Relevant bibliographies by topics / Radiomics analysis

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Radiomics analysis'

Author: Grafiati
Published: 14 December 2024
Last updated: 31 July 2025

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Journal articles on the topic "Radiomics analysis"

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Hu, Shuyi, Xiajie Lyu, Weifeng Li, et al. "Radiomics Analysis on Noncontrast CT for Distinguishing Hepatic Hemangioma (HH) and Hepatocellular Carcinoma (HCC)." Contrast Media & Molecular Imaging 2022 (June 25, 2022): 1–8. http://dx.doi.org/10.1155/2022/7693631.

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Background. To form a radiomic model on the basis of noncontrast computed tomography (CT) to distinguish hepatic hemangioma (HH) and hepatocellular carcinoma (HCC). Methods. In this retrospective study, a total of 110 patients were reviewed, including 72 HCC and 38 HH. We accomplished feature selection with the least absolute shrinkage and operator (LASSO) and built a radiomics signature. Another improved model (radiomics index) was established using forward conditional multivariate logistic regression. Both models were tested in an internal validation group (38 HCC and 21 HH). Results. The ra
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Yin, Yunchao, Derya Yakar, Rudi A. J. O. Dierckx, Kim B. Mouridsen, Thomas C. Kwee, and Robbert J. de Haas. "Combining Hepatic and Splenic CT Radiomic Features Improves Radiomic Analysis Performance for Liver Fibrosis Staging." Diagnostics 12, no. 2 (2022): 550. http://dx.doi.org/10.3390/diagnostics12020550.

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Background: The exact focus of computed tomography (CT)-based artificial intelligence techniques when staging liver fibrosis is still not exactly known. This study aimed to determine both the added value of splenic information to hepatic information, and the correlation between important radiomic features and information exploited by deep learning models for liver fibrosis staging by CT-based radiomics. Methods: The study design is retrospective. Radiomic features were extracted from both liver and spleen on portal venous phase CT images of 252 consecutive patients with histologically proven l
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Xia, Zhen, Xiao-Chen Huang, Xin-Yu Xu, et al. "Ultrasound-Based Deep Learning Radiomics Models for Predicting Primary and Secondary Salivary Gland Malignancies: A Multicenter Retrospective Study." Bioengineering 12, no. 4 (2025): 391. https://doi.org/10.3390/bioengineering12040391.

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Background: Primary and secondary salivary gland malignancies differ significantly in treatment and prognosis. However, conventional ultrasonography often struggles to differentiate between these malignancies due to overlapping imaging features. We aimed to develop and evaluate noninvasive diagnostic models based on traditional ultrasound features, radiomics, and deep learning—independently or in combination—for distinguishing between primary and secondary salivary gland malignancies. Methods: This retrospective study included a total of 140 patients, comprising 68 with primary and 72 with sec
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Gelardi, Fabrizia, Lara Cavinato, Rita De Sanctis, et al. "The Predictive Role of Radiomics in Breast Cancer Patients Imaged by [18F]FDG PET: Preliminary Results from a Prospective Cohort." Diagnostics 14, no. 20 (2024): 2312. http://dx.doi.org/10.3390/diagnostics14202312.

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Background: Recently, radiomics has emerged as a possible image-derived biomarker, predominantly stemming from retrospective analyses. We aimed to prospectively assess the predictive role of [18F]FDG-PET radiomics in breast cancer (BC). Methods: Patients affected by stage I–III BC eligible for neoadjuvant chemotherapy (NAC) staged with [18F]FDG-PET/CT were prospectively enrolled. The pathological response to NAC was assessed on surgical specimens. From each primary breast lesion, we extracted radiomic PET features and their predictive role with respect to pCR was assessed. Uni- and multivariat
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Chilaca-Rosas, Maria-Fatima, Melissa Garcia-Lezama, Sergio Moreno-Jimenez, and Ernesto Roldan-Valadez. "Diagnostic Performance of Selected MRI-Derived Radiomics Able to Discriminate Progression-Free and Overall Survival in Patients with Midline Glioma and the H3F3AK27M Mutation." Diagnostics 13, no. 5 (2023): 849. http://dx.doi.org/10.3390/diagnostics13050849.

