MRI 放射组学特征预测局部进展期直肠癌新辅助放化疗后淋巴结转移

MRI radiomics signature to predict lymph node metastasis after neoadjuvant chemoradiation therapy in locally advanced rectal cancer.

机构信息

Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, 32# Second Section of First Ring Road, Qingyang District, Chengdu, 610070, Sichuan, China.

Department of Radiology, Affiliated Cancer Hospital of Medical School, University of Electronic Science and Technology of China, Sichuan Cancer Hospital, 55#Four Section of South Renmin Road, Wuhou District, Chengdu, 610000, China.

出版信息

Abdom Radiol (NY). 2023 Jul;48(7):2270-2283. doi: 10.1007/s00261-023-03910-4. Epub 2023 Apr 21.

DOI:10.1007/s00261-023-03910-4

PMID:37085730

Abstract

PURPOSE

To investigative the performance of MRI-radiomics analysis derived from T2WI and apparent diffusion coefficients (ADC) images before and after neoadjuvant chemoradiation therapy (nCRT) separately or simultaneously for predicting post-nCRT lymph node status in patients with locally advanced rectal cancer (LARC). MATERIALS AND METHODS: Eighty-three patients (training cohort, n = 57; validation cohort, n = 26) with LARC between June 2017 and December 2022 were retrospectively enrolled. All the radiomics features were extracted from volume of interest on T2WI and ADC images from baseline and post-nCRT MRI. Delta-radiomics features were defined as the difference between radiomics features before and after nCRT. Seven clinical-radiomics models were constructed by combining the most predictive radiomics signatures and clinical parameters selected from support vector machine. Receiver operating characteristic curve (ROC) was used to evaluate the performance of models. The optimum model-based LNM was applied to assess 5-years disease-free survival (DFS) using Kaplan-Meier analysis. The end point was clinical or radiological locoregional recurrence or distant metastasis during postoperative follow-up.

RESULTS

Clinical-deltaADC radiomics combined model presented good performance for predicting post-CRT LNM in the training (AUC = 0.895,95%CI:0.838-0.953) and validation cohort (AUC = 0.900,95%CI:0.771-1.000). Clinical-deltaADC radiomics-postT2WI radiomics combined model also showed good performances (AUC = 0.913,95%CI:0.838-0.953) in the training and (AUC = 0.912,95%CI:0.771-1.000) validation cohort. As for subgroup analysis, clinical-deltaADC radiomics combined model showed good performance predicting LNM in ypT0-T2 (AUC = 0.827;95%CI:0.649-1.000) and ypT3-T4 stage (AUC = 0.934;95%CI:0.864-1.000). In ypT0-T2 stage, clinical-deltaADC radiomics combined model-based LNM could assess 5-years DFS (P = 0.030).

CONCLUSION

Clinical-deltaADC radiomics combined model could predict post-nCRT LNM, and this combined model-based LNM was associated with 5-years DFS in ypT0-T2 stage.

摘要

目的

分别或同时研究新辅助放化疗（nCRT）前后 MRI 放射组学分析从 T2WI 和表观扩散系数（ADC）图像得出的结果，以预测局部晚期直肠癌（LARC）患者 nCRT 后淋巴结状态。

材料与方法

回顾性纳入 2017 年 6 月至 2022 年 12 月间 83 例 LARC 患者（训练队列，n=57；验证队列，n=26）。所有放射组学特征均从基线和 nCRT 后 MRI 的 T2WI 和 ADC 图像的感兴趣区域提取。Delta 放射组学特征定义为 nCRT 前后放射组学特征的差异。通过从支持向量机中选择最具预测性的放射组学特征和临床参数，构建了 7 个临床放射组学模型。使用接收器操作特征曲线（ROC）评估模型性能。基于最优模型的 LNM 用于通过 Kaplan-Meier 分析评估 5 年无病生存率（DFS）。终点为术后随访期间临床或影像学局部区域复发或远处转移。

结果

临床-deltaADC 放射组学联合模型在训练队列（AUC=0.895，95%CI：0.838-0.953）和验证队列（AUC=0.900，95%CI：0.771-1.000）中对预测 nCRT 后 LNM 具有良好的性能。临床-deltaADC 放射组学-T2WI 放射组学联合模型在训练队列（AUC=0.913，95%CI：0.838-0.953）和验证队列（AUC=0.912，95%CI：0.771-1.000）中也表现出良好的性能。对于亚组分析，临床-deltaADC 放射组学联合模型在 ypT0-T2（AUC=0.827；95%CI：0.649-1.000）和 ypT3-T4 期（AUC=0.934；95%CI：0.864-1.000）中对预测 LNM 具有良好的性能。在 ypT0-T2 期，基于临床-deltaADC 放射组学联合模型的 LNM 可评估 5 年 DFS（P=0.030）。