenchymal patterns and mode of detection: implications for the breast screening programme." Journal of Medical Screening 5, no. 4 (December 1998): 207–12. http://dx.doi.org/10.1136/jms.5.4.207.

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32

Sala, E., L. Solomon, R. Warren, J. McCann, S. Duffy, R. Luben, and N. Day. "Size, node status and grade of breast tumours: association with mammographic parenchymal patterns." European Radiology 10, no. 1 (January 10, 2000): 157–61. http://dx.doi.org/10.1007/s003300050025.

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33

Huo, Zhimin, Maryellen L. Giger, Dulcy E. Wolverton, Weiming Zhong, Shelly Cumming, and Olufunmilayo I. Olopade. "Computerized analysis of mammographic parenchymal patterns for breast cancer risk assessment: Feature selection." Medical Physics 27, no. 1 (January 2000): 4–12. http://dx.doi.org/10.1118/1.598851.

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34

Gastounioti, Aimilia, Andrew Oustimov, Meng-Kang Hsieh, Lauren Pantalone, Emily F. Conant, and Despina Kontos. "Using Convolutional Neural Networks for Enhanced Capture of Breast Parenchymal Complexity Patterns Associated with Breast Cancer Risk." Academic Radiology 25, no. 8 (August 2018): 977–84. http://dx.doi.org/10.1016/j.acra.2017.12.025.

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35

Nie, Ke, Daniel Chang, Jeon-Hor Chen, Chieh-Chih Hsu, Orhan Nalcioglu, and Min-Ying Su. "Quantitative analysis of breast parenchymal patterns using 3D fibroglandular tissues segmented based on MRI." Medical Physics 37, no. 1 (December 9, 2009): 217–26. http://dx.doi.org/10.1118/1.3271346.

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36

Bayram, Yalcin, Cihan Sahin, Celalettin Sever, Huseyin Karagoz, and Yalcin Kulahci. "Custom-made approach to a patient with post-burn breast deformity." Indian Journal of Plastic Surgery 47, no. 01 (January 2014): 127–31. http://dx.doi.org/10.4103/0970-0358.129646.

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ABSTRACTSecond and third degree burns on breasts at preadolescent period may cause severe breast deformations. This deformation can be variable depending on severity and location of the burns, personal adolescent patterns, and treatment modality in acute burn period. A 21 year old female patient admitted to our department for her breast deformation due to burn contracture at the inferior pole of the right breast. On physical examination we defined that development of the volume of the right breast was equal to the left, and inferior pole of the right breast was flattened due to contracture, and nipple was projected to inferior. We found that inframammary crease of the right breast was 2 cm lower than that of left; andthe distance of nipple-inframamary crease was 4.7 cm while areola-inframmary crease was 2 cm. New nipple-areola complex level was identified according to left breast’s level. Medial and lateral lines were planned to merge inferiorly at 2 cm above inframmary crease in a plan similar to vertical mammaplasty. Superior pedicle carrying nipple areola was desepitelised. Lower parenchymal V flap was transposed superiorly and attached to the pectoral muscle. Inferior parts of the lateral and medial glandular flaps were excised to form new inframammary crease. The desired laxity of skin at the lower pole was obtained by performing a new Z- plasty between lateral and medial skin flaps. Breast symmetry was confirmed by postoperative objective measurements between left and right breasts. Patient’s satisfaction and aesthetic appearance levels were high. Breasts deformation patterns caused by burns, trauma and mass exsicion due to cancer could not be addressed with traditional defined techniques. Special deformations can be corrected by custom made plannings as we presented here.

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37

Hinton, CP, EJ Roebuck, MR Williams, RW Blamey, J. Glaves, RI Nicholson, and K. Griffiths. "Mammographic parenchymal patterns: value as a predictor of hormone dependency and survival in breast cancer." American Journal of Roentgenology 144, no. 6 (June 1985): 1103–7. http://dx.doi.org/10.2214/ajr.144.6.1103.

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38

Howard, Daniel, Simon C. Roberts, Conor Ryan, and Adrian Brezulianu. "Textural Classification of Mammographic Parenchymal Patterns with the SONNET Selforganizing Neural Network." Journal of Biomedicine and Biotechnology 2008 (2008): 1–11. http://dx.doi.org/10.1155/2008/526343.

