Clinical radiology · 2026
Machine learning for differentiating primary lung cancer from pulmonary metastases and non-small cell lung cancer histological subtype classification: a systematic review.
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Zare O, Ostovari M, Mahmoudi T
Аннотация
AIM: This study aimed to systematically review machine learning (ML) methods applied to computed tomography (CT) and positron emission tomography (PET)/CT for differentiating primary lung cancer from pulmonary metastases and for lung cancer histological subtype classification.
MATERIALS AND METHODS: PubMed/MEDLINE, Web of Science, and IEEE Xplore were searched for original peer-reviewed studies published from January 2010 to May 31, 2026. Eligible studies applied ML, deep learning, or radiomics to CT, PET, or PET/CT for primary versus metastatic lung lesion differentiation or major lung cancer histological subtype classification. Methodological quality was assessed with Quality assessment of comparative diagnostic accuracy studies (QUADAS-2), and reporting was evaluated using 10 prespecified Checklist for Artificial Intelligence in Medical Imaging-aligned items.
RESULTS: Twenty-two studies were included after eligibility reassessment. Twenty-one provided a numeric study-level Area Under the Curve (AUC); the median representative AUC was 0.889 (IQR 0.820-0.936). The median AUC was 0.923 for primary/metastatic differentiation and 0.874 for histological subtype classification. Only 5 of 22 studies (22.7%) used independent external testing; among 4 externally validated studies with numeric AUCs, the median was 0.720. QUADAS-2 showed frequent uncertainty in patient selection (17/22, 77.3%), while CLAIM-aligned assessment identified limited external testing, code/model availability (3/22, 13.6%), and explainability reporting (4/22, 18.2%).
CONCLUSION: CT- and PET/CT-based ML models show promising diagnostic performance for lung lesion-origin and histological subtype classification, but performance is lower in independent external evaluation. Broader multicentre validation, transparent reporting, reproducible code/model access, and prospective assessment are required before routine clinical implementation.
MATERIALS AND METHODS: PubMed/MEDLINE, Web of Science, and IEEE Xplore were searched for original peer-reviewed studies published from January 2010 to May 31, 2026. Eligible studies applied ML, deep learning, or radiomics to CT, PET, or PET/CT for primary versus metastatic lung lesion differentiation or major lung cancer histological subtype classification. Methodological quality was assessed with Quality assessment of comparative diagnostic accuracy studies (QUADAS-2), and reporting was evaluated using 10 prespecified Checklist for Artificial Intelligence in Medical Imaging-aligned items.
RESULTS: Twenty-two studies were included after eligibility reassessment. Twenty-one provided a numeric study-level Area Under the Curve (AUC); the median representative AUC was 0.889 (IQR 0.820-0.936). The median AUC was 0.923 for primary/metastatic differentiation and 0.874 for histological subtype classification. Only 5 of 22 studies (22.7%) used independent external testing; among 4 externally validated studies with numeric AUCs, the median was 0.720. QUADAS-2 showed frequent uncertainty in patient selection (17/22, 77.3%), while CLAIM-aligned assessment identified limited external testing, code/model availability (3/22, 13.6%), and explainability reporting (4/22, 18.2%).
CONCLUSION: CT- and PET/CT-based ML models show promising diagnostic performance for lung lesion-origin and histological subtype classification, but performance is lower in independent external evaluation. Broader multicentre validation, transparent reporting, reproducible code/model access, and prospective assessment are required before routine clinical implementation.