Scholarlink

Purpose <p>This study aimed to develop a deep learning model based on magnetic resonance imaging (MRI) and clinical features for predicting PSM risk after radical prostatectomy (RP).</p> Methods <p>This retrospective multicenter study included 1177 prostate cancer patients who underwent preoperative MRI and RP across eight institutions. A total of 1022 patients from two institutions were used for model training, while 155 patients from six independent centers formed the external validation cohort. A feature disentanglement-based deep learning model (DESM) was developed to isolate disease-specific features from hospital-specific variations. A multimodal fusion model (MDESM) was further constructed by integrating the DESM-derived imaging signature with clinical variables to enhance prediction accuracy and generalizability. Gradient-weighted class activation mapping was applied to provide interpretability by highlighting model attention regions.</p> Results <p>In the external validation cohort, MDESM achieved an area under the receiver operating characteristic curve of 0.843 (95% CI, 0.770–0.911), significantly outperforming the DESM (0.711, 95% CI, 0.607–0.800, <i>p</i> = 0.003, Z = 2.936) and the clinical-only model (0.676, 95% CI, 0.577–0.770, <i>p</i> &lt; 0.001, Z = 3.420). Decision curve analysis demonstrated the highest net benefit for MDESM across a range of threshold probabilities.</p> Conclusion <p>The MDESM model, based on a feature disentanglement and multimodal fusion strategy, demonstrates the potential to effectively combine MRI and clinical data to achieve accurate PSM prediction. This approach offers a promising tool for preoperative risk stratification and surgical planning in prostate cancer.</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

Preoperative prediction of positive surgical margins in prostate cancer using multimodal deep learning model: a multicenter study

  • 期刊论文

  • 发表日期：

    2026年7月25日

查看全文
  • Xu Fu,
  • Jie Bao,
  • Xiaomeng Qiao,
  • Junkang Shen,
  • Yueyue Zhang,
  • Pengfei Jin,
  • Yanting Ji,
  • Ji Zhang,
  • Yueting Su,
  • Libiao Ji,
  • Zhenkai Li,
  • Chunhong Hu,
  • Jian Lu,
  • Ximing Wang,
  • Hailin Shen,
  • Jiangang Liu

摘要

Purpose

This study aimed to develop a deep learning model based on magnetic resonance imaging (MRI) and clinical features for predicting PSM risk after radical prostatectomy (RP).

Methods

This retrospective multicenter study included 1177 prostate cancer patients who underwent preoperative MRI and RP across eight institutions. A total of 1022 patients from two institutions were used for model training, while 155 patients from six independent centers formed the external validation cohort. A feature disentanglement-based deep learning model (DESM) was developed to isolate disease-specific features from hospital-specific variations. A multimodal fusion model (MDESM) was further constructed by integrating the DESM-derived imaging signature with clinical variables to enhance prediction accuracy and generalizability. Gradient-weighted class activation mapping was applied to provide interpretability by highlighting model attention regions.

Results

In the external validation cohort, MDESM achieved an area under the receiver operating characteristic curve of 0.843 (95% CI, 0.770–0.911), significantly outperforming the DESM (0.711, 95% CI, 0.607–0.800, p = 0.003, Z = 2.936) and the clinical-only model (0.676, 95% CI, 0.577–0.770, p < 0.001, Z = 3.420). Decision curve analysis demonstrated the highest net benefit for MDESM across a range of threshold probabilities.

Conclusion

The MDESM model, based on a feature disentanglement and multimodal fusion strategy, demonstrates the potential to effectively combine MRI and clinical data to achieve accurate PSM prediction. This approach offers a promising tool for preoperative risk stratification and surgical planning in prostate cancer.