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Responsible Radiomics Research for Faster Clinical Translation

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Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms

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Radiomics for classification of bone mineral loss: A machine learning study

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A Delta-radiomics model for preoperative evaluation of Neoadjuvant chemotherapy response in high-grade osteosarcoma

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Noninvasive Fuhrman grading of clear cell renal cell carcinoma using computed tomography radiomic features and machine learning

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Radiomics-based machine learning model to predict risk of death within 5-years in clear cell renal cell carcinoma patients

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Delta-radiomics signature predicts treatment outcomes after preoperative chemoradiotherapy and surgery in rectal cancer

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Radiomics-Based Pretherapeutic Prediction of Non-response to Neoadjuvant Therapy in Locally Advanced Rectal Cancer

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CT imaging markers to improve radiation toxicity prediction in prostate cancer radiotherapy by stacking regression algorithm

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Machine Learning-based Analysis of Rectal Cancer MRI Radiomics for Prediction of Metachronous Liver Metastasis

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Repeatability of radiomic features in magnetic resonance imaging of glioblastoma: Test–retest and image registration analyses

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Delta-radiomics features during radiotherapy improve the prediction of late xerostomia

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Oropharyngeal squamous cell carcinoma: radiomic machine-learning classifiers from multiparametric MR images for determination of HPV infection status

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Quantitative radiomic biomarkers for discrimination between neuromyelitis optica spectrum disorder and multiple sclerosis

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Association between Race and Cancer-Related Mortality among Patients with Colorectal Cancer in the United States: A Retrospective Cohort Study

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Prediction of Response to Neoadjuvant Chemoradiotherapy by MRI-Based Machine Learning Texture Analysis in Rectal Cancer Patients

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PET/CT Radiomic Sequencer for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients

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Robustness of radiomic breast features of benign lesions and luminal A cancers across MR magnet strengths

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Residual haemopoietic damage in the mouse after fractionated gamma-irradiation, down to 0.1 Gy per fraction

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Delta radiomics: a systematic review

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Non-small cell lung carcinoma histopathological subtype phenotyping using high-dimensional multinomial multiclass CT radiomics signature

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Non-contrast Cine Cardiac Magnetic Resonance image radiomics features and machine learning algorithms for myocardial infarction detection

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Impact of feature harmonization on radiogenomics analysis: Prediction of EGFR and KRAS mutations from non-small cell lung cancer PET/CT images

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[18F]FDG-PET/CT Radiomics and Artificial Intelligence in Lung Cancer: Technical Aspects and Potential Clinical Applications

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COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients

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Overall Survival Prediction in Renal Cell Carcinoma Patients Using Computed Tomography Radiomic and Clinical Information

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AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging

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Artificial intelligence for dementia prevention

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Myocardial Perfusion SPECT Imaging Radiomic Features and Machine Learning Algorithms for Cardiac Contractile Pattern Recognition

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Role of Machine Learning in Precision Oncology: Applications in Gastrointestinal Cancers

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Prediction of Response to Induction Chemotherapy Plus Concurrent Chemoradiotherapy for Nasopharyngeal Carcinoma Based on MRI Radiomics and Delta Radiomics: A Two-Center Retrospective Study

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Radiomics-guided radiation therapy: opportunities and challenges

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Ability of Delta Radiomics to Predict a Complete Pathological Response in Patients with Loco-Regional Rectal Cancer Addressed to Neoadjuvant Chemo-Radiation and Surgery

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A clinical‐radiomics model incorporating T2‐weighted and diffusion‐weighted magnetic resonance images predicts the existence of lymphovascular invasion / perineural invasion in patients with colorectal cancer

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Towards reliable head and neck cancers locoregional recurrence prediction using delta‐radiomics and learning with rejection option

Wang, K., Dohopolski, M. J., Zhang, Q., Sher, D. J., & Wang, J. (2022) · Medical Physics · doi:10.1002/mp.16132 · Cited by 26 · see citing works

MRI-based pre-Radiomics and delta-Radiomics models accurately predict the post-treatment response of rectal adenocarcinoma to neoadjuvant chemoradiotherapy

