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Background <p>Early 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.</p> Methods <p>The 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).</p> Results <p>The 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%.</p> Conclusions <p>The 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.</p>

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Enhancing the positive predictive value of early-stage ovarian cancer detection using a two-step machine learning framework

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月1日

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  • Mikio Mikami,
  • Kazuhiro Tanabe,
  • Saki Nagaki,
  • Yuya Nogami,
  • Tadashi Imanishi,
  • Masae Ikeda,
  • Hiroshi Yoshida,
  • Masanori Hasegawa,
  • Muneaki Shimada,
  • Shogo Shigeta,
  • Mitsuya Ishikawa,
  • Mayumi Kato,
  • Haruya Saji,
  • Yoichi Kobayashi,
  • Tohru Morisada,
  • Nao Suzuki,
  • Tatsuru Ohhara,
  • Kyoko Tanaka,
  • Isao Murakami,
  • Tomoko Katahira,
  • Chihiro Hayashi,
  • Brendan H. Grubbs,
  • Wataru Yamagami,
  • Koji Matsuo

摘要

Background

Early 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.

Methods

The 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).

Results

The 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%.

Conclusions

The 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.