Scholarlink

Background <p>Accurate identification of pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC) remains a key clinical challenge. Clinical complete response is an imperfect surrogate, and pCR can only be definitively established after surgery. We developed a fully automated, segmentation-free deep learning model to support post-treatment response assessment using routine T2-weighted MRI.</p> Methods <p>A longitudinal three-dimensional (3D) siamese convolutional neural network was trained using paired pre- and post-nCRT axial T2-weighted MRI volumes and a normalized signed voxel-wise difference map. The multitask framework simultaneously predicted rectal wall response (good response: modified Ryan score 0–1 vs poor response ≥ 2) and nodal status (ypN0 vs ypN +), from which pCR probability (ypT0N0) was derived. A retrospective single-center cohort of 195 patients was divided into training and independent test sets stratified by pCR status. Performance was evaluated using AUC-ROC and standard classification metrics with bootstrap-derived 95% confidence intervals.</p> Results <p>In the independent test set (<i>n</i> = 49; pCR prevalence 18.5%), the model achieved an AUC-ROC of 0.71 (95% CI: 0.55–0.85) for pCR prediction. At the selected operating threshold, sensitivity was 100% (95% CI: 70.1–100) and negative predictive value (NPV) was 100% (95% CI: 81.6–100), with a specificity of 42.5% (95% CI: 28.5–57.8). The high NPV reflects the low prevalence of pCR in the study cohort and may vary across external populations.</p> Conclusions <p>This fully automated longitudinal deep learning model demonstrated moderate discrimination and a high-sensitivity profile for pCR detection. Its performance suggests potential utility as a screening or triage tool to support multidisciplinary assessment, rather than to directly guide organ-preserving strategies. External multicenter validation is required before clinical implementation.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Automated deep learning model for predicting pathological complete response in rectal cancer: A tool to organ-preserving strategies

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月22日

查看全文
  • Martin-Arevalo J.,
  • Lopez-Mozos F.,
  • Moro-Valdezate D.,
  • Perez-Santiago L.,
  • Palomo-Lopez I.,
  • Garcia-Botello S. A.,
  • Tarazona-Llavero N.,
  • Cabrera-Perez B.,
  • Riera-Cardona M.,
  • Lillo-Albert G.,
  • Millan M,
  • Pla-Marti V.

摘要

Background

Accurate identification of pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC) remains a key clinical challenge. Clinical complete response is an imperfect surrogate, and pCR can only be definitively established after surgery. We developed a fully automated, segmentation-free deep learning model to support post-treatment response assessment using routine T2-weighted MRI.

Methods

A longitudinal three-dimensional (3D) siamese convolutional neural network was trained using paired pre- and post-nCRT axial T2-weighted MRI volumes and a normalized signed voxel-wise difference map. The multitask framework simultaneously predicted rectal wall response (good response: modified Ryan score 0–1 vs poor response ≥ 2) and nodal status (ypN0 vs ypN +), from which pCR probability (ypT0N0) was derived. A retrospective single-center cohort of 195 patients was divided into training and independent test sets stratified by pCR status. Performance was evaluated using AUC-ROC and standard classification metrics with bootstrap-derived 95% confidence intervals.

Results

In the independent test set (n = 49; pCR prevalence 18.5%), the model achieved an AUC-ROC of 0.71 (95% CI: 0.55–0.85) for pCR prediction. At the selected operating threshold, sensitivity was 100% (95% CI: 70.1–100) and negative predictive value (NPV) was 100% (95% CI: 81.6–100), with a specificity of 42.5% (95% CI: 28.5–57.8). The high NPV reflects the low prevalence of pCR in the study cohort and may vary across external populations.

Conclusions

This fully automated longitudinal deep learning model demonstrated moderate discrimination and a high-sensitivity profile for pCR detection. Its performance suggests potential utility as a screening or triage tool to support multidisciplinary assessment, rather than to directly guide organ-preserving strategies. External multicenter validation is required before clinical implementation.