Scholarlink

<p>This study aimed to develop a fully automated survival prediction (FASP) system that analyzes whole-liver regions from preoperative contrast-enhanced CT scans for predicting recurrence-free survival (RFS) after curative resection in Hepatocellular carcinoma (HCC). FASP comprised three consecutive components: automatic liver and tumor segmentation models, and a RFS prediction model, LiverSurv, all based on deep convolutional neural networks. FASP was compared against a clinical model leveraging clinical factors and three tumor-based methods using semantic, radiomic, or deep learning features. A total of 827 patients were included across the development, internal test, and external test sets. In the internal and external test sets, FASP achieved concordance indices (C-indices) of 0.646 (95% CI: 0.566, 0.725) and 0.786 (95% CI: 0.726, 0.846), respectively, outperforming the clinical model (both adjusted <i>P&lt;</i>.05). Integrating clinical factors improved C-indices to 0.664 (95% CI: 0.591, 0.736) internally and 0.800 (95% CI: 0.750, 0.849) externally. FASP also surpassed the tumor-based models, which yielded C-indices ranging from 0.623 to 0.632 internally and 0.523 to 0.775 externally. Visualization analysis demonstrated that FASP captured prognostic information from both tumor and background liver regions. These findings suggest whole-liver-based deep learning provides a promising non-invasive approach to predict recurrence risk for HCC patients before surgery.</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

Prediction of recurrence after resection in hepatocellular carcinoma via whole liver deep learning on preoperative contrast-enhanced CT

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月2日

查看全文
  • Chao Huang,
  • Yu Tian,
  • Qi Zhang,
  • Yiwei Gao,
  • Peijun Hu,
  • Yangyang Wang,
  • Yingying Xu,
  • Jing Liu,
  • Weibin Wang,
  • Tianshu Zhou,
  • Mu Zhou,
  • Jingsong Li

摘要

This study aimed to develop a fully automated survival prediction (FASP) system that analyzes whole-liver regions from preoperative contrast-enhanced CT scans for predicting recurrence-free survival (RFS) after curative resection in Hepatocellular carcinoma (HCC). FASP comprised three consecutive components: automatic liver and tumor segmentation models, and a RFS prediction model, LiverSurv, all based on deep convolutional neural networks. FASP was compared against a clinical model leveraging clinical factors and three tumor-based methods using semantic, radiomic, or deep learning features. A total of 827 patients were included across the development, internal test, and external test sets. In the internal and external test sets, FASP achieved concordance indices (C-indices) of 0.646 (95% CI: 0.566, 0.725) and 0.786 (95% CI: 0.726, 0.846), respectively, outperforming the clinical model (both adjusted P<.05). Integrating clinical factors improved C-indices to 0.664 (95% CI: 0.591, 0.736) internally and 0.800 (95% CI: 0.750, 0.849) externally. FASP also surpassed the tumor-based models, which yielded C-indices ranging from 0.623 to 0.632 internally and 0.523 to 0.775 externally. Visualization analysis demonstrated that FASP captured prognostic information from both tumor and background liver regions. These findings suggest whole-liver-based deep learning provides a promising non-invasive approach to predict recurrence risk for HCC patients before surgery.