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In nationwide mammography screening, thousands of mammography examinations must be processed. Each consists of two standard views of each breast, and each mammogram must be visually examined by an experienced radiologist to assess it for any anomalies. The ability to detect an anomaly in mammographic texture is important to successful outcomes in mammography screening and, in this study, a large number of mammograms were digitized with a highly accurate scanner; and textural features were derived from the mammograms as input data to a SONNET selforganizing neural network. The paper discusses how SONNET was used to produce a taxonomic organization of the mammography archive in an unsupervised manner. This process is subject to certain choices of SONNET parameters, in these numerical experiments using the craniocaudal view, and typically produced O(10), for example, 39 mammogram classes, by analysis of features from O() mammogram images. The mammogram taxonomy captured typical subtleties to discriminate mammograms, and it is submitted that this may be exploited to aid the detection of mammographic anomalies, for example, by acting as a preprocessing stage to simplify the task for a computational detection scheme, or by ordering mammography examinations by mammogram taxonomic class prior to screening in order to encourage more successful visual examination during screening. The resulting taxonomy may help train screening radiologists and conceivably help to settle legal cases concerning a mammography screening examination because the taxonomy can reveal the frequency of mammographic patterns in a population.

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39

DE STAVOLA, B. L., I. H. GRAVELLE, D. Y. WANG, D. S. ALLEN, R. D. BULBROOK, I. S. FENTIMAN, J. L. HAYWARD, and M. C. CHAUDARY. "Relationship of Mammographic Parenchymal Patterns with Breast Cancer Risk Factors and Risk of Breast Cancer in a Prospective Study." International Journal of Epidemiology 19, no. 2 (1990): 247–54. http://dx.doi.org/10.1093/ije/19.2.247.

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40

Vineis, Paolo, Giuseppe Sinistrero, Aurelio Temporelli, Livio Azzoni, Aldo Bigo, Paolo Burke, Giovannino Ciccone, et al. "Inter-Observer Variability in the Interpretation of Mammograms." Tumori Journal 74, no. 3 (June 1988): 275–79. http://dx.doi.org/10.1177/030089168807400306.

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Inter-observer agreement was tested in the interpretation by 8 radiologists of mammograms from 45 women (for a total of 180 films per radiologist). The radiologists were representative of the whole range of those involved in mammography in the town of Torino, with a number of films read per year ranging from 100 to 4000. Out of the 45, 9 women were affected by breast cancer (histologically proved), 25 had benign disease (diagnosed with fine-needle aspiration) and 11 had normal breasts. Weighted kappa values were in the range 0.27–0.82 (median 0.60) for parenchymal patterns; 0.33–0.67 (0.48) for diagnosis in five categories; and 0.22–0.57 (0.38) for indications for further diagnostic tests. These values are comparable with those reported from other investigations.

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41

Kim, Ki Hwan, Hyeonseob Nam, Eunji Lim, and Chan-Young Ock. "Development of AI-powered imaging biomarker for breast cancer risk assessment on the basis of mammography alone." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): 10519. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.10519.

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10519 Background: There is increasing interest in early detection of breast cancer by utilizing MRI in high-risk populations. However, it is still challenging to define and enrich the high-risk population. In this study, we developed an artificial intelligence (AI)-powered Imaging Biomarker in Mammography (IBM) to discover unique mammographic patterns, beyond simple density evaluations, that are related to breast cancer. Methods: A total of 49,577 mammography exams were collected to develop the AI-powered IBM, in which 6,218 were cancers. First, we evaluated the hypothesis that the unaffected breast of cancer patients would have a different pattern than that of non-cancer patients, by training AI (IBM-A) with unaffected breast in cancer patients and breasts of non-cancer patients. We then utilized further images of the cancer patients to train AI (IBM-B). This time we used both affected and unaffected breasts of cancer patients and breasts of non-cancer patients, allowing IBM-B to additionally learn patterns related to breast cancer. The IBMs were evaluated using the internal data (n = 2,058) that included 719 cancers. To demonstrate the feasibility of early detection by using IBM-B, it was tested with external data (n = 4,158) from an independent institution. This included pre-index exams (n = 292) taken prior to index exams acquired at the time of cancer diagnosis. Results: With the internal data, IBM-A showed AUC of 0.842, suggesting that AI could learn the difference between the normal breast of cancer patients and non-cancer patients. With IBM-B, which used additional cancer images to train, AUC was improved to 0.852. Based on the internal validation, IBM-B was chosen for the external validation, in which pre-index examinations were used only. IBM-B showed AUC of 0.777 in discriminating the pre-index exams of cancer patients and those of non-cancer patients. The radiologists excluded the apparent missed cancers (n = 87) by reviewing the pre-index exams retrospectively. After, the recalculated AUC of IBM-B was 0.770, suggesting that IBM-B can distinguish between mammograms of patients who will develop breast cancer in the future and those who will not. The mean IBM-B scores in pre-index exams of cancer group (0.580) were significantly higher than those in the normal (0.258, P < 0.001) and benign (0.258, P < 0.001) groups. Conclusions: AI-powered IBM could detect the unique parenchymal pattern associated with high breast cancer risks, and we show the potential of the AI-powered IBM to be used as an independent biomarker to select high-risk populations based on mammography alone.[Table: see text]

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42

Kaufman, Z., W. I. H. Garstin, R. Hayes, M. J. Michell, and M. Baum. "The mammographic parenchymal patterns of nulliparous women and women with a family history of breast cancer." Clinical Radiology 43, no. 6 (June 1991): 385–88. http://dx.doi.org/10.1016/s0009-9260(05)80565-3.

