PDF(Pubmed)
Abstract：
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
摘要：
■中国临床肿瘤学会人工智能系统（CSCOAI）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CSCOAI提供的乳腺癌治疗建议与其在临床环境中的实际应用之间的一致性。
■回顾性分析2017年1月至2022年12月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。熟练的高级肿瘤学研究人员将患者数据手动输入到CSCOAI系统中。通过使我们的治疗方案与CSCOAI建议中的分类系统保持一致，定义了“一致”和“不一致”治疗类别。最初显示不一致的病例接受了医院多学科治疗（MDT）小组的第二次评估。当MDTS的治疗建议处于“一致”类别时，就达到了一致性。
在所有乳腺癌患者的实际治疗方案和CSCOAI建议之间观察到令人印象深刻的80.4%的一致性。值得注意的是,与II期患者(76.06%,P=0.023)。此外，浸润性导管癌和小叶癌之间存在显着一致性（88.46％）。有趣的是,与其他分子亚型相比，三阴性乳腺癌（TNBC）的一致性率高（87.50％）。将MDT推荐的治疗方法与CSCOAI决策进行对比时，达成了总体92.4%的协议。此外，Logistic多变量分析强调了年龄的统计学意义，月经状态,肿瘤类型,分子亚型,肿瘤大小,和TNM阶段影响一致性。
■在乳腺癌治疗领域，CSCOAI提供的建议与MDT提供的建议之间的一致性占主导地位。CSCOAI可以成为乳腺癌治疗决策的有用工具。
■回顾性分析2017年1月至2022年12月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。熟练的高级肿瘤学研究人员将患者数据手动输入到CSCOAI系统中。通过使我们的治疗方案与CSCOAI建议中的分类系统保持一致，定义了“一致”和“不一致”治疗类别。最初显示不一致的病例接受了医院多学科治疗（MDT）小组的第二次评估。当MDTS的治疗建议处于“一致”类别时，就达到了一致性。
在所有乳腺癌患者的实际治疗方案和CSCOAI建议之间观察到令人印象深刻的80.4%的一致性。值得注意的是,与II期患者(76.06%,P=0.023)。此外，浸润性导管癌和小叶癌之间存在显着一致性（88.46％）。有趣的是,与其他分子亚型相比，三阴性乳腺癌（TNBC）的一致性率高（87.50％）。将MDT推荐的治疗方法与CSCOAI决策进行对比时，达成了总体92.4%的协议。此外，Logistic多变量分析强调了年龄的统计学意义，月经状态,肿瘤类型,分子亚型,肿瘤大小,和TNM阶段影响一致性。
■在乳腺癌治疗领域，CSCOAI提供的建议与MDT提供的建议之间的一致性占主导地位。CSCOAI可以成为乳腺癌治疗决策的有用工具。
