ArticleBMC medical imaging2026
Automated opportunistic cardiovascular risk assessment in non-small cell lung cancer patients on routine chest CT using an optimised nnU-net framework.
Article in BMC medical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundCardiovascular disease (CVD) and non-small cell lung cancer (NSCLC) are the global leading causes of overall and cancer-related deaths, respectively. NSCLC patients have a higher CVD risk than the general population which is frequently underdiagnosed. Coronary artery calcification (CAC), a marker of CVD, is commonly detected on routinely acquired CT from NSCLC work-up but often not reported. We present an automated CAC assessment tool validated for NSCLC patients using a deep learning-based framework to provide a non-invasive CVD screening opportunity without incurring extra workload or radiation exposure.
methodsWe trained nnU-Net models on ungated, unenhanced chest CTs (n = 97) from Stanford AIMI dataset, and tested them on three mutually independent datasets: (1) ungated unenhanced CTs from AIMI (n = 95), (2) attenuation correction CTs from PET-CT scans of NSCLC patients at our institution (ICHNT, n = 87; age 67.8 ± 10.1 years; M:F 174:113), and (3) CAC-negative scans from TCIA (n = 50); and used the best performing model to produce CAC segmentations, post-processed with TotalSegmentator, to stratify patients into CVD risk groups, informing the need for dedicated cardiac clinic assessment.
resultsFor a CAC threshold of 100, the model achieved accuracy: 83.6%, sensitivity: 91.9%, specificity: 70.8%, positive predictive value (PPV): 82.9%, negative predictive value (NPV): 85.1%, F1-score: 0.87, kappa coefficient: 0.65 and Area Under the Receiver Operating Characteristic Curve (AUC) score of 0.899. For a threshold of 400, accuracy: 84.5%, sensitivity: 90.9%, specificity: 79.5%, PPV: 77.6%, NPV: 91.8%, F1-score: 0.84, and kappa coefficient: 0.69 as well as an AUC of 0.926.
conclusionOur optimised deep learning model can benefit NSCLC patients by providing CVD risk information from their routine CT scans which may not acted upon otherwise, thus enabling a practical opportunistic screening solution for these patients.
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