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Background: Radiomics refers to a recent area of knowledge that studies features extracted from different imaging techniques and subsequently transformed into high-dimensional data that can be associated with biological events. Diffuse midline gliomas (DMG) are one of the most devastating types of cancer, with a median survival of approximately 11 months after diagnosis and 4–5 months after radiological and clinical progression. Methods: A retrospective study. From a database of 91 patients with DMG, only 12 had the H3.3K27M mutation and brain MRI DICOM files available. Radiomic features were
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Hu, Yumin, Qiaoyou Weng, Haihong Xia, et al. "A radiomic nomogram based on arterial phase of CT for differential diagnosis of ovarian cancer." Abdominal Radiology 46, no. 6 (2021): 2384–92. http://dx.doi.org/10.1007/s00261-021-03120-w.

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Abstract Purpose To develop and validate a radiomic nomogram based on arterial phase of CT to discriminate the primary ovarian cancers (POCs) and secondary ovarian cancers (SOCs). Methods A total of 110 ovarian cancer patients in our hospital were reviewed from January 2010 to December 2018. Radiomic features based on the arterial phase of CT were extracted by Artificial Intelligence Kit software (A.K. software). The least absolute shrinkage and selection operation regression (LASSO) was employed to select features and construct the radiomics score (Rad-score) for further radiomics signature c
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Cinarer, Gokalp, and Bulent Gursel Emiroglu. "Statistical analysis of radiomic features in differentiation of glioma grades." New Trends and Issues Proceedings on Advances in Pure and Applied Sciences, no. 12 (April 30, 2020): 68–79. http://dx.doi.org/10.18844/gjpaas.v0i12.4988.

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Radiomics is an important quantitative feature extraction tool used in many areas such as image processing and computer-aided diagnosis. In this study, the discriminability of brain cancer tumour grades (Grade II and Grade III) with radiomic features were analysed statistically. The data set consists of 121 patients, 77 patients with Grade II tumours and 44 patients with Grade III tumours. A total of 107 radiomic features were extracted, including three groups of radiomic features such as morphological, first-order and texture. Relationships between the characteristics of each group were teste
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Hu, Lili, Jingjing Zhang, Xiaofei Wu, et al. "CT-based multi-regional radiomics model for predicting contrast medium extravasation in patients with tumors: A case-control study." PLOS ONE 20, no. 3 (2025): e0314601. https://doi.org/10.1371/journal.pone.0314601.

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Objective To develop a non-contrast CT based multi-regional radiomics model for predicting contrast medium (CM) extravasation in patients with tumors. Methods A retrospective analysis of non-contrast CT scans from 282 tumor patients across two medical centers led to the development of a radiomics model, using 157 patients for training, 68 for validation, and 57 from an external center as an independent test cohort. The different volumes of interest from right common carotid artery/right internal jugular vein, right subclavian artery/vein and thoracic aorta were delineated. Radiomics features f
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Wei, Zhi-Yao, Zhe Zhang, Dong-Li Zhao, Wen-Ming Zhao, and Yuan-Guang Meng. "Magnetic resonance imaging-based radiomics model for preoperative assessment of risk stratification in endometrial cancer." World Journal of Clinical Cases 12, no. 26 (2024): 5908–21. http://dx.doi.org/10.12998/wjcc.v12.i26.5908.

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BACKGROUND Preoperative risk stratification is significant for the management of endometrial cancer (EC) patients. Radiomics based on magnetic resonance imaging (MRI) in combination with clinical features may be useful to predict the risk grade of EC. AIM To construct machine learning models to predict preoperative risk stratification of patients with EC based on radiomics features extracted from MRI. METHODS The study comprised 112 EC patients. The participants were randomly separated into training and validation groups with a 7:3 ratio. Logistic regression analysis was applied to uncover ind
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Lei, Chu-qian, Wei Wei, Zhen-yu Liu, et al. "Radiomics analysis for pathological classification prediction in BI-RADS category 4 mammographic calcifications." Journal of Clinical Oncology 37, no. 15_suppl (2019): e13055-e13055. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e13055.

