{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T22:32:37Z","timestamp":1787697157768,"version":"build-2784847793"},"reference-count":18,"publisher":"Oxford University Press (OUP)","issue":"7","license":[{"start":{"date-parts":[[2023,8,12]],"date-time":"2023-08-12T00:00:00Z","timestamp":1691798400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/100000054","name":"National Cancer Institute at the National Institutes of Health","doi-asserted-by":"crossref","award":["R01CA184476"],"award-info":[{"award-number":["R01CA184476"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100006235","name":"Lawrence Berkeley National Laboratory","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006235","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100005595","name":"University of California","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100005595","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000015","name":"Department of Energy","doi-asserted-by":"publisher","award":["DE AC02-05CH11231"],"award-info":[{"award-number":["DE AC02-05CH11231"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000015","name":"Department of Energy","doi-asserted-by":"publisher","award":["PID2020-118527RB-I00"],"award-info":[{"award-number":["PID2020-118527RB-I00"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000015","name":"Department of Energy","doi-asserted-by":"publisher","award":["MCIN\/AEI\/10.13039\/501100011039"],"award-info":[{"award-number":["MCIN\/AEI\/10.13039\/501100011039"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000015","name":"Department of Energy","doi-asserted-by":"publisher","award":["PDC2021-121735-I00"],"award-info":[{"award-number":["PDC2021-121735-I00"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000015","name":"Department of Energy","doi-asserted-by":"publisher","award":["MCIN\/AEI\/10.13039\/501100011039"],"award-info":[{"award-number":["MCIN\/AEI\/10.13039\/501100011039"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]},{"name":"European Union Next Generation EU\/PRTR"},{"name":"Regional Government of Castile and Le\u00f3n","award":["CSI234P18"],"award-info":[{"award-number":["CSI234P18"]}]},{"name":"Regional Government of Castile and Le\u00f3n","award":["CSI144P20"],"award-info":[{"award-number":["CSI144P20"]}]},{"name":"Regional Government of Castile and Le\u00f3n","award":["GRS2549\/A\/22"],"award-info":[{"award-number":["GRS2549\/A\/22"]}]},{"DOI":"10.13039\/501100004587","name":"Instituto de Salud Carlos III","doi-asserted-by":"publisher","award":["PI21\/2017"],"award-info":[{"award-number":["PI21\/2017"]}],"id":[{"id":"10.13039\/501100004587","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Programa de Intensificaci\u00f3n","award":["INT20\/00074"],"award-info":[{"award-number":["INT20\/00074"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,6,25]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Risk stratification of cutaneous squamous cell carcinoma (cSCC) is essential for managing patients.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Objectives<\/jats:title>\n                  <jats:p>To determine if artificial intelligence and machine learning might help to stratify patients with cSCC by risk using more than solely clinical and histopathological factors.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>We retrieved a retrospective cohort of 104 patients whose cSCCs had been excised with clear margins. Clinical and histopathological risk factors were evaluated. Haematoxylin and eosin-stained slides were scanned and analysed by an algorithm based on the stacked predictive sparse decomposition technique. Cellular morphometric biomarkers (CMBs) were identified via machine learning and used to derive a cellular morphometric risk score (CMRS) that classified cSCCs into clusters of differential prognoses. Concordance analysis, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy were calculated and compared with results obtained with the Brigham and Women\u2019s Hospital (BWH) staging system. The performance of the combination of the BWH staging system and the CMBs was also analysed.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>There were no differences among the CMRS groups in terms of clinical and histopathological risk factors and T-stage assignment, but there were significant differences in prognosis. Combining the CMRS with BWH staging systems increased distinctiveness and improved prognostic performance. C-indices were 0.91 local recurrence and 0.91 for nodal metastasis when combining the two approaches. The NPV was 94.41% and 96.00%, the PPV was 36.36% and 41.67%, and accuracy reached 86.75% and 89.16%, respectively, with the combined approach.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>CMRS is helpful for cSCC risk stratification beyond classic clinical and histopathological risk features. Combining the information from the CMRS and the BWH staging system offers outstanding prognostic performance for patients with high-risk cSCC.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/ced\/llad264","type":"journal-article","created":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T12:12:14Z","timestamp":1691755934000},"page":"692-698","source":"Crossref","is-referenced-by-count":11,"title":["Artificial intelligence-empowered cellular morphometric risk score improves prognostic stratification of cutaneous squamous cell carcinoma"],"prefix":"10.1093","volume":"49","author":[{"given":"Manuel J","family":"P\u00e9rez-Baena","sequence":"first","affiliation":[{"name":"Instituto de Biolog\u00eda Molecular y Celular del C\u00e1ncer, Universidad de Salamanca\/CSIC , Salamanca , Spain"},{"name":"Instituto de Investigaci\u00f3n Biom\u00e9dica de Salamanca"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian-Hua","family":"Mao","sequence":"additional","affiliation":[{"name":"Berkeley Biomedical Data Science Center"},{"name":"Biological Systems and Engineering Division; Lawrence Berkeley National Laboratory , Berkeley, CA , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jes\u00fas","family":"P\u00e9rez-Losada","sequence":"additional","affiliation":[{"name":"Instituto de Biolog\u00eda Molecular y Celular del C\u00e1ncer, Universidad de Salamanca\/CSIC , Salamanca , Spain"},{"name":"Instituto de Investigaci\u00f3n Biom\u00e9dica de Salamanca"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"\u00c1ngel","family":"Santos-Briz","sequence":"additional","affiliation":[{"name":"Instituto de Investigaci\u00f3n Biom\u00e9dica de Salamanca"},{"name":"Servicio de Anatom\u00eda 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