An overview of artificial intelligence in medical physics and radiation oncology

Xing, 2021 Huynh, 2020, Artificial intelligence in radiation oncology, Nat Rev Clin Oncol, 17, 771, 10.1038/s41571-020-0417-8 Mardani, 2016, Deep-learning based prediction of achievable dose for personalizing inverse treatment planning, Intl J Radiat Oncol Biol Phys, 96, E419, 10.1016/j.ijrobp.2016.06.1685 Ibragimov, 2016, TH-CD-206-05: machine-learning based segmentation of organs at risks for head and neck radiotherapy planning, Med Phys, 43, 3883, 10.1118/1.4958186 Ibragimov, 2017, Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks, Med Phys, 44, 547, 10.1002/mp.12045 Ibragimov, 2017, Combining deep learning with anatomical analysis for segmentation of the portal vein for liver SBRT planning, Phys Med Biol, 62, 8943, 10.1088/1361-6560/aa9262 Ibragimov, 2017, Segmentation of pathological structures by landmark-assisted deformable models, IEEE Trans Med Imaging, 36, 1457, 10.1109/TMI.2017.2667578 Arik, 2017, Fully automated quantitative cephalometry using convolutional neural networks, J Med Imaging, 4, 10.1117/1.JMI.4.1.014501 Zhao, 2020, Fiducial-free image-guided spinal stereotactic radiosurgery enabled via deep learning, Intl J Radiat Oncol Bio Phys, 108, e357, 10.1016/j.ijrobp.2020.07.2348 Zhao, 2019, Incorporating imaging information from deep neural network layers into image guided radiation therapy (IGRT), Radiother Oncol, 140, 167, 10.1016/j.radonc.2019.06.027 Li, 2018, H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes, IEEE Trans Med Imaging, 37, 2663, 10.1109/TMI.2018.2845918 Seo, 2020, Modified U-Net (mU-Net) with incorporation of object-dependent high level features for improved liver and liver-tumor segmentation in CT images, IEEE Trans Med Imaging, 39, 1316, 10.1109/TMI.2019.2948320 Maes, 1997, Multimodality image registration by maximization of mutual information, IEEE Trans Med Imaging, 16, 187, 10.1109/42.563664 Men, 2017, Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks, Med Phys, 44, 6377, 10.1002/mp.12602 Men, 2017, Deep deconvolutional neural network for target segmentation of nasopharyngeal cancer in planning computed tomography images, Front Oncol, 7, 315, 10.3389/fonc.2017.00315 Ermis, 2020, Fully automated brain resection cavity delineation for radiation target volume definition in glioblastoma patients using deep learning, Radiat Oncol, 15, 100, 10.1186/s13014-020-01553-z Jin, 2021, DeepTarget: gross tumor and clinical target volume segmentation in esophageal cancer radiotherapy, Med Image Anal, 68, 10.1016/j.media.2020.101909 Guo, 2019, Gross tumor volume segmentation for head and neck cancer radiotherapy using deep dense multi-modality network, Phys Med Biol, 64, 10.1088/1361-6560/ab440d Ma, 2023, Deep learning-based internal gross target volume definition in 4D CT images of lung cancer patients, Med Phys, 50, 2303, 10.1002/mp.16106 Momin, 2021, Lung tumor segmentation in 4D CT images using motion convolutional neural networks, Med Phys, 48, 7141, 10.1002/mp.15204 Zhou, 2022, Development of a deep learning-based patient-specific target contour prediction model for markerless tumor positioning, Med Phys, 49, 1382, 10.1002/mp.15456 Mao, 2006, Supervised learning-based cell image segmentation for p53 immunohistochemistry, IEEE Trans Biomed Eng, 53, 1153, 10.1109/TBME.2006.873538 Noh, 2015, Learning deconvolution network for semantic segmentation, 1520 He, 2016, Deep residual learning for image recognition, 770 Huang, 2017, Densely connected convolutional networks, 2261 Peeken, 2019, Deep learning derived tumor infiltration maps for personalized target definition in glioblastoma radiotherapy, Radiother Oncol, 138, 166, 10.1016/j.radonc.2019.06.031 Litjens, 2017, A survey on deep learning in medical image analysis, Med Image Anal, 42, 60, 10.1016/j.media.2017.07.005 Christensen, 2007, Tracking lung tissue motion and expansion/compression with inverse consistent image registration and spirometry, Med Phys, 34, 2155, 10.1118/1.2731029 Reed, 2009, Automatic segmentation of whole breast using atlas approach and deformable image registration, Int J Radiat Oncol Biol Phys, 73, 1493, 10.1016/j.ijrobp.2008.07.001 Schreibmann, 2008, Four-dimensional image registration for image-guided radiotherapy, Int J Radiat Oncol Biol Phys, 71, 578, 10.1016/j.ijrobp.2008.01.042 Olteanu, 2012, Evaluation of deformable image coregistration in adaptive dose painting by numbers for head-and-neck cancer, Int J Radiat Oncol Biol Phys, 83, 696, 10.1016/j.ijrobp.2011.07.037 Balakrishnan, 2019, VoxelMorph: a learning framework for deformable medical image registration, IEEE Trans Med Imaging, 10.1109/TMI.2019.2897538 Simonovsky M, Gutiérrez-Becker B, Mateus D, et al. A deep metric for multimodal registration. In: Ourselin S, Joskowicz L, Sabuncu M, Unal G, Wells W, eds. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016. Lecture Notes in Computer Science. Vol 9902. Springer;2016:10–18. doi:10.1007/978-3-319-46726-9_2 Biomedical Image Analysis Group, Imperial College London. IXI dataset. Accessed August 3, 2023. http://brain-development.org/ixi-dataset/. Gousias, 2012, Magnetic resonance imaging of the newborn brain: manual segmentation of labelled atlases in term-born and preterm infants, Neuroimage, 62, 1499, 10.1016/j.neuroimage.2012.05.083 Sedghi, 2021, Image registration: maximum likelihood, minimum entropy and deep learning, Med Image Anal, 69, 10.1016/j.media.2020.101939 Teng, 2021, Respiratory deformation registration in 4D-CT/cone beam CT using deep learning, Quant Imaging Med Surg, 11, 737, 10.21037/qims-19-1058 Sokooti, 2017, Nonrigid image registration using multi-scale 3D convolutional neural networks, 232 Stolk, 2007, Progression parameters for emphysema: a clinical investigation, Respir Med, 101, 1924, 10.1016/j.rmed.2007.04.016 Sokooti H, de Vos B, Berendsen F, et al. 3D convolutional neural networks image registration based on efficient supervised learning from artificial deformations. Posted online August 27, 2019. arXiv:1908.10235v1[eess.IV]. doi:10.48550/arXiv.1908.10235. Li B, Niessen WJ, Klein S, et al. A hybrid deep learning framework for integrated segmentation and registration: evaluation on longitudinal white matter tract changes. In: Shen, D, Liu T, Peters TM, et al, eds. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science, vol 11766. Springer;2019:645–653. doi:10.1007/978-3-030-32248-9_72. Fan, 2019, BIRNet: brain image registration using dual-supervised fully convolutional networks, Med Image Anal, 54, 193, 10.1016/j.media.2019.03.006 Klein, 2009, Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration, Neuroimage, 46, 786, 10.1016/j.neuroimage.2008.12.037 Marcus, 2007, Open Access Series of Imaging Studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults, J Cogn Neurosci, 19, 1498, 10.1162/jocn.2007.19.9.1498 Di Martino, 2014, The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism, Mol Psychiatry, 19, 659, 10.1038/mp.2013.78 Consortium, 2012, The ADHD-200 consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience, Front Syst Neurosci, 6, 62 Gollub, 2013, The MCIC collection: a shared repository of multi-modal, multi-site brain image data from a clinical investigation of schizophrenia, Neuroinformatics, 11, 367, 10.1007/s12021-013-9184-3 Marek, 2011, The Parkinson Progression Marker Initiative (PPMI), Prog Neurobiol, 95, 629, 10.1016/j.pneurobio.2011.09.005 Dagley, 2017, Harvard aging brain study: dataset and accessibility, Neuroimage, 144, 255, 10.1016/j.neuroimage.2015.03.069 Holmes, 2015, Brain Genomics Superstruct Project initial data release with structural, functional, and behavioral measures, Sci Data, 2, 10.1038/sdata.2015.31 Fischl, 2012, FreeSurfer, Neuroimage, 62, 774, 10.1016/j.neuroimage.2012.01.021 Xiao, 2021, A review of deep learning-based three-dimensional medical image registration methods, Quant Imaging Med Surg, 11, 4895, 10.21037/qims-21-175 Vishnevskiy, 2017, Isotropic total variation regularization of displacements in parametric image registration, IEEE Trans Med Imaging, 36, 385, 10.1109/TMI.2016.2610583 Zhu, 2018, Image reconstruction by domain-transform manifold learning, Nature, 555, 487, 10.1038/nature25988 Shen, 2022, A geometry-informed deep learning framework for ultra-sparse 3D tomographic image reconstruction, Comput Biol Med, 10.1016/j.compbiomed.2022.105710 Mardani, 2019, Deep generative adversarial neural networks for compressive sensing MRI, IEEE