ArticleJournal of translational medicine2026
Enhancing the positive predictive value of early-stage ovarian cancer detection using a two-step machine learning framework.
Article in Journal of translational medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundEarly detection of epithelial ovarian cancer (EOC) remains a major clinical challenge. Although serum tumor markers are widely used for detection, their diagnostic performance remains limited. We previously developed a comprehensive serum glycopeptide spectrum analysis (CSGSA) approach that integrates tumor marker measurements and enriched glycopeptides (EGPs) using convolutional neural networks. In this study, we evaluated whether a two-step LightGBM framework incorporating cancer antigen 125 (CA125), human epididymis protein 4 (HE4), cancer antigen 72 - 4 (CA72-4), and EGPs could improve the diagnostic specificity and projected positive predictive value (PPV) for EOC detection compared with conventional biomarker-based approaches.
methodsThe study included 553 patients with EOC and 1,144 non-EOC controls (healthy individuals or patients with benign conditions). Serum levels of CA125, HE4, and CA72-4 were measured along with 1,712 EGPs. Diagnostic models were developed using machine learning algorithms and evaluated for accuracy, area under the receiver operating characteristic curve (ROC-AUC), PPV, and negative predictive value (NPV).
resultsThe highest diagnostic performance was achieved using a two-step classification framework. First, patients were stratified into high-, intermediate-, and low-risk groups based on tumor markers and age. Second, the intermediate-risk group was reclassified using a model incorporating EGP-derived features. Among the evaluated algorithms, LightGBM achieved the best performance, yielding a prevalence-adjusted (projected) PPV of 18.7% and an NPV of 99.99%. At a predefined specificity of 99.5%, the corresponding sensitivity was 65%.
conclusionsThe CSGSA method combined with a two-step LightGBM framework demonstrated promising diagnostic performance in an internally validated cohort, with improved specificity and prevalence-adjusted PPV compared with conventional biomarker-based approaches. However, prospective external validation in independent populations is required before clinical implementation or generalizability can be established.
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