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ORIGINAL RESEARCH article

Front. Artif. Intell.

Sec. Medicine and Public Health

Volume 8 - 2025 | doi: 10.3389/frai.2025.1675969

Artificial Analysis Applied to Treatment of Granulosa Cell Tumors of the Ovary

Provisionally accepted
OPhir  NaveOPhir Nave1*Pnina  BarasheshetPnina Barasheshet2
  • 1Jerusalem College of Technology, Jerusalem, Israel
  • 2Ben-Gurion University of the Negev, Be'er Sheva, Israel

The final, formatted version of the article will be published soon.

We propose a novel method that integrates a mathematical model describing the combined action of TRAIL-producing oncolytic virus and PAC-1 therapy for granulosa cell tumors of the ovary with machine learning (ML) algorithms to enhance the prediction of tumor dynamics. This approach follows a structured approach using a few stages. First, the ML algorithm is trained to predict tumor burden based on clinico-pathological data and imaging findings collected at multiple time points during treatment. Second, the mathematical model is personalized for individual patients, incorporating initial tumor characteristics. Third, critical variables from the model solutions are extracted and added as new features for ML training. Finally, various ML algorithms are applied to the enriched dataset. Our comparative analysis showed that integrating the mathematical model reduced the root mean square error in linear regression predictions and improved both accuracy and F1-scores in neural network models. These results were confirmed across distinct patient cohorts treated with TRAIL-oncolytic virus and PAC-1 therapy.

Keywords: artificial intelligence, mathematical model, PAC-1, oncolytic virus, Granulosa Cells, ovarian cancer, machine learning

Received: 31 Jul 2025; Accepted: 19 Sep 2025.

Copyright: © 2025 Nave and Barasheshet. 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: OPhir Nave, ophirn@g.jct.ac.il

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