Frontiers in oncology · 2026
Metabolic dynamic score and machine learning: a novel approach to predicting pathological complete response in rectal cancer after neoadjuvant chemoradiotherapy.
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Peng Tengyi, Zhang Qiqi, Lai Xingrong, Pan Zhen, Chen Bin, Zhuang Zhicheng, Zheng Shaoqing, Liu Xing, Zhuang Jinfu, Lu XingRong, Guan Guoxian, Li Shoufeng
Аннотация
BACKGROUND: This study developed a scoring system based on the dynamic changes in biochemical indicators in advance of and subsequent to neoadjuvant chemoradiotherapy (NCRT) in individuals with locally advanced rectal cancer (LARC). The scoring system, combined with other clinical features, was used to develop a machine learning (ML) model aimed at predicting a pathological complete response (pCR).
METHODS: A review of earlier data was performed on the data of 1300 patients with LARC treated at Center1 and Center 2. To determine factors linked to pCR and create the scoring system, uni and multivariate logistic regression analyses were conducted. The development of predictive models involved the use of 10 ML methods, while model performance was evaluated using the area under the receiver operating characteristic curve (AUC), area under the precision-recall curve (AUPRC), decision curve analysis, and calibration curves. Additionally, SHapley Additive exPlanations values were applied to enhance model interpretability.
RESULTS: Multivariate analysis identified the MDS, tumor size, clinical T stage, and clinical N stage as independent predictors, which were incorporated into the ML models. Among the 10 ML models, the XGBoost model demonstrated the best and most generalizable predictive performance (training set: AUC = 0.93, AUPRC = 0.732; external validation set: AUC = 0.92, AUPRC = 0.779) and was selected as the optimal model.
CONCLUSION: This study analyzed changes in biochemical indicators pre- versus post-NCRT and developed a promising MDS. By leveraging ML predictive models to estimate pCR, this approach may serve as a convenient tool to support the clinical decision-making process pending further prospective validation.
METHODS: A review of earlier data was performed on the data of 1300 patients with LARC treated at Center1 and Center 2. To determine factors linked to pCR and create the scoring system, uni and multivariate logistic regression analyses were conducted. The development of predictive models involved the use of 10 ML methods, while model performance was evaluated using the area under the receiver operating characteristic curve (AUC), area under the precision-recall curve (AUPRC), decision curve analysis, and calibration curves. Additionally, SHapley Additive exPlanations values were applied to enhance model interpretability.
RESULTS: Multivariate analysis identified the MDS, tumor size, clinical T stage, and clinical N stage as independent predictors, which were incorporated into the ML models. Among the 10 ML models, the XGBoost model demonstrated the best and most generalizable predictive performance (training set: AUC = 0.93, AUPRC = 0.732; external validation set: AUC = 0.92, AUPRC = 0.779) and was selected as the optimal model.
CONCLUSION: This study analyzed changes in biochemical indicators pre- versus post-NCRT and developed a promising MDS. By leveraging ML predictive models to estimate pCR, this approach may serve as a convenient tool to support the clinical decision-making process pending further prospective validation.