AI-assisted clinical decision making (CDM) for dose prescription in radiosurgery of brain metastases using three-path three-dimensional CNN

Clinical and Translational Radiation Oncology - Tập 39 - Trang 100565 - 2023
Yufeng Cao1, Dan Kunaprayoon1, Junliang Xu1, Lei Ren1
1Department of Radiation Oncology, University of Maryland, Baltimore, MD, USA

Tài liệu tham khảo

Fraass, 1998, American association of physicists in medicine radiation therapy committee task group 53: quality assurance for clinical radiotherapy treatment planning, Med Phys, 25, 1773, 10.1118/1.598373 Huynh, 2020, Artificial intelligence in radiation oncology, Nat Rev Clin Oncol, 17, 771, 10.1038/s41571-020-0417-8 Ezzell, 2003, Guidance document on delivery, treatment planning, and clinical implementation of IMRT: report of the IMRT Subcommittee of the AAPM radiation therapy committee, Med Phys, 30, 2089, 10.1118/1.1591194 Barton, 2006, Role of radiotherapy in cancer control in low-income and middle-income countries, Lancet Oncol, 7, 584, 10.1016/S1470-2045(06)70759-8 Zubizarreta, 2015, Need for radiotherapy in low and middle income countries–the silent crisis continues, Clin Oncol, 27, 107, 10.1016/j.clon.2014.10.006 Wang D, Wang L, Zhang Z, Wang D, Zhu H, Gao Y, et al. “Brilliant AI Doctor” in Rural Clinics: Challenges in AI-Powered Clinical Decision Support System Deployment. Proc. 2021 CHI Conf. Hum. Factors Comput. Syst., 2021, p. 1–18. Kompa, 2021, Second opinion needed: communicating uncertainty in medical machine learning, NPJ Digit Med, 4, 1, 10.1038/s41746-020-00367-3 Hosny, 2019, Artificial intelligence for global health, Science, 366, 955, 10.1126/science.aay5189 Louis, 2016, The 2016 World Health Organization classification of tumors of the central nervous system: a summary, Acta Neuropathol (Berl), 131, 803, 10.1007/s00401-016-1545-1 Frid-Adar, 2018, GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification, Neurocomputing, 321, 321, 10.1016/j.neucom.2018.09.013 Cao, 2021, Automatic detection and segmentation of multiple brain metastases on magnetic resonance image using asymmetric UNet architecture, Phys Med Biol, 66, 015003, 10.1088/1361-6560/abca53 Vassantachart, 2022, Automatic differentiation of Grade I and II meningiomas on magnetic resonance image using an asymmetric convolutional neural network, Sci Rep, 12, 10.1038/s41598-022-07859-0 Jiang, 2020, A multi-scale framework with unsupervised joint training of convolutional neural networks for pulmonary deformable image registration, Phys Med Biol, 65, 015011, 10.1088/1361-6560/ab5da0 Mohammadi, 2021, Deep learning-based Auto-segmentation of Organs at Risk in High-Dose Rate Brachytherapy of Cervical Cancer, Radiother Oncol, 159, 231, 10.1016/j.radonc.2021.03.030 Xu, 2019, Deep learning predicts lung cancer treatment response from serial medical imaging, Clin Cancer Res, 25, 3266, 10.1158/1078-0432.CCR-18-2495 Zhang, 2022, Patient-specific deep learning model to enhance 4D-CBCT image for radiomics analysis, Phys Med Biol, 67, 085003, 10.1088/1361-6560/ac5f6e Jiang, 2021, Enhancement of Four-dimensional Cone-beam Computed Tomography (4D-CBCT) using a Dual-encoder Convolutional Neural Network (DeCNN), IEEE Trans Radiat Plasma Med Sci Jiang, 2021, Enhancing digital tomosynthesis (DTS) for lung radiotherapy guidance using patient-specific deep learning model, Phys Med Biol, 66, 035009, 10.1088/1361-6560/abcde8 Jiang, 2019, Augmentation of CBCT reconstructed from under-sampled projections using deep learning, IEEE Trans Med Imaging, 38, 2705, 10.1109/TMI.2019.2912791 Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. ArXiv Prepr ArXiv14091556 2014. Ahmad, 2021, Diagn Pathol, 16, 1, 10.1186/s13000-021-01085-4 Jiang, 2017, Artificial intelligence in healthcare: past, present and future, Stroke Vasc Neurol, 2, 230, 10.1136/svn-2017-000101 LeBlanc, 2017, Patient experiences of acute myeloid leukemia: a qualitative study about diagnosis, illness understanding, and treatment decision-making, Psychooncology, 26, 2063, 10.1002/pon.4309 Strickland, 2019, IBM Watson, heal thyself: how IBM overpromised and underdelivered on AI health care, IEEE Spectr, 56, 24, 10.1109/MSPEC.2019.8678513 Lee, 2018, Assessing concordance with Watson for Oncology, a cognitive computing decision support system for colon cancer treatment in Korea, JCO Clin Cancer Inform, 2, 1 Shaw, 2000, Single dose radiosurgical treatment of recurrent previously irradiated primary brain tumors and brain metastases: final report of RTOG protocol 90–05, Int J Radiat Oncol Biol Phys, 47, 291, 10.1016/S0360-3016(99)00507-6 Glorot, 2010, Understanding the difficulty of training deep feedforward neural networks, Proc Thirteen Int Conf Artif Intell Stat, 249 Kingma DP, Ba J. Adam: A method for stochastic optimization. ArXiv Prepr ArXiv14126980 2014. Fawcett, 2006, An introduction to ROC analysis, Pattern Recognit Lett, 27, 861, 10.1016/j.patrec.2005.10.010 Pedregosa, 2011, Scikit-learn: machine learning in Python, J Mach Learn Res, 12, 2825