{"title":"CSCO AI 与乳腺癌多学科临床决策团队的一致性:回顾性研究","authors":"Weimin Xu, Xinyu Wang, Lei Yang, Muzi Meng, Chenyu Sun, Wanwan Li, Jia Li, Lu Zheng, Tong Tang, WenJun Jia, Xiao Chen","doi":"10.2147/BCTT.S419433","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>The Chinese Society of Clinical Oncology Artificial Intelligence System (CSCO AI) serves as a clinical decision support system developed utilizing Chinese breast cancer data. Our study delved into the congruence between breast cancer treatment recommendations provided by CSCO AI and their practical application in clinical settings.</p><p><strong>Methods: </strong>A retrospective analysis encompassed 537 breast cancer patients treated at the Second Affiliated Hospital of Anhui Medical University between January 2017 and December 2022. Proficient senior oncology researchers manually input patient data into the CSCO AI system. \"Consistent\" and \"Inconsistent\" treatment categories were defined by aligning our treatment protocols with the classification system in the CSCO AI recommendations. Cases that initially showed inconsistency underwent a second evaluation by the Multi-Disciplinary Treatment (MDT) team at the hospital. Concordance was achieved when MDTs' treatment suggestions were in the 'Consistent' categories.</p><p><strong>Results: </strong>An impressive 80.4% concurrence was observed between actual treatment protocols and CSCO AI recommendations across all breast cancer patients. Notably, the alignment was markedly higher for stage I (85.02%) and stage III (88.46%) patients in contrast to stage II patients (76.06%, P=0.023). Moreover, there was a significant concordance between invasive ductal carcinoma and lobular carcinoma (88.46%). Interestingly, triple-negative breast cancer (TNBC) exhibited a high concordance rate (87.50%) compared to other molecular subtypes. When contrasting MDT-recommended treatments with CSCO AI decisions, an overall 92.4% agreement was established. Furthermore, a logistic multivariate analysis highlighted the statistical significance of age, menstrual status, tumor type, molecular subtype, tumor size, and TNM stage in influencing consistency.</p><p><strong>Conclusion: </strong>In the realm of breast cancer treatment, the alignment between recommendations offered by CSCO AI and those from MDT is predominant. CSCO AI can be a useful tool for breast cancer treatment decisions.</p>","PeriodicalId":9106,"journal":{"name":"Breast Cancer : Targets and Therapy","volume":"16 ","pages":"413-422"},"PeriodicalIF":3.3000,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11296359/pdf/","citationCount":"0","resultStr":"{\"title\":\"Consistency of CSCO AI with Multidisciplinary Clinical Decision-Making Teams in Breast Cancer: A Retrospective Study.\",\"authors\":\"Weimin Xu, Xinyu Wang, Lei Yang, Muzi Meng, Chenyu Sun, Wanwan Li, Jia Li, Lu Zheng, Tong Tang, WenJun Jia, Xiao Chen\",\"doi\":\"10.2147/BCTT.S419433\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>The Chinese Society of Clinical Oncology Artificial Intelligence System (CSCO AI) serves as a clinical decision support system developed utilizing Chinese breast cancer data. Our study delved into the congruence between breast cancer treatment recommendations provided by CSCO AI and their practical application in clinical settings.</p><p><strong>Methods: </strong>A retrospective analysis encompassed 537 breast cancer patients treated at the Second Affiliated Hospital of Anhui Medical University between January 2017 and December 2022. Proficient senior oncology researchers manually input patient data into the CSCO AI system. \\\"Consistent\\\" and \\\"Inconsistent\\\" treatment categories were defined by aligning our treatment protocols with the classification system in the CSCO AI recommendations. Cases that initially showed inconsistency underwent a second evaluation by the Multi-Disciplinary Treatment (MDT) team at the hospital. Concordance was achieved when MDTs' treatment suggestions were in the 'Consistent' categories.