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e13055 Background: To establish and validate a radiomics-based imaging diagnostic model to predict Breast Imaging Reporting and Data System (BI-RADS) category 4 calcification of breast with mammographic images before biopsy and assess its value. Methods: A total of 212 BI-RADS category 4 pathology-proven mammographic calcifications without obvious mass on mammography were retrospectively enrolled (159 in primary cohort and 53 in validation cohort). All patients received ultrasound inspection and the results were available. 8286 radiomic features were extracted from each mammography images. We
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Dissertations / Theses on the topic "Radiomics analysis"

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Xu, Chongrui. "Quantitative Radiomic Analysis for Prognostic Medical Applications." Thesis, The University of Sydney, 2019. https://hdl.handle.net/2123/21517.

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Radiomics, a non-invasive and quantitative mining medical imaging information method, could extract molecular biological features and enormous feature combinations to customise individualised treatment and solve the problem of heterogeneity, satisfying the standards of precision medicine. However, it faces many challenges in the feature selection process, including redundant features, irrelevant features and the overfitting risk. More important, people know little about radiomics biological background and its connection to radiology, so it is difficult to apply radiology directly to medicine a
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Ortiz, Ramón Rafael. "Radiomics for diagnosis and assessing brain diseases: an approach based on texture analysis on magnetic resonance imaging." Doctoral thesis, Universitat Politècnica de València, 2019. http://hdl.handle.net/10251/119118.

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[ES] En los últimos años, los investigadores han intentado explotar la información de las imágenes médicas a través de la evaluación de parámetros cuantitativos para ayudar a los clínicos con el diagnóstico de enfermedades. Esta práctica ha sido bautizada como radiomics. El análisis de texturas proporciona una gran variedad de parámetros que permiten cuantificar la heterogeneidad característica de diferentes tejidos, especialmente cuando se obtienen de imagen por resonancia magnética (IRM). Basándonos en esto, decidimos estudiar las posibilidades de los parámetros texturales extraídos de IRM p
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Iyer, Sukanya Raj. "Deformation heterogeneity radiomics to predict molecular sub-types and overall survival in pediatric Medulloblastoma." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1588601774292049.

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Wang, Dingqian. "Quantitative analysis with machine learning models for multi-parametric brain imaging data." Thesis, The University of Sydney, 2019. https://hdl.handle.net/2123/22245.

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Gliomas are considered to be the most common primary adult malignant brain tumor. With the dramatic increases in computational power and improvements in image analysis algorithms, computer-aided medical image analysis has been introduced into clinical applications. Precision tumor grading and genotyping play an indispensable role in clinical diagnosis, treatment and prognosis. Gliomas diagnostic procedures include histopathological imaging tests, molecular imaging scans and tumor grading. Pathologic review of tumor morphology in histologic sections is the traditional method for cancer classif
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Boughdad, Sarah. "Contributions of radiomics in ¹⁸F-FDG PET/CT and in MRI in breast cancer." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS500.

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Le cancer du sein est une pathologie fréquente pour lequel les examens TEP/TDM au ¹⁸F-FDG et IRM mammaire sont fréquemment réalisés en routine. Il existe cependant une sous-utilisation des informations apportées par chacune de ces techniques d'imagerie. En pratique, l’interprétation de ces examens est principalement basée sur l’analyse visuelle et l'analyse « quantitative » se résume généralement au SUVmax seul en TEP/TDM et à l’étude du rehaussement du signal après injection de produit de contraste en IRM mammaire (DCE-MRI). L’arrivée de nouvelles machines hybrides TEP/ IRM, nous a amené à év
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Mahon, Rebecca N. "Advanced Imaging Analysis for Predicting Tumor Response and Improving Contour Delineation Uncertainty." VCU Scholars Compass, 2018. https://scholarscompass.vcu.edu/etd/5516.

