Dataset Information

The Singapore Liver Cancer Recurrence (SLICER) Score for relapse prediction in patients with surgically resected hepatocellular carcinoma.

ABSTRACT: Surgery is the primary curative option in patients with hepatocellular carcinoma (HCC). Current prognostic models for HCC are developed on datasets of primarily patients with advanced cancer, and may be less relevant to resectable HCC. We developed a postoperative nomogram, the Singapore Liver Cancer Recurrence (SLICER) Score, to predict outcomes of HCC patients who have undergone surgical resection.Records for 544 consecutive patients undergoing first-line curative surgery for HCC in one institution from 1992-2007 were reviewed, with 405 local patients selected for analysis. Freedom from relapse (FFR) was the primary outcome measure. An outcome-blinded modeling strategy including clustering, data reduction and transformation was used. We compared the performance of SLICER in estimating FFR with other HCC prognostic models using concordance-indices and likelihood analysis.A nomogram predicting FFR was developed, incorporating non-neoplastic liver cirrhosis, multifocality, preoperative alpha-fetoprotein level, Child-Pugh score, vascular invasion, tumor size, surgical margin and symptoms at presentation. Our nomogram outperformed other HCC prognostic models in predicting FFR by means of log-likelihood ratio statistics with good calibration demonstrated at 3 and 5 years post-resection and a concordance index of 0.69. Using decision curve analysis, SLICER also demonstrated superior net benefit at higher threshold probabilities.The SLICER score enables well-calibrated individualized predictions of relapse following curative HCC resection, and may represent a novel tool for biomarker research and individual counseling.

SUBMITTER: Ang SF

PROVIDER: S-EPMC4382157 | biostudies-other | 2015

REPOSITORIES: biostudies-other

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The Singapore Liver Cancer Recurrence (SLICER) Score for relapse prediction in patients with surgically resected hepatocellular carcinoma.

Ang Soo Fan SF Ng Elizabeth Shu-Hui ES Li Huihua H Ong Yu-Han YH Choo Su Pin SP Ngeow Joanne J Toh Han Chong HC Lim Kiat Hon KH Yap Hao Yun HY Tan Chee Kiat CK Ooi London Lucien Peng Jin LL Cheow Peng Chung PC Chung Alexander Yaw Fui AY Chow Pierce Kah Hoe PK Foo Kian Fong KF Tan Min-Han MH

PloS one 20150401 4

<h4>Background and aims</h4>Surgery is the primary curative option in patients with hepatocellular carcinoma (HCC). Current prognostic models for HCC are developed on datasets of primarily patients with advanced cancer, and may be less relevant to resectable HCC. We developed a postoperative nomogram, the Singapore Liver Cancer Recurrence (SLICER) Score, to predict outcomes of HCC patients who have undergone surgical resection.<h4>Methods</h4>Records for 544 consecutive patients undergoing first ...[more]

PMID: 25830231

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Project description:BackgroundThere is a growing need for new improved classifiers of prognosis in hepatocellular carcinoma (HCC) patients to stratify them effectively.MethodsA deep learning model was developed on a total of 1118 patients from 4 independent cohorts. A nucleus map set (n = 120) was used to train U-net to capture the nuclear architecture. The training set (n = 552) included HCC patients that had been treated by resection. The liver transplantation (LT) set (n = 144) contained patients with HCC that had been treated by LT. The train set and its nuclear architectural information extracted by U-net were used to train the MobileNet V2-based classifier (MobileNetV2_HCC_class). The classifier was then independently tested on the LT set and externally validated on the TCGA set (n = 302). The primary outcome was recurrence free survival (RFS).ResultsThe MobileNetV2_HCC_class was a strong predictor of RFS in both LT set and TCGA set. The classifier provided a hazard ratio of 3.44 (95% CI 2.01-5.87, p < 0.001) for high risk versus low risk in the LT set, and 2.55 (95% CI 1.64-3.99, p < 0.001) when known prognostic factors, remarkable in univariable analyses on the same cohort, were adjusted. The MobileNetV2_HCC_class maintained a relatively higher discriminatory power [time-dependent accuracy and area under curve (AUC)] than other factors after LT or resection in the independent validation set (LT and TCGA set). Net reclassification improvement (NRI) analysis indicated MobileNetV2_HCC_class exhibited better net benefits for the Stage_AJCC beyond other independent factors. A pathological review demonstrated that tumoral areas with the highest recurrence predictability featured the following features: the presence of stroma, a high degree of cytological atypia, nuclear hyperchromasia, and a lack of immune cell infiltration.ConclusionA prognostic classifier for clinical purposes had been proposed based on the use of deep learning on histological slides from HCC patients. This classifier assists in refining the prognostic prediction of HCC patients and identifies patients who have been benefited from more intensive management.

Project description:Background:Hepatocellular carcinoma (HCC) is one of the most aggressive malignancies with poor prognosis. There are many selectable treatments with good prognosis in Barcelona Clinic Liver Cancer- (BCLC-) 0, A, and B HCC patients, but the most crucial factor affecting survival is the high recurrence rate after treatments. Therefore, it is of great significance to predict the recurrence of BCLC-0, BCLC-A, and BCLC-B HCC patients. Aim:To develop a gene signature to enhance the prediction of recurrence among HCC patients. Materials and Methods:The RNA expression data and clinical data of HCC patients were obtained from the Gene Expression Omnibus (GEO) database. Univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were conducted to screen primarily prognostic biomarkers in GSE14520. Multivariate Cox regression analysis was introduced to verify the prognostic role of these genes. Ultimately, 5 genes were demonstrated to be related with the recurrence of HCC patients and a gene signature was established. GSE76427 was adopted to further verify the accuracy of gene signature. Subsequently, a nomogram based on gene signature was performed to predict recurrence. Gene functional enrichment analysis was conducted to investigate the potential biological processes and pathways. Results:We identified a five-gene signature which performs a powerful predictive ability in HCC patients. In the training set of GSE14520, area under the curve (AUC) for the five-gene predictive signature of 1, 2, and 3 years were 0.813, 0.786, and 0.766. Then, the relative operating characteristic (ROC) curves of five-gene predictive signature were verified in the GSE14520 validation set, the whole GSE14520, and GSE76427, showed good performance. A nomogram comprising the five-gene signature was built so as to show a good accuracy for predicting recurrence-free survival of HCC patients. Conclusion:The novel five-gene signature showed potential feasibility of recurrence prediction for early-stage HCC.

Dataset Information

The Singapore Liver Cancer Recurrence (SLICER) Score for relapse prediction in patients with surgically resected hepatocellular carcinoma.

Publications

The Singapore Liver Cancer Recurrence (SLICER) Score for relapse prediction in patients with surgically resected hepatocellular carcinoma.

Similar Datasets

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