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Objective <p>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.</p> Materials and methods <p>This retrospective study included 204 patients with RC from June 2023 to May 2025, divided into a training cohort (<i>n</i> = 133) and a validation cohort (<i>n</i> = 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.</p> Results <p>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).</p> Conclusion <p>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.</p> Relevance statement <p>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.</p> Key Points <p><UnorderedList Mark="Bullet"> <ItemContent> <p>A histogram-based model using APTw and ADC maps can predict TB grade in RC preoperatively.</p> </ItemContent> <ItemContent> <p>A combined model integrating clinical factors and histogram features improves predictive accuracy.</p> </ItemContent> <ItemContent> <p>This interpretable, contrast-free method provides a practical tool for clinicians to personalize treatment planning.</p> </ItemContent> </UnorderedList></p> Graphical Abstract <p></p>

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Preoperative prediction of tumor budding grade in rectal cancer by combining APT histogram analysis and ADC MRI

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月20日

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  • Yingying Zhang,
  • Yunxia Du,
  • Jinghuan Huang,
  • Ou Yang,
  • Juntao Gong,
  • Xiaoyue Zhang,
  • Yun Sun,
  • Feixiang Li,
  • Jiaqi Wang,
  • Gang Huang

摘要

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.

Graphical Abstract