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Updated: Jan 8, 2026

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Simpler predictive models provide higher accuracy for ovarian cancer detection.

Derrick E Wood1, Joseph Roy1,2,3, Bari J Ballew1

  • 1Blackjack Biotechnologies, Baltimore, MD, United States of America.

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|December 22, 2025
PubMed
Summary

Ovarian cancer screening using cell-free DNA (cfDNA) and protein biomarkers may not improve accuracy. Simpler models combining CA125 and HE4 proteins show comparable performance to complex cfDNA models.

Keywords:
Cancer detectionCancer diagnosticsCancer screeningMachine learningOvarian cancer

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Area of Science:

  • Oncology
  • Biomarker Discovery
  • Machine Learning in Medicine

Background:

  • Ovarian cancer poses a significant threat to women's health, necessitating improved screening methods.
  • Established protein biomarkers CA125 and HE4 demonstrate high accuracy in ovarian cancer detection, especially when combined.
  • Previous research introduced DELFI-Pro, a logistic regression (LR) model integrating cell-free DNA (cfDNA) features with protein concentrations.

Purpose of the Study:

  • To re-evaluate the efficacy of the DELFI-Pro screening model by addressing potential confounding factors in its training data.
  • To determine if the cfDNA features in DELFI-Pro offer a significant advantage over protein-only biomarkers for ovarian cancer detection.

Main Methods:

  • Analysis of a dataset used in prior DELFI-Pro research, focusing on cfDNA-derived features and protein concentrations (CA125, HE4).
  • Identification and removal of training data samples with anomalous chromosomal copy number values that could introduce technical variation.
  • Comparative performance evaluation of the refined DELFI-Pro model against protein-only logistic regression classifiers using cross-validation.

Main Results:

  • Confounding technical variation was identified within the cfDNA features of the original DELFI-Pro training data.
  • Exclusion of 42 outlier cancer samples from the training set revealed that DELFI-Pro did not outperform protein-only models.
  • The combined CA125 and HE4 protein model achieved an area under the curve (AUC) of 0.99, indicating high accuracy.

Conclusions:

  • The cfDNA features in DELFI-Pro do not provide sufficient added value to justify their inclusion over simpler, protein-based models.
  • Simpler machine learning models, like the protein-only classifiers, tend to generalize better to new data.
  • Current evidence does not adequately support the complex DELFI-Pro model for ovarian cancer screening.