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ADVANCED IMAGING ANALYSIS FOR PREDICTING TUMOR RESPONSE AND IMPROVING CONTOUR DELINEATION UNCERTAINTY By Rebecca Nichole Mahon, MS A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at Virginia Commonwealth University. Virginia Commonwealth University, 2018 Major Director: Dr. Elisabeth Weiss, Professor, Department of Radiation Oncology Radiomics, an advanced form of imaging analysis, is a growing field of interest in medicine. Radiomics seeks to extract quantitative information from images through use of computer vision techniques to as
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Oliver, Jasmine Alexandria. "Increasing 18F-FDG PET/CT Capabilities in Radiotherapy for Lung and Esophageal Cancer via Image Feature Analysis." Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6123.

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Positron Emission Tomography (PET) is an imaging modality that has become increasingly beneficial in Radiotherapy by improving treatment planning (1). PET reveals tumor volumes that are not well visualized on computed tomography CT or MRI, recognizes metastatic disease, and assesses radiotherapy treatment (1). It also reveals areas of the tumor that are more radiosensitive allowing for dose painting - a non-homogenous dose treatment across the tumor (1). However, PET is not without limitations. The quantitative unit of PET images, the Standardized Uptake Value (SUV), is affected by many factor
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Prasanna, Prateek. "NOVEL RADIOMICS FOR SPATIALLY INTERROGATING TUMOR HABITAT: APPLICATIONS IN PREDICTING TREATMENT RESPONSE AND SURVIVAL IN BRAIN TUMORS." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case149624929700524.

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Chirra, Prathyush V. Chirra. "EMPIRICAL EVALUATION OFCROSS-SITE REPRODUCIBILITY ANDDISCRIMINABILITY OF RADIOMICFEATURES FOR CHARACTERIZINGTUMOR APPEARANCE ON PROSTATEMRI." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1528456281983062.

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Basu, Satrajit. "Developing Predictive Models for Lung Tumor Analysis." Scholar Commons, 2012. http://scholarcommons.usf.edu/etd/3963.

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A CT-scan of lungs has become ubiquitous as a thoracic diagnostic tool. Thus, using CT-scan images in developing predictive models for tumor types and survival time of patients afflicted with Non-Small Cell Lung Cancer (NSCLC) would provide a novel approach to non-invasive tumor analysis. It can provide an alternative to histopathological techniques such as needle biopsy. Two major tumor analysis problems were addressed in course of this study, tumor type classification and survival time prediction. CT-scan images of 109 patients with NSCLC were used in this study. The first involved classifyi
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Books on the topic "Radiomics analysis"

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Ma, Xuelei, Lei Deng, Rong Tian, and Chunxiao Guo, eds. Novel Methods for Oncologic Imaging Analysis: Radiomics, Machine Learning, and Artificial Intelligence. Frontiers Media SA, 2021. http://dx.doi.org/10.3389/978-2-88971-347-9.

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Book chapters on the topic "Radiomics analysis"

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Veeraraghavan, Harini. "Radiomics analysis for gynecologic cancers." In Radiomics and Radiogenomics. Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9781351208277-19.

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Ghosh, Adarsh, and Suraj D. Serai. "Radiomics and Texture Analysis." In Advanced Clinical MRI of the Kidney. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-40169-5_27.

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Chen, Qingfeng. "Fusion and Radiomics Study of Multimodal Medical Images." In Association Analysis Techniques and Applications in Bioinformatics. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-8251-6_10.

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Yang, Jiancheng, Rongyao Fang, Bingbing Ni, Yamin Li, Yi Xu, and Linguo Li. "Probabilistic Radiomics: Ambiguous Diagnosis with Controllable Shape Analysis." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32226-7_73.

