{"title":"A prospective study comparing highly qualified Molecular Tumor Boards with AI-powered software as a medical device.","authors":"Hideaki Bando, Yoichi Naito, Tomoyuki Yamada, Takao Fujisawa, Mitsuho Imai, Yasutoshi Sakamoto, Yusuke Saigusa, Kouji Yamamoto, Yutaka Tomioka, Nobuyoshi Takeshita, Kuniko Sunami, Megumi Futamura, Chiemi Notake, Satoko Aoki, Kazunori Okano, Takayuki Yoshino","doi":"10.1007/s10147-024-02684-z","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>The implementation of cancer precision medicine in Japan is deeply intertwined with insurance reimbursement policies and requires case-by-case reviews by Molecular Tumor Boards (MTBs), which impose considerable operational burdens on healthcare facilities. The extensive preparation and review times required by MTBs hinder their ability to efficiently assess comprehensive genomic profiling (CGP) test results. Despite attempts to optimize MTB operations, significant challenges remain. This study aims to evaluate the effectiveness of QA Commons, an artificial intelligence-driven system designed to improve treatment planning using CGP analysis. QA Commons utilizes a comprehensive knowledge base of drugs, regulatory approvals, and clinical trials linked to genetic biomarkers, thereby enabling the delivery of consistent and standardized treatment recommendations. Initial assessments revealed that the QA Commons' recommendations closely matched the ideal treatment recommendations (consensus annotations), outperforming the average results of MTBs at Cancer Genomic Medicine Core Hospitals.</p><p><strong>Methods: </strong>A clinical performance evaluation study will be conducted by comparing the QA Commons' treatment recommendations with those of the Academia Assembly, which includes medical professionals from the Cancer Genomic Medicine Core and Hub Hospitals. One hundred cases selected from the \"Registry of the Academia Assembly,\" based on defined inclusion and exclusion criteria, will be analyzed to assess the concordance of recommendations.</p><p><strong>Conclusion: </strong>The expected outcomes suggest that QA Commons could reduce the workload of MTB members, standardize the quality of MTB discussions, and provide consistent outcomes in repeated patient consultations. In addition, the global expansion of QA Commons could promote worldwide adoption of Japan's pioneering precision oncology system.</p>","PeriodicalId":13869,"journal":{"name":"International Journal of Clinical Oncology","volume":" ","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Clinical Oncology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s10147-024-02684-z","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Background: The implementation of cancer precision medicine in Japan is deeply intertwined with insurance reimbursement policies and requires case-by-case reviews by Molecular Tumor Boards (MTBs), which impose considerable operational burdens on healthcare facilities. The extensive preparation and review times required by MTBs hinder their ability to efficiently assess comprehensive genomic profiling (CGP) test results. Despite attempts to optimize MTB operations, significant challenges remain. This study aims to evaluate the effectiveness of QA Commons, an artificial intelligence-driven system designed to improve treatment planning using CGP analysis. QA Commons utilizes a comprehensive knowledge base of drugs, regulatory approvals, and clinical trials linked to genetic biomarkers, thereby enabling the delivery of consistent and standardized treatment recommendations. Initial assessments revealed that the QA Commons' recommendations closely matched the ideal treatment recommendations (consensus annotations), outperforming the average results of MTBs at Cancer Genomic Medicine Core Hospitals.
Methods: A clinical performance evaluation study will be conducted by comparing the QA Commons' treatment recommendations with those of the Academia Assembly, which includes medical professionals from the Cancer Genomic Medicine Core and Hub Hospitals. One hundred cases selected from the "Registry of the Academia Assembly," based on defined inclusion and exclusion criteria, will be analyzed to assess the concordance of recommendations.
Conclusion: The expected outcomes suggest that QA Commons could reduce the workload of MTB members, standardize the quality of MTB discussions, and provide consistent outcomes in repeated patient consultations. In addition, the global expansion of QA Commons could promote worldwide adoption of Japan's pioneering precision oncology system.
期刊介绍：
The International Journal of Clinical Oncology (IJCO) welcomes original research papers on all aspects of clinical oncology that report the results of novel and timely investigations. Reports on clinical trials are encouraged. Experimental studies will also be accepted if they have obvious relevance to clinical oncology. Membership in the Japan Society of Clinical Oncology is not a prerequisite for submission to the journal. Papers are received on the understanding that: their contents have not been published in whole or in part elsewhere; that they are subject to peer review by at least two referees and the Editors, and to editorial revision of the language and contents; and that the Editors are responsible for their acceptance, rejection, and order of publication.
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