AI-assisted clinical decision making (CDM) for dose prescription in radiosurgery of brain metastases using three-path three-dimensional CNN. (March 2023)

• Record Type: Journal Article
• Authors: Cao, Yufeng; Kunaprayoon, Dan; Xu, Junliang; Ren, Lei
• Is Part Of: Clinical and translational radiation oncology. Volume 39(2023)
• Publication Date: 2023-03
• Subjects: Clinical decision-making -- Metastases -- Deep learning; Cancer -- Radiotherapy -- Periodicals; Oncology -- Periodicals; Cancer -- Radiotherapy; Oncology; Radiation Oncology; Neoplasms -- radiotherapy; Translational Medical Research; Periodicals; Electronic journals; Periodicals; 616.9940642
• DOI: 10.1016/j.ctro.2022.100565
• Languages: English
• ISSNs: 2405-6308

Abstract

Highlights: Developed a practical AI model to learn from the actual treatment records with the incorporation of physicians' logical decision process to mimic physicians' clinical decision-making (CDM). The model was trained intentionally to be institution-specific or physician-specific to address the practice variations in clinical practice. This is the first time an AI model has been developed to predict dose prescription based on both image and non-image clinical parameters for CDM of radiotherapy. Results demonstrated the effectiveness of the model, which can become a valuable tool for secondary opinion consultation for patients or QA and training of physician practices. Abstract: Purpose: AI modeling physicians' clinical decision-making (CDM) can improve the efficiency and accuracy of clinical practice or serve as a surrogate to provide initial consultations to patients seeking secondary opinions. In this study, we developed an AI network to model radiotherapy CDM and used dose prescription as an example to demonstrate its feasibility. Materials/Methods: 152 patients with brain metastases treated by radiosurgery from 2017 to 2021 were included. CT images and tumor and organ-at-risk (OAR) contours were exported. Eight relevant clinical parameters were extracted and digitized, including age, numbers of lesions, performance status (ECOG), presence of symptoms, arrangement with surgery (pre- or post-surgery radiation therapy), re-treatment, primary cancer type, and metastasis to other sites. A 3D convolutional neural network (CNN) architecture was built using three encoding paths with the same kernel and filters to capture the different image and contour features. Specifically, one path was built to capture the tumor feature, including the size and location of the tumor, another path was built to capture the relative spatial relationship between the tumor and OARs, and the third path was built to capture the clinical parameters. The model combines information from three paths to predict dose prescription. The actual prescription in the patient record was used as ground truth for model training. The model performance was assessed by 19-fold-cross-validation, with each fold consisting of randomly selected 128 training, 16 validation, and 8 testing subjects. Result: The dose prescriptions of 152 patient cases included 48 cases with 1 × 24 Gy, 48 cases with 1 × 20–22 Gy, 32 cases with 3 × 9 Gy, and 24 cases with 5 × 6 Gy prescribed by 8 physicians. The AI model prescribed correctly for 124 (82 %) cases, including 44 (92 %) cases with 1 × 24 Gy, 36 (75 %) cases with 1 × 20–22 Gy, 25 (78 %) cases with 3 × 9 Gy, and 19 (79 %) cases with 5 × 6 Gy. Analysis of the failed cases showed the potential cause of practice variations across individual physicians, which were not accounted for in the model trained by the group data. Including clinical parameters improved the overall prediction accuracy by 20 %. Conclusion: To our best knowledge, this is the first study to demonstrate the feasibility of AI in predicting dose prescription in CDM in radiation therapy. Such CDM models can serve as vital tools to address healthcare disparities by providing preliminary consultations to patients in underdeveloped areas or as a valuable quality assurance (QA) tool for physicians to cross-check intra- and inter-institution practices.