Department of Surgical Oncology, Faculty of Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Surgery Department, Sanno Hospital, International University of Health and Welfare, Tokyo, Japan.
Ann Surg Oncol. 2018 May;25(5):1366-1373. doi: 10.1245/s10434-018-6403-z. Epub 2018 Mar 5.
Detection of peritoneal metastasis remains challenging due to the limited sensitivity of current examination methods. This study aimed to establish a prediction model for estimating the individual risk of postoperative peritoneal metastasis from colon cancer to facilitate early interventions for high-risk patients.
This study investigated 1720 patients with stages 1-3 colon cancer who underwent curative resection at the University of Tokyo Hospital between 1997 and 2015. The data for the patients were retrospectively retrieved from their medical records. The risk score was developed using the elastic net techniques in a derivation cohort (973 patients treated in 1997-2009) and validated in a validation cohort (747 patients treated in 2010-2015).
The factors selected using the elastic net approaches included the T stage, N stage, number of examined lymph nodes, preoperative carcinoembryonic antigen level, large bowel obstruction, and anastomotic leakage. The model had good discrimination (c-index, 0.85) and was well-calibrated after application of the bootstrap resampling method. Discrimination and calibration were favorable in external validation (c-index, 0.83). The model presented a clear stratification of patients' risk for postoperative peritoneal recurrence, and decision curve analysis showed its net benefit across a wide range of threshold probabilities.
This study established and validated a prediction model that can aid clinicians in optimizing postoperative surveillance and therapeutic strategies according to the individual patient risk of peritoneal recurrence.
由于目前检查方法的敏感性有限，因此检测腹膜转移仍然具有挑战性。本研究旨在建立一种预测模型，以估计结肠癌患者术后腹膜转移的个体风险，从而为高危患者提供早期干预。
本研究调查了 1997 年至 2015 年期间在东京大学医院接受根治性切除术的 1720 例 1-3 期结肠癌患者。从他们的病历中回顾性检索了患者的数据。使用弹性网络技术在推导队列（1997-2009 年治疗的 973 例患者）中开发风险评分，并在验证队列（2010-2015 年治疗的 747 例患者）中进行验证。
弹性网络方法选择的因素包括 T 分期、N 分期、检查的淋巴结数量、术前癌胚抗原水平、大肠梗阻和吻合口漏。该模型具有良好的判别能力（c 指数为 0.85），并且在应用引导重采样方法后校准良好。外部验证的判别和校准效果良好（c 指数为 0.83）。该模型清楚地对患者术后腹膜复发的风险进行了分层，决策曲线分析表明其在广泛的阈值概率范围内具有净收益。
本研究建立并验证了一种预测模型，可帮助临床医生根据患者腹膜复发的个体风险优化术后监测和治疗策略。
