Clinical and Translational Radiation Oncology (Jan 2023)

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

Journal volume & issue
Vol. 38
pp. 175 – 182

Abstract

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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, pinteraction = 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.

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