Effects of MRI radiomics combined with clinical data in evaluating lymph node metastasis in mrT1-3a staging rectal cancer

Xue Dong; Gang Ren; Yanhong Chen; Huifang Yong; Tingting Zhang; Qiufeng Yin; Zhongyang Zhang; Shijun Yuan; Yaqiong Ge; Shaofeng Duan; Huanhuan Liu; Dengbin Wang

doi:10.3389/fonc.2023.1194120

Effects of MRI radiomics combined with clinical data in evaluating lymph node metastasis in mrT1-3a staging rectal cancer

Front Oncol. 2023 Oct 16:13:1194120. doi: 10.3389/fonc.2023.1194120. eCollection 2023.

Authors

Affiliations

¹ Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
² Department of Radiology, Integrated Traditional Chinese and Western Medicine Hospital, Shanghai, China.
³ Department of Medicine, GE Healthcare China, Shanghai, China.

Abstract

Objective: To investigate the value of a clinical-MRI radiomics model based on clinical characteristics and T2-weighted imaging (T2WI) for preoperatively evaluating lymph node (LN) metastasis in patients with MRI-predicted low tumor (T) staging rectal cancer (mrT1, mrT2, and mrT3a with extramural spread ≤ 5 mm).

Methods: This retrospective study enrolled 303 patients with low T-staging rectal cancer (training cohort, n = 213, testing cohort n = 90). A total of 960 radiomics features were extracted from T2WI. Minimum redundancy and maximum relevance (mRMR) and support vector machine were performed to select the best performed radiomics features for predicting LN metastasis. Multivariate logistic regression analysis was then used to construct the clinical and clinical-radiomics combined models. The model performance for predicting LN metastasis was assessed by receiver operator characteristic curve (ROC) and clinical utility implementing a nomogram and decision curve analysis (DCA). The predictive performance for LN metastasis was also compared between the combined model and human readers (2 seniors).

Results: Fourteen radiomics features and 2 clinical characteristics were selected for predicting LN metastasis. In the testing cohort, a higher positive predictive value of 75.9% for the combined model was achieved than those of the clinical model (44.8%) and two readers (reader 1: 54.9%, reader 2: 56.3%) in identifying LN metastasis. The interobserver agreement between 2 readers was moderate with a kappa value of 0.416. A clinical-radiomics nomogram and decision curve analysis demonstrated that the combined model was clinically useful.

Conclusion: T2WI-based radiomics combined with clinical data could improve the efficacy in noninvasively evaluating LN metastasis for the low T-staging rectal cancer and aid in tailoring treatment strategies.

Keywords: lymph node; magnetic resonance imaging; metastasis; radiomics; rectal cancer.

Grants and funding

This study was supported by National Natural Science Foundation of China (No.81901695), Shanghai Sailing Program (No.19YF1433100), Shanghai Municipal Health Youth Talent Program (No. 2022YQ042), and Research Fund of Hospital Project (No.18YJ16). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.