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43

Mehnati, Parinaz, Hamed Alizadeh, and Haleh Hoda. "Relation between Mammographic Parenchymal Patterns and Breast Cancer Risk: Considering BMI, Compressed Breast Thickness and Age of Women in Tabriz, Iran." Asian Pacific Journal of Cancer Prevention 17, no. 4 (June 1, 2016): 2259–63. http://dx.doi.org/10.7314/apjcp.2016.17.4.2259.

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44

Boehm, Holger F., Tanja Schneider, Sonja M. Buhmann-Kirchhoff, Thomas Schlossbauer, Dorothea Rjosk-Dendorfer, Stefanie Britsch, and Maximilian Reiser. "Automated Classification of Breast Parenchymal Density: Topologic Analysis of X-Ray Attenuation Patterns Depicted with Digital Mammography." American Journal of Roentgenology 191, no. 6 (December 2008): W275—W282. http://dx.doi.org/10.2214/ajr.07.3588.

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Li, Hui, Maryellen L. Giger, Zhimin Huo, Olufunmilayo I. Olopade, Li Lan, Barbara L. Weber, and Ioana Bonta. "Computerized analysis of mammographic parenchymal patterns for assessing breast cancer risk: Effect of ROI size and location." Medical Physics 31, no. 3 (February 17, 2004): 549–55. http://dx.doi.org/10.1118/1.1644514.

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Jakes, RW, SW Duffy, FC Ng, F. Gao, and EH Ng. "Mammographic parenchymal patterns and risk of breast cancer at and after a prevalence screen in Singaporean women." International Journal of Epidemiology 29, no. 1 (February 2000): 11–19. http://dx.doi.org/10.1093/ije/29.1.11.

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Boca (Bene), Ioana, Anca Ileana Ciurea, Cristiana Augusta Ciortea, Paul Andrei Ștefan, Lorena Alexandra Lisencu, and Sorin Marian Dudea. "Differentiating Breast Tumors from Background Parenchymal Enhancement at Contrast-Enhanced Mammography: The Role of Radiomics—A Pilot Reader Study." Diagnostics 11, no. 7 (July 13, 2021): 1248. http://dx.doi.org/10.3390/diagnostics11071248.

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Background: The purpose of this study was to assess the effectiveness of the radiomic analysis of contrast-enhanced spectral mammography (CESM) in discriminating between breast cancers and background parenchymal enhancement (BPE). Methods: This retrospective study included 38 patients that underwent CESM examinations for clinical purposes between January 2019–December 2020. A total of 57 malignant breast lesions and 23 CESM examinations with 31 regions of BPE were assessed through radiomic analysis using MaZda software. The parameters that demonstrated to be independent predictors for breast malignancy were exported into the B11 program and a k-nearest neighbor classifier (k-NN) was trained on the initial groups of patients and was tested using a validation group. Histopathology results obtained after surgery were considered the gold standard. Results: Radiomic analysis found WavEnLL_s_2 parameter as an independent predictor for breast malignancies with a sensitivity of 68.42% and a specificity of 83.87%. The prediction model that included CH1D6SumAverg, CN4D6Correlat, Kurtosis, Perc01, Perc10, Skewness, and WavEnLL_s_2 parameters had a sensitivity of 73.68% and a specificity of 80.65%. Higher values were obtained of WavEnLL_s_2 and the prediction model for tumors than for BPEs. The comparison between the ROC curves provided by the WaveEnLL_s_2 and the entire prediction model did not show statistically significant results (p = 0.0943). The k-NN classifier based on the parameter WavEnLL_s_2 had a sensitivity and specificity on training and validating groups of 71.93% and 45.16% vs. 60% and 44.44%, respectively. Conclusion: Radiomic analysis has the potential to differentiate CESM between malignant lesions and BPE. Further quantitative insight into parenchymal enhancement patterns should be performed to facilitate the role of BPE in personalized clinical decision-making and risk assessment.

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Furberg, Anne-Sofie, Grazyna Jasienska, Nils Bjurstam, Peter A. Torjesen, Aina Emaus, Susan F. Lipson, Peter T. Ellison, and Inger Thune. "Metabolic and Hormonal Profiles: HDL Cholesterol as a Plausible Biomarker of Breast Cancer Risk. The Norwegian EBBA Study." Cancer Epidemiology, Biomarkers & Prevention 14, no. 1 (January 1, 2005): 33–40. http://dx.doi.org/10.1158/1055-9965.33.14.1.

