{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T21:57:49Z","timestamp":1792533469429,"version":"4.5.0"},"reference-count":24,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T00:00:00Z","timestamp":1781136000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate preoperative identification of high nodal burden (pathologic N2 or N3; hereafter N2+) is important in esophageal squamous cell carcinoma, but contrast-enhanced computed tomography criteria based mainly on nodal size and morphology have limited sensitivity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      In this retrospective multicohort study, 1,060 consecutive patients with esophageal squamous cell carcinoma who underwent preoperative contrast-enhanced computed tomography and curative-intent esophagectomy with lymphadenectomy were enrolled from two centers. Center A contributed a development cohort (\n                      <jats:italic>n<\/jats:italic>\n                      =\u202f612; train\/validation\/internal test, 428\/92\/92) and a temporally held-out cohort (\n                      <jats:italic>n<\/jats:italic>\n                      =\u202f238), and Center B contributed an external test cohort (\n                      <jats:italic>n<\/jats:italic>\n                      =\u202f210). A three-dimensional residual convolutional neural network encoder was pretrained on 3,200 unlabeled chest computed tomography examinations using masked-volume reconstruction and then fine-tuned on tumor-centered volumes comprising the primary tumor plus a 5-mm margin.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The self-supervised model achieved area under the receiver operating characteristic curve values of 0.881 (95% confidence interval, 0.793\u20130.955) in the internal test cohort, 0.860 (95% confidence interval, 0.810\u20130.903) in the temporal cohort, and 0.860 (95% confidence interval, 0.810\u20130.906) in the external cohort. In the external cohort, sensitivity and specificity at the main operating point were 0.581 and 0.845 for the self-supervised model versus 0.339 and 0.784 for the guideline-inspired comparator. Calibration also improved with self-supervised pretraining (Brier score, 0.148; expected calibration error, 0.053).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>A contrast-enhanced computed tomography-only self-supervised three-dimensional model predicted high pathologic nodal burden in esophageal squamous cell carcinoma with robust temporal and external validation and showed numerically higher performance than a transparent computed tomography-only comparator. Calibrated risk estimates may help prioritize additional nodal workup when staging resources are limited or routine computed tomography findings are equivocal.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1841260","type":"journal-article","created":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T04:41:06Z","timestamp":1781152866000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Self-supervised 3D deep learning on preoperative contrast-enhanced computed tomography for predicting high pathologic nodal burden in esophageal squamous cell carcinoma: temporal and external multicohort validation"],"prefix":"10.3389","volume":"13","author":[{"given":"Qian","family":"Li","sequence":"first","affiliation":[{"name":"Department of Thoracic Surgery, The Fourth Hospital of Hebei Medical University","place":["Shijiazhuang, China"]},{"name":"Department of Thoracic Surgery, Beijing Genertec Aerospace Hospital","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongxin","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Thoracic Surgery, Beijing Genertec Aerospace Hospital","place":["Beijing, China"]},{"name":"School of Automation and Intelligence, Beijing Jiaotong University","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Bai","sequence":"additional","affiliation":[{"name":"Department of Pharmacy, The Fourth Hospital of Hebei Medical University","place":["Shijiazhuang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinze","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Thoracic Surgery, The Fourth Hospital of Hebei Medical University","place":["Shijiazhuang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenkai","family":"Nie","sequence":"additional","affiliation":[{"name":"Department of Thoracic Surgery, Beijing Genertec Aerospace Hospital","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanxin","family":"Ren","sequence":"additional","affiliation":[{"name":"Department of Thoracic Surgery, Beijing Genertec Aerospace Hospital","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhantao","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Thoracic Surgery, Beijing Genertec Aerospace Hospital","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Medical Technology, Beijing Institute of Technology","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Fourth Hospital of Hebei Medical University","place":["Shijiazhuang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingting","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Radiology, Yichang Central People's Hospital","place":["Yichang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinbo","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Thoracic Surgery, The Fourth Hospital of Hebei Medical University","place":["Shijiazhuang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,6,11]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"992","DOI":"10.1016\/j.annonc.2022.07.003","article-title":"Oesophageal cancer: ESMO 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