Huapeng Lin1,2, Lingfeng Zeng3, Jing Yang1, Wei Hu1, Ying Zhu1. 1. Department of Intensive Care Unit, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China. 2. Department of Hepatobiliary Surgery, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China. 3. Department of Nephrology, The Second Xiangya Hospital of Central South University, Changsha, China.
Abstract
OBJECTIVE: We sought to develop and validate a novel prognostic model for predicting survival of patients with Barcelona Clinic Liver Cancer Stages (BCLC) stage B hepatocellular carcinoma (HCC) using a machine learning approach based on random survival forests (RSF). METHODS: We retrospectively analyzed overall survival rates of patients with BCLC stage B HCC using a training (n = 602), internal validation (n = 301), and external validation (n = 343) groups. We extracted twenty-one clinical and biochemical parameters with established strategies for preprocessing, then adopted the RSF classifier for variable selection and model development. We evaluated model performance using the concordance index (c-index) and area under the receiver operator characteristic curves (AUROC). RESULTS: RSF revealed that five parameters, namely size of the tumor, BCLC-B sub-classification, AFP level, ALB level, and number of lesions, were strong predictors of survival. These were thereafter used for model development. The established model had a c-index of 0.69, whereas AUROC for predicting survival outcomes of the first three years reached 0.72, 0.71, and 0.73, respectively. Additionally, the model had better performance relative to other eight Cox proportional-hazards models, and excellent performance in the subgroup of BCLC-B sub-classification B I and B II stages. CONCLUSION: The RSF-based model, established herein, can effectively predict survival of patients with BCLC stage B HCC, with better performance than previous Cox proportional hazards models.
OBJECTIVE: We sought to develop and validate a novel prognostic model for predicting survival of patients with Barcelona Clinic Liver Cancer Stages (BCLC) stage B hepatocellular carcinoma (HCC) using a machine learning approach based on random survival forests (RSF). METHODS: We retrospectively analyzed overall survival rates of patients with BCLC stage B HCC using a training (n = 602), internal validation (n = 301), and external validation (n = 343) groups. We extracted twenty-one clinical and biochemical parameters with established strategies for preprocessing, then adopted the RSF classifier for variable selection and model development. We evaluated model performance using the concordance index (c-index) and area under the receiver operator characteristic curves (AUROC). RESULTