{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T07:42:31Z","timestamp":1790667751491,"version":"4.1.0"},"reference-count":178,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T00:00:00Z","timestamp":1655424000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology of Taiwan","doi-asserted-by":"publisher","award":["MOST 109-2221-E-011-018-MY3"],"award-info":[{"award-number":["MOST 109-2221-E-011-018-MY3"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Precision oncology, which ensures optimized cancer treatment tailored to the unique biology of a patient\u2019s disease, has rapidly developed and is of great clinical importance. Deep learning has become the main method for precision oncology. This paper summarizes the recent deep-learning approaches relevant to precision oncology and reviews over 150 articles within the last six years. First, we survey the deep-learning approaches categorized by various precision oncology tasks, including the estimation of dose distribution for treatment planning, survival analysis and risk estimation after treatment, prediction of treatment response, and patient selection for treatment planning. Secondly, we provide an overview of the studies per anatomical area, including the brain, bladder, breast, bone, cervix, esophagus, gastric, head and neck, kidneys, liver, lung, pancreas, pelvis, prostate, and rectum. Finally, we highlight the challenges and discuss potential solutions for future research directions.<\/jats:p>","DOI":"10.3390\/diagnostics12061489","type":"journal-article","created":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T11:45:44Z","timestamp":1655466344000},"page":"1489","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["A Survey on Deep Learning for Precision Oncology"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9992-6863","authenticated-orcid":false,"given":"Ching-Wei","family":"Wang","sequence":"first","affiliation":[{"name":"Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan"},{"name":"Graduate Institute of Applied Science and Technology, National Taiwan University of Science and Technology, Taipei 106335, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad-Adil","family":"Khalil","sequence":"additional","affiliation":[{"name":"Graduate Institute of Applied Science and Technology, National Taiwan University of Science and Technology, Taipei 106335, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3068-8198","authenticated-orcid":false,"given":"Nabila","family":"Firdi","sequence":"additional","affiliation":[{"name":"Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1038\/s41698-018-0055-0","article-title":"Recent advances in precision oncology research","volume":"2","author":"Bode","year":"2018","journal-title":"NPJ Precis. Oncol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"916","DOI":"10.1016\/j.ccell.2021.04.002","article-title":"Artificial intelligence for clinical oncology","volume":"39","author":"Kann","year":"2021","journal-title":"Cancer Cell"},{"key":"ref_3","first-page":"2177","article-title":"A review on application of deep learning algorithms in external beam radiotherapy automated treatment planning","volume":"10","author":"Wang","year":"2020","journal-title":"Front. Oncol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1002\/acm2.13130","article-title":"Feasibility of automated planning for whole-brain radiation therapy using deep learning","volume":"22","author":"Yu","year":"2021","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1002\/mp.14563","article-title":"Deep-learning-based radiomics predicts response to chemotherapy in colorectal liver metastases","volume":"48","author":"Wei","year":"2021","journal-title":"Med. Phys."},{"key":"ref_6","first-page":"743","article-title":"A multi-omics-based serial deep-learning approach to predict clinical outcomes of single-agent anti-PD-1\/PD-L1 immunotherapy in advanced stage non-small-cell lung cancer","volume":"13","author":"Yang","year":"2021","journal-title":"Am. J. Transl. Res."},{"key":"ref_7","first-page":"1","article-title":"Deep learning\u2013based survival analysis identified associations between molecular subtype and optimal adjuvant treatment of patients with gastric cancer","volume":"2","author":"Lee","year":"2018","journal-title":"JCO Clin. Cancer Inform."},{"key":"ref_8","first-page":"901","article-title":"Prediction of Target-Drug Therapy by Identifying Gene Mutations in Lung Cancer With Histopathological Stained Image and Deep Learning Techniques","volume":"11","author":"Huang","year":"2021","journal-title":"Front. Oncol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5441","DOI":"10.1007\/s00330-019-06082-2","article-title":"Convolutional neural network to predict the local recurrence of giant cell tumor of bone after curettage based on pre-surgery magnetic resonance images","volume":"29","author":"He","year":"2019","journal-title":"Eur. Radiol."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Liu, Y., Stojadinovic, S., Hrycushko, B., Wardak, Z., Lau, S., Lu, W., Yan, Y., Jiang, S.B., Zhen, X., and Timmerman, R. (2017). A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0185844"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Nie, D., Zhang, H., Adeli, E., Liu, L., and Shen, D. (2016, January 17\u201321). 3D deep learning for multi-modal imaging-guided survival time prediction of brain tumor patients. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Athens, Greece.","DOI":"10.1007\/978-3-319-46723-8_25"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"421","DOI":"10.18383\/j.tom.2016.00184","article-title":"Bladder cancer segmentation in CT for treatment response assessment: Application of deep-learning convolution neural network\u2014A pilot study","volume":"2","author":"Cha","year":"2016","journal-title":"Tomography"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"8738","DOI":"10.1038\/s41598-017-09315-w","article-title":"Bladder cancer treatment response assessment in CT using radiomics with deep-learning","volume":"7","author":"Cha","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"201","DOI":"10.18383\/j.tom.2018.00036","article-title":"Deep-learning approach for assessment of bladder cancer treatment response","volume":"5","author":"Wu","year":"2019","journal-title":"Tomography"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1016\/j.ijrobp.2020.05.006","article-title":"Dosimetry-driven quality measure of brain pseudo computed tomography generated from deep learning for MRI-only radiation therapy treatment planning","volume":"108","author":"Andres","year":"2020","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1002\/acm2.13350","article-title":"Clinical implementation of automated treatment planning for whole-brain radiotherapy","volume":"22","author":"Han","year":"2021","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Jalalifar, A., Soliman, H., Sahgal, A., and Sadeghi-Naini, A. (2020, January 20\u201324). A Cascaded Deep-Learning Framework for Segmentation of Metastatic Brain Tumors Before and After Stereotactic Radiation Therapy. Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada.","DOI":"10.1109\/EMBC44109.2020.9175489"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.radonc.2019.03.026","article-title":"MRI-only brain radiotherapy: Assessing the dosimetric accuracy of synthetic CT images generated using a deep-learning approach","volume":"136","author":"Kazemifar","year":"2019","journal-title":"Radiother. Oncol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1002\/acm2.12856","article-title":"Dosimetric evaluation of synthetic CT generated with GANs for MRI-only proton therapy treatment planning of brain tumors","volume":"21","author":"Kazemifar","year":"2020","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1223","DOI":"10.21037\/qims-19-885","article-title":"Magnetic resonance image (MRI) synthesis from brain computed tomography (CT) images based on deep-learning methods for magnetic resonance (MR)-guided radiotherapy","volume":"10","author":"Li","year":"2020","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.radonc.2020.09.029","article-title":"Deep-learning-based synthetic CT generation for paediatric brain MR-only photon and proton radiotherapy","volume":"153","author":"Maspero","year":"2020","journal-title":"Radiother. Oncol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3133","DOI":"10.1002\/mp.13560","article-title":"Learning-based automatic segmentation of arteriovenous malformations on contrast CT images in brain stereotactic radiosurgery","volume":"46","author":"Wang","year":"2019","journal-title":"Med. Phys."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yoon, H.G., Cheon, W., Jeong, S.W., Kim, H.S., Kim, K., Nam, H., Han, Y., and Lim, D.H. (2020). Multi-parametric deep-learning model for prediction of overall survival after postoperative concurrent chemoradiotherapy in glioblastoma patients. Cancers, 12.","DOI":"10.3390\/cancers12082284"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.phro.2021.01.006","article-title":"Development and evaluation of radiotherapy deep learning dose prediction models for breast cancer","volume":"17","author":"Bakx","year":"2021","journal-title":"Phys. Imaging Radiat. Oncol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1109\/JBHI.2020.3008040","article-title":"Early prediction of response to neoadjuvant chemotherapy in breast cancer sonography using Siamese convolutional neural networks","volume":"25","author":"Byra","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"245013","DOI":"10.1088\/1361-6560\/aaf0d6","article-title":"Deep-learning based surface region selection for deep inspiration breath hold (DIBH) monitoring in left breast cancer radiotherapy","volume":"63","author":"Chen","year":"2018","journal-title":"Phys. Med. Biol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1491","DOI":"10.1007\/s11548-020-02209-9","article-title":"Multi-input deep-learning architecture for predicting breast tumor response to chemotherapy using quantitative MR images","volume":"15","author":"Drisis","year":"2020","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1016\/j.radonc.2018.04.011","article-title":"Automatic quantification of calcifications in the coronary arteries and thoracic aorta on radiotherapy planning CT scans of Western and Asian breast cancer patients","volume":"127","author":"Gernaat","year":"2018","journal-title":"Radiother. Oncol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1007\/s10278-018-0144-1","article-title":"Prior to initiation of chemotherapy, can we predict breast tumor response? Deep learning convolutional neural networks approach using a breast MRI tumor dataset","volume":"32","author":"Ha","year":"2019","journal-title":"J. Digit. Imaging"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.ejca.2021.01.028","article-title":"Ultrasound-based deep learning radiomics in the assessment of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer","volume":"147","author":"Jiang","year":"2021","journal-title":"Eur. J. Cancer"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1111\/1759-7714.13309","article-title":"Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using a deep learning (DL) method","volume":"11","author":"Qu","year":"2020","journal-title":"Thorac. Cancer"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"677","DOI":"10.3389\/fonc.2019.00677","article-title":"A full-image deep segmenter for CT images in breast cancer radiotherapy treatment","volume":"9","author":"Schreier","year":"2019","journal-title":"Front. Oncol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"102270","DOI":"10.1016\/j.media.2021.102270","article-title":"Deep learning for bone marrow cell detection and classification on whole-slide images","volume":"75","author":"Wang","year":"2022","journal-title":"Med. Image Anal."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1533033820957002","DOI":"10.1177\/1533033820957002","article-title":"Automated Intensity Modulated Radiation Therapy Treatment Planning for Cervical Cancer Based on Convolution Neural Network","volume":"19","author":"Jihong","year":"2020","journal-title":"Technol. Cancer Res. Treat."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1016\/j.ijrobp.2020.10.038","article-title":"Automatic segmentation using deep learning to enable online dose optimization during adaptive radiation therapy of cervical cancer","volume":"109","author":"Rigaud","year":"2021","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"165008","DOI":"10.1088\/1361-6560\/ab2f47","article-title":"Fully automatic catheter segmentation in MRI with 3D convolutional neural networks: Application to MRI-guided gynecologic brachytherapy","volume":"64","author":"Zaffino","year":"2019","journal-title":"Phys. Med. Biol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"16244","DOI":"10.1038\/s41598-021-95545-y","article-title":"Artificial intelligence-assisted fast screening cervical high grade squamous intraepithelial lesion and squamous cell carcinoma diagnosis and treatment planning","volume":"11","author":"Wang","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1016\/j.radonc.2020.09.014","article-title":"Computed tomography-based deep-learning prediction of neoadjuvant chemoradiotherapy treatment response in esophageal squamous cell carcinoma","volume":"154","author":"Hu","year":"2021","journal-title":"Radiother. Oncol."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Jiang, D., Li, T., Mao, R., Du, C., and Liu, J. (2019, January 23\u201327). Deep Learning Based Dosimetry Evaluation at Organs-at-Risk in Esophageal Radiation Treatment Planning. Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany.","DOI":"10.1109\/EMBC.2019.8856819"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"4735","DOI":"10.1002\/mp.14434","article-title":"Convolutional neural network-based dosimetry evaluation of esophageal radiation treatment planning","volume":"47","author":"Jiang","year":"2020","journal-title":"Med. Phys."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.radonc.2020.06.010","article-title":"A deep learning risk prediction model for overall survival in patients with gastric cancer: A multicenter study","volume":"150","author":"Zhang","year":"2020","journal-title":"Radiother. Oncol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.ebiom.2018.12.028","article-title":"Developed and validated a prognostic nomogram for recurrence-free survival after complete surgical resection of local primary gastrointestinal stromal tumors based on deep learning","volume":"39","author":"Chen","year":"2019","journal-title":"EBioMedicine"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1016\/j.ijrobp.2020.10.005","article-title":"Generating High-Quality Lymph Node Clinical Target Volumes for Head and Neck Cancer Radiation Therapy Using a Fully Automated Deep Learning-Based Approach","volume":"109","author":"Cardenas","year":"2021","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1002\/mp.13262","article-title":"A feasibility study on an automated method to generate patient-specific dose distributions for radiotherapy using deep learning","volume":"46","author":"Chen","year":"2019","journal-title":"Med. Phys."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2764","DOI":"10.1038\/s41598-019-39206-1","article-title":"Deep learning in head & neck cancer outcome prediction","volume":"9","author":"Diamant","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"4095","DOI":"10.1002\/mp.13663","article-title":"Dosimetric evaluation of synthetic CT for head and neck radiotherapy generated by a patch-based three-dimensional convolutional neural network","volume":"46","author":"Dinkla","year":"2019","journal-title":"Med. Phys."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1002\/mp.13271","article-title":"Automatic treatment planning based on three-dimensional dose distribution predicted from deep learning technique","volume":"46","author":"Fan","year":"2019","journal-title":"Med. Phys."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"6322","DOI":"10.1007\/s00330-020-06982-8","article-title":"Deep learning analysis using FDG-PET to predict treatment outcome in patients with oral cavity squamous cell carcinoma","volume":"30","author":"Fujima","year":"2020","journal-title":"Eur. Radiol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"5567","DOI":"10.1002\/mp.14827","article-title":"Dose prediction for head and neck radiotherapy using a three-dimensional dense dilated U-net architecture","volume":"48","author":"Gronberg","year":"2021","journal-title":"Med. Phys."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.phro.2020.06.002","article-title":"A convolutional neural network for contouring metastatic lymph nodes on diffusion-weighted magnetic resonance images for assessment of radiotherapy response","volume":"15","author":"Kieselmann","year":"2020","journal-title":"Phys. Imaging Radiat. Oncol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1002\/mp.12045","article-title":"Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks","volume":"44","author":"Ibragimov","year":"2017","journal-title":"Med. Phys."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"6994","DOI":"10.1038\/s41598-019-43372-7","article-title":"Deep-learning-based survival prediction of oral cancer patients","volume":"9","author":"Kim","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Kim, N., Chun, J., Chang, J.S., Lee, C.G., Keum, K.C., and Kim, J.S. (2021). Feasibility of continual deep-learning-based segmentation for personalized adaptive radiation therapy in head and neck area. Cancers, 13.","DOI":"10.3390\/cancers13040702"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.ejmp.2020.08.018","article-title":"Deep-learning-based metal artifact reduction using cycle-consistent adversarial network for intensity-modulated head and neck radiation therapy treatment planning","volume":"78","author":"Koike","year":"2020","journal-title":"Phys. Med."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1002\/mp.13925","article-title":"Deep-learning-based virtual noncontrast CT for volumetric modulated arc therapy planning: Comparison with a dual-energy CT-based approach","volume":"47","author":"Koike","year":"2020","journal-title":"Med. Phys."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"245022","DOI":"10.1088\/1361-6560\/ab9fcb","article-title":"Evaluation of CBCT scatter correction using deep convolutional neural networks for head and neck adaptive proton therapy","volume":"65","author":"Lalonde","year":"2020","journal-title":"Phys. Med. Biol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1961","DOI":"10.1007\/s00330-018-5748-9","article-title":"Deep-learning-based detection and segmentation of organs at risk in nasopharyngeal carcinoma computed tomographic images for radiotherapy planning","volume":"29","author":"Liang","year":"2019","journal-title":"Eur. Radiol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2714","DOI":"10.1002\/mp.14770","article-title":"An artificial intelligence-driven agent for real-time head-and-neck IMRT plan generation using conditional generative adversarial network (cGAN)","volume":"48","author":"Li","year":"2021","journal-title":"Med. Phys."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1148\/radiol.2019182012","article-title":"Deep learning for automated contouring of primary tumor volumes by MRI for nasopharyngeal carcinoma","volume":"291","author":"Lin","year":"2019","journal-title":"Radiology"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1972","DOI":"10.1002\/mp.13490","article-title":"A deep-learning method for prediction of three-dimensional dose distribution of helical tomotherapy","volume":"46","author":"Liu","year":"2019","journal-title":"Med. Phys."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1298","DOI":"10.1002\/cam4.2802","article-title":"Deep learning pathological microscopic features in endemic nasopharyngeal cancer: Prognostic value and protentional role for individual induction chemotherapy","volume":"9","author":"Liu","year":"2020","journal-title":"Cancer Med."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"101953","DOI":"10.1016\/j.compmedimag.2021.101953","article-title":"CT synthesis from MRI using multi-cycle GAN for head-and-neck radiation therapy","volume":"91","author":"Liu","year":"2021","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1002\/mp.13296","article-title":"More accurate and efficient segmentation of organs-at-risk in radiotherapy with convolutional neural networks cascades","volume":"46","author":"Men","year":"2019","journal-title":"Med. Phys."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1429","DOI":"10.1080\/0284186X.2019.1630754","article-title":"Evaluation of proton and photon dose distributions recalculated on 2D and 3D Unet-generated pseudoCTs from T1-weighted MR head scans","volume":"58","author":"Neppl","year":"2019","journal-title":"Acta Oncol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"065020","DOI":"10.1088\/1361-6560\/ab039b","article-title":"3D radiotherapy dose prediction on head and neck cancer patients with a hierarchically densely connected U-net deep-learning architecture","volume":"64","author":"Nguyen","year":"2019","journal-title":"Phys. Med. Biol."},{"key":"ref_66","unstructured":"Nikolov, S., Blackwell, S., Zverovitch, A., Mendes, R., Livne, M., De Fauw, J., Patel, Y., Meyer, C., Askham, H., and Romera-Paredes, B. (2018). Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy. arXiv."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"4271","DOI":"10.1158\/1078-0432.CCR-18-3065","article-title":"Prognostic value of deep learning PET\/CT-based radiomics: Potential role for future individual induction chemotherapy in advanced nasopharyngeal carcinoma","volume":"25","author":"Peng","year":"2019","journal-title":"Clin. Cancer Res."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1880","DOI":"10.1002\/mp.14075","article-title":"Multi-sequence MR image-based synthetic CT generation using a generative adversarial network for head and neck MRI-only radiotherapy","volume":"47","author":"Qi","year":"2020","journal-title":"Med. Phys."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"4558","DOI":"10.1002\/mp.13147","article-title":"Fully automatic multi-organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks","volume":"45","author":"Tong","year":"2018","journal-title":"Med. Phys."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1016\/j.ijrobp.2019.02.040","article-title":"Deep-learning-based delineation of head and neck organs at risk: Geometric and dosimetric evaluation","volume":"104","author":"Dahele","year":"2019","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1592","DOI":"10.3389\/fonc.2020.01592","article-title":"Dose-distribution-driven PET image-based outcome prediction (DDD-PIOP): A deep learning study for oropharyngeal cancer IMRT application","volume":"10","author":"Wang","year":"2020","journal-title":"Front. Oncol."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1002\/mp.13300","article-title":"AnatomyNet: Deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy","volume":"46","author":"Zhu","year":"2019","journal-title":"Med. Phys."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.radonc.2020.06.050","article-title":"A deep learning MR-based radiomic nomogram may predict survival for nasopharyngeal carcinoma patients with stage T3N1M0","volume":"151","author":"Zhong","year":"2020","journal-title":"Radiother. Oncol."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.radonc.2020.09.056","article-title":"Deep learning-enabled MRI-only photon and proton therapy treatment planning for paediatric abdominal tumours","volume":"153","author":"Florkow","year":"2020","journal-title":"Radiother. Oncol."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.radonc.2020.11.026","article-title":"Deep learning prediction of proton and photon dose distributions for paediatric abdominal tumours","volume":"156","author":"Guerreiro","year":"2021","journal-title":"Radiother. Oncol."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"215","DOI":"10.3389\/fonc.2018.00215","article-title":"Deep learning renal segmentation for fully automated radiation dose estimation in unsealed source therapy","volume":"8","author":"Jackson","year":"2018","journal-title":"Front. Oncol."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"101894","DOI":"10.1016\/j.compmedimag.2021.101894","article-title":"An imageomics and multi-network based deep-learning model for risk assessment of liver transplantation for hepatocellular cancer","volume":"89","author":"He","year":"2021","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"4763","DOI":"10.1002\/mp.13122","article-title":"Development of deep neural network for individualized hepatobiliary toxicity prediction after liver SBRT","volume":"45","author":"Ibragimov","year":"2018","journal-title":"Med. Phys."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1821","DOI":"10.1109\/JBHI.2019.2904078","article-title":"Neural networks for deep radiotherapy dose analysis and prediction of liver SBRT outcomes","volume":"23","author":"Ibragimov","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"3721","DOI":"10.1002\/mp.14235","article-title":"Deep learning for identification of critical regions associated with toxicities after liver stereotactic body radiation therapy","volume":"47","author":"Ibragimov","year":"2020","journal-title":"Med. Phys."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1007\/s00330-019-06318-1","article-title":"Residual convolutional neural network for predicting response of transarterial chemoembolization in hepatocellular carcinoma from CT imaging","volume":"30","author":"Peng","year":"2020","journal-title":"Eur. Radiol."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1717","DOI":"10.1002\/ijc.33427","article-title":"Deep learning-assisted magnetic resonance imaging prediction of tumor response to chemotherapy in patients with colorectal liver metastases","volume":"148","author":"Zhu","year":"2021","journal-title":"Int. J. Cancer"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"3679","DOI":"10.1002\/mp.13597","article-title":"Three-dimensional dose prediction for lung IMRT patients with deep neural networks: Robust learning from heterogeneous beam configurations","volume":"46","author":"Nguyen","year":"2019","journal-title":"Med. Phys."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1093\/jrr\/rraa132","article-title":"Automated approach for segmenting gross tumor volumes for lung cancer stereotactic body radiation therapy using CT-based dense V-networks","volume":"62","author":"Cui","year":"2021","journal-title":"J. Radiat. Res."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.phro.2020.05.009","article-title":"Cardio-pulmonary substructure segmentation of radiotherapy computed tomography images using convolutional neural networks for clinical outcomes analysis","volume":"14","author":"Haq","year":"2020","journal-title":"Phys. Imaging Radiat. Oncol."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"e000550","DOI":"10.1136\/jitc-2020-000550","article-title":"Predicting response to immunotherapy in advanced non-small-cell lung cancer using tumor mutational burden radiomic biomarker","volume":"8","author":"He","year":"2020","journal-title":"J. Immunother. Cancer"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"1500","DOI":"10.3389\/fonc.2019.01500","article-title":"Prediction of radiation pneumonitis with dose distribution: A convolutional neural network (CNN) based model","volume":"9","author":"Liang","year":"2020","journal-title":"Front. Oncol."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"e136","DOI":"10.1016\/S2589-7500(19)30058-5","article-title":"An image-based deep learning framework for individualising radiotherapy dose: A retrospective analysis of outcome prediction","volume":"1","author":"Lou","year":"2019","journal-title":"Lancet Digit. Health"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"e002118","DOI":"10.1136\/jitc-2020-002118","article-title":"Non-invasive measurement of PD-L1 status and prediction of immunotherapy response using deep learning of PET\/CT images","volume":"9","author":"Mu","year":"2021","journal-title":"J. Immunother. Cancer"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"2098","DOI":"10.7150\/thno.48027","article-title":"Assessing PD-L1 expression in non-small cell lung cancer and predicting responses to immune checkpoint inhibitors using deep learning on computed tomography images","volume":"11","author":"Tian","year":"2021","journal-title":"Theranostics"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"6690","DOI":"10.1002\/mp.12625","article-title":"Deep reinforcement learning for automated radiation adaptation in lung cancer","volume":"44","author":"Tseng","year":"2017","journal-title":"Med. Phys."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1002\/acm2.12937","article-title":"Boosting radiotherapy dose calculation accuracy with deep learning","volume":"21","author":"Xing","year":"2020","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"3266","DOI":"10.1158\/1078-0432.CCR-18-2495","article-title":"Deep learning predicts lung cancer treatment response from serial medical imaging","volume":"25","author":"Xu","year":"2019","journal-title":"Clin. Cancer Res."},{"key":"ref_94","first-page":"121","article-title":"Identification and Validation of Efficacy of Immunological Therapy for Lung Cancer From Histopathological Images Based on Deep Learning","volume":"12","author":"Yang","year":"2021","journal-title":"Front. Genet."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1158\/1541-7786.MCR-17-0378","article-title":"Precision oncology beyond targeted therapy: Combining omics data with machine learning matches the majority of cancer cells to effective therapeutics","volume":"16","author":"Ding","year":"2018","journal-title":"Mol. Cancer Res."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"3367","DOI":"10.1016\/j.celrep.2019.11.017","article-title":"A deep learning framework for predicting response to therapy in cancer","volume":"29","author":"Sakellaropoulos","year":"2019","journal-title":"Cell Rep."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.phro.2020.04.002","article-title":"A single neural network for cone-beam computed tomography-based radiotherapy of head-and-neck, lung and breast cancer","volume":"14","author":"Maspero","year":"2020","journal-title":"Phys. Imaging Radiat. Oncol."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1002\/mp.13338","article-title":"Deep learning for patient-specific quality assurance: Identifying errors in radiotherapy delivery by radiomic analysis of gamma images with convolutional neural networks","volume":"46","author":"Nyflot","year":"2019","journal-title":"Med. Phys."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"4568","DOI":"10.1002\/mp.13141","article-title":"Autosegmentation for thoracic radiation treatment planning: A grand challenge at AAPM 2017","volume":"45","author":"Yang","year":"2018","journal-title":"Med. Phys."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"4316","DOI":"10.1002\/mp.14386","article-title":"CT-based multi-organ segmentation using a 3D self-attention U-net network for pancreatic radiotherapy","volume":"47","author":"Liu","year":"2020","journal-title":"Med. Phys."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"100672","DOI":"10.1016\/j.adro.2021.100672","article-title":"Deep Learning\u2013Based Fluence Map Prediction for Pancreas Stereotactic Body Radiation Therapy With Simultaneous Integrated Boost","volume":"6","author":"Wang","year":"2021","journal-title":"Adv. Radiat. Oncol."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"5218","DOI":"10.1002\/mp.13187","article-title":"Comparative study of algorithms for synthetic CT generation from MRI: Consequences for MRI-guided radiation planning in the pelvic region","volume":"45","author":"Arabi","year":"2018","journal-title":"Med. Phys."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"185001","DOI":"10.1088\/1361-6560\/aada6d","article-title":"Dose evaluation of fast synthetic-CT generation using a generative adversarial network for general pelvis MR-only radiotherapy","volume":"63","author":"Maspero","year":"2018","journal-title":"Phys. Med. Biol."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1080\/0284186X.2020.1775290","article-title":"Automatic segmentation of pelvic organs-at-risk using a fusion network model based on limited training samples","volume":"59","author":"Ju","year":"2020","journal-title":"Acta Oncol."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"3898","DOI":"10.1002\/mp.14374","article-title":"Using deep learning to predict beam-tunable Pareto optimal dose distribution for intensity-modulated radiation therapy","volume":"47","author":"Bohara","year":"2020","journal-title":"Med. Phys."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"5659","DOI":"10.1002\/mp.13247","article-title":"U-net-generated synthetic CT images for magnetic resonance imaging-only prostate intensity-modulated radiation therapy treatment planning","volume":"45","author":"Chen","year":"2018","journal-title":"Med. Phys."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1186\/s13014-020-01528-0","article-title":"Clinical implementation of MRI-based organs-at-risk auto-segmentation with convolutional networks for prostate radiotherapy","volume":"15","author":"Savenije","year":"2020","journal-title":"Radiat. Oncol."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"3329","DOI":"10.1002\/mp.13620","article-title":"Robust contour propagation using deep learning and image registration for online adaptive proton therapy of prostate cancer","volume":"46","author":"Elmahdy","year":"2019","journal-title":"Med. Phys."},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Elmahdy, M.S., Ahuja, T., van der Heide, U.A., and Staring, M. (2020, January 3\u20137). Patient-specific finetuning of deep-learning models for adaptive radiotherapy in prostate CT. Proceedings of the 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Iowa City, IA, USA.","DOI":"10.1109\/ISBI45749.2020.9098702"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1007\/s12194-018-0472-3","article-title":"Automated prediction of dosimetric eligibility of patients with prostate cancer undergoing intensity-modulated radiation therapy using a convolutional neural network","volume":"11","author":"Kajikawa","year":"2018","journal-title":"Radiol. Phys. Technol."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1093\/jrr\/rrz051","article-title":"A convolutional neural network approach for IMRT dose distribution prediction in prostate cancer patients","volume":"60","author":"Kajikawa","year":"2019","journal-title":"J. Radiat. Res."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.radonc.2020.10.027","article-title":"Dose prediction with deep learning for prostate cancer radiation therapy: Model adaptation to different treatment planning practices","volume":"153","author":"Kandalan","year":"2020","journal-title":"Radiother. Oncol."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"11073","DOI":"10.1038\/s41598-020-68062-7","article-title":"DoseGAN: A generative adversarial network for synthetic dose prediction using attention-gated discrimination and generation","volume":"10","author":"Kearney","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Kiljunen, T., Akram, S., Niemel\u00e4, J., L\u00f6yttyniemi, E., Sepp\u00e4l\u00e4, J., Heikkil\u00e4, J., Vuolukka, K., K\u00e4\u00e4ri\u00e4inen, O.S., Heikkil\u00e4, V.P., and Lehti\u00f6, K. (2020). A deep-learning-based automated CT segmentation of prostate cancer anatomy for radiation therapy planning-a retrospective multicenter study. Diagnostics, 10.","DOI":"10.3390\/diagnostics10110959"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"075013","DOI":"10.1088\/1361-6560\/ab7630","article-title":"DeepDose: Towards a fast dose calculation engine for radiation therapy using deep learning","volume":"65","author":"Kontaxis","year":"2020","journal-title":"Phys. Med. Biol."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1016\/j.ijrobp.2019.08.049","article-title":"Comparison of deep-learning-based and patch-based methods for pseudo-CT generation in MRI-based prostate dose planning","volume":"105","author":"Largent","year":"2019","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"175014","DOI":"10.1088\/1361-6560\/aba5eb","article-title":"Automatic IMRT planning via static field fluence prediction (AIP-SFFP): A deep learning algorithm for real-time prostate treatment planning","volume":"65","author":"Li","year":"2020","journal-title":"Phys. Med. Biol."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"2978","DOI":"10.1002\/mp.13618","article-title":"Dose distribution prediction in isodose feature-preserving voxelization domain using deep convolutional neural network","volume":"46","author":"Ma","year":"2019","journal-title":"Med. Phys."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"125017","DOI":"10.1088\/1361-6560\/ab2146","article-title":"Incorporating dosimetric features into the prediction of 3D VMAT dose distributions using deep convolutional neural network","volume":"64","author":"Ma","year":"2019","journal-title":"Phys. Med. Biol."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"4438","DOI":"10.1002\/mp.15025","article-title":"A feasibility study on deep-learning-based individualized 3D dose distribution prediction","volume":"48","author":"Ma","year":"2021","journal-title":"Med. Phys."},{"key":"ref_121","doi-asserted-by":"crossref","unstructured":"Murakami, Y., Magome, T., Matsumoto, K., Sato, T., Yoshioka, Y., and Oguchi, M. (2020). Fully automated dose prediction using generative adversarial networks in prostate cancer patients. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0232697"},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.ejmp.2020.09.004","article-title":"Simple low-cost approaches to semantic segmentation in radiation therapy planning for prostate cancer using deep learning with non-contrast planning CT images","volume":"78","author":"Nemoto","year":"2020","journal-title":"Phys. Med."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"1076","DOI":"10.1038\/s41598-018-37741-x","article-title":"A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning","volume":"9","author":"Nguyen","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1002\/mp.13955","article-title":"Incorporating human and learned domain knowledge into training deep neural networks: A differentiable dose-volume histogram and adversarial inspired framework for generating Pareto optimal dose distributions in radiation therapy","volume":"47","author":"Nguyen","year":"2020","journal-title":"Med. Phys."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"880","DOI":"10.1002\/mp.13986","article-title":"A fast deep-learning approach for beam orientation optimization for prostate cancer treated with intensity-modulated radiation therapy","volume":"47","author":"Ogunmolu","year":"2020","journal-title":"Med. Phys."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"101919","DOI":"10.1016\/j.media.2020.101919","article-title":"ProsRegNet: A deep learning framework for registration of MRI and histopathology images of the prostate","volume":"68","author":"Shao","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"2443","DOI":"10.3389\/fonc.2020.593381","article-title":"Dose Super-Resolution in Prostate Volumetric Modulated Arc Therapy Using Cascaded Deep Learning Networks","volume":"10","author":"Shin","year":"2020","journal-title":"Front. Oncol."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.ejmp.2020.03.023","article-title":"A convolution neural network for higher resolution dose prediction in prostate volumetric modulated arc therapy","volume":"72","author":"Sumida","year":"2020","journal-title":"Phys. Med."},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1002\/mp.13953","article-title":"A feasibility study on deep-learning-based radiotherapy dose calculation","volume":"47","author":"Xing","year":"2020","journal-title":"Med. Phys."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"12611","DOI":"10.1038\/s41598-018-30657-6","article-title":"Deep learning and radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer","volume":"8","author":"Bibault","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.radonc.2020.11.027","article-title":"Multicentre, deep learning, synthetic-CT generation for ano-rectal MR-only radiotherapy treatment planning","volume":"156","author":"Bird","year":"2021","journal-title":"Radiother. Oncol."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"1851","DOI":"10.1038\/s41467-021-22188-y","article-title":"Predicting treatment response from longitudinal images using multi-task deep learning","volume":"12","author":"Jin","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"103442","DOI":"10.1016\/j.ebiom.2021.103442","article-title":"Deep learning radiomics-based prediction of distant metastasis in patients with locally advanced rectal cancer after neoadjuvant chemoradiotherapy: A multicentre study","volume":"69","author":"Liu","year":"2021","journal-title":"EBioMedicine"},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"6377","DOI":"10.1002\/mp.12602","article-title":"Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks","volume":"44","author":"Men","year":"2017","journal-title":"Med. Phys."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.mri.2019.05.003","article-title":"Machine learning for prediction of chemoradiation therapy response in rectal cancer using pre-treatment and mid-radiation multi-parametric MRI","volume":"61","author":"Shi","year":"2019","journal-title":"Magn. Reson. Imaging"},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.radonc.2020.01.020","article-title":"Automatic delineation of the clinical target volume and organs at risk by deep learning for rectal cancer postoperative radiotherapy","volume":"145","author":"Song","year":"2020","journal-title":"Radiother. Oncol."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"2560","DOI":"10.1002\/mp.12918","article-title":"A deep-learning-based autosegmentation of rectal tumors in MR images","volume":"45","author":"Wang","year":"2018","journal-title":"Med. Phys."},{"key":"ref_138","first-page":"572","article-title":"Application of convolutional neural network to risk evaluation of positive circumferential resection margin of rectal cancer by magnetic resonance imaging","volume":"23","author":"Xu","year":"2020","journal-title":"Zhonghua Wei Chang. Wai Zhi = Chin. J. Gastrointest. Surg."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1148\/radiol.2020190936","article-title":"Predicting rectal cancer response to neoadjuvant chemoradiotherapy using deep learning of diffusion kurtosis MRI","volume":"296","author":"Zhang","year":"2020","journal-title":"Radiology"},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1002\/acm2.12849","article-title":"A method of using deep learning to predict three-dimensional dose distributions for intensity-modulated radiotherapy of rectal cancer","volume":"21","author":"Zhou","year":"2020","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"ref_141","doi-asserted-by":"crossref","unstructured":"Wang, C.W., Lee, Y.C., Chang, C.C., Lin, Y.J., Liou, Y.A., Hsu, P.C., Chang, C.C., Sai, A.K.O., Wang, C.H., and Chao, T.K. (2022). A Weakly Supervised Deep Learning Method for Guiding Ovarian Cancer Treatment and Identifying an Effective Biomarker. Cancers, 14.","DOI":"10.3390\/cancers14071651"},{"key":"ref_142","doi-asserted-by":"crossref","unstructured":"Lin, Y.J., Chao, T.K., Khalil, M.A., Lee, Y.C., Hong, D.Z., Wu, J.J., and Wang, C.W. (2021). Deep Learning Fast Screening Approach on Cytological Whole Slides for Thyroid Cancer Diagnosis. Cancers, 13.","DOI":"10.3390\/cancers13153891"},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","article-title":"Fully convolutional networks for semantic segmentation","volume":"39","author":"Shelhamer","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_144","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst., 25."},{"key":"ref_145","doi-asserted-by":"crossref","unstructured":"Liu, S., and Deng, W. (2015, January 3\u20136). Very deep convolutional neural network based image classification using small training sample size. Proceedings of the 2015 third IAPR Asian Conference on Pattern Recognition (ACPR), Kuala Lampur, Malaysia.","DOI":"10.1109\/ACPR.2015.7486599"},{"key":"ref_146","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_147","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.ejmp.2021.02.021","article-title":"Radiation therapy dose prediction for left-sided breast cancers using two-dimensional and three-dimensional deep-learning models","volume":"83","author":"Hedden","year":"2021","journal-title":"Phys. Med."},{"key":"ref_149","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., and Ahmadi, S.A. (2016, January 25\u201328). V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA.","DOI":"10.1109\/3DV.2016.79"},{"key":"ref_150","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_151","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the Inception Architecture for Computer Vision. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_152","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_153","unstructured":"Sabour, S., Frosst, N., and Hinton, G.E. (2017, January 4\u20139). Dynamic Routing between Capsules. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_154","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A. (2015). Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs. arXiv."},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_156","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."},{"key":"ref_157","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 23\u201328). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European conference on computer vision (ECCV), Glasgow, UK.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"39670","DOI":"10.1109\/ACCESS.2019.2906890","article-title":"RP-Net: A 3D Convolutional Neural Network for Brain Segmentation From Magnetic Resonance Imaging","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"1822","DOI":"10.1109\/TMI.2018.2806309","article-title":"Automatic Multi-Organ Segmentation on Abdominal CT With Dense V-Networks","volume":"37","author":"Gibson","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_160","doi-asserted-by":"crossref","unstructured":"Kaur, M., and Mohta, A. (2019, January 27\u201329). A Review of Deep Learning with Recurrent Neural Network. Proceedings of the 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India.","DOI":"10.1109\/ICSSIT46314.2019.8987837"},{"key":"ref_161","first-page":"2553","article-title":"Deep Neural Networks for Object Detection","volume":"26","author":"Szegedy","year":"2013","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_162","unstructured":"Gulgec, N., Tak\u00e1\u010d, M., and Pakzad, S. (2017). Model Validation and Uncertainty Quantification, Springer."},{"key":"ref_163","doi-asserted-by":"crossref","unstructured":"Katzman, J.L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., and Kluger, Y. (2018). DeepSurv: Personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Med. Res. Methodol., 18.","DOI":"10.1186\/s12874-018-0482-1"},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1111\/j.2517-6161.1972.tb00899.x","article-title":"Regression Models and Life-Tables","volume":"34","author":"Cox","year":"1972","journal-title":"J. R. Stat. Soc. Ser. (Methodol.)"},{"key":"ref_165","unstructured":"Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N., and Weinberger, K.Q. (2014, January 8\u201313). Generative adversarial nets. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1109\/TNNLS.2020.2995319","article-title":"Transformation-consistent self-ensembling model for semisupervised medical image segmentation","volume":"32","author":"Li","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.media.2018.07.002","article-title":"Weakly-supervised convolutional neural networks for multimodal image registration","volume":"49","author":"Hu","year":"2018","journal-title":"Med. Image Anal."},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"1193","DOI":"10.1038\/s41467-021-21467-y","article-title":"An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning","volume":"12","author":"Chen","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_169","doi-asserted-by":"crossref","first-page":"193","DOI":"10.3389\/fmed.2019.00193","article-title":"A high-performance system for robust stain normalization of whole-slide images in histopathology","volume":"6","author":"Anghel","year":"2019","journal-title":"Front. Med."},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"101544","DOI":"10.1016\/j.media.2019.101544","article-title":"Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology","volume":"58","author":"Tellez","year":"2019","journal-title":"Med. Image Anal."},{"key":"ref_171","unstructured":"Ghazvinian Zanjani, F., Zinger, S., Bejnordi, B.E., van der Laak, J.A., and de With, P.H.N. (2018, January 4\u20136). Histopathology stain-color normalization using deep generative models. Proceedings of the 1st Conference on Medical Imaging with Deep Learning (MIDL), Amsterdam, The Netherlands."},{"key":"ref_172","doi-asserted-by":"crossref","unstructured":"Shaban, M.T., Baur, C., Navab, N., and Albarqouni, S. (2019, January 8\u201311). Staingan: Stain style transfer for digital histological images. Proceedings of the 2019 Ieee 16th international symposium on biomedical imaging (Isbi 2019), Venice, Italy.","DOI":"10.1109\/ISBI.2019.8759152"},{"key":"ref_173","doi-asserted-by":"crossref","first-page":"746307","DOI":"10.3389\/fmed.2021.746307","article-title":"Stainnet: A fast and robust stain normalization network","volume":"8","author":"Kang","year":"2021","journal-title":"Front. Med."},{"key":"ref_174","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.compmedimag.2014.05.009","article-title":"Applying latent semantic analysis to large-scale medical image databases","volume":"39","author":"Stathopoulos","year":"2015","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_175","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.compmedimag.2012.04.001","article-title":"Interactive GPU-based maximum intensity projection of large medical data sets using visibility culling based on the initial occluder and the visible block classification","volume":"36","author":"Kye","year":"2012","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_176","doi-asserted-by":"crossref","unstructured":"Xu, Y., Li, Y., Shen, Z., Wu, Z., Gao, T., Fan, Y., Lai, M., Eric, I., and Chang, C. (2017). Parallel multiple instance learning for extremely large histopathology image analysis. BMC Bioinform., 18.","DOI":"10.1186\/s12859-017-1768-8"},{"key":"ref_177","doi-asserted-by":"crossref","first-page":"1817","DOI":"10.1109\/TMI.2021.3066295","article-title":"Detection of prostate cancer in whole-slide images through end-to-end training with image-level labels","volume":"40","author":"Pinckaers","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_178","doi-asserted-by":"crossref","unstructured":"Cruz-Roa, A., Gilmore, H., Basavanhally, A., Feldman, M., Ganesan, S., Shih, N., Tomaszewski, J., Madabhushi, A., and Gonz\u00e1lez, F. (2018). High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: Application to invasive breast cancer detection. 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