MRI-based radiomics to predict neoadjuvant chemoradiotherapy outcomes in locally advanced rectal cancer: A multicenter study

Yirong Xiang; Shuai Li; Hongzhi Wang; Maxiaowei Song; Ke Hu; Fengwei Wang; Zhi Wang; Zhiyong Niu; Jin Liu; Yong Cai; Yongheng Li; Xianggao Zhu; Jianhao Geng; Yangzi Zhang; Huajing Teng; Weihu Wang

doi:10.1016/j.ctro.2022.11.009

MRI-based radiomics to predict neoadjuvant chemoradiotherapy outcomes in locally advanced rectal cancer: A multicenter study

Clin Transl Radiat Oncol. 2022 Nov 17:38:175-182. doi: 10.1016/j.ctro.2022.11.009. eCollection 2023 Jan.

Authors

Yirong Xiang¹, Shuai Li¹, Hongzhi Wang¹, Maxiaowei Song¹, Ke Hu², Fengwei Wang³, Zhi Wang⁴, Zhiyong Niu⁴, Jin Liu⁴, Yong Cai¹, Yongheng Li¹, Xianggao Zhu¹, Jianhao Geng¹, Yangzi Zhang¹, Huajing Teng¹, Weihu Wang¹

Affiliations

¹ Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, China.
² Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, China.
³ Department of Oncology, Tianjin Union Medical Center, China.
⁴ Blot Info & Tech (Beijing) Co. Ltd, China.

Abstract

Background and purpose: Predicting tumour response would be useful for selecting patients with locally advanced rectal cancer (LARC) for organ preservation strategies. We aimed to develop and validate a prediction model for T downstaging (ypT0-2) in LARC patients after neoadjuvant chemoradiotherapy and to identify those who may benefit from consolidation chemotherapy.

Materials and methods: cT3-4 LARC patients at three tertiary medical centers from January 2012 to January 2019 were retrospectively included, while a prospective cohort was recruited from June 2021 to March 2022. Eight filter (principal component analysis, least absolute shrinkage and selection operator, partial least-squares discriminant analysis, random forest)-classifier (support vector machine, logistic regression) models were established to select radiomic features. A nomogram combining radiomics and significant clinical features was developed and validated by calibration curve and decision curve analysis. Interaction test was conducted to investigate the consolidation chemotherapy benefits.

Results: A total of 634 patients were included (426 in training cohort, 174 in testing cohort and 34 in prospective cohort). A radiomic prediction model using partial least-squares discriminant analysis and a support vector machine showed the best performance (AUC: 0.832 [training]; 0.763 [testing]). A nomogram combining radiomics and clinical features showed significantly better prognostic performance (AUC: 0.842 [training]; 0.809 [testing]) than the radiomic model. The model was also tested in the prospective cohort with AUC 0.727. High-probability group (score > 81.82) may have potential benefits from ≥ 4 cycles consolidation chemotherapy (OR: 4.173, 95 % CI: 0.953-18.276, p = 0.058, p_interaction = 0.021).

Conclusion: We identified and validated a model based on multicenter pre-treatment radiomics to predict ypT0-2 in cT3-4 LARC patients, which may facilitate individualised treatment decision-making for organ-preservation strategies and consolidation chemotherapy.

Keywords: AUC, area under the curve; CEA, Carcinoembryonic antigen; DA, discriminant analysis; FOLFOX, fluorouracil plus oxaliplatin; IDI, integrated discrimination improvement; LARC, locally advanced rectal cancer; LASSO, least absolute shrinkage and selection operator; MRI, magnetic resonance imaging; NRI, net reclassification improvement; Neoadjuvant treatment; PCA, principal component analysis; PLS, partial least squares; RF, random forest; ROC, receiver operating characteristic; ROI, region-of-interest; Radiomics; Rectal cancer; SVM, support vector machine; T2WI, T2-weighted imaging; TEM, endoscopic microsurgery; TME, total mesorectal excision; XELOX, capecitabine plus oxaliplatin.