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This study evaluated radiologist-defined semantic features for differentiation of second primary lung cancer (SPLC) versus lung metastasis (LM).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>651 single-timepoint, pre-treatment CT thorax scans from the multicentre retrospective AI-SONAR biomarker study (IRAS 331656 REC 23\/NE\/0151) were divided for review by nine thoracic oncology radiologists to evaluate eight semantic features. Logistic regression analysis was undertaken to identify significant features and a developed \u2018Second Malignancy Aetiology Recognition Tool\u2019 model (SMART) was compared to real-world clinical reader performance using McNemar\u2019s test.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      649 scans were technically usable, 299 SPLC and 350 LM. Emphysema (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.0001, OR 0.20 [95% CI 0.14\u20130.29]), irregular contour (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.0001, OR 0.31 [95% CI 0.20\u20130.48]) and spiculation (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.013, OR 0.51 [95% CI 0.30\u20130.89]) were more prevalent in SPLC (OR\u2009&lt;\u20091 indicates association with SPLC). Peripheral lung distribution (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.003, OR 1.80 [95% CI 1.20\u20132.68]) was more common in LM (OR\u2009&gt;\u20091 indicates metastasis). SMART model AUC was 0.81 (95% CI 0.78\u20130.84), LM classification accuracy 75% vs 69% by radiology reader and McNemar\n                      <jats:italic>p<\/jats:italic>\n                      -value\u2009&lt;\u20090.01 for comparative accuracy. 550\/649 cases were predicted SPLC or LM by radiologists, in which the SMART model LM classification accuracy was 74% vs 77% by reader and McNemar\n                      <jats:italic>p<\/jats:italic>\n                      -value 0.20.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>In new lesions after prior treated cancer, radiologist readers called SPLC more often than LM. The SMART model performed comparably with expert thoracic radiologists in the diagnosis of LM.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Question<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Differentiating the malignant aetiology of indeterminate lung lesions after prior cancer presents a growing diagnostic challenge. The literature and nodule guidelines are sparse for this setting.<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Findings<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>A SMART model derived from semantic CT imaging features correctly classified malignant lung lesions as metastasis or lung cancer, more often than thoracic radiologists.<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Clinical relevance<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>The SMART model could improve the stratification of malignant new lung lesions after prior cancer. This may lead to earlier diagnosis and optimise patient management, treatment selection and downstream outcomes.<\/jats:italic>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00330-026-12557-w","type":"journal-article","created":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T14:37:34Z","timestamp":1778078254000},"page":"6969-6980","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Semantic CT features and differentiation model: new primary lung cancer versus metastasis after previous malignancy"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5722-0034","authenticated-orcid":false,"given":"Hardeep Singh","family":"Kalsi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kristofer","family":"Linton-Reid","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changhyun","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mitchell","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Victoria","family":"Crowe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Esubalew","family":"Alemu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samir","family":"Mahboobani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Gibeon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Procter","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohsen","family":"Hajhosseiny","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cara","family":"Owens","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emily C.","family":"Bartlett","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nuria","family":"Porta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thesha","family":"Thavaraja","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simon","family":"Doran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anand","family":"Devaraj","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bhupinder","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arjun","family":"Nair","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric O.","family":"Aboagye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard W.","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,6]]},"reference":[{"key":"12557_CR1","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3322\/caac.21660","volume":"71","author":"H Sung","year":"2021","unstructured":"Sung H, Ferlay J, Siegel RL et al (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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Lee.","order":1,"name":"Ethics","label":"Guarantor","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors of this manuscript declare relationships with the following companies: Dr Kalsi is funded by the Royal Marsden and ICR National Institute of Health and Care Research (NIHR) Biomedical Research Centre, and Royal Marsden Cancer Charity. Dr Lee is funded by the Royal Marsden and ICR National Institute of Health and Care Research (NIHR) Biomedical Research Centre, and Royal Marsden Cancer Charity. Dr Lee\u2019s institution receives compensation for time spent in a secondment role for the NHS England lung health check programme and as National Specialty Lead for the NIHR. He has received research funding from CRUK, Innovate UK (co-funded by GE Healthcare, Roche Diagnostics, Optellum, Elliptica, and RNA Guardian); SBRI (including as a co-applicant with QURE.AI), RM Partners Cancer Alliance; and NIHR (including co-applicant in grants with Optellum). He has received honoraria, speaker\/advisory fees, and\/or hospitality\/travel expenses from CRUK, Roche Diagnostics, Johnson & Johnson, Guardant, AstraZeneca, and King Faisal Hospital, Saudi Arabia. He also undertakes medical private practice. Professor Aboagye is funded by the Imperial College London Biomedical Research Centre (ICL-BRC), Experimental Cancer Medicines Centre (ECMC) and National Cancer Imaging Translational Accelerator Consortium (NCITA). Professor Aboagye also acknowledges the Medical Research Council (MRC). Professor Devaraj is employed by and has stocks in Brainomix. He receives consulting fees from Roche and Boehringer Ingelheim. Dr Nair receives research grants from the Department of Health\u2019s NIHR Biomedical Research Centre and GRAIL. He has received consulting fees from Aidence BV, Faculty Science Limited and MSD. He has received a travel bursary from Takeda. He participates on advisory boards for Aidence BV and Faculty Science Limited. He has leadership roles within the British Society of Thoracic Imaging, the British Lung Foundation and the NHS England Targeted Lung Health Checks Programme.","order":2,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Dr Nuria Porta kindly provided statistical advice for this manuscript.","order":3,"name":"Ethics","label":"Statistics and biometry","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Written informed consent was waived by the Institutional Review Board.","order":4,"name":"Ethics","label":"Informed consent","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Institutional Review Board approval was obtained.","order":5,"name":"Ethics","label":"Ethical approval","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Not applicable.","order":6,"name":"Ethics","label":"Study subjects or cohorts overlap","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"order":7,"name":"Ethics","label":"Methodology","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}}]}}