Predicting Mesothelioma Using Artificial Intelligence: A Scoping Review of Common Models and Applications Publisher Pubmed



Ram M¹ ; Afrash MR² ; Moulaei K³ ; Esmaeeli E⁴ ; Khorashadizadeh MS⁵ ; Garavand A⁶ ; Amiri P⁶ ; Sabahi A⁷
Source: Technology in Cancer Research and Treatment Published:2025

Abstract

Introduction: Mesothelioma is a type of lung cancer caused by asbestos exposure, and early diagnosis is crucial for improving survival chances. Artificial intelligence offers a potential solution for the timely diagnosis and staging of the disease. This study aims to review the latest research conducted in artificial intelligence applications to predict mesothelioma. Methods: Until April 24, 2023, PubMed, Scopus, and Web of Science databases were searched comprehensively for articles on artificial intelligence in mesothelioma management. The data was gathered using a standardized extraction form, and the findings were reported in figures and tables. Results: One hundred and seventy-three articles were identified from database searches, which were then reduced to 151 after eliminating duplicates. Finally, 19 articles were selected for inclusion in our study. The applications of artificial intelligence in these articles primarily focused on tumor diagnosis and classification (73.69%), followed by prevention and prognosis (21.05%) and tumor volumetric measurement of malignant pleural mesothelioma (5.26%). The most frequently used AI models include types of neural networks (NN), decision trees (DT), random forests (RF), logistic regression (LogR), Naive Bayes (NB), and support vector machines (SVM). SVM, DT, and RF emerged as prominent models, achieving high accuracies ranging from 78.3% to 99.97%. Genetic algorithms, correlation-based algorithms, and Neural Networks were employed for risk factor identification and feature selection. Conclusion: Artificial intelligence, particularly machine learning models such as neural networks, decision trees, support vector machines, and random forests, holds promise in predicting and managing mesothelioma, potentially enhancing early detection and improving patient outcomes. © The Author(s) 2025.