Artificial Intelligence and Machine Learning in Predicting the Response to Immunotherapy in Non-small Cell Lung Carcinoma: A Systematic Review.

Autor:	Sinha T; Internal Medicine, Tribhuvan University, Kathmandu, NPL., Khan A; Medicine, Liaquat College of Medicine and Dentistry, Karachi, PAK., Awan M; General Practice, Liaquat National Hospital and Medical College, Karachi, PAK., Bokhari SFH; Surgery, King Edward Medical University, Lahore, PAK., Ali K; Medicine and Surgery, King Edward Medical University, Lahore, PAK., Amir M; Medicine and Surgery, King Edward Medical University, Lahore, PAK., Jadhav AN; Pediatrics, Bharat Ratna Dr. Babasaheb Ambedkar Memorial Hospital, Mumbai, IND., Bakht D; Medicine and Surgery, Mayo Hospital, Lahore, PAK., Puli ST; Internal Medicine, Bhaskar Medical College, Hyderabad, IND., Burhanuddin M; Medicine, Bhaskar Medical College, Hyderabad, IND.
Jazyk:	angličtina
Zdroj:	Cureus [Cureus] 2024 May 28; Vol. 16 (5), pp. e61220. Date of Electronic Publication: 2024 May 28 (Print Publication: 2024).
DOI:	10.7759/cureus.61220
Abstrakt:	Non-small cell lung carcinoma (NSCLC) is a prevalent and aggressive form of lung cancer, with a poor prognosis for metastatic disease. Immunotherapy, particularly immune checkpoint inhibitors (ICIs), has revolutionized the management of NSCLC, but response rates are highly variable. Identifying reliable predictive biomarkers is crucial to optimize patient selection and treatment outcomes. This systematic review aimed to evaluate the current state of artificial intelligence (AI) and machine learning (ML) applications in predicting the response to immunotherapy in NSCLC. A comprehensive literature search identified 19 studies that met the inclusion criteria. The studies employed diverse AI/ML techniques, including deep learning, artificial neural networks, support vector machines, and gradient boosting methods, applied to various data modalities such as medical imaging, genomic data, clinical variables, and immunohistochemical markers. Several studies demonstrated the ability of AI/ML models to accurately predict immunotherapy response, progression-free survival, and overall survival in NSCLC patients. However, challenges remain in data availability, quality, and interpretability of these models. Efforts have been made to develop interpretable AI/ML techniques, but further research is needed to improve transparency and explainability. Additionally, translating AI/ML models from research settings to clinical practice poses challenges related to regulatory approval, data privacy, and integration into existing healthcare systems. Nonetheless, the successful implementation of AI/ML models could enable personalized treatment strategies, improve treatment outcomes, and reduce unnecessary toxicities and healthcare costs associated with ineffective treatments. Competing Interests: Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work. (Copyright © 2024, Sinha et al.)
Databáze:	MEDLINE
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