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Abstract Low serum high-density lipoprotein cholesterol (HDL-C) is an important component of the metabolic syndrome and has recently been related to increased breast cancer risk in overweight and obese women. We therefore questioned whether serum HDL-C might be a biologically sound marker of breast cancer risk. We obtained cross-sectional data among 206 healthy women ages 25 to 35 years who participated in the Norwegian EBBA study. We included salivary ovarian steroid concentrations assessed by daily samples throughout one entire menstrual cycle, metabolic profile with measures of adiposity [body mass index (BMI) and truncal fat percentage], serum concentrations of lipids and hormones (insulin, leptin, testosterone, dehydroepiandrostendione sulfate, insulin-like growth factor-I, and its principal binding protein), and mammographic parenchymal pattern. We examined how components of the metabolic syndrome, including low serum HDL-C, were related to levels of hormones, and free estradiol concentration in particular, and studied predictors of mammographic parenchymal patterns in regression models. In women with BMI ≥ 23.6 kg/m2 (median), overall average salivary estradiol concentration dropped by 2.4 pmol/L (0.7 pg/mL; 13.2% change in mean for the total population) by each 0.33 mmol/L (12.8 mg/dl; 1SD) increase in serum HDL-C (P = 0.03; Pinteraction = 0.03). A subgroup of women characterized by both relatively high BMI (≥23.6 kg/m2) and high serum LDL-C/HDL-C ratio (≥ 2.08; 75 percentile) had substantially higher levels of salivary estradiol by cycle day than other women (P = 0.001). BMI was the strongest predictor of overall average estradiol with a direct relationship (P< 0.001). Serum HDL-C was inversely related to serum leptin, insulin, and dehydroepiandrostendione sulfate (P < 0.001, P < 0.01, and P < 0.05, respectively). There was a direct relationship between breast density and healthy metabolic profiles (low BMI, high serum HDL-C; P < 0.001) and salivary progesterone concentrations (P < 0.05). Our findings support the hypothesis that low serum HDL-C might reflect an unfavorable hormonal profile with, in particular, increased levels of estrogens and gives further clues to biomarkers of breast cancer risk especially in overweight and obese women.

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Giess, Catherine S., Eren D. Yeh, Sughra Raza, and Robyn L. Birdwell. "Background Parenchymal Enhancement at Breast MR Imaging: Normal Patterns, Diagnostic Challenges, and Potential for False-Positive and False-Negative Interpretation." RadioGraphics 34, no. 1 (January 2014): 234–47. http://dx.doi.org/10.1148/rg.341135034.

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Robichaux, Jacqulyne P., John W. Fuseler, Shrusti S. Patel, Steven W. Kubalak, Adam Hartstone-Rose, and Ann F. Ramsdell. "Left–right analysis of mammary gland development in retinoid X receptor-α +/− mice." Philosophical Transactions of the Royal Society B: Biological Sciences 371, no. 1710 (December 19, 2016): 20150416. http://dx.doi.org/10.1098/rstb.2015.0416.

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Left–right (L–R) differences in mammographic parenchymal patterns are an early predictor of breast cancer risk; however, the basis for this asymmetry is unknown. Here, we use retinoid X receptor alpha heterozygous null (RXRα +/− ) mice to propose a developmental origin: perturbation of coordinated anterior–posterior (A–P) and L–R axial body patterning. We hypothesized that by analogy to somitogenesis—in which retinoic acid (RA) attenuation causes anterior somite pairs to develop L–R asynchronously—that RA pathway perturbation would likewise result in asymmetric mammary development. To test this, mammary glands of RXRα +/− mice were quantitatively assessed to compare left- versus right-side ductal epithelial networks. Unlike wild-type controls, half of the RXRα +/− thoracic mammary gland (TMG) pairs exhibited significant L–R asymmetry, with left-side reduction in network size. In RXRα +/− TMGs in which symmetry was maintained, networks had bilaterally increased size, with left networks showing greater variability in area and pattern. Reminiscent of posterior somites, whose bilateral symmetry is refractory to RA attenuation, inguinal mammary glands (IMGs) also had bilaterally increased network size, but no loss of symmetry. Together, these results demonstrate that mammary glands exhibit differential A–P sensitivity to RXRα heterozygosity, with ductal network symmetry markedly compromised in anterior but not posterior glands. As TMGs more closely model human breast development than IMGs, these findings raise the possibility that for some women, breast cancer risk may initiate with subtle axial patterning defects that result in L–R asymmetric growth and pattern of the mammary ductal epithelium. This article is part of the themed issue ‘Provocative questions in left–right asymmetry’.

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