Wang Hui, Chen Xiaoyong, Ding Jingfeng, Deng Shuitang, Mao Guoqun, Tian Shuyuan, Zhu Xiandi, Ao Weiqun
Department of Radiology, Tongde Hospital of Zhejiang Province, No. 234, Gucui Road, Hangzhou, 310012, Zhejiang, China.
Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Abdom Radiol (NY). 2023 Feb;48(2):471-485. doi: 10.1007/s00261-022-03759-z. Epub 2022 Dec 12.
To investigate the feasibility and efficacy of a nomogram that combines clinical and radiomic features of magnetic resonance imaging (MRI) for preoperative perirectal fat invasion (PFI) prediction in rectal cancer.
This was a retrospective study. A total of 363 patients from two centers were included in the study. Patients in the first center were randomly divided into training cohort (n = 212) and internal validation cohort (n = 91) at the ratio of 7:3. Patients in the second center were allocated to the external validation cohort (n = 60). Among the training cohort, the numbers of patients who were PFI positive and PFI negative were 108 and 104, respectively. The radiomics features of preoperative T-weighted images, diffusion-weighted images and enhanced T-weighted images were extracted, and the total Radscore of each patient was obtained. We created Clinic model and Radscore model, respectively, according to clinical data or Radscore only. And that, we assembled the combined model using the clinical data and Radscore. We used DeLong's test, receiver operating characteristic, calibration and decision curve analysis to assess the models' performance.
The three models had good performance. Clinic model and Radscore model showed equivalent performance with AUCs of 0.85, 0.82 (accuracy of 81%, 81%) in the training cohort, AUCs of 0.78, 0.86 (accuracy of 74%, 84%) in the internal cohort, and 0.84, 0.84 (accuracy of 80%, 82%) in the external cohort without statistical difference (DeLong's test, p > 0.05). AUCs and accuracy of Combined model were 0.89 and 87%, 0.90 and 88%, and 0.90 and 88% in the three cohorts, respectively, which were higher than that of Clinic model and Radscore model, but only in the training cohort with a statistical difference (DeLong's test, p < 0.05). The calibration curves of the nomogram exhibited acceptable consistency, and the decision curve analysis indicated higher net benefit in clinical practice.
A nomogram combining clinical and radiomic features of MRI to compute the probability of PFI in rectal cancer was developed and validated. It has the potential to serve as a preoperative biomarker for predicting pathological PFI of rectal cancer.
探讨一种结合磁共振成像（MRI）临床和影像组学特征的列线图用于预测直肠癌术前直肠周围脂肪浸润（PFI）的可行性和有效性。
这是一项回顾性研究。共纳入来自两个中心的363例患者。第一个中心的患者按7:3的比例随机分为训练队列（n = 212）和内部验证队列（n = 91）。第二个中心的患者被分配到外部验证队列（n = 60）。在训练队列中，PFI阳性和PFI阴性的患者数量分别为108例和104例。提取术前T加权图像、扩散加权图像和增强T加权图像的影像组学特征，获得每位患者的总Radscore。我们分别根据临床数据或仅根据Radscore创建了临床模型和Radscore模型。然后，我们使用临床数据和Radscore组装了联合模型。我们使用德龙检验、受试者工作特征曲线、校准和决策曲线分析来评估模型的性能。
这三个模型表现良好。临床模型和Radscore模型在训练队列中的AUC分别为0.85、0.82（准确率分别为81%、81%），在内部队列中的AUC分别为0.78、0.86（准确率分别为74%、84%），在外部队列中的AUC分别为0.84、0.84（准确率分别为80%、82%），无统计学差异（德龙检验，p > 0.05）。联合模型在三个队列中的AUC和准确率分别为0.89和87%、0.90和88%、0.90和88%，均高于临床模型和Radscore模型，但仅在训练队列中有统计学差异（德龙检验，p < 0.05）。列线图的校准曲线显示出可接受的一致性，决策曲线分析表明在临床实践中具有更高的净效益。
开发并验证了一种结合MRI临床和影像组学特征来计算直肠癌PFI概率的列线图。它有可能作为预测直肠癌病理PFI的术前生物标志物。
