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Utilizing Machine Learning for Pre- and Postoperative Assessment of Patients Undergoing Resection for BCLC-0, A and B Hepatocellular Carcinoma: Implications for Resection Beyond the BCLC Guidelines

2020-03Journal article Open access
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dc.contributor.authorTsilimigras, D
dc.contributor.authorMehta, R
dc.contributor.authorMoris, D
dc.contributor.authorSahara, K
dc.contributor.authorBagante, F
dc.contributor.authorParedes, A
dc.contributor.authorFarooq, A
dc.contributor.authorRatti, F
dc.contributor.authorPinto Marques, H
dc.contributor.authorSilva, S
dc.contributor.authorSoubrane, O
dc.contributor.authorLam, V
dc.contributor.authorPoultsides, G
dc.contributor.authorPopescu, I
dc.contributor.authorGrigorie, R
dc.contributor.authorAlexandrescu, S
dc.contributor.authorMartel, G
dc.contributor.authorWorkneh, A
dc.contributor.authorGuglielmi, A
dc.contributor.authorHugh, T
dc.contributor.authorAldrighetti, L
dc.contributor.authorEndo, I
dc.contributor.authorPawlik, T
dc.date.accessioned2021-06-15T10:23:09Z
dc.date.available2021-06-15T10:23:09Z
dc.date.issued2020-03
dc.description.abstractBackground: There is an ongoing debate about expanding the resection criteria for hepatocellular carcinoma (HCC) beyond the Barcelona Clinic Liver Cancer (BCLC) guidelines. We sought to determine the factors that held the most prognostic weight in the pre- and postoperative setting for each BCLC stage by applying a machine learning method. Methods: Patients who underwent resection for BCLC-0, A and B HCC between 2000 and 2017 were identified from an international multi-institutional database. A Classification and Regression Tree (CART) model was used to generate homogeneous groups of patients relative to overall survival (OS) based on pre- and postoperative factors. Results: Among 976 patients, 63 (6.5%) had BCLC-0, 745 (76.3%) had BCLC-A, and 168 (17.2%) had BCLC-B HCC. Five-year OS among BCLC-0/A and BCLC-B patients was 64.2% versus 50.2%, respectively (p = 0.011). The preoperative CART model selected α-fetoprotein (AFP) and Charlson comorbidity score (CCS) as the first and second most important preoperative factors of OS among BCLC-0/A patients, whereas radiologic tumor burden score (TBS) was the best predictor of OS among BCLC-B patients. The postoperative CART model revealed lymphovascular invasion as the best postoperative predictor of OS among BCLC-0/A patients, whereas TBS remained the best predictor of long-term outcomes among BCLC-B patients in the postoperative setting. On multivariable analysis, pathologic TBS independently predicted worse OS among BCLC-0/A (hazard ratio [HR] 1.04, 95% confidence interval [CI] 1.02-1.07) and BCLC-B patients (HR 1.13, 95% CI 1.06-1.19) undergoing resection. Conclusion: Prognostic stratification of patients undergoing resection for HCC within and beyond the BCLC resection criteria should include assessment of AFP and comorbidities for BCLC-0/A patients, as well as tumor burden for BCLC-B patients.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationAnn Surg Oncol. 2020 Mar;27(3):866-874.pt_PT
dc.identifier.doi10.1245/s10434-019-08025-zpt_PT
dc.identifier.urihttp://hdl.handle.net/10400.17/3727
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringerpt_PT
dc.subjectAgedpt_PT
dc.subjectBiomarkers, Tumorpt_PT
dc.subjectCarcinoma, Hepatocellularpt_PT
dc.subjectFemalept_PT
dc.subjectFollow-Up Studiespt_PT
dc.subjectHepatectomypt_PT
dc.subjectHumanspt_PT
dc.subjectLiver Neoplasmspt_PT
dc.subjectMalept_PT
dc.subjectMiddle Agedpt_PT
dc.subjectNeoplasm Stagingpt_PT
dc.subjectPostoperative Complicationspt_PT
dc.subjectPractice Guidelines as Topicpt_PT
dc.subjectRetrospective Studiespt_PT
dc.subjectSurvival Ratept_PT
dc.subjectTumor Burdenpt_PT
dc.subjectMachine Learningpt_PT
dc.subjectPreoperative Carept_PT
dc.subjectHCC CIRpt_PT
dc.titleUtilizing Machine Learning for Pre- and Postoperative Assessment of Patients Undergoing Resection for BCLC-0, A and B Hepatocellular Carcinoma: Implications for Resection Beyond the BCLC Guidelinespt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage874pt_PT
oaire.citation.issue3pt_PT
oaire.citation.startPage866pt_PT
oaire.citation.titleAnnals of Surgical Oncologypt_PT
oaire.citation.volume27pt_PT
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT

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