Diagnostic accuracy of CT and PET/CT radiomics in predicting lymph node metastasis in non-small cell lung cancer

Objectives

This study evaluates the accuracy of radiomics in predicting lymph node metastasis in non-small cell lung cancer, which is crucial for patient management and prognosis.

Methods

Adhering to PRISMA and AMSTAR guidelines, we systematically reviewed literature from March 2012 to December 2023 using databases including PubMed, Web of Science, and Embase. Radiomics studies utilizing computed tomography (CT) and positron emission tomography (PET)/CT imaging were included. The quality of studies was appraised with QUADAS-2 and RQS tools, and the TRIPOD checklist assessed model transparency. Sensitivity, specificity, and AUC values were synthesized to determine diagnostic performance, with subgroup and sensitivity analyses probing heterogeneity and a Fagan plot evaluating clinical applicability.

Results

Our analysis incorporated 42 cohorts from 22 studies. CT-based radiomics demonstrated a sensitivity of 0.84 (95% CI: 0.79–0.88, p < 0.01) and specificity of 0.82 (95% CI: 0.75–0.87, p < 0.01), with an AUC of 0.90 (95% CI: 0.87–0.92), indicating no publication bias (p-value = 0.54 > 0.05). PET/CT radiomics showed a sensitivity of 0.82 (95% CI: 0.76–0.86, p < 0.01) and specificity of 0.86 (95% CI: 0.81–0.90, p < 0.01), with an AUC of 0.90 (95% CI: 0.87–0.93), with a slight publication bias (p-value = 0.03 < 0.05). Despite high clinical utility, subgroup analysis did not clarify heterogeneity sources, suggesting influences from possible factors like lymph node location and small subgroup sizes.

Conclusions

Radiomics models show accuracy in predicting lung cancer lymph node metastasis, yet further validation with larger, multi-center studies is necessary.

Clinical relevance statement

Radiomics models using CT and PET/CT imaging may improve the prediction of lung cancer lymph node metastasis, aiding personalized treatment strategies.

Research registration unique identifying number (UIN)

International Prospective Register of Systematic Reviews (PROSPERO), CRD42023494701. This study has been registered on the PROSPERO platform with a registration date of 18 December 2023. https://www.crd.york.ac.uk/prospero/

Key Points

The study explores radiomics for lung cancer lymph node metastasis detection, impacting surgery and prognosis.

Radiomics improves the accuracy of lymph node metastasis prediction in lung cancer.

Radiomics can aid in the prediction of lymph node metastasis in lung cancer and personalized treatment.