Zhang Yuanxin, Qin Xiusen, Li Yang, Zhang Xi, Luo Rui, Wu Zhijie, Li Victoria, Han Shuai, Wang Hui, Wang Huaiming
Department of Colorectal Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Front Oncol. 2022 Jul 13;12:943951. doi: 10.3389/fonc.2022.943951. eCollection 2022.
The early diagnosis of occult peritoneal metastasis (PM) remains a challenge due to the low sensitivity on computed tomography (CT) images. Exploratory laparoscopy is the gold standard to confirm PM but should only be proposed in selected patients due to its invasiveness, high cost, and port-site metastasis risk. In this study, we aimed to develop an individualized prediction model to identify occult PM status and determine optimal candidates for exploratory laparoscopy.
A total of 622 colorectal cancer (CRC) patients from 2 centers were divided into training and external validation cohorts. All patients' PM status was first detected as negative on CT imaging but later confirmed by exploratory laparoscopy. Multivariate analysis was used to identify independent predictors, which were used to build a prediction model for identifying occult PM in CRC. The concordance index (C-index), calibration plot and decision curve analysis were used to evaluate its predictive accuracy and clinical utility.
The C-indices of the model in the development and validation groups were 0.850 (95% CI 0.815-0.885) and 0.794 (95% CI, 0.690-0.899), respectively. The calibration curve showed consistency between the observed and predicted probabilities. The decision curve analysis indicated that the prediction model has a great clinical value between thresholds of 0.10 and 0.72. At a risk threshold of 30%, a total of 40% of exploratory laparoscopies could have been prevented, while still identifying 76.7% of clinically occult PM cases. A dynamic online platform was also developed to facilitate the usage of the proposed model.
Our individualized risk model could reduce the number of unnecessary exploratory laparoscopies while maintaining a high rate of diagnosis of clinically occult PM. These results warrant further validation in prospective studies.
https://www.isrctn.com, identifier ISRCTN76852032.
由于计算机断层扫描（CT）图像的敏感性较低，隐匿性腹膜转移（PM）的早期诊断仍然是一项挑战。探索性腹腔镜检查是确诊PM的金标准，但由于其侵入性、高成本和穿刺部位转移风险，仅应在选定的患者中进行。在本研究中，我们旨在开发一种个体化预测模型，以识别隐匿性PM状态并确定探索性腹腔镜检查的最佳候选者。
来自2个中心的622例结直肠癌（CRC）患者被分为训练队列和外部验证队列。所有患者的PM状态最初在CT成像上被检测为阴性，但后来通过探索性腹腔镜检查得到证实。采用多变量分析来识别独立预测因素，这些因素被用于构建一个识别CRC隐匿性PM的预测模型。一致性指数（C指数）、校准图和决策曲线分析被用于评估其预测准确性和临床实用性。
该模型在开发组和验证组中的C指数分别为0.850（95%CI 0.815 - 0.885）和0.794（95%CI，0.690 - 0.899）。校准曲线显示观察到的概率与预测概率之间具有一致性。决策曲线分析表明，该预测模型在0.10至0.72的阈值之间具有很大的临床价值。在30%的风险阈值下，总共可以避免40%的探索性腹腔镜检查，同时仍能识别76.7%的临床隐匿性PM病例。还开发了一个动态在线平台，以方便所提出模型的使用。
我们的个体化风险模型可以减少不必要的探索性腹腔镜检查数量，同时保持对临床隐匿性PM的高诊断率。这些结果有待在前瞻性研究中进一步验证。