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Morvan, Ludivine, Cristina Nanni, Anne-Victoire Michaud, et al. "Learned Deep Radiomics for Survival Analysis with Attention." In Predictive Intelligence in Medicine. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59354-4_4.

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El Naqa, Issam. "Computerized Prediction of Treatment Outcomes and Radiomics Analysis." In Image-Based Computer-Assisted Radiation Therapy. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-2945-5_14.

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Shi, Zhenwei, Chong Zhang, Inge Compter, et al. "A Feature-Pooling and Signature-Pooling Method for Feature Selection for Quantitative Image Analysis: Application to a Radiomics Model for Survival in Glioma." In Radiomics and Radiogenomics in Neuro-oncology. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40124-5_8.

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Klontzas, Michail E., and Renato Cuocolo. "Machine Learning Methods for Radiomics Analysis: Algorithms Made Easy." In Imaging Informatics for Healthcare Professionals. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-25928-9_4.

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Piantadosi, Gabriele, Giampaolo Bovenzi, Giuseppe Argenziano, et al. "Skin Lesions Classification: A Radiomics Approach with Deep CNN." In New Trends in Image Analysis and Processing – ICIAP 2019. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30754-7_26.

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Ali, Muhammad, Viviana Benfante, Giuseppe Cutaia, et al. "Prostate Cancer Detection: Performance of Radiomics Analysis in Multiparametric MRI." In Image Analysis and Processing - ICIAP 2023 Workshops. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-51026-7_8.

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Conference papers on the topic "Radiomics analysis"

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Yadav, Neha, Andrew Turangan, Huawei Han, et al. "Systematic Approach to Identifying Sources of Variation in CT Radiomics: A Phantom Study." In 2024 20th International Symposium on Medical Information Processing and Analysis (SIPAIM). IEEE, 2024. https://doi.org/10.1109/sipaim62974.2024.10783586.

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Filos, Dimitris, Dimitris Fotopoulos, Maria Anastasia Rouni, and Ioanna Chouvarda. "Machine Learning-Based Whole Gland Radiomics Analysis for Prostate Cancer Classification." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635588.

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Fields, Jacquelyn, Steven Cen, Xiaomeng Lei, et al. "CEM Radiomics for Distinguishing Benign vs Malignant Lesions in Patients with Invasive Breast Cancer or Benign Breast Lesions." In 2024 20th International Symposium on Medical Information Processing and Analysis (SIPAIM). IEEE, 2024. https://doi.org/10.1109/sipaim62974.2024.10783603.

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Mylona, Eugenia, Dimitrios I. Zaridis, Charalampos N. Kalantzopoulos, et al. "Large-Scale Radiomics Analysis for Prostate Cancer Detection Harnessing Machine and Deep Learning Models." In 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI). IEEE, 2025. https://doi.org/10.1109/isbi60581.2025.10980708.

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Smith, R. L., K. Al-Battat, R. John, et al. "From Radiomics to Deep Learning: Leveraging Gramian Matrix Features in CNNs for NSCLC Survival Analysis." In 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD). IEEE, 2024. http://dx.doi.org/10.1109/nss/mic/rtsd57108.2024.10657697.

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Peoples, Jacob J., Mohammad Hamghalam, Joshua Virani-Wall, et al. "Worse is better? Performance and bias implications of feature selection in radiomics-based survival analysis." In Computer-Aided Diagnosis, edited by Susan M. Astley and Axel Wismüller. SPIE, 2025. https://doi.org/10.1117/12.3047247.

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Taş, Muhammed Oğuz, and Hasan Serhan Yavuz. "Survival Analysis in Lung Cancer: A Comparative Study of Different Approaches Using NSCLC-Radiomics (Lung1) Data." In 2024 Innovations in Intelligent Systems and Applications Conference (ASYU). IEEE, 2024. https://doi.org/10.1109/asyu62119.2024.10757041.

