Department of Digestive Surgery, Xijing Hospital of Digestive Diseases, Air Force Military Medical University, Xi'an, 710032, China.
Department of Radiology, The First Affiliated Hospital of Air Force Military Medical University, Xi'an, 710032, China.
J Robot Surg. 2024 May 29;18(1):229. doi: 10.1007/s11701-024-01978-8.
The aim of this study is to evaluate the predictive ability of MRI-based radiomics combined with tumor markers for TN staging in patients with rectal cancer and to develop a prediction model for TN staging. A total of 190 patients with rectal adenocarcinoma who underwent total mesorectal excision at the First Affiliated Hospital of the Air Force Medical University between January 2016 and December 2020 were included in the study. An additional 54 patients from a prospective validation cohort were included between August 2022 and August 2023. Preoperative tumor markers and MRI imaging data were collected from all enrolled patients. The 190 patients were divided into a training cohort (n = 133) and a validation cohort (n = 57). Radiomics features were extracted by outlining the region of interest (ROI) on T2WI sequence images. Feature selection and radiomics score (Rad-score) construction were performed using least absolute shrinkage and selection operator regression analysis (LASSO). The postoperative pathology TNM stage was used to differentiate locally advanced rectal cancer (T3/4 or N1/2) from locally early rectal cancer (T1/2, N0). Logistic regression was used to construct separate prediction models for T stage and N stage. The models' predictive performance was evaluated using DCA curves and calibration curves. The T staging model showed that Rad-score, based on 8 radiomics features, was an independent predictor of T staging. When combined with CEA, tumor diameter, mesoretal fascia (MRF), and extramural venous invasion (EMVI), it effectively differentiated between T1/2 and T3/4 stage rectal cancers in the training cohort (AUC 0.87 [95% CI: 0.81-0.93]). The N-staging model found that Rad-score, based on 10 radiomics features, was an independent predictor of N-staging. When combined with CA19.9, degree of differentiation, and EMVI, it effectively differentiated between N0 and N1/2 stage rectal cancers. The training cohort had an AUC of 0.84 (95% CI: 0.77-0.91). The calibration curves demonstrated good precision between the predicted and actual results. The DCA curves indicated that both sets of predictive models could provide net clinical benefits for diagnosis. MRI-based radiomics features are independent predictors of T staging and N staging. When combined with tumor markers, they have good predictive efficacy for TN staging of rectal cancer.
本研究旨在评估基于 MRI 的放射组学与肿瘤标志物联合用于预测直肠癌 TN 分期的预测能力，并建立用于 TN 分期的预测模型。共纳入 2016 年 1 月至 2020 年 12 月在空军军医大学第一附属医院行全直肠系膜切除术的 190 例直肠腺癌患者为研究对象。另外，纳入 2022 年 8 月至 2023 年 8 月前瞻性验证队列中的 54 例患者。收集所有入组患者的术前肿瘤标志物和 MRI 影像学数据。190 例患者被分为训练队列（n=133）和验证队列（n=57）。通过勾画 T2WI 序列图像的感兴趣区（ROI）提取放射组学特征。采用最小绝对值收缩和选择算子回归分析（LASSO）进行特征选择和放射组学评分（Rad-score）构建。术后病理 TNM 分期用于区分局部进展期直肠癌（T3/4 或 N1/2）和局部早期直肠癌（T1/2、N0）。采用 logistic 回归分别构建 T 分期和 N 分期的预测模型。采用 DCA 曲线和校准曲线评估模型的预测性能。T 分期模型显示，基于 8 个放射组学特征的 Rad-score 是 T 分期的独立预测因子。当与 CEA、肿瘤直径、系膜筋膜（MRF）和外膜静脉侵犯（EMVI）联合使用时，在训练队列中可有效区分 T1/2 和 T3/4 期直肠癌（AUC 0.87 [95%CI：0.81-0.93]）。N 分期模型发现，基于 10 个放射组学特征的 Rad-score 是 N 分期的独立预测因子。当与 CA19.9、分化程度和 EMVI 联合使用时，可有效区分 N0 和 N1/2 期直肠癌。训练队列的 AUC 为 0.84（95%CI：0.77-0.91）。校准曲线显示预测结果与实际结果之间具有良好的一致性。DCA 曲线表明，两组预测模型均能为诊断提供净临床效益。基于 MRI 的放射组学特征是 T 分期和 N 分期的独立预测因子。当与肿瘤标志物联合使用时，对直肠癌的 TN 分期具有良好的预测效果。
