Ching-Wen Chiu, Chih-Ming Su, Li-Min Liao, Chang-Siang Su, Thanh-Phuc Phan, Ka-Wai Tam
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Two prediction models were built, in which patients who met at least one (Model A) or two (Model B) of the predictors would be predicted to upstage in the final pathology. We compared the accuracy of our models with National Comprehensive Cancer Network (NCCN) guideline and original data.</p><p><strong>Results: </strong>The analyses included 249 patients, of which 67 DCIS patients upstaged in final pathology. The excess treatment in Model A (70%) was lower than the original data (80.2%). The incomplete treatment in Model A (3%) was lower than the NCCN guideline model (38.8%) and the original data (7.5%). Both Model A and Model B yielded a higher receiver operating characteristic (AUC) curve compared with original data.</p><p><strong>Conclusions: </strong>Our Model A derived from the systematic review of the real-world data reduced the incomplete treatment rate of SLNB. Our Model B also showed the highest predictive value. With the two models, we provided a clearer indication for surgeons to perform SLNB in DCIS patients and demonstrated proof of concept, allowing ready input of patient data.</p>","PeriodicalId":17111,"journal":{"name":"Journal of Surgical Oncology","volume":" ","pages":""},"PeriodicalIF":2.0000,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Upstaging Prediction Model to Guide the Application of Sentinel Lymph Node Biopsy in Patients With Ductal Carcinoma In Situ: A Retrospective Comparative Study.\",\"authors\":\"Ching-Wen Chiu, Chih-Ming Su, Li-Min Liao, Chang-Siang Su, Thanh-Phuc Phan, Ka-Wai Tam\",\"doi\":\"10.1002/jso.27983\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background and objectives: </strong>Indication for sentinel lymph node (SLN) biopsy in ductal carcinoma in situ (DCIS) patients with high-upstaging risk remains inconsistent. 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引用次数: 0
摘要
背景和目的:具有高分期风险的导管原位癌(DCIS)患者进行前哨淋巴结(SLN)活检的指征仍不一致。我们之前的系统综述和荟萃分析报告了五个变量,这些变量在上行分期组中明显较高。我们建立了 "高风险上行分期模型",并对其预测性和准确性进行了研究:研究对象包括2011年至2020年间在一家医疗中心初诊的DCIS患者。患者的临床病理数据通过网络手术病历数据库获取。我们建立了两个预测模型,其中符合至少一个(模型A)或两个(模型B)预测指标的患者将被预测为最终病理结果为上期。我们将模型的准确性与美国国家综合癌症网络(NCCN)指南和原始数据进行了比较:分析包括249名患者,其中67名DCIS患者在最终病理结果中升期。模型 A 中的过度治疗率(70%)低于原始数据(80.2%)。模型 A 中的治疗不完全率(3%)低于 NCCN 指南模型(38.8%)和原始数据(7.5%)。与原始数据相比,模型A和模型B的接收者操作特征曲线(AUC)都更高:结论:我们通过对真实世界数据的系统回顾得出的模型 A 降低了 SLNB 的不完全治疗率。我们的模型 B 也显示出了最高的预测价值。通过这两个模型,我们为外科医生在 DCIS 患者中实施 SLNB 提供了更明确的指征,并证明了这一概念,使患者数据的输入成为可能。
Upstaging Prediction Model to Guide the Application of Sentinel Lymph Node Biopsy in Patients With Ductal Carcinoma In Situ: A Retrospective Comparative Study.
Background and objectives: Indication for sentinel lymph node (SLN) biopsy in ductal carcinoma in situ (DCIS) patients with high-upstaging risk remains inconsistent. Our previous systematic review and meta-analysis had reported five variables that were significantly higher in the upstaging group. We developed the "high-risk upstaging model" and investigated its predictivity and accuracy.
Methods: The study included patients initially diagnosed with DCIS in a medical center between 2011 and 2020. Patients' clinicopathological data were obtained through web-based surgical medical record database. Two prediction models were built, in which patients who met at least one (Model A) or two (Model B) of the predictors would be predicted to upstage in the final pathology. We compared the accuracy of our models with National Comprehensive Cancer Network (NCCN) guideline and original data.
Results: The analyses included 249 patients, of which 67 DCIS patients upstaged in final pathology. The excess treatment in Model A (70%) was lower than the original data (80.2%). The incomplete treatment in Model A (3%) was lower than the NCCN guideline model (38.8%) and the original data (7.5%). Both Model A and Model B yielded a higher receiver operating characteristic (AUC) curve compared with original data.
Conclusions: Our Model A derived from the systematic review of the real-world data reduced the incomplete treatment rate of SLNB. Our Model B also showed the highest predictive value. With the two models, we provided a clearer indication for surgeons to perform SLNB in DCIS patients and demonstrated proof of concept, allowing ready input of patient data.
期刊介绍:
The Journal of Surgical Oncology offers peer-reviewed, original papers in the field of surgical oncology and broadly related surgical sciences, including reports on experimental and laboratory studies. As an international journal, the editors encourage participation from leading surgeons around the world. The JSO is the representative journal for the World Federation of Surgical Oncology Societies. Publishing 16 issues in 2 volumes each year, the journal accepts Research Articles, in-depth Reviews of timely interest, Letters to the Editor, and invited Editorials. Guest Editors from the JSO Editorial Board oversee multiple special Seminars issues each year. These Seminars include multifaceted Reviews on a particular topic or current issue in surgical oncology, which are invited from experts in the field.