Mortality Predicted Accuracy for Hepatocellular Carcinoma Patients with Hepatic Resection Using Artificial Neural Network

Chiu, Herng‐Chia; Ho, Te-Wei; Lee, King‐Teh; Chen, Hong-Yaw; Ho, Wen-Hsien

doi:10.1155/2013/201976

Cited by 36 publications

(24 citation statements)

References 36 publications

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“…In all training sets and testing sets, accuracy, sensitivity, specificity, F-measure, and AUC were higher in the 3, 7, and 14 days' models built by ANN than in those constructed by LR; this is consistent with other reports in which ANN outperformed LR in both training and testing (37)(38)(39)(40).…”

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confidence: 90%

A Neural Network Approach to Predict Acute Allograft Rejection in Liver Transplant Recipients Using Routine Laboratory Data

Zare

Zarei

et al. 2017

Hepat Mon

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Background: Discovery of non-invasive methods for acute rejection in liver transplant patients would contribute to preservation of liver function in the graft. Recently, however, outcome prediction based on biostatistical models like artificial neural networks (ANNs) is increasingly becoming impressive in medicine. Objectives: The aim of this study was to obtain a predictive model based on ANN technique and to figure out the best time for early prediction of acute allograft rejection after transplantation in liver transplant recipients. Methods: Feed-forward, back-propagation neural network was developed to predict acute rejection in liver transplant recipients using clinical and biochemical data from 148 liver transplant recipients over days 3, 7, and 14 post-transplantation. Sensitivity and receiver-operating characteristic (ROC) analysis were done to reveal the importance of input variables and the performance of the neural network. Results:The results were compared with a logistic regression (LR) model using the same data. Our results showed that the data related to day 7 gave the best results in terms of ANN performance; and the most important factors in the predictive model were aspartate aminotransferase (AST) and alanine aminotransferase (ALT). The ANN's accuracy was 90%, sensitivity was 87%, specificity was 90% in the testing set, and the performance of the ANN was better than that of the LR model. The ANN recognized correctly eight out of ten acute rejection patients and 34 out of 36 non-rejection ones in the testing set. Conclusions: This study suggests that ANN could be a valuable adjunct to conventional liver function tests for monitoring liver transplant recipients in the early postoperative period.

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confidence: 90%

A Neural Network Approach to Predict Acute Allograft Rejection in Liver Transplant Recipients Using Routine Laboratory Data

Zare

Zarei

et al. 2017

Hepat Mon

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“…One criticism of the ANN is that it is difficult to assess the relative contribution of each variable to the final prediction put forth by the model [ 24 ]. Additionally, the ANN does not provide detailed information, such as the hazard ratio, which generally indicates the direction and magnitude of influence each variable has on the outcome [ 44 ]. Some other limitations included: First, patients declared dead on arrival at the hospital or at the scene of the accident were not recorded in the registered database [ 45 , 46 ] and may have resulted in potential sample bias.…”

Section: Discussionmentioning

confidence: 99%

Mortality prediction in patients with isolated moderate and severe traumatic brain injury using machine learning models

et al. 2018

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BackgroundThe purpose of this study was to build a model of machine learning (ML) for the prediction of mortality in patients with isolated moderate and severe traumatic brain injury (TBI).MethodsHospitalized adult patients registered in the Trauma Registry System between January 2009 and December 2015 were enrolled in this study. Only patients with an Abbreviated Injury Scale (AIS) score ≥ 3 points related to head injuries were included in this study. A total of 1734 (1564 survival and 170 non-survival) and 325 (293 survival and 32 non-survival) patients were included in the training and test sets, respectively.ResultsUsing demographics and injury characteristics, as well as patient laboratory data, predictive tools (e.g., logistic regression [LR], support vector machine [SVM], decision tree [DT], naive Bayes [NB], and artificial neural networks [ANN]) were used to determine the mortality of individual patients. The predictive performance was evaluated by accuracy, sensitivity, and specificity, as well as by area under the curve (AUC) measures of receiver operator characteristic curves. In the training set, all five ML models had a specificity of more than 90% and all ML models (except the NB) achieved an accuracy of more than 90%. Among them, the ANN had the highest sensitivity (80.59%) in mortality prediction. Regarding performance, the ANN had the highest AUC (0.968), followed by the LR (0.942), SVM (0.935), NB (0.908), and DT (0.872). In the test set, the ANN had the highest sensitivity (84.38%) in mortality prediction, followed by the SVM (65.63%), LR (59.38%), NB (59.38%), and DT (43.75%).ConclusionsThe ANN model provided the best prediction of mortality for patients with isolated moderate and severe TBI.

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“…In contrast, we used a fully connected ANN based on a multitude of clinical parameters (n = 46) to predict OS prior to the second TACE. Similar approaches using an ANN have already been used following tumour resection . Regarding its use in interventional oncology, Wu et al tried to predict disease free survival in patients with HCC after radiofrequency ablation .…”

Section: Discussionmentioning

confidence: 99%

“…Similar approaches using an ANN have already been used following tumour resection. [40][41][42] Regarding its use in interventional oncology, Wu et al tried to predict disease free survival in patients with HCC after radiofrequency ablation. 43 Until now, it has never been tried in the setting of TACE.…”

Section: It Describes How Different Factors Including Inherent Characmentioning

confidence: 99%

Predicting survival after transarterial chemoembolization for hepatocellular carcinoma using a neural network: A Pilot Study

Mähringer‐Kunz

Wagner

Hähn

et al. 2020

Liver International

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Background and aims Deciding when to repeat and when to stop transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC) can be difficult even for experienced investigators. Our aim was to develop a survival prediction model for such patients undergoing TACE using novel machine learning algorithms and to compare it to conventional prediction scores, ART, ABCR and SNACOR. Methods For this retrospective analysis, 282 patients who underwent TACE for HCC at our tertiary referral centre between January 2005 and December 2017 were included in the final analysis. We built an artificial neural network (ANN) including all parameters used by the aforementioned risk scores and other clinically meaningful parameters. Following an 80:20 split, the first 225 patients were used for training; the more recently treated 20% were used for validation. Results The ANN had a promising performance at predicting 1‐year survival, with an area under the ROC curve (AUC) of 0.77 ± 0.13. Internal validation yielded an AUC of 0.83 ± 0.06, a positive predictive value of 87.5% and a negative predictive value of 68.0%. The sensitivity was 77.8% and specificity 81.0%. In a head‐to‐head comparison, the ANN outperformed the aforementioned scoring systems, which yielded lower AUCs (SNACOR 0.73 ± 0.07, ABCR 0.70 ± 0.07 and ART 0.54 ± 0.08). This difference reached significance for ART (P < .001); for ABCR and SNACOR significance was not reached (P = .143 and P = .201). Conclusions Artificial neural networks could be better at predicting patient survival after TACE for HCC than traditional scoring systems. Once established, such prediction models could easily be deployed in clinical routine and help determine optimal patient care.

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Mortality Predicted Accuracy for Hepatocellular Carcinoma Patients with Hepatic Resection Using Artificial Neural Network

Cited by 36 publications

References 36 publications

A Neural Network Approach to Predict Acute Allograft Rejection in Liver Transplant Recipients Using Routine Laboratory Data

A Neural Network Approach to Predict Acute Allograft Rejection in Liver Transplant Recipients Using Routine Laboratory Data

Mortality prediction in patients with isolated moderate and severe traumatic brain injury using machine learning models

Predicting survival after transarterial chemoembolization for hepatocellular carcinoma using a neural network: A Pilot Study

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