AccScience Publishing / AIH / Volume 1 / Issue 4 / DOI: 10.36922/aih.3541
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REVIEW

Prognostic evaluation using radiomics after stereotactic body radiotherapy in early-stage lung cancer

Melek Yakar1*
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1 Department of Radiation Oncology, Faculty of Medicine, Osmangazi University, Eskişehir, Turkey
AIH 2024, 1(4), 1–11; https://doi.org/10.36922/aih.3541
Submitted: 30 April 2024 | Accepted: 1 August 2024 | Published: 16 October 2024
© 2024 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Non-small cell lung cancer (NSCLC), the leading cause of cancer-related deaths, is the most common subtype of lung cancer with an incidence of 85%. Stereotactic body radiotherapy (SBRT) is a curative treatment option for patients with early-stage NSCLC who cannot undergo surgery due to medical reasons or who refuse surgery. Radiomics non-invasively extracts advanced imaging features invisible to the human eye from medical images. Radiomics has prognostic value in predicting oncological outcomes after lung SBRT. Although studies on this subject are available in the literature, they are quite heterogeneous. There is a need for large-scale multicenter studies in which standard imaging techniques are used to obtain radiomic features, artificial intelligence-based segmentations are used to eliminate differences between contours, and SBRT dose schemes with appropriate therapeutic indexes are applied. This review aimed to interpret the existing studies and emphasize the clinical importance of radiomics, which can contribute to personalized treatment. A comprehensive literature search was conducted through the PubMed database using a wide range of keywords, which yielded 11 peer-reviewed articles published between 2017 and 2024. Seven articles evaluated computed tomography radiomics, and four evaluated fluorodeoxyglucose positron emission tomography-computed tomography radiomics. Oncological outcomes are not always identical in patients with a similar history receiving similar treatments at the same stage and age. Clinical, demographic, or treatment-related data are insufficient to predict prognosis and determine personalized treatment. Incorporating radiomics to these data can help establish models with higher accuracy and achieve personalized treatment.

Keywords
Artificial intelligence
Lung cancer
Stereotactic body radiotherapy
Prognosis
Radiomics
Funding
None.
Conflict of interest
The author declares having no competing interests.
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Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Print ISSN: 3041-0894, Published by AccScience Publishing
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