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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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We trained nnU-Net models on ungated, unenhanced chest CTs (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200997) from Stanford AIMI dataset, and tested them on three mutually independent datasets: (1) ungated unenhanced CTs from AIMI (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200995), (2) attenuation correction CTs from PET-CT scans of NSCLC patients at our institution (ICHNT,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200987; age 67.8\u2009\u00b1\u200910.1 years; M:F 174:113), and (3) CAC-negative scans from TCIA (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200950); 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.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12880-026-02252-z","type":"journal-article","created":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T02:16:32Z","timestamp":1772504192000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated opportunistic cardiovascular risk assessment in non-small cell lung cancer patients on routine chest CT using an optimised nnU-net framework"],"prefix":"10.1186","volume":"26","author":[{"given":"Jubril Olayinka","family":"Anifowose","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zechen","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Girija","family":"Agarwal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric O.","family":"Aboagye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Declan P.","family":"O\u2019Regan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ben","family":"Ariff","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susan J.","family":"Copley","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mitchell","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,3]]},"reference":[{"key":"2252_CR1","unstructured":"World Health Organization. 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