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Prediction and validation of pathologic complete response for locally advanced rectal cancer under neoadjuvant chemoradiotherapy based on a novel predictor using interpretable machine learning.
Eur J Surg Oncol
December 2024
Department of Colorectal Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China; Department of Colorectal Surgery, National Regional Medical Center, Binhai Campus of the First Afliated Hospital, Fuzhou, China. Electronic address:
Article Synopsis
- This study focuses on the importance of accurately predicting pathological complete response (pCR) in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiotherapy, highlighting limitations in existing predictive models that mainly consider pre-treatment factors.
- The researchers developed a new predictor using dynamic changes in systemic inflammation-nutritional marker indexes (SINI) throughout the treatment process and created machine-learning models to enhance prediction accuracy.
- By analyzing data from two patient cohorts and applying advanced statistical methods, including logistic regression and machine learning, the study aims to improve the identification of independent risk factors for pCR and enhance treatment strategies for patients.