Wang, L., Wu, X., Tian, R., Ma, H., Jiang, Z., Zhao, W., Cui, G., Li, M., Hu, Q., Yu, X., et al. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1133008 · Cited by 26 · see citing works

Diagnosis of COVID-19 Using CT image Radiomics Features: A Comprehensive Machine Learning Study Involving 26,307 PatientsPreprint

Shiri, I., Salimi, Y., Saberi, A., Pakbin, M., Hajianfar, G., Avval, A. H., Sanaat, A., Akhavanallaf, A., Mostafaei, S., Mansouri, Z., et al. (2021) · medRxiv · doi:10.1101/2021.12.07.21267367 · Cited by 26 · see citing works

Advancements and Challenges in the Image-Based Diagnosis of Lung and Colon Cancer: A Comprehensive Review

Patharia, P., Sethy, P. K., & Nanthaamornphong, A. (2024) · Cancer Informatics · doi:10.1177/11769351241290608 · Cited by 24 · see citing works

CT-based radiomics signature analysis for evaluation of response to induction chemotherapy and progression-free survival in locally advanced hypopharyngeal carcinoma

Liu, X., Long, M., Sun, C., Yang, Y., Lin, P., Shen, Z., Xia, S., & Shen, W. (2022) · European Radiology · doi:10.1007/s00330-022-08859-4 · Cited by 23 · see citing works

A Tumoral and Peritumoral CT-Based Radiomics and Machine Learning Approach to Predict the Microsatellite Instability of Rectal Carcinoma

Yuan, H., Peng, Y., Xu, X., Tu, S., Wei, Y., & Ma, Y. (2022) · Cancer Management and Research · doi:10.2147/cmar.s377138 · Cited by 18 · see citing works

MRI-based radiomics to predict neoadjuvant chemoradiotherapy outcomes in locally advanced rectal cancer: A multicenter study

Xiang, Y., Li, S., Wang, H., Song, M., Hu, K., Wang, F., Wang, Z., Niu, Z., Liu, J., Cai, Y., et al. (2022) · Clinical and Translational Radiation Oncology · doi:10.1016/j.ctro.2022.11.009 · Cited by 14 · see citing works

Construction of machine learning-based models for cancer outcomes in low and lower-middle income countries: A scoping review

Adeoye, J., Akinshipo, A., Koohi‐Moghadam, M., Thomson, P. J., & Su, Y. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.976168 · Cited by 14 · see citing works

Response prediction for neoadjuvant treatment in locally advanced rectal cancer patients-improvement in decision-making: A systematic review

Boldrini, L., Charles-Davies, D., Romano, A., Mancino, M., Nacci, I., Tran, H. E., Bono, F., Boccia, E., Gambacorta, M. A., & Chiloiro, G. (2024) · European Journal of Surgical Oncology · doi:10.1016/j.ejso.2024.109463 · Cited by 12 · see citing works

Radiomic Phenotypes for Improving Early Prediction of Survival in Stage III Non-Small Cell Lung Cancer Adenocarcinoma after Chemoradiation

Luna, J. M., Barsky, A. R., Shinohara, R. T., Roshkovan, L., Hershman, M., Dreyfuss, A. D., Horng, H., Lou, C., Noël, P. B., Cengel, K. A., et al. (2022) · Cancers · doi:10.3390/cancers14030700 · Cited by 12 · see citing works

Radiomics in Hypopharyngeal Cancer Management: A State-of-the-Art Review

Chiesa-Estomba, C. M., Mayo‐Yáñez, M., Guntinas‐Lichius, O., Poorten, V. V., Takes, R. P., de Bree, R., Halmos, G. B., Saba, N. F., Nuyts, S., & Ferlito, A. (2023) · Biomedicines · doi:10.3390/biomedicines11030805 · Cited by 11 · see citing works

Two-stage machine learning models for bowel lesions characterisation using self-propelled capsule dynamics

Afebu, K. O., Tian, J., Papatheou, E., Liu, Y., & Prasad, S. (2023) · Nonlinear Dynamics · doi:10.1007/s11071-023-08852-6 · Cited by 8 · see citing works

Significance of MRI-based radiomics in predicting pathological complete response to neoadjuvant chemoradiotherapy of locally advanced rectal cancer: A narrative review

Li, Y., Liu, X., Gu, M., Xu, T., Ge, C., & Chang, P. D. (2024) · Cancer/Radiothérapie · doi:10.1016/j.canrad.2024.04.003 · Cited by 8 · see citing works

Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine

Dang, M., Liu, B., Chen, Y., & Zhang, Z. (2026) · Psychoradiology · doi:10.1093/psyrad/kkag007 · Cited by 7 · see citing works

PET Image Radiomics Feature Variability in Lung Cancer: Impact of Image Segmentation

Hosseini, S. A., Hajianfar, G., Shiri, I., & Zaidi, H. (2021) · IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) · doi:10.1109/nss/mic44867.2021.9875904 · Cited by 7 · see citing works

Development and validation of machine-learning model based on dynamic tumor markers in predicting pathological complete response after neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer: a multicenter cohort study

Chen, B., Peng, T., Pan, Z., Li, S., Wang, Y., Zheng, S., Zhuang, J., Liu, X., Lu, X., Zeng, C., et al. (2025) · International Journal of Colorectal Disease · doi:10.1007/s00384-025-04993-9 · Cited by 6 · see citing works

Is It Really Gone? Assessing Response to Neoadjuvant Therapy in Rectal Cancer

Kimura, C. M. S., Crowder, S., & Kin, C. (2022) · Journal of Gastrointestinal Cancer · doi:10.1007/s12029-022-00889-x · Cited by 6 · see citing works

Improving prediction of treatment response and prognosis in colorectal cancer with AI-based medical image analysis

Liu, X.-Y., Zhang, S., Shao, L., Sun, C., Li, B., Wei, W., Ying, Z., Liu, Z., & Tian, J. (2024) · The Innovation Medicine · doi:10.59717/j.xinn-med.2024.100069 · Cited by 8 · see citing works

Survival Prognostic Modeling Using PET/CT Image Radiomics: The Quest for Optimal Approaches

Amini, M., Hajianfar, G., Nazari, M., Mehri-Kakavand, G., Shiri, I., & Zaidi, H. (2021) · IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) · doi:10.1109/nss/mic44867.2021.9875619 · Cited by 6 · see citing works

Prediction of Radiation Treatment Response for Locally Advanced Rectal Cancer via a Longitudinal Trend Analysis Framework on Cone-Beam CT

Li, Z., Raldow, A. C., Weidhaas, J. B., Zhou, Q., & Qi, X. S. (2023) · Cancers · doi:10.3390/cancers15215142 · Cited by 5 · see citing works

Colon Cancer Detection using Deep Learning Algorithm

Srivani, E. N., & Seshikala, G. (2024) · International Conference on Integrated Circuits and Communication Systems (ICICACS) · doi:10.1109/icicacs60521.2024.10498515 · Cited by 5 · see citing works

Application of Radiomics in Predicting Treatment Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer: Strategies and Challenges

Chen, R., Fu, Y. H. H., Yi, X., Pei, Q., Zai, H., & Chen, B. T. (2022) · Journal of Oncology · doi:10.1155/2022/1590620 · Cited by 4 · see citing works

Do treated rectal tumors appear differently on MRI after chemotherapy versus chemoradiotherapy?

Yu, S., Wen, Y., Bi, L., Yang, X., Gong, X., Deng, X., Meng, W., & Wang, Z. (2023) · Abdominal Radiology · doi:10.1007/s00261-023-04115-5 · Cited by 4 · see citing works

Lymphovascular Invasion Prediction in Lung Cancer Using Multi-Segmentation PET Radiomics and Multi-Machine Learning Algorithms

Hosseini, S. A., Hajianfar, G., Shiri, I., & Zaidi, H. (2021) · IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) · doi:10.1109/nss/mic44867.2021.9875817 · Cited by 4 · see citing works

