Journal of thoracic disease · 2026
Artificial intelligence and radiomics in small cell lung cancer: a narrative review of imaging and multisource data integration.
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Yang Yue, Sun Jialu, Liu Jiayi, Liu Jiaxu, Deng Shiyang, Du Jiang
Аннотация
BACKGROUND AND OBJECTIVE: Artificial intelligence (AI) and radiomics are increasingly used to analyze imaging and multisource data in small cell lung cancer (SCLC), but the directness and clinical maturity of the available evidence remain uncertain. This narrative review aimed to summarize and critically evaluate SCLC-specific, mixed-cohort, and indirect evidence across diagnostic classification, prognostic prediction, treatment-associated outcomes, and lesion segmentation.
METHODS: A structured literature search was conducted to identify human studies evaluating AI, deep learning (DL), radiomics, or machine learning within imaging-based or imaging-integrated workflows. Studies were selected according to clinical relevance, directness of SCLC evidence, methodological strength, and reporting completeness, and were classified as SCLC-specific, mixed-cohort, or indirect/transferable evidence.
KEY CONTENT AND FINDINGS: Direct SCLC-specific evidence was strongest for multicenter computed tomography (CT)-based prognostic models and lesion segmentation, whereas diagnostic classification relied mainly on mixed cohorts or public image-level datasets. Treatment-response models showed preliminary value but were predominantly retrospective and lacked prospective clinical validation. Retrospective studies suggested associations between model-derived risk scores and outcomes following prophylactic cranial irradiation (PCI), but did not establish treatment-benefit prediction. Pathomic and radiogenomic analyses also identified associations between quantitative features, immune-cell infiltration, genomic alterations, and treatment response; these findings remain hypothesis-generating. Interpretation is limited by small SCLC cohorts, retrospective single-center designs, non-independent image-level analyses, and limited external validation and interpretability.
CONCLUSIONS: AI and radiomics show potential in SCLC, particularly for CT-based prognosis and segmentation. However, treatment-response prediction, non-invasive subtype classification, and clinical decision support remain investigational and require prospective multicenter validation and patient-level external evaluation before clinical implementation.
METHODS: A structured literature search was conducted to identify human studies evaluating AI, deep learning (DL), radiomics, or machine learning within imaging-based or imaging-integrated workflows. Studies were selected according to clinical relevance, directness of SCLC evidence, methodological strength, and reporting completeness, and were classified as SCLC-specific, mixed-cohort, or indirect/transferable evidence.
KEY CONTENT AND FINDINGS: Direct SCLC-specific evidence was strongest for multicenter computed tomography (CT)-based prognostic models and lesion segmentation, whereas diagnostic classification relied mainly on mixed cohorts or public image-level datasets. Treatment-response models showed preliminary value but were predominantly retrospective and lacked prospective clinical validation. Retrospective studies suggested associations between model-derived risk scores and outcomes following prophylactic cranial irradiation (PCI), but did not establish treatment-benefit prediction. Pathomic and radiogenomic analyses also identified associations between quantitative features, immune-cell infiltration, genomic alterations, and treatment response; these findings remain hypothesis-generating. Interpretation is limited by small SCLC cohorts, retrospective single-center designs, non-independent image-level analyses, and limited external validation and interpretability.
CONCLUSIONS: AI and radiomics show potential in SCLC, particularly for CT-based prognosis and segmentation. However, treatment-response prediction, non-invasive subtype classification, and clinical decision support remain investigational and require prospective multicenter validation and patient-level external evaluation before clinical implementation.