{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T09:21:40Z","timestamp":1767172900638,"version":"build-2238731810"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,12,23]],"date-time":"2022-12-23T00:00:00Z","timestamp":1671753600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,12,23]],"date-time":"2022-12-23T00:00:00Z","timestamp":1671753600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Hospital da luz lisboa","award":["ID LH.INV.F2019027"],"award-info":[{"award-number":["ID LH.INV.F2019027"]}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["UIDB\/00006\/2020"],"award-info":[{"award-number":["UIDB\/00006\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cancer Imaging"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>To construct a model based on magnetic resonance imaging (MRI) features and histological and clinical variables for the prediction of pathology-detected extracapsular extension (pECE) in patients with prostate cancer (PCa).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We performed a prospective 3\u2009T MRI study comparing the clinical and MRI data on pECE obtained from patients treated using robotic-assisted radical prostatectomy (RARP) at our institution. The covariates under consideration were prostate-specific antigen (PSA) levels, the patient\u2019s age, prostate volume, and MRI interpretative features for predicting pECE based on the Prostate Imaging\u2013Reporting and Data System (PI-RADS) version 2.0 (v2), as well as tumor capsular contact length (TCCL), length of the index lesion, and prostate biopsy Gleason score (GS). Univariable and multivariable logistic regression models were applied to explore the statistical associations and construct the model. We also recruited an additional set of participants\u2014which included 59 patients from external institutions\u2014to validate the model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The study participants included 185 patients who had undergone RARP at our institution, 26% of whom were pECE+ (i.e., pECE positive). Significant predictors of pECE+ were TCCL, capsular disruption, measurable ECE on MRI, and a GS of \u22657(4\u2009+\u20093) on a prostate biopsy. The strongest predictor of pECE+ is measurable ECE on MRI, and in its absence, a combination of TCCL and prostate biopsy GS was significantly effective for detecting the patient\u2019s risk of being pECE+. Our predictive model showed a satisfactory performance at distinguishing between patients with pECE+ and patients with pECE\u2212, with an area under the ROC curve (AUC) of 0.90 (86.0\u201395.8%), high sensitivity (86%), and moderate specificity (70%).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our predictive model, based on consistent MRI features (i.e., measurable ECE and TCCL) and a prostate biopsy GS, has satisfactory performance and sufficiently high sensitivity for predicting pECE+. Hence, the model could be a valuable tool for surgeons planning preoperative nerve sparing, as it would reduce positive surgical margins.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s40644-022-00509-8","type":"journal-article","created":{"date-parts":[[2022,12,22]],"date-time":"2022-12-22T22:02:47Z","timestamp":1671746567000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Early biomarkers of extracapsular extension of prostate cancer using MRI-derived semantic features"],"prefix":"10.1186","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0095-5662","authenticated-orcid":false,"given":"Adalgisa","family":"Guerra","sequence":"first","affiliation":[]},{"given":"Filipe Caseiro","family":"Alves","sequence":"additional","affiliation":[]},{"given":"Kris","family":"Maes","sequence":"additional","affiliation":[]},{"given":"Steven","family":"Joniau","sequence":"additional","affiliation":[]},{"given":"Jo\u00e3o","family":"Cassis","sequence":"additional","affiliation":[]},{"given":"Rui","family":"Maio","sequence":"additional","affiliation":[]},{"given":"Mar\u00edlia","family":"Cravo","sequence":"additional","affiliation":[]},{"given":"Helena","family":"Mouri\u00f1o","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,12,23]]},"reference":[{"key":"509_CR1","unstructured":"Global Cancer Observatory [Internet]. 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Written consents were obtained from all subjects involved in the study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no conflict of interest.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"74"}}