Scholarlink

Purpose <p>To develop and validate a 2.5D multi-angle deep learning (MADL) model for preoperative T-staging in patients with gastric cancer (GC) and to explore the predictive potential for survival outcome.</p> Materials and methods <p>In this retrospective study, GC datasets from four centers were used to develop a quaternary for preoperative T-staging model by integrating CT images with nine 2D slicers and three angles (0°, + 45°, -45°) from transverse, sagittal, and coronal views. Diagnostic performance (accuracy, sensitivity, specificity, F1-score) were compared between the 2.5D MADL and radiologists. The prognostic potential of 2.5D MADL-derived features was evaluated using Kaplan-Meier analysis and multivariate Cox regression.</p> Results <p>A total of 433 patients were divided into the training (<i>n</i> = 346, mean age, 64.8 years ± 10.5 [SD]; 95 female) and the internal validation (<i>n</i> = 87, mean age, 63.0 years ± 11.9; 23 female) sets from Centers 1–3, and 41 patients (mean age, 64.6 years ± 8.9; 10 female) formed the external testing set from Center 4. The 2.5D MADL model exhibited excellent diagnostic performance for T-staging in external testing set (AUCs of T1-T4: 0.962, 0.722, 0.913, 0.962). For T1 staging, it outperformed radiologists in accuracy (95% vs. 83%), sensitivity (75% vs. 12%), and F1 score (86% vs. 22%) in external testing set. Survival analysis showed significant overall survival differences between high- and low-risk groups stratified (<i>P</i> &lt; 0.001), with high-risk patients having shorter survival (HR = 1.632, 95% CI:1.239–2.150).</p> Conclusion <p>The 2.5D MADL model achieves accurate and reliable preoperative T staging of gastric cancer, with performance comparable to radiologists and superior accuracy for early-stage disease.</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

A CT-based 2.5D deep learning model for preoperative T-staging in gastric cancer: a retrospective multicenter study

  • 期刊论文

  • 发表日期：

    2025年11月14日

查看全文
  • Ying Liu,
  • Xiao Zhang,
  • Wangyang He,
  • Yang Li,
  • Fubi Hu

摘要

Purpose

To develop and validate a 2.5D multi-angle deep learning (MADL) model for preoperative T-staging in patients with gastric cancer (GC) and to explore the predictive potential for survival outcome.

Materials and methods

In this retrospective study, GC datasets from four centers were used to develop a quaternary for preoperative T-staging model by integrating CT images with nine 2D slicers and three angles (0°, + 45°, -45°) from transverse, sagittal, and coronal views. Diagnostic performance (accuracy, sensitivity, specificity, F1-score) were compared between the 2.5D MADL and radiologists. The prognostic potential of 2.5D MADL-derived features was evaluated using Kaplan-Meier analysis and multivariate Cox regression.

Results

A total of 433 patients were divided into the training (n = 346, mean age, 64.8 years ± 10.5 [SD]; 95 female) and the internal validation (n = 87, mean age, 63.0 years ± 11.9; 23 female) sets from Centers 1–3, and 41 patients (mean age, 64.6 years ± 8.9; 10 female) formed the external testing set from Center 4. The 2.5D MADL model exhibited excellent diagnostic performance for T-staging in external testing set (AUCs of T1-T4: 0.962, 0.722, 0.913, 0.962). For T1 staging, it outperformed radiologists in accuracy (95% vs. 83%), sensitivity (75% vs. 12%), and F1 score (86% vs. 22%) in external testing set. Survival analysis showed significant overall survival differences between high- and low-risk groups stratified (P < 0.001), with high-risk patients having shorter survival (HR = 1.632, 95% CI:1.239–2.150).

Conclusion

The 2.5D MADL model achieves accurate and reliable preoperative T staging of gastric cancer, with performance comparable to radiologists and superior accuracy for early-stage disease.