Dataset Information

Assessment of a Deep Learning Model to Predict Hepatocellular Carcinoma in Patients With Hepatitis C Cirrhosis.

ABSTRACT: Importance:Deep learning, a family of machine learning models that use artificial neural networks, has achieved great success at predicting outcomes in nonmedical domains. Objective:To examine whether deep learning recurrent neural network (RNN) models that use raw longitudinal data extracted directly from electronic health records outperform conventional regression models in predicting the risk of developing hepatocellular carcinoma (HCC). Design, Setting, and Participants:This prognostic study included 48?151 patients with hepatitis C virus (HCV)-related cirrhosis in the national Veterans Health Administration who had at least 3 years of follow-up after the diagnosis of cirrhosis. Patients were identified by having at least 1 positive HCV RNA test between January 1, 2000, to January 1, 2016, and were followed up from the diagnosis of cirrhosis to January 1, 2019, for the development of incident HCC. A total of 3 models predicting HCC during a 3-year period were developed and compared, as follows: (1) logistic regression (LR) with cross-sectional inputs (cross-sectional LR); (2) LR with longitudinal inputs (longitudinal LR); and (3) RNN with longitudinal inputs. Data analysis was conducted from April 2018 to August 2020. Exposures:Development of HCC. Main Outcomes and Measures:Area under the receiver operating characteristic curve, area under the precision-recall curve, and Brier score. Results:During a mean (SD) follow-up of 11.6 (5.0) years, 10?741 of 48?151 patients (22.3%) developed HCC (annual incidence, 3.1%), and a total of 52?983 samples (51?948 [98.0%] from men) were collected. Patients who developed HCC within 3 years were older than patients who did not (mean [SD] age, 58.2 [6.6] years vs 56.9 [6.9] years). RNN models had superior mean (SD) area under the receiver operating characteristic curve (0.759 [0.009]) and mean (SD) Brier score (0.136 [0.003]) than cross-sectional LR (0.689 [0.009] and 0.149 [0.003], respectively) and longitudinal LR (0.682 [0.007] and 0.150 [0.003], respectively) models. Using the RNN model, the samples with the mean (SD) highest 51% (1.5%) of HCC risk, in which 80% of all HCCs occurred, or the mean (SD) highest 66% (1.2%) of HCC risk, in which 90% of all HCCs occurred, could potentially be targeted. Among samples from patients who achieved sustained virologic response, the performance of the RNN models was even better (mean [SD] area under receiver operating characteristic curve, 0.806 [0.025]; mean [SD] Brier score, 0.117 [0.007]). Conclusions and Relevance:In this study, deep learning RNN models outperformed conventional LR models, suggesting that RNN models could be used to identify patients with HCV-related cirrhosis with a high risk of developing HCC for risk-based HCC outreach and surveillance strategies.

SUBMITTER: Ioannou GN

PROVIDER: S-EPMC7489819 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Assessment of a Deep Learning Model to Predict Hepatocellular Carcinoma in Patients With Hepatitis C Cirrhosis.

Ioannou George N GN Tang Weijing W Beste Lauren A LA Tincopa Monica A MA Su Grace L GL Van Tony T Tapper Elliot B EB Singal Amit G AG Zhu Ji J Waljee Akbar K AK

JAMA network open 20200901 9

<h4>Importance</h4>Deep learning, a family of machine learning models that use artificial neural networks, has achieved great success at predicting outcomes in nonmedical domains.<h4>Objective</h4>To examine whether deep learning recurrent neural network (RNN) models that use raw longitudinal data extracted directly from electronic health records outperform conventional regression models in predicting the risk of developing hepatocellular carcinoma (HCC).<h4>Design, setting, and participants</h4 ...[more]

PMID: 32870314

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Project description:Background & aimsPersonalised risk prediction of the development of hepatocellular carcinoma (HCC) among patients with liver cirrhosis on potent antiviral therapy is important for targeted screening and individualised intervention. This study aimed to develop and validate a new model for risk prediction of HCC development based on deep learning, and to compare it with previously reported risk models.MethodsA novel deep-learning-based model was developed from a cohort of 424 patients with HBV-related cirrhosis on entecavir therapy with 2 residual blocks, including 7 layers of a neural network, and it was validated using an independent external cohort (n = 316). The deep-learning-based model was compared to 6 previously reported models (platelet, age, and gender-hepatitis B score [PAGE-B], Chinese University HCC score [CU-HCC], HCC-Risk Estimating Score in CHB patients Under Entecavir [HCC-RESCUE], age, diabetes, race, etiology of cirrhosis, sex, and severity HCC score [ADRESS-HCC], modified PAGE-B score [mPAGE], and Toronto HCC risk index [THRI]) using Harrell's concordance (c)-index.ResultsDuring a median 5.2 yr of follow-up (inter-quartile range 2.8-6.9 yr), 86 patients (20.3%) developed HCC. The deep-learning-based model had a Harrell's c-index of 0.719 in the derivation cohort and 0.782 in the validation cohort. Goodness of fit was confirmed by the Hosmer-Lemeshow test (p >0.05). Moreover, this model in the validation cohort had the highest c-index among the 6 previously reported models: PAGE-B (0.570), CU-HCC (0.548), HCC-RESCUE (0.577), ADRESS-HCC (0.551), mPAGE (0.598), and THRI (0.587) (all p <0.001). The misclassification rate of this model was 23.7% (model accuracy: 76.3%) in the validation group.ConclusionsThe deep-learning-based model had better performance than the previous models for predicting the HCC risk in patients with HBV-related cirrhosis on potent antivirals.Lay summaryFor early detection of hepatocellular carcinoma, it is important to maintain regular surveillance. However, there is currently no standard prediction model for risk stratification that can be used to establish a personalised surveillance strategy. We develop and validate a deep-learning-based model that showed better performance than previous models.

