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Background <p>Optimal prognostic stratification for colorectal liver metastases (CRLM) patients undergoing surgery with neoadjuvant therapy (NAT) remains elusive. This study aimed to develop and validate dual-time-point radiomic models for CRLM prognosis prediction using pre- and post-NAT imaging features.</p> Methods <p>Radiomic features were extracted from four MRI sequences in 100 cases of CRLM patients who underwent NAT and radical resection. RAD scores were generated, and clinical/pathologic variables were incorporated into uni- and multivariate Cox regression analyses to construct prognosis models. Time-ROC, time-C index, decision curve analysis (DCA), and calibration curves assessed the predictive performance of Fong score and pre- and post-NAT models for overall survival (OS) and disease-free survival (DFS) in a testing set.</p> Results <p>The final models included four variables for OS and three variables for DFS. The post-NAT models outperformed the pre-NAT models in time-ROC, time-C index, calibration, and DCA analysis, except for the 1-year DFS area under the curve (AUC). The Fong score models underperformed. The post-NAT OS RAD score effectively stratified patients into prognostic subgroups.</p> Conclusions <p>The radiomic models incorporating pre- and post-NAT MRI features and clinical/pathologic variables effectively stratified CRLM patients prognositically. The post-NAT models demonstrated superior performance.</p>

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Dual-Time-Point Radiomics for Prognosis Prediction in Colorectal Liver Metastasis Treated with Neoadjuvant Therapy Before Radical Resection: A Two-Center Study

  • 期刊论文

  • 发表日期：

    2025年2月5日

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  • Zhuo-fu Li,
  • Jia-ning Zhang,
  • Song Tian,
  • Chao Sun,
  • Ying Ma,
  • Zhao-xiang Ye

摘要

Background

Optimal prognostic stratification for colorectal liver metastases (CRLM) patients undergoing surgery with neoadjuvant therapy (NAT) remains elusive. This study aimed to develop and validate dual-time-point radiomic models for CRLM prognosis prediction using pre- and post-NAT imaging features.

Methods

Radiomic features were extracted from four MRI sequences in 100 cases of CRLM patients who underwent NAT and radical resection. RAD scores were generated, and clinical/pathologic variables were incorporated into uni- and multivariate Cox regression analyses to construct prognosis models. Time-ROC, time-C index, decision curve analysis (DCA), and calibration curves assessed the predictive performance of Fong score and pre- and post-NAT models for overall survival (OS) and disease-free survival (DFS) in a testing set.

Results

The final models included four variables for OS and three variables for DFS. The post-NAT models outperformed the pre-NAT models in time-ROC, time-C index, calibration, and DCA analysis, except for the 1-year DFS area under the curve (AUC). The Fong score models underperformed. The post-NAT OS RAD score effectively stratified patients into prognostic subgroups.

Conclusions

The radiomic models incorporating pre- and post-NAT MRI features and clinical/pathologic variables effectively stratified CRLM patients prognositically. The post-NAT models demonstrated superior performance.