</p><p><strong>Results: </strong>An impressive 80.4% concurrence was observed between actual treatment protocols and CSCO AI recommendations across all breast cancer patients. Notably, the alignment was markedly higher for stage I (85.02%) and stage III (88.46%) patients in contrast to stage II patients (76.06%, P=0.023). Moreover, there was a significant concordance between invasive ductal carcinoma and lobular carcinoma (88.46%). Interestingly, triple-negative breast cancer (TNBC) exhibited a high concordance rate (87.50%) compared to other molecular subtypes. When contrasting MDT-recommended treatments with CSCO AI decisions, an overall 92.4% agreement was established. Furthermore, a logistic multivariate analysis highlighted the statistical significance of age, menstrual status, tumor type, molecular subtype, tumor size, and TNM stage in influencing consistency.</p><p><strong>Conclusion: </strong>In the realm of breast cancer treatment, the alignment between recommendations offered by CSCO AI and those from MDT is predominant. CSCO AI can be a useful tool for breast cancer treatment decisions.</p>\",\"PeriodicalId\":9106,\"journal\":{\"name\":\"Breast Cancer : Targets and Therapy\",\"volume\":\"16 \",\"pages\":\"413-422\"},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2024-07-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11296359/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Breast Cancer : Targets and Therapy\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2147/BCTT.S419433\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Breast Cancer : Targets and Therapy","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2147/BCTT.S419433","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
背景:中国临床肿瘤学会人工智能系统(CSCO AI中国临床肿瘤学会人工智能系统(CSCO AI)是利用中国乳腺癌数据开发的临床决策支持系统。我们的研究探讨了 CSCO AI 提供的乳腺癌治疗建议与临床实际应用之间的一致性:回顾性分析涵盖了 2017 年 1 月至 2022 年 12 月期间在安徽医科大学第二附属医院接受治疗的 537 例乳腺癌患者。熟练的资深肿瘤学研究人员将患者数据手动输入 CSCO AI 系统。通过将我们的治疗方案与 CSCO AI 建议中的分类系统进行比对,确定了 "一致 "和 "不一致 "的治疗类别。最初出现不一致的病例将由医院的多学科治疗团队(MDT)进行第二次评估。当多学科治疗小组的治疗建议属于 "一致 "类别时,即为一致:在所有乳腺癌患者中,实际治疗方案与 CSCO AI 建议的一致性达到了令人印象深刻的 80.4%。值得注意的是,I 期(85.02%)和 III 期(88.46%)患者的一致性明显高于 II 期患者(76.06%,P=0.023)。此外,浸润性导管癌和小叶癌(88.46%)之间存在明显的一致性。有趣的是,与其他分子亚型相比,三阴性乳腺癌(TNBC)的一致性更高(87.50%)。当将 MDT 推荐的治疗方法与 CSCO AI 决定进行对比时,总体一致率为 92.4%。此外,逻辑多变量分析强调了年龄、月经状况、肿瘤类型、分子亚型、肿瘤大小和 TNM 分期在影响一致性方面的统计学意义:在乳腺癌治疗领域,CSCO AI 提供的建议与 MDT 提出的建议之间的一致性占主导地位。CSCO AI 可以作为乳腺癌治疗决策的有用工具。
Consistency of CSCO AI with Multidisciplinary Clinical Decision-Making Teams in Breast Cancer: A Retrospective Study.
Background: The Chinese Society of Clinical Oncology Artificial Intelligence System (CSCO AI) serves as a clinical decision support system developed utilizing Chinese breast cancer data. Our study delved into the congruence between breast cancer treatment recommendations provided by CSCO AI and their practical application in clinical settings.
Methods: A retrospective analysis encompassed 537 breast cancer patients treated at the Second Affiliated Hospital of Anhui Medical University between January 2017 and December 2022. Proficient senior oncology researchers manually input patient data into the CSCO AI system. "Consistent" and "Inconsistent" treatment categories were defined by aligning our treatment protocols with the classification system in the CSCO AI recommendations. Cases that initially showed inconsistency underwent a second evaluation by the Multi-Disciplinary Treatment (MDT) team at the hospital. Concordance was achieved when MDTs' treatment suggestions were in the 'Consistent' categories.
Results: An impressive 80.4% concurrence was observed between actual treatment protocols and CSCO AI recommendations across all breast cancer patients. Notably, the alignment was markedly higher for stage I (85.02%) and stage III (88.46%) patients in contrast to stage II patients (76.06%, P=0.023). Moreover, there was a significant concordance between invasive ductal carcinoma and lobular carcinoma (88.46%). Interestingly, triple-negative breast cancer (TNBC) exhibited a high concordance rate (87.50%) compared to other molecular subtypes. When contrasting MDT-recommended treatments with CSCO AI decisions, an overall 92.4% agreement was established. Furthermore, a logistic multivariate analysis highlighted the statistical significance of age, menstrual status, tumor type, molecular subtype, tumor size, and TNM stage in influencing consistency.
Conclusion: In the realm of breast cancer treatment, the alignment between recommendations offered by CSCO AI and those from MDT is predominant. CSCO AI can be a useful tool for breast cancer treatment decisions.