Project description:Serum levels of ?-fetoprotein (AFP) are influenced not only by the presence of hepatocellular carcinoma (HCC), but also by the underlying severity and activity of liver disease, which is reflected by liver function tests. We constructed an AFP-based algorithm that included these factors to identify patients at risk for HCC, and tested its predictive ability in a large set of patients with cirrhosis.We used the national Department of Veterans Affairs Hepatitis C Virus Clinical Case Registry to identify patients with cirrhosis, results from at least 1 AFP test, and 6 months of follow-up. Our algorithm included data on age; levels of aspartate aminotransferase, alanine aminotransferase (ALT), alkaline phosphatase, total bilirubin, albumin, creatinine, and hemoglobin; prothrombin time; and numbers of platelets and white cells. We examined the operating characteristics (calibration, discrimination, predictive values) of several different algorithms for identification of patients who would develop HCC within 6 months of the AFP test. We assessed our final model in the development and validation subsets.We identified 11,721 patients with hepatitis C virus-related cirrhosis in whom 35,494 AFP tests were performed, and 987 patients developed HCC. A predictive model that included data on levels of AFP, ALT, and platelets, along with age at time of AFP test (and interaction terms between AFP and ALT, and AFP and platelets), best discriminated between patients who did and did not develop HCC. Using this AFP-adjusted model, the predictive accuracy increased at different AFP cutoffs compared with AFP alone. At any given AFP value, low numbers of platelets and ALT and older age were associated with increased risk of HCC, and high levels of ALT and normal/high numbers of platelets were associated with low risk for HCC. For example, the probabilities of HCC, based only on 20 ng/mL and 120 ng/mL AFP, were 3.5% and 11.4%, respectively. However, patients with the same AFP values (20 ng/mL and 120 ng/mL) who were 70 years old, with ALT levels of 40 IU/mL and platelet counts of 100,000, had probabilities of developing HCC of 8.1% and 29.0%, respectively.We developed and validated an algorithm based on levels of AFP, platelets, and ALT, along with age, which increased the predictive value for identifying patients with hepatitis C virus-associated cirrhosis likely to develop HCC within 6 months. If validated in other patient groups, this model would have immediate clinical applicability.

Project description:BackgroundModels predicting future macrovascular invasion in hepatocellular carcinoma are constructed to assist timely interventions.MethodsA total of 366 HCC cases were retrospectively collected from five Chinese hospitals between April 2007 and November 2016: the training dataset comprised 281 patients from four hospitals; the external validation dataset comprised 85 patients from another hospital. Multi-task deep learning network-based models were constructed to predict future macrovascular invasion. The discrimination, calibration, and decision curves were compared to identify the best model. We compared the time to macrovascular invasion and overall survival using the best model and related image heterogeneity scores (H-score). Then, we determined the need for a segmentation subnet or the replacement deep learning algorithm by logistic regression in screening clinical/radiological factors. Finally, an applet was constructed for future application.FindingsThe best model combined clinical/radiological factors and radiomic features. It achieved best discrimination (areas under the curve: 0·877 in the training dataset and 0·836 in the validation dataset), calibration, and decision curve. Its performance was not affected by the treatments and disease stages. The subgroups had statistical significance for time to macrovascular invasion (training: hazard ratio [HR] = 0·073, 95% confidence interval [CI]: 0·032-0·167, p < 0·001 and validation: HR = 0·090, 95%CI: 0·022-0·366, p < 0·001) and overall survival (training: HR = 0·344, 95%CI: 0·246-0·547, p < 0·001 and validation: HR = 0·489, 95%CI: 0·279 - 0·859, p = 0·003). Similar results were achieved when the patients were subdivided by the H-score. The subnet for segmentation and end-to-end deep learning algorithms improved the performance of the model.InterpretationOur multi-task deep learning network-based model successfully predicted future macrovascular invasion. In high-risk populations, besides the current first-line treatments, more therapies may be explored for macrovascular invasion.

Dataset Information

Assessment of a Deep Learning Model to Predict Hepatocellular Carcinoma in Patients With Hepatitis C Cirrhosis.

Publications

Assessment of a Deep Learning Model to Predict Hepatocellular Carcinoma in Patients With Hepatitis C Cirrhosis.

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