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Disease-Free Survival after Hepatic Resection in Hepatocellular Carcinoma Patients: A Prediction Approach Using Artificial Neural Network

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  • Wen-Hsien Ho
  • King-Teh Lee
  • Hong-Yaw Chen
  • Te-Wei Ho
  • Herng-Chia Chiu

Abstract

Background: A database for hepatocellular carcinoma (HCC) patients who had received hepatic resection was used to develop prediction models for 1-, 3- and 5-year disease-free survival based on a set of clinical parameters for this patient group. Methods: The three prediction models included an artificial neural network (ANN) model, a logistic regression (LR) model, and a decision tree (DT) model. Data for 427, 354 and 297 HCC patients with histories of 1-, 3- and 5-year disease-free survival after hepatic resection, respectively, were extracted from the HCC patient database. From each of the three groups, 80% of the cases (342, 283 and 238 cases of 1-, 3- and 5-year disease-free survival, respectively) were selected to provide training data for the prediction models. The remaining 20% of cases in each group (85, 71 and 59 cases in the three respective groups) were assigned to validation groups for performance comparisons of the three models. Area under receiver operating characteristics curve (AUROC) was used as the performance index for evaluating the three models. Conclusions: The ANN model outperformed the LR and DT models in terms of prediction accuracy. This study demonstrated the feasibility of using ANNs in medical decision support systems for predicting disease-free survival based on clinical databases in HCC patients who have received hepatic resection.

Suggested Citation

  • Wen-Hsien Ho & King-Teh Lee & Hong-Yaw Chen & Te-Wei Ho & Herng-Chia Chiu, 2012. "Disease-Free Survival after Hepatic Resection in Hepatocellular Carcinoma Patients: A Prediction Approach Using Artificial Neural Network," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-9, January.
  • Handle: RePEc:plo:pone00:0029179
    DOI: 10.1371/journal.pone.0029179
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    References listed on IDEAS

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    1. Martin MacDowell & Eugene Somoza & Kenneth Rothe & Richard Fry & Kim Brady & Albert Bocklet, 2001. "Understanding Birthing Mode Decision Making Using Artificial Neural Networks," Medical Decision Making, , vol. 21(6), pages 433-443, December.
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    Cited by:

    1. Hue-Yu Wang & Ching-Feng Wen & Yu-Hsien Chiu & I-Nong Lee & Hao-Yun Kao & I-Chen Lee & Wen-Hsien Ho, 2013. "Leuconostoc Mesenteroides Growth in Food Products: Prediction and Sensitivity Analysis by Adaptive-Network-Based Fuzzy Inference Systems," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-16, May.
    2. Chong-Jian Wang & Yu-Qian Li & Ling Wang & Lin-Lin Li & Yi-Rui Guo & Ling-Yun Zhang & Mei-Xi Zhang & Rong-Hai Bie, 2012. "Development and Evaluation of a Simple and Effective Prediction Approach for Identifying Those at High Risk of Dyslipidemia in Rural Adult Residents," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-7, August.
    3. Hyo Soung Cha & Jip Min Jung & Seob Yoon Shin & Young Mi Jang & Phillip Park & Jae Wook Lee & Seung Hyun Chung & Kui Son Choi, 2019. "The Korea Cancer Big Data Platform (K-CBP) for Cancer Research," IJERPH, MDPI, vol. 16(13), pages 1-13, June.
    4. Hon-Yi Shi & King-Teh Lee & Hao-Hsien Lee & Wen-Hsien Ho & Ding-Ping Sun & Jhi-Joung Wang & Chong-Chi Chiu, 2012. "Comparison of Artificial Neural Network and Logistic Regression Models for Predicting In-Hospital Mortality after Primary Liver Cancer Surgery," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-6, April.
    5. Lei Liu & Zhiwei Wang & Songqi Jiang & Bingfeng Shao & Jibing Liu & Suqing Zhang & Yilong Zhou & Yuan Zhou & Yixin Zhang, 2013. "Perioperative Allogenenic Blood Transfusion Is Associated with Worse Clinical Outcomes for Hepatocellular Carcinoma: A Meta-Analysis," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-10, May.
    6. Guowei Li & Lehana Thabane & Thomas Delate & Daniel M Witt & Mitchell A H Levine & Ji Cheng & Anne Holbrook, 2016. "Can We Predict Individual Combined Benefit and Harm of Therapy? Warfarin Therapy for Atrial Fibrillation as a Test Case," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-16, August.

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