Computed tomography-based radiomics predicts prognostic and treatment-related levels of immune infiltration in the immune microenvironment of clear cell renal cell carcinoma

Song, S., Ge, W., Qi, X., Che, X., Wang, Q., & Wu, G. (2025) · BMC Medical Imaging · doi:10.1186/s12880-025-01749-3 · Cited by 3 · see citing works

Multi-Scale Temporal Imaging: From Micro- and Meso- to Macro-scale-time Nuclear Medicine

Farhadi, F., Rajagopal, J. R., Veziroglu, E. M., Abdollahi, H., Shiri, I., Nikpanah, M., Morris, M. A., Zaidi, H., Rahmim, A., & Saboury, B. (2022) · PET Clinics · doi:10.1016/j.cpet.2022.09.008 · Cited by 3 · see citing works

Feasibility of delta radiomics–based pCR prediction for rectal cancer patients treated with magnetic resonance–guided adaptive radiotherapy

Wu, J., Xiao, J., Li, Y., Wu, F., Peng, Q., Li, C., Tang, B., & Orlandini, L. C. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1230519 · Cited by 3 · see citing works

Machine-learning based Computed Tomography Radiomics Nomogram for Predicting Perineural Invasion in Gastric Cancer

Huang, P. Y., Li, S., Deng, Z., Hu, F., Jin, D., Xiong, S., & Fan, B. (2025) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/0115734056323323250102073559 · Cited by 3 · see citing works

Lung Cancer Recurrence Prediction Using Radiomics Features of PET Tumor Sub-Volumes and Multi-Machine Learning Algorithms

Hosseini, S. A., Hajianfar, G., Shiri, I., & Zaidi, H. (2021) · IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) · doi:10.1109/nss/mic44867.2021.9875889 · Cited by 3 · see citing works

Machine Learning for Radiomics in Oncology: Challenges, Limitations, and Future Directions

Missaoui, R., Saadaoui, W., Coco, M. D., Helali, A., Leo, M., & Carcagnì, P. (2026) · Sensors · doi:10.3390/s26144619 · Cited by 2 · see citing works

ComBat Harmonization of Image Reconstruction Parameters to Improve the Repeatability of Radiomics Features

Hajianfar, G., Avval, A. H., Sabouri, M., Khateri, M., Jenabi, E., Geramifar, P., Oveisi, M., Shiri, I., & Zaidi, H. (2021) · IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) · doi:10.1109/nss/mic44867.2021.9875440 · Cited by 2 · see citing works

AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging: Towards Radiophenomics

Yousefirizi, F., Decazes, P., Amyar, A., Ruan, S., Saboury, B., & Rahmim, A. (2021) · PubMed · doi:10.48550/arxiv.2110.10332 · Cited by 2 · see citing works

Cardiac Pattern Recognition from SPECT Images Using Machine Learning Algorithms

Sabouri, M., Hajianfar, G., Amini, M., Hosseini, Z., Madadi, S., Ghaedian, T., Ghassed, M., Rastgou, F., Bitarafan‐Rajabi, A., Shiri, I., et al. (2021) · IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) · doi:10.1109/nss/mic44867.2021.9875936 · Cited by 2 · see citing works

Pathological Prostate Gleason Score Prediction Using MRI Radiomics and Machine Learning Algorithms

Bagheri, S., Hajianfar, G., Saberi, A., Oveisi, M., Shiri, I., & Zaidi, H. (2021) · IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) · doi:10.1109/nss/mic44867.2021.9875791 · Cited by 2 · see citing works

Ischemic Stroke Post-treatment Prediction using Machine Learning to develop Web Application for Healthcare Centers in Thailand

Worraviseat, K., Danvirutai, P., Anutrakulchai, S., Kasemsap, N., & Srichan, C. (2023) · International Electrical Engineering Congress (iEECON) · doi:10.1109/ieecon56657.2023.10126975 · Cited by 2 · see citing works

On the Removal of Inter/Intra Observer Variability of PET Radiomics Features

Shiri, I., Amini, M., Hajianfar, G., Avval, A. H., Sanaat, A., Arabi, H., & Zaidi, H. (2021) · IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) · doi:10.1109/nss/mic44867.2021.9875640 · Cited by 1 · see citing works

Application of contrast-enhanced CT-based radiomics with machine learning to predict WHO/ISUP grading in clear cell renal cell carcinoma

Li, X., Song, S., Ge, W., Wang, H., Liu, W., Qi, X., & Zhao, Y. (2026) · BMC Cancer · doi:10.1186/s12885-026-15794-w · Cited by 1 · see citing works

MRI Radiomics Features for Prediction of Treatment Response in Colorectal Patients

Shayesteh, S. P., Nazari, M., Salahshour, A., Sandoughdaran, S., Jozian, F., Joybari, A. Y., Hajianfar, G., Feyzabad, S. H. F., Khateri, M., Shiri, I., et al. (2020) · IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) · doi:10.1109/nss/mic42677.2020.9508060 · Cited by 1 · see citing works

Survival-Based Treatment Planning Using Stage-Specific Machine Learning Models

Farrag, A., Fadlullah, Z. M., Fouda, M. M., & Almalki, N. S. (2023) · IEEE Access · doi:10.1109/access.2023.3337117 · Cited by 1 · see citing works

Radiomics prediction of response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer

Lyu, J., Pang, Z., & Sun, J. (2024) · Radiology Science · doi:10.15212/radsci-2023-0005 · Cited by 1 · see citing works

Late Gadolinium Enhanced Cardiac Magnetic Resonance Imaging Radiomics For High Precision Differentiation of Scar and Viable Cardiac Tissues

Avard, E., Shiri, I., Hajianfar, G., Abdollahi, H., Kasani, K., Rezaei‐Kalantari, K., Bitarafan‐Rajabi, A., Deevband, M. R., Oveisi, M., & Zaidi, H. (2020) · IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) · doi:10.1109/nss/mic42677.2020.9507746 · Cited by 1 · see citing works

Threat Matrix: A Fast Algorithm for Human–Machine Chinese Ludo Gaming

Han, F., & Zhou, M. (2022) · Electronics · doi:10.3390/electronics11111699 · Cited by 1 · see citing works

A tumoral and peritumoral CT-based Radiomics and Machine learning approach to predict the microsatellite instability of rectal carcinomaPreprint

Ma, Y., Yuan, H., Peng, Y., Xu, X., Tu, S., & Wei, Y. (2022) · Research Square · doi:10.21203/rs.3.rs-1305223/v1 · Cited by 1 · see citing works

Developing Bayesian Networks based Prognostic Radiomics Model for Clear Cell Renal Cell Carcinoma Patients

Nazari, M., Shiri, I., & Zaidi, H. (2020) · IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) · doi:10.1109/nss/mic42677.2020.9507825

A tumoral and peritumoral CT-based Radiomics and Machine learning approach to predict the microsatellite instability of rectal carcinomaPreprint

Yu, P., Xu, X., Tu, S., Wei, Y., Ma, Y., & Yuan, H. (2022) · Research Square · doi:10.21203/rs.3.rs-1305223/v2

CT delta-radiomics for identification of advanced EGFR-Mutated lung adenocarcinoma Sensitive to Therapies Targeting EGFR PathwayPreprint

Zou, J., Mao, H., Zhu, F., He, B., Zhao, L., Yang, C., & Zhao, Z. (2022) · Research Square · doi:10.21203/rs.3.rs-1746619/v1

Holistic MRI-based Prognostic Model Development Using Genomic and Histopathologic Features in Glioblastoma Patients

Avval, A. H., Mostafaei, S., Oveisi, M., Shiri, I., & Zaidi, H. (2021) · IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) · doi:10.1109/nss/mic44867.2021.9875744

Prediction of Myocardial Function after Coronary Artery Bypass Graft using Cardiac Magnetic Resonance Imaging Radiomics

Arian, F., Avval, A. H., Mostafaei, S., Shahbazi, Z., Bitarafan‐Rajabi, A., Rezaei‐Kalantari, K., Kasani, K., Bagherpour, Z., Oveisi, M., Shiri, I., et al. (2021) · IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) · doi:10.1109/nss/mic44867.2021.9875764

Recognizing Non-small Cell Lung Cancer Subtypes by a Constraint-Based Causal Network from CT Images

Deng, Z., Qian, S., Qi, J., Liu, L., & Xu, B. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26422-1_24

Prediction of the efficacy of neoadjuvant chemoradiotherapy in patients with rectal cancer based on a texture analysis of T2-weighted magnetic resonance tumor image obtained at primary staging

Dayneko, Y. A., Berezovskaya, T. P., Mirzeabasov, O. A., Старков, С. О., Myalina, S. A., Nevolskikh, A. A., Иванов, С. А., & Каприн, А. Д. (2024) · Digital Diagnostics · doi:10.17816/dd628304

Artificial intelligence applied to the diagnosis of colorectal cancer

Jiménez-Pastor, A., & Prieto-de-la-Lastra, C. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-29048-0.00016-1

Real time multimodal diagnosis and treatment planning for comprehensive oncological care

Priyadharshini, K., Mohan, D., Sharma, M., & Priyaa, S. (2025) · International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC) · doi:10.1109/isacc65211.2025.10969344

A Systematic Review of AI-Based Image Processing Techniques for Colon Cancer Diagnosis

Li, J., Goh, W., & Jhanjhi, N. Z. (2025) · International Conference on Emerging Trends in Networks and Computer Communications (ETNCC) · doi:10.1109/etncc66224.2025.11299760

Nodal Staging and Response Assessment in Locally Advanced Mismatch Repair–Deficient Colon and Rectal Cancer in the Era of Neoadjuvant Immune Checkpoint Inhibitors

Ryan, É. J., O’Reilly, M., Rogers, E. L., MacDermott, R., Cotter, M., Keane, F., McDermott, R., Martin, S., Sheahan, K. D., & Winter, D. (2026) · Lymphatics · doi:10.3390/lymphatics4030036

MRI Radiomics Features for Prediction of Treatment Response in Colorectal Patients

Shayesteh, S. P., Nazari, M., Salahshour, A., Sandoughdaran, S., Jozian, F., Joybari, A. Y., Hajianfar, G., Feyzabad, S. H. F., Khateri, M., Shiri, I., et al. (2020) · IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) · doi:10.1109/nss/mic42677.2020.9508060 · Cited by 1 · see citing works

Radiomics of locally advanced rectal cancer: machine learning-based prediction of response to neoadjuvant chemoradiotherapy using pre-treatment sagittal T2-weighted MRI

Yardımcı, A. H., Koçak, B., Sel, İ., Bulut, H., Bektaş, C. T., Cin, M., Dursun, N., Bektaş, H., Mermut, Ö., Yardımcı, V. H., et al. (2022) · Japanese Journal of Radiology · doi:10.1007/s11604-022-01325-7 · Cited by 36 · see citing works

MRI-based pre-Radiomics and delta-Radiomics models accurately predict the post-treatment response of rectal adenocarcinoma to neoadjuvant chemoradiotherapy

Wang, L., Wu, X., Tian, R., Ma, H., Jiang, Z., Zhao, W., Cui, G., Li, M., Hu, Q., Yu, X., et al. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1133008 · Cited by 26 · see citing works

MRI-based clinical-radiomics model predicts tumor response before treatment in locally advanced rectal cancer

Pizzi, A. D., Chiarelli, A. M., Chiacchiaretta, P., d’Annibale, M., Croce, P., Rosa, C., Mastrodicasa, D., Trebeschi, S., Lambregts, D. M. J., Caposiena, D., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-84816-3 · Cited by 98 · see citing works

Prediction of pathological response and lymph node metastasis after neoadjuvant therapy in rectal cancer through tumor and mesorectal MRI radiomic features

Qin, S., Liu, K., Chen, Y., Zhou, Y., Zhao, W., Yan, R., Xin, P., Zhu, Y., Wang, H., & Lang, N. (2024) · Scientific Reports · doi:10.1038/s41598-024-72916-9 · Cited by 21 · see citing works