{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T23:58:42Z","timestamp":1793404722084,"version":"4.5.0"},"reference-count":21,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2021,12,23]],"date-time":"2021-12-23T00:00:00Z","timestamp":1640217600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100008237","name":"Department of Surgery","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100008237","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Tokyo Medical University from FUJIFILM Corporation"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,3,24]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p\/>\n               <jats:sec>\n                  <jats:title>OBJECTIVES<\/jats:title>\n                  <jats:p>Indications of limited resection, such as segmentectomy, have recently been reported for patients with solid-predominant lung cancers \u22642\u2009cm. This study aims to identify unfavourable prognostic factors using three-dimensional imaging analysis with artificial intelligence (AI) technology.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>METHODS<\/jats:title>\n                  <jats:p>A total of 157 patients who had clinical N0 non-small cell lung cancer with a radiological size \u22642\u2009cm, and a consolidation tumour ratio &amp;gt; 0.5, who underwent anatomical lung resection between 2011 and 2017 were enrolled. To evaluate the three-dimensional structure, the ground-glass nodule\/Solid Automatic Identification AI software Beta Version (AI software; Fujifilm Corporation, Japan) was used.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>RESULTS<\/jats:title>\n                  <jats:p>Maximum standardized uptake value (SUVmax) and solid-part volume measured by AI software (AI-SV) showed significant differences between the 139 patients with adenocarcinoma and the 18 patients with non-adenocarcinoma. Among the adenocarcinoma patients, 42 patients (30.2%) were found to be pathological upstaging. Multivariable analysis demonstrated that high SUVmax, high carcinoembryonic antigen level and high AI-SV were significant prognostic factors for recurrence-free survival (RFS; P\u2009&amp;lt;\u20090.05). The 5-year RFS was compared between patients with tumours showing high SUVmax and those showing low SUVmax (67.7% vs 95.4%, respectively, P\u2009&amp;lt;\u20090.001). The 5-year RFS was 91.0% in patients with small AI-SV and 68.1% in those with high AI-SV (P\u2009=\u20090.001).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>CONCLUSIONS<\/jats:title>\n                  <jats:p>High AI-SV, high SUVmax and abnormal carcinoembryonic antigen level were unfavourable prognostic factors of patients with solid-predominant lung adenocarcinoma with a radiological size \u22642\u2009cm. Our results suggest that lobectomy should be preferred to segmentectomy for patients with these prognostic factors.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/ejcts\/ezab541","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T20:12:29Z","timestamp":1638389549000},"page":"751-760","source":"Crossref","is-referenced-by-count":19,"title":["Artificial intelligence analysis of three-dimensional imaging data derives factors associated with postoperative recurrence in patients with radiologically solid-predominant small-sized lung cancers"],"prefix":"10.1093","volume":"61","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3187-9970","authenticated-orcid":false,"given":"Yujin","family":"Kudo","sequence":"first","affiliation":[{"name":"Department of Surgery, Tokyo Medical University, Tokyo, 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