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Machine learning for epithelial ovarian cancer platinum resistance recurrence identification using routine clinical data

  • 0Hematology Oncology Department, the Southern Central Hospital of Yunnan Province, Honghe, Yunnan, China.
Frontiers in Oncology +

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November 25, 2024

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November 25, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

Machine learning models predict epithelial ovarian cancer platinum resistance recurrence using clinical data. The XGBoost model showed the best performance, offering a valuable tool for early intervention in high-risk patients.

Area Of Science

  • Oncology
  • Machine Learning
  • Biostatistics

Background

  • Epithelial ovarian cancer (EOC) frequently recurs after treatment.
  • Identifying high-risk patients is crucial for timely intervention and improved outcomes.
  • Predictive models for platinum resistance recurrence in EOC are needed.

Purpose Of The Study

  • To develop and validate machine learning models for predicting platinum resistance recurrence in epithelial ovarian cancer.
  • To identify key clinical and laboratory variables associated with platinum resistance recurrence.
  • To compare the performance of machine learning models against traditional logistic regression.

Main Methods

  • Retrospective cohort analysis of 1,392 EOC patients treated with platinum-based chemotherapy.
  • Variable selection using Lasso and multiple logistic regression.
  • Development and comparison of five machine learning models (DTA, KNN, SVM, RF, XGBoost) against logistic regression.
  • Internal validation using five-fold cross-validation and performance metrics (AUC, sensitivity, specificity, accuracy).

Main Results

  • Multiple logistic regression identified eight variables, while Lasso regression identified seven, associated with platinum resistance recurrence.
  • The XGBoost model, using variables from multiple logistic regression, achieved the highest performance (AUC: 0.784, accuracy: 80.4%).
  • A logistic regression model based on Lasso regression also showed good performance (AUC: 0.738, accuracy: 79.6%).

Conclusions

  • Successfully developed predictive models for platinum-resistant recurrence in EOC using routine clinical and laboratory data.
  • The XGBoost model demonstrated superior predictive performance and is recommended for clinical use.
  • Continuous model evolution is necessary to adapt to changing influencing factors over time.

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