Trans Med Imaging, 38, 167, 10.1109/TMI.2018.2858752 Wu, 2020, Incorporating prior knowledge via volumetric deep residual network to optimize the reconstruction of sparsely sampled MRI, Magn Reson Imaging, 66, 93, 10.1016/j.mri.2019.03.012 Shen, 2019, Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning, Nat Biomed Eng, 3, 880, 10.1038/s41551-019-0466-4 Krizhevsky, 2017, ImageNet classification with deep convolutional neural networks, Commun ACM, 60, 84, 10.1145/3065386 Fan, 2016, MGH-USC human connectome project datasets with ultra-high b-value diffusion MRI, Neuroimage, 124, 1108, 10.1016/j.neuroimage.2015.08.075 Chen, 2019, Ultra–low-dose 18F-florbetaben amyloid PET imaging using deep learning with multi-contrast MRI inputs, Radiology, 290, 649, 10.1148/radiol.2018180940 Ouyang, 2019, Ultra-low-dose PET reconstruction using generative adversarial network with feature matching and task-specific perceptual loss, Med Phys, 46, 3555, 10.1002/mp.13626 Nguyen, 2022, Federated learning for smart healthcare: a survey, ACM Computing Surveys, 55, 1, 10.1145/3501296 Li, 2021, Synthesizing CT images from MR images with deep learning: model generalization for different datasets through transfer learning, Biomed Phys Eng Express, 7, 10.1088/2057-1976/abe3a7 Li, 2022, Virtual contrast-enhanced magnetic resonance images synthesis for patients with nasopharyngeal carcinoma using multimodality-guided synergistic neural network, Int J Radiat Oncol Biol Phys, 112, 1033, 10.1016/j.ijrobp.2021.11.007 Ma, 2019, Incorporating dosimetric features into the prediction of 3D VMAT dose distributions using deep convolutional neural network, Phys Med Biol, 64, 10.1088/1361-6560/ab2146 Ma, 2019, Dose distribution prediction in isodose feature-preserving voxelization domain using deep convolutional neural network, Med Phys, 46, 2978, 10.1002/mp.13618 Fan, 2019, Automatic treatment planning based on three-dimensional dose distribution predicted from deep learning technique, Med Phys, 46, 370, 10.1002/mp.13271 Dong, 2020, Deep DoseNet: a deep neural network for accurate dosimetric transformation between different spatial resolutions and/or different dose calculation algorithms for precision radiation therapy, Phys Med Biol, 65, 10.1088/1361-6560/ab652d Fan, 2020, Data-driven dose calculation algorithm based on deep U-Net, Phys Med Biol, 65, 10.1088/1361-6560/abca05 Huang, 2022, Meta-optimization for fully automated radiation therapy treatment planning, Phys Med Biol, 67, 10.1088/1361-6560/ac5672 Huang, 2021, Fully automated noncoplanar radiation therapy treatment planning, Med Phys, 48, 7439, 10.1002/mp.15223 Li, 2022, Explainable attention guided adversarial deep network for 3D radiotherapy dose distribution prediction, Knowl-Based Syst, 241, 10.1016/j.knosys.2022.108324 Yue, 2022, Dose prediction via distance-guided deep learning: Initial development for nasopharyngeal carcinoma radiotherapy, Radiother Oncol, 170, 198, 10.1016/j.radonc.2022.03.012 Zhang, 2021, Performance of a multileaf collimator system for a 1.5T MR-linac, Med Phys, 48, 546, 10.1002/mp.14608 Adler, 1997, The Cyberknife: a frameless robotic system for radiosurgery, Stereotact Funct Neurosurg, 69, 124, 10.1159/000099863 Jaffray, 2002, Flat-panel cone-beam computed tomography for image-guided radiation therapy, Int J Radiat Oncol Biol Phys, 53, 1337, 10.1016/S0360-3016(02)02884-5 Shirato, 1999, Real-time tumour-tracking radiotherapy, Lancet, 353, 1331, 10.1016/S0140-6736(99)00700-X Mackie, 1999, Tomotherapy, Sem Radiat Oncol, 9, 108, 10.1016/S1053-4296(99)80058-7 Liu, 2019, A deep learning method for prediction of three-dimensional dose distribution of helical tomotherapy, Med Phys, 46, 1972, 10.1002/mp.13490 Xu, 2016, Radiation-induced CT number changes in GTV and parotid glands during the course of radiation therapy for nasopharyngeal cancer, Br J Radiol, 89, 10.1259/bjr.20140819 Kluter, 2019, Technical design and concept of a 0.35 T MR-linac, Clin Transl Radiat Oncol, 18, 98 LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539 Zhao, 2019, Markerless pancreatic tumor target localization enabled by deep learning, Int J Radiat Oncol Biol Phys, 105, 432, 10.1016/j.ijrobp.2019.05.071 Zhao, 2018, Visualizing the invisible