European radiology experimental · 2026
Preoperative prediction of tumor budding grade in rectal cancer by combining APT histogram analysis and ADC MRI.
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Zhang Yingying, Du Yunxia, Huang Jinghuan, Yang Ou, Gong Juntao, Zhang Xiaoyue, Sun Yun, Li Feixiang, Wang Jiaqi, Huang Gang
Аннотация
OBJECTIVE: Tumor budding (TB) is a histopathological marker of aggressive behavior and poor prognosis in rectal cancer (RC), yet not reliably evaluated preoperatively. We assessed whether histogram features from amide proton transfer-weighted (APTw) imaging and apparent diffusion coefficient (ADC) maps could serve as noninvasive biomarkers for preoperative TB grade prediction.
MATERIALS AND METHODS: This retrospective study included 204 patients with RC from June 2023 to May 2025, divided into a training cohort (n = 133) and a validation cohort (n = 71) using a temporal split. All patients underwent preoperative APTw and diffusion-weighted imaging, and TB grade was determined histopathologically. Histogram features were extracted from whole-tumor volumes on APTw and ADC maps. Feature selection used a machine learning-based classifier, followed by univariate and multivariate logistic regression to identify independent predictors. SHapley Additive exPlanations (SHAP) were applied for interpretability, and a nomogram integrating histogram and clinical variables was constructed.
RESULTS: Five key histogram features (ADC-90%, ADC-Minimum, ADC-Range, APTw-10%, and APTw-Median) were selected. The histogram model achieved areas under the curve (AUROCs) of 0.85 (95% confidence interval [CI]: 0.79-0.92) and 0.86 (95% CI: 0.78-0.95) in the training and validation cohorts. SHAP analysis identified ADC-90% and ADC-Minimum as the most influential predictors. The combined model with histogram and clinical factors showed improved performance, with AUROCs of 0.88 (95% CI: 0.82-0.94) and 0.87 (95% CI: 0.79-0.96).
CONCLUSION: APTw and ADC histogram features can independently predict TB grade in RC. The combined model, integrating both histogram and clinical features, further enhanced preoperative predictive accuracy.
RELEVANCE STATEMENT: This study investigates the role of imaging biomarkers in the preoperative stratification of RC, with the potential to enhance clinical decision-making and improve patient outcomes by providing more accurate, noninvasive prognostic information.
KEY POINTS: A histogram-based model using APTw and ADC maps can predict TB grade in RC preoperatively. A combined model integrating clinical factors and histogram features improves predictive accuracy. This interpretable, contrast-free method provides a practical tool for clinicians to personalize treatment planning.
MATERIALS AND METHODS: This retrospective study included 204 patients with RC from June 2023 to May 2025, divided into a training cohort (n = 133) and a validation cohort (n = 71) using a temporal split. All patients underwent preoperative APTw and diffusion-weighted imaging, and TB grade was determined histopathologically. Histogram features were extracted from whole-tumor volumes on APTw and ADC maps. Feature selection used a machine learning-based classifier, followed by univariate and multivariate logistic regression to identify independent predictors. SHapley Additive exPlanations (SHAP) were applied for interpretability, and a nomogram integrating histogram and clinical variables was constructed.
RESULTS: Five key histogram features (ADC-90%, ADC-Minimum, ADC-Range, APTw-10%, and APTw-Median) were selected. The histogram model achieved areas under the curve (AUROCs) of 0.85 (95% confidence interval [CI]: 0.79-0.92) and 0.86 (95% CI: 0.78-0.95) in the training and validation cohorts. SHAP analysis identified ADC-90% and ADC-Minimum as the most influential predictors. The combined model with histogram and clinical factors showed improved performance, with AUROCs of 0.88 (95% CI: 0.82-0.94) and 0.87 (95% CI: 0.79-0.96).
CONCLUSION: APTw and ADC histogram features can independently predict TB grade in RC. The combined model, integrating both histogram and clinical features, further enhanced preoperative predictive accuracy.
RELEVANCE STATEMENT: This study investigates the role of imaging biomarkers in the preoperative stratification of RC, with the potential to enhance clinical decision-making and improve patient outcomes by providing more accurate, noninvasive prognostic information.
KEY POINTS: A histogram-based model using APTw and ADC maps can predict TB grade in RC preoperatively. A combined model integrating clinical factors and histogram features improves predictive accuracy. This interpretable, contrast-free method provides a practical tool for clinicians to personalize treatment planning.