Automated opportunistic cardiovascular risk assessment in non-small cell lung cancer patients on routine chest CT using an optimised nnU-net framework.
[BACKGROUND] Cardiovascular disease (CVD) and non-small cell lung cancer (NSCLC) are the global leading causes of overall and cancer-related deaths, respectively.
- Sensitivity 91.9%
- Specificity 70.8%
APA
Anifowose JO, Li Z, et al. (2026). Automated opportunistic cardiovascular risk assessment in non-small cell lung cancer patients on routine chest CT using an optimised nnU-net framework.. BMC medical imaging, 26(1). https://doi.org/10.1186/s12880-026-02252-z
MLA
Anifowose JO, et al.. "Automated opportunistic cardiovascular risk assessment in non-small cell lung cancer patients on routine chest CT using an optimised nnU-net framework.." BMC medical imaging, vol. 26, no. 1, 2026.
PMID
41772492
Abstract
[BACKGROUND] Cardiovascular 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.
[METHODS] We trained nnU-Net models on ungated, unenhanced chest CTs ( = 97) from Stanford AIMI dataset, and tested them on three mutually independent datasets: (1) ungated unenhanced CTs from AIMI ( = 95), (2) attenuation correction CTs from PET-CT scans of NSCLC patients at our institution (ICHNT, = 87; age 67.8 ± 10.1 years; M:F 174:113), and (3) CAC-negative scans from TCIA ( = 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.
[RESULTS] For 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.
[CONCLUSION] Our 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.
[SUPPLEMENTARY INFORMATION] The online version contains supplementary material available at 10.1186/s12880-026-02252-z.
[METHODS] We trained nnU-Net models on ungated, unenhanced chest CTs ( = 97) from Stanford AIMI dataset, and tested them on three mutually independent datasets: (1) ungated unenhanced CTs from AIMI ( = 95), (2) attenuation correction CTs from PET-CT scans of NSCLC patients at our institution (ICHNT, = 87; age 67.8 ± 10.1 years; M:F 174:113), and (3) CAC-negative scans from TCIA ( = 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.
[RESULTS] For 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.
[CONCLUSION] Our 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.
[SUPPLEMENTARY INFORMATION] The online version contains supplementary material available at 10.1186/s12880-026-02252-z.