The final, formatted version of the article will be published soon.
ORIGINAL RESEARCH article
Front. Oncol.
Sec. Gynecological Oncology
Volume 14 - 2024 |
doi: 10.3389/fonc.2024.1457531
Machine Learning-Driven Survival Prediction in Gestational Trophoblastic Neoplasms: A Focus on PSTT and ETT Prognosis
Provisionally accepted- 1 Faculty of Medicine, Jordan University of Science and Technology, Irbid, Irbid, Jordan
- 2 Faculty of Medicine, University of Aleppo, Aleppo, Syria
- 3 Other, Tangier, Morocco
The clinicopathological characteristics and prognosis of placental site trophoblastic tumor (PSTT) and epithelioid trophoblastic tumor (ETT) have not been well summarized. Consequently, we conducted the largest to date series of samples of both types and employed machine learning (ML) to assess treatment effectiveness and develop accurate prognostic models for patients with GTN. Gestational choriocarcinoma (GCC) was used as the control group to show the clinical features of PTSS and ETT.The Surveillance, Epidemiology, and End Results (SEER) database provided the data used for this study's analysis. To identify the prognostic variables, we conducted Cox regression analysis and constructed prognostic models using five ML algorithms to predict the 5-year survival. A validation method incorporating the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to validate the accuracy and reliability of the ML models. We also investigated the role of multiple therapeutic options using the Kaplan-Meier survival analysis.The study population comprised 725 patients. Among them, 139 patients had ETT, 107 had PSTT, and 479 had GCC. There were no significant differences in survival between the different tumor groups. Multivariate Cox regression analysis revealed that metastasis was a significant prognostic factor for GCC, while older age and radiotherapy were significant prognostic factors for PTSS and ETT. ML models revealed that the Gradient Boosting classifier accurately predicted the outcomes, followed by the random forest classifier, K-Nearest Neighbors, Logistic Regression, and multilayer perceptron models. The most significant contributing factors were tumor size, year of diagnosis, age, and race.Our study provides a method for treatment and prognostic assessment of patients with GTN. The ML we developed can be used as a convenient individualized tool to facilitate clinical decision making.
Keywords: clinical decision making, machine learning, Placenta, prognosis, survival analysis, treatment outcome, trophoblastic tumor
Received: 30 Jun 2024; Accepted: 09 Sep 2024.
Copyright: © 2024 Alshwayyat, Haddadin, Alshwayyat, Alshwayyat, Odat, Al-kurdi and Kharmoum. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Mohammed A. Al-kurdi, Faculty of Medicine, University of Aleppo, Aleppo, Syria
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.