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BMC Cancer
Published in: BMC Cancer 1/2024

Open Access 01-12-2024 | Ovarian Cancer | Research

Comprehensive machine learning-based preoperative blood features predict the prognosis for ovarian cancer

Authors: Meixuan Wu, Sijia Gu, Jiani Yang, Yaqian Zhao, Jindan Sheng, Shanshan Cheng, Shilin Xu, Yongsong Wu, Mingjun Ma, Xiaomei Luo, Hao Zhang, Yu Wang, Aimin Zhao

Published in: BMC Cancer | Issue 1/2024

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Abstract

Purpose

Significant advancements in improving ovarian cancer (OC) outcomes have been limited over the past decade. To predict prognosis and improve outcomes of OC, we plan to develop and validate a robust prognosis signature based on blood features.

Methods

We screened age and 33 blood features from 331 OC patients. Using ten machine learning algorithms, 88 combinations were generated, from which one was selected to construct a blood risk score (BRS) according to the highest C-index in the test dataset.

Results

Stepcox (both) and Enet (alpha = 0.7) performed the best in the test dataset with a C-index of 0.711. Meanwhile, the low RBS group possessed observably prolonged survival in this model. Compared to traditional prognostic-related features such as age, stage, grade, and CA125, our combined model had the highest AUC values at 3, 5, and 7 years. According to the results of the model, BRS can provide accurate predictions of OC prognosis. BRS was also capable of identifying various prognostic stratifications in different stages and grades. Importantly, developing the nomogram may improve performance by combining BRS and stage.

Conclusion

This study provides a valuable combined machine-learning model that can be used for predicting the individualized prognosis of OC patients.
Appendix
Available only for authorised users
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Metadata
Title
Comprehensive machine learning-based preoperative blood features predict the prognosis for ovarian cancer
Authors
Meixuan Wu
Sijia Gu
Jiani Yang
Yaqian Zhao
Jindan Sheng
Shanshan Cheng
Shilin Xu
Yongsong Wu
Mingjun Ma
Xiaomei Luo
Hao Zhang
Yu Wang
Aimin Zhao
Publication date
01-12-2024
Publisher
BioMed Central
Published in
BMC Cancer / Issue 1/2024
Electronic ISSN: 1471-2407
DOI
https://doi.org/10.1186/s12885-024-11989-1

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