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Integrative Predictive Nomograms for Treatment Decision-Making in Resectable Synchronous Colorectal Liver Metastases.

Yujuan Jiang1,2, Dedi Jiang1, Jinghua Chen3

  • 1Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Journal of Cancer
|February 24, 2025
PubMed
Summary
This summary is machine-generated.

This study developed personalized web-based tools to predict outcomes for patients with resectable synchronous colorectal liver metastases (CRLM), aiding treatment decisions between upfront surgery and neoadjuvant therapy.

Keywords:
CRLMneoadjuvant therapyprognostic modelsupfront surgery

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Area of Science:

  • Oncology
  • Surgical Oncology
  • Medical Informatics

Background:

  • No standard management exists for resectable synchronous colorectal liver metastases (CRLM).
  • Treatment decisions involve upfront surgery versus neoadjuvant therapy.
  • Integrating diverse clinical data can improve management strategies.

Purpose of the Study:

  • To develop and validate predictive models for resectable synchronous CRLM.
  • To assist clinicians in choosing between upfront surgery and neoadjuvant therapy.
  • To create accessible web-based tools for clinical decision support.

Main Methods:

  • Retrospective cohort study of 386 patients with resectable synchronous CRLM (2008-2018).
  • Development of prediction nomograms using univariate and multivariate Cox analyses.
  • Validation via calibration curves, C-index, DCA, and ROC curves; web server development.

Main Results:

  • Nomograms incorporated nine predictors: tumor count, cN stage, KRAS/BRAF mutations, age, tumor location, neutrophil/platelet counts, D-Dimer.
  • Models demonstrated good calibration and predictive accuracy (C-index > 0.7, ROC AUC > 0.7 for 1-, 3-, 5-year outcomes).
  • Web-based application developed for practical use.

Conclusions:

  • Personalized web-based predictive models show moderate accuracy for resectable synchronous CRLM.
  • These tools support clinical decision-making for upfront surgery versus neoadjuvant therapy.
  • The models enhance personalized treatment strategies for CRLM patients.