Prediction of survival outcomes in patients with epithelial ovarian cancer using machine learning methods

E Sun Paik, Jeong‐Won Lee, Jeong‐Yeol Park, Ju-Hyun Kim, Mijung Kim, Tae‐Joong Kim, Chel Hun Choi, Byoung‐Gie Kim et al.

Journal of Gynecologic Oncology 2019 article cited by 75

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importance 6/10 partially solved Ovarian cancer diagnosis and treatment oncology stage: research open-source model

Problem

Conventional statistical methods poorly predict survival outcomes in epithelial ovarian cancer patients.

Why it matters

Better survival prediction can guide treatment for a lethal cancer.

What the AI did

A gradient boosting prognostic model was trained on 1,128 patients and externally validated on 229 patients, compared against conventional Cox regression.

Results

AUC 0.830 (training) and 0.843 (external validation) for 2-year overall survival; Cox comparison truncated in abstract.

Impact

Model was externally validated at a second hospital, showing high predictive accuracy.

Record

Venue	Journal of Gynecologic Oncology
Published	2019
Work type	article
Authors	E Sun Paik, Jeong‐Won Lee, Jeong‐Yeol Park, Ju-Hyun Kim, Mijung Kim, Tae‐Joong Kim, Chel Hun Choi, Byoung‐Gie Kim et al.
Citations	75 (OpenAlex, at import time)
References	28
Model or method	gradient boosting (GB)
Organization	Samsung Medical Center; Asan Medical Center
Primary source	https://doi.org/10.3802/jgo.2019.30.e65
Open access	diamond
Keywords	gradient boosting, ovarian cancer, survival prediction, prognosis, external validation

Importance rating

6 / 10

significant — thousands to millions affected

Prognostic classification for epithelial ovarian cancer guides treatment of a serious cancer; abstract reports model development and validation only, with no scale or impact figures.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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