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Preoperative serum level of CA153 and a new model to predict the sub-optimal primary debulking surgery in patients with advanced epithelial ovarian cancer

  • 0Department of Gynecology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, P. R. China, 650118.
World Journal of Surgical Oncology +

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February 23, 2024

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February 23, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

A new predictive model combining the Suidan score with HE4, CA125, CA153, and ROMA index can predict suboptimal debulking surgery in advanced ovarian cancer patients. This tool aids in non-invasively assessing surgical outcomes for better patient management.

Area Of Science

  • Gynecologic Oncology
  • Surgical Oncology
  • Biomarker Discovery

Background

  • Advanced ovarian cancer (AOC) poses significant treatment challenges.
  • Primary debulking surgery (PDS) is a cornerstone of AOC management.
  • Predicting surgical outcomes preoperatively is crucial for optimizing patient care.

Purpose Of The Study

  • To develop a preoperative model for predicting suboptimal debulking surgery (SDS) in AOC patients.
  • To integrate the Suidan predictive model with serum biomarkers (HE4, CA125, CA153) and the ROMA index.
  • To enhance the non-invasive prediction of PDS outcomes.

Main Methods

  • Retrospective analysis of 76 AOC patients (FIGO stage III-IV) who underwent PDS.
  • Collection of preoperative serum levels of HE4, CA125, CA153, and calculation of the ROMA index.
  • Logistic regression and ROC curve analysis to identify predictors of SDS.
  • Construction of a predictive index value (PIV) model.

Main Results

  • Optimal surgical cytoreduction was achieved in 61.84% of patients.
  • Lower levels of CA125, HE4, CA153, ROMA index, and Suidan score were associated with optimal debulking surgery (ODS).
  • The PIV model demonstrated an AUC of 0.770 for predicting SDS, with diagnostic accuracy of 73.7%.

Conclusions

  • Preoperative serum CA153 is a novel non-invasive predictor of SDS in AOC.
  • The developed PIV model, incorporating Suidan's model and biomarkers, can non-invasively predict SDS in AOC patients.
  • Further validation of the PIV model's accuracy in larger cohorts is warranted.

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