Department of Medicine Imaging Center, Kunming Medical University, Qujing First People's Hospital, Yunnan, China.
Department of Radiology, Sichuan University, West China Hospital, Sichuan, China.
Medicine (Baltimore). 2023 Oct 13;102(41):e34865. doi: 10.1097/MD.0000000000034865.
The objective is to develop and validate a combined model for noninvasive preoperative differentiating tumor deposits (TDs) from lymph node metastasis (LNM) in patients with rectal cancer (RC). A total of 204 patients were enrolled and randomly divided into 2 sets (training and validation set) at a ratio of 8:2. Radiomics features of tumor and peritumor fat were extracted by using Pyradiomics software from the axial T2-weighted imaging of MRI. Rad-score based on extracted Radiomics features were calculated by combination of feature selection and the machine learning method. Factors (Rad-score, laboratory test factor, clinical factor, traditional characters of tumor on MRI) with statistical significance were integrated to build a combined model. The combined model was visualized by a nomogram, and its distinguish ability, diagnostic accuracy, and clinical utility were evaluated by the receiver operating characteristic curve (ROC) analysis, calibration curve, and clinical decision curve, respectively. Carbohydrate antigen (CA) 19-9, MRI reported node stage (MRI-N stage), tumor volume (cm3), and Rad-score were all included in the combined model (odds ratio = 3.881 for Rad-score, 2.859 for CA19-9, 0.411 for MRI-N stage, and 1.055 for tumor volume). The distinguish ability of the combined model in the training and validation cohorts was area under the summary receiver operating characteristic curve (AUC) = 0.863, 95% confidence interval (CI): 0.8-0.911 and 0.815, 95% CI: 0.663-0.919, respectively. And the combined model outperformed the clinical model in both training and validation cohorts (AUC = 0.863 vs 0.749, 0.815 vs 0.627, P = .0022, .0302), outperformed the Rad-score model only in training cohorts (AUC = 0.863 vs 0.819, P = .0283). The combined model had highest net benefit and showed good diagnostic accuracy. The combined model incorporating Rad-score and clinical factors could provide a preoperative differentiation of TD from LNM and guide clinicians in making individualized treatment strategy for patients with RC.
目的是开发和验证一种联合模型，用于术前无创区分直肠癌患者的肿瘤沉积物 (TDs) 和淋巴结转移 (LNM)。共纳入 204 例患者，按 8:2 的比例随机分为 2 组 (训练集和验证集)。使用 Pyradiomics 软件从 MRI 的轴位 T2 加权图像中提取肿瘤和肿瘤周围脂肪的放射组学特征。基于提取的放射组学特征的 Rad-score 通过特征选择和机器学习方法的组合来计算。将具有统计学意义的因素 (Rad-score、实验室检查因素、临床因素、MRI 上肿瘤的传统特征) 整合到联合模型中。通过列线图可视化联合模型，并通过接收者操作特征曲线 (ROC) 分析、校准曲线和临床决策曲线分别评估其鉴别能力、诊断准确性和临床实用性。癌抗原 19-9 (CA19-9)、MRI 报告的淋巴结分期 (MRI-N 分期)、肿瘤体积 (cm3) 和 Rad-score 均包含在联合模型中 (Rad-score 的优势比 = 3.881，CA19-9 的优势比 = 2.859，MRI-N 分期的优势比 = 0.411，肿瘤体积的优势比 = 1.055)。联合模型在训练和验证队列中的鉴别能力的曲线下面积 (AUC) 分别为 0.863、95%置信区间 (CI)：0.8-0.911 和 0.815、95%CI：0.663-0.919。联合模型在训练和验证队列中的表现均优于临床模型 (AUC = 0.863 vs 0.749，0.815 vs 0.627，P =.0022，.0302)，仅在训练队列中优于 Rad-score 模型 (AUC = 0.863 vs 0.819，P =.0283)。联合模型具有最高的净收益，表现出良好的诊断准确性。联合模型结合了 Rad-score 和临床因素，可以为术前区分 TD 和 LNM 提供依据，并指导临床医生为 RC 患者制定个体化治疗策略。
