{
    "abstract": "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.\n152 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.\nThe dose prescriptions of 152 patient cases included 48 cases with 1\u00a0\u00d7\u00a024\u00a0Gy, 48 cases with 1\u00a0\u00d7\u00a020-22\u00a0Gy, 32 cases with 3\u00a0\u00d7\u00a09\u00a0Gy, and 24 cases with 5\u00a0\u00d7\u00a06\u00a0Gy prescribed by 8 physicians. The AI model prescribed correctly for 124 (82\u00a0%) cases, including 44 (92\u00a0%) cases with 1\u00a0\u00d7\u00a024\u00a0Gy, 36 (75\u00a0%) cases with 1\u00a0\u00d7\u00a020-22\u00a0Gy, 25 (78\u00a0%) cases with 3\u00a0\u00d7\u00a09\u00a0Gy, and 19 (79\u00a0%) cases with 5\u00a0\u00d7\u00a06\u00a0Gy. 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\u00a0%.\nTo 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.",
    "authors": [
        {
            "affiliation": "Department of Radiation Oncology, University of Maryland, Baltimore, MD, USA.",
            "firstname": "Yufeng",
            "initials": "Y",
            "lastname": "Cao"
        },
        {
            "affiliation": "Department of Radiation Oncology, University of Maryland, Baltimore, MD, USA.",
            "firstname": "Dan",
            "initials": "D",
            "lastname": "Kunaprayoon"
        },
        {
            "affiliation": "Department of Radiation Oncology, University of Maryland, Baltimore, MD, USA.",
            "firstname": "Junliang",
            "initials": "J",
            "lastname": "Xu"
        },
        {
            "affiliation": "Department of Radiation Oncology, University of Maryland, Baltimore, MD, USA.",
            "firstname": "Lei",
            "initials": "L",
            "lastname": "Ren"
        }
    ],
    "conclusions": "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.",
    "copyrights": "\u00a9 2022 Published by Elsevier B.V. on behalf of European Society for Radiotherapy and Oncology.",
    "doi": "10.1016/j.ctro.2022.100565",
    "journal": "Clinical and translational radiation oncology",
    "keywords": [
        "Clinical decision-making",
        "Deep learning",
        "Metastases"
    ],
    "methods": null,
    "publication_date": "2023-01-04",
    "pubmed_id": "36594076\n9800687\n32843739\n12945975\n16814210\n25455407\n33402680\n31753987\n27157931\n33186927\n35264655\n31783390\n33831446\n31010833\n35313293\n35386935\n33238249\n31021791\n33731170\n29507784\n27862591\n30652564\n10802351",
    "results": null,
    "title": "AI-assisted clinical decision making (CDM) for dose prescription in radiosurgery of brain metastases using three-path three-dimensional CNN.",
    "xml": "<Element 'PubmedArticle' at 0x7885036844a0>"
}