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

AI Article Synopsis

  • The study aimed to create and validate a prediction model for tumor downstaging (ypT0-2) in patients with locally advanced rectal cancer (LARC) undergoing chemoradiotherapy, assisting in patient selection for organ preservation techniques.
  • A total of 634 patients were analyzed through various classification models, ultimately developing a nomogram that combined radiomic features and clinical data, showing improved prediction accuracy (AUC up to 0.842 in training and 0.809 in testing cohorts).
  • The findings suggest that patients with a high prediction score may gain significant benefits from additional consolidation chemotherapy, emphasizing the model's potential utility in treatment planning.

Article 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 = 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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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9719068PMC
http://dx.doi.org/10.1016/j.ctro.2022.11.009DOI Listing

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