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Ahmadyar, Y., R. Samimi, A. Kamali-Asl, J. Majidpour, H. Arabi, and H. Zaidi. "Predicting Neoadjuvant Therapy Response in Breast Cancer Patients via Radiomics Analysis of Dynamic Contrast-Enhanced MRI Imaging Features." In 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD). IEEE, 2024. http://dx.doi.org/10.1109/nss/mic/rtsd57108.2024.10655295.

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Azarianpour Esfahani, Sepideh, Ammar Hoori, Tao Hu, Sadeer Al-Kindi, Sanjay Rajagopalan, and David L. Wilson. "Improving cardiovascular risk assessment through comprehensive radiomics analysis of epicardial adipose tissue in screening non-contrast CT calcium score images." In Clinical and Biomedical Imaging, edited by Barjor S. Gimi and Andrzej Krol. SPIE, 2025. https://doi.org/10.1117/12.3047461.

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Placzek, Fabian, Katarína Benčurová, Khashayar Memarpour, et al. "Optical coherence tomography (OCT) as a new tool for xenograft development assessment: automated radiomics on OCT/OCT-angiography data of an in ovo xenograft model derived from colorectal cancer liver metastasis." In Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XXIII, edited by Attila Tarnok, Jessica P. Houston, and Xuantao Su. SPIE, 2025. https://doi.org/10.1117/12.3041727.

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Reports on the topic "Radiomics analysis"

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Ouyang, Zhiqiang, Qian Li, Guangrong Zheng, Tengfei Ke, Jun Yang, and Chengde Liao. Radiomics for predicting tumor microenvironment phenotypes in non-small cell lung cance: A systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.9.0060.

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Review question / Objective: Tumor microenvironment (TIME) phenotype is an important factor to affect the response and prognosis of immunotherapy in non-small cell lung cancer (NSCLC). Recently, accumulating studies have noninvasivly perdited the TIME phenotypes of NSCLC by using CT or PET/CT based radiomics. We will conduct this study by means of meta-analysis to eveluate the power and value of CT or PET/CT based radiomics for predicting TIME phenotypes in NSCLC patients. Condition being studied: At present, several recent prospective or retrospective cohort studies and randomized controlled
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Chen, Jie, Xinyue Zhang, Chi Xu, and Kefu Liu. Diagnostic Performance of Radiomics Analysis for Pulmonary Cancer Airway Spread: A Systematic Review and Meta-Analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2024. http://dx.doi.org/10.37766/inplasy2024.10.0103.

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Wang, Chih-Keng, Ting-Wei Wang, Chia-Fung Lu, and Yu-Te Wu. Deciphering the Prognostic Efficacy of MRI Radiomics in Nasopharyngeal Carcinoma: A Comprehensive Meta-Analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2024. http://dx.doi.org/10.37766/inplasy2024.2.0101.

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Chang, Ke-Vin. Ultrasound Radiomics for Diagnosing Carpal Tunnel Syndrome: a Protocol for Systematic Review and Meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2023. http://dx.doi.org/10.37766/inplasy2023.9.0069.

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Yang, Jiawen, Shuzong You, Limin Zhang, et al. Prediction Power of Radiomics in Early Recurrence of Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.1.0099.

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Wang, Yingxuan, Cheng Yan, and Liqin Zhao. The value of radiomics-based machine learning for hepatocellular carcinoma after TACE: a systematic evaluation and Meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.6.0100.

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Review question / Objective: Meta-analysis was performed to predict the efficacy and survival status of patients with hepatocellular carcinoma after the application of TACE, applying clinical models, radiomic models and combined models for non-invasive assessment.We performed a Meta-analysis on the prediction of efficacy and survival status after TACE for hepatocellular carcinoma. Condition being studied: Patients were scanned using CT or MR machines, and some patients had multiple follow-up records, and imaging feature extraction software was applied to extract regions of interest and build m
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zheng, xiushan. CT-based radiomics for prediction of lymph node metastasis in lung cancer A protocol for systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.3.0167.

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