Abstract：
OBJECTIVE: To develop a radiomic nomogram based on multiparametric magnetic resonance imaging for the preoperative prediction of lymph node metastasis (LNM) in rectal cancer.
METHODS: This retrospective study included 318 patients with pathologically proven rectal adenocarcinoma from two hospitals. Radiomic features were extracted from T2-weighted imaging, diffusion-weighted imaging, and contrast-enhanced T1-weighted imaging scans of the training cohort, and the radsore model was then constructed. The combined model was obtained by integrating the Radscore and clinical models. The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic effectiveness of each model, and the best-performing model was used to develop the nomogram.
RESULTS: The Radscore and clinical models exhibited similar diagnostic efficacy (DeLong\'s test, P > 0.05). The AUC of the combined model was significantly higher than those of the clinical and Radscore models in the training cohort (AUC: 0.837 vs. 0.763 and 0.787, P: 0.02120 and 0.02309) and the external validation cohort (AUC: 0.880 vs. 0.797 and 0.779, P: 0.02310 and 0.02471). However, the diagnostic performance of the three models was comparable in the internal validation cohort (P > 0.05). Thus, among the three models, the combined model exhibited the highest diagnostic efficiency. The calibration curve exhibited satisfactory consistency between the nomogram predictions and the actual results. DCA confirmed the considerable clinical usefulness of the nomogram.
CONCLUSIONS: The radiomics nomogram can accurately and noninvasively predict LNM in rectal cancer before surgery, serving as a convenient visualization tool for informing treatment decisions, including the choice of surgical approach and the need for neoadjuvant therapy.
METHODS: This retrospective study included 318 patients with pathologically proven rectal adenocarcinoma from two hospitals. Radiomic features were extracted from T2-weighted imaging, diffusion-weighted imaging, and contrast-enhanced T1-weighted imaging scans of the training cohort, and the radsore model was then constructed. The combined model was obtained by integrating the Radscore and clinical models. The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic effectiveness of each model, and the best-performing model was used to develop the nomogram.
RESULTS: The Radscore and clinical models exhibited similar diagnostic efficacy (DeLong\'s test, P > 0.05). The AUC of the combined model was significantly higher than those of the clinical and Radscore models in the training cohort (AUC: 0.837 vs. 0.763 and 0.787, P: 0.02120 and 0.02309) and the external validation cohort (AUC: 0.880 vs. 0.797 and 0.779, P: 0.02310 and 0.02471). However, the diagnostic performance of the three models was comparable in the internal validation cohort (P > 0.05). Thus, among the three models, the combined model exhibited the highest diagnostic efficiency. The calibration curve exhibited satisfactory consistency between the nomogram predictions and the actual results. DCA confirmed the considerable clinical usefulness of the nomogram.
CONCLUSIONS: The radiomics nomogram can accurately and noninvasively predict LNM in rectal cancer before surgery, serving as a convenient visualization tool for informing treatment decisions, including the choice of surgical approach and the need for neoadjuvant therapy.
摘要：
目的：建立基于多参数磁共振成像的放射组学列线图，用于直肠癌淋巴结转移（LNM）的术前预测。
方法：这项回顾性研究包括来自两家医院的318例经病理证实的直肠腺癌患者。从T2加权成像中提取影像组学特征，弥散加权成像,和训练队列的对比增强T1加权成像扫描，然后构建了radso模型。通过整合Radscore和临床模型获得组合模型。受试者工作特征曲线下面积（AUC）用于评估每个模型的诊断有效性，并使用性能最佳的模型来开发列线图。
结果：Radscore和临床模型显示出相似的诊断功效（DeLong检验，P>0.05)。在训练队列中，组合模型的AUC显着高于临床和Radscore模型的AUC（AUC：0.837vs.0.763和0.787，P：0.02120和0.02309）和外部验证队列（AUC：0.880vs.0.797和0.779,P:0.02310和0.02471)。然而,3种模型在内部验证队列中的诊断性能具有可比性(P>0.05).因此，在这三个模型中，组合模型显示出最高的诊断效率.校准曲线在列线图预测和实际结果之间表现出令人满意的一致性。DCA证实了列线图的相当大的临床有用性。
结论：影像组学列线图可以准确、无创地预测直肠癌术前的LNM，作为一个方便的可视化工具，用于告知治疗决策，包括手术方式的选择和新辅助治疗的需要。
方法：这项回顾性研究包括来自两家医院的318例经病理证实的直肠腺癌患者。从T2加权成像中提取影像组学特征，弥散加权成像,和训练队列的对比增强T1加权成像扫描，然后构建了radso模型。通过整合Radscore和临床模型获得组合模型。受试者工作特征曲线下面积（AUC）用于评估每个模型的诊断有效性，并使用性能最佳的模型来开发列线图。
结果：Radscore和临床模型显示出相似的诊断功效（DeLong检验，P>0.05)。在训练队列中，组合模型的AUC显着高于临床和Radscore模型的AUC（AUC：0.837vs.0.763和0.787，P：0.02120和0.02309）和外部验证队列（AUC：0.880vs.0.797和0.779,P:0.02310和0.02471)。然而,3种模型在内部验证队列中的诊断性能具有可比性(P>0.05).因此，在这三个模型中，组合模型显示出最高的诊断效率.校准曲线在列线图预测和实际结果之间表现出令人满意的一致性。DCA证实了列线图的相当大的临床有用性。
结论：影像组学列线图可以准确、无创地预测直肠癌术前的LNM，作为一个方便的可视化工具，用于告知治疗决策，包括手术方式的选择和新辅助治疗的需要。