in prostate radiation therapy: markerless prostate target localization via a deep learning model and monoscopic kV projection X-ray image, Intl J Radiat Oncol Biol Phys, 102, S128, 10.1016/j.ijrobp.2018.06.319 Isaksson, 2005, On using an adaptive neural network to predict lung tumor motion during respiration for radiotherapy applications, Med Phys, 32, 3801, 10.1118/1.2134958 Kakar, 2005, Respiratory motion prediction by using the adaptive neuro fuzzy inference system (ANFIS), Phys Med Biol, 50, 4721, 10.1088/0031-9155/50/19/020 Murphy, 2009, Optimization of an adaptive neural network to predict breathing, Med Phys, 36, 40, 10.1118/1.3026608 Valdes, 2016, A mathematical framework for virtual IMRT QA using machine learning, Med Phys, 43, 4323, 10.1118/1.4953835 Carlson, 2016, A machine learning approach to the accurate prediction of multi-leaf collimator positional errors, Phys Med Biol, 61, 2514, 10.1088/0031-9155/61/6/2514 Li, 2017, Predictive time-series modeling using artificial neural networks for linac beam symmetry: an empirical study, Ann N Y Acad Sci, 1387, 84, 10.1111/nyas.13215 Li, 2019, Machine learning for patient-specific quality assurance of VMAT: prediction and classification accuracy, Int J Radiat Oncol Biol Phys, 105, 893, 10.1016/j.ijrobp.2019.07.049 Valdes, 2017, IMRT QA using machine learning: a multi-institutional validation, J Appl Clin Med Phys, 18, 279, 10.1002/acm2.12161 Chan, 2020, Integration of AI and machine learning in radiotherapy QA, Front Artif Intell, 3, 10.3389/frai.2020.577620 Zhao, 2020, Beam data modeling of linear accelerators (linacs) through machine learning and its potential applications in fast and robust linac commissioning and quality assurance, Radiother Oncol, 153, 122, 10.1016/j.radonc.2020.09.057 Fan, 2020, Verification of the machine delivery parameters of a treatment plan via deep learning, Phys Med Biol, 65, 10.1088/1361-6560/aba165 Fan, 2021, Independent verification of brachytherapy treatment plan by using deep learning inference modeling, Phys Med Biol, 66, 10.1088/1361-6560/ac067f Keall, 2021, AAPM Task Group 264: The safe clinical implementation of MLC tracking in radiotherapy, Med Phys, 48, e44, 10.1002/mp.14625 Ibragimov B, Toesca DA, Yuan Y, et al. Deep 3D dose analysis for prediction of outcomes after liver stereotactic body radiation therapy. In: Frangi A, Schnabel J, Davatzikos C, Alberola-López C, Fichtinger G, eds. Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. Lecture Notes in Computer Science. Vol 11071. Springer;2018:684–692. doi:10.1007/978-3-030-00934-2_76. Ibragimov, 2020, Deep learning for identification of critical regions associated with toxicities after liver stereotactic body radiation therapy, Med Phys, 47, 3721, 10.1002/mp.14235 Bibault, 2021, Development and validation of a model to predict survival in colorectal cancer using a gradient-boosted machine, Gut, 70, 884, 10.1136/gutjnl-2020-321799 Liu, 2021, Integrate sequence information of dose volume histogram in training LSTM-based deep learning model for lymphopenia diagnosis, Intl J Radiat Oncol Biol Phys, 111, e112, 10.1016/j.ijrobp.2021.07.520 Appelt, 2022, Deep learning for radiotherapy outcome prediction using dose data - a review, Clin Oncol (R Coll Radiol), 34, e87, 10.1016/j.clon.2021.12.002 Cruz Rivera, 2020, Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension, Nat Med, 26, 1351, 10.1038/s41591-020-1037-7 Liu, 2020, Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension, BMJ, 370, m3164, 10.1136/bmj.m3164 Luo, 2016, Guidelines for developing and reporting machine learning predictive models in biomedical research: a multidisciplinary view, J Med Internet Res, 18, e323, 10.2196/jmir.5870 Rodríguez-Barroso, 2020, Federated learning and differential privacy: software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy, Info Fusion, 64, 270, 10.1016/j.inffus.2020.07.009 Shneiderman, 2020, Bridging the gap between ethics and practice, ACM Trans Interact Intell Syst, 10, 1, 10.1145/3419764 Hagendorff, 2020, The ethics of AI ethics: an evaluation of guidelines, Minds Machs, 30, 99, 10.1007/s11023-020-09517-8 Jobin, 2019, The global landscape of AI ethics guidelines, Nat Mach Intell, 1, 389, 10.1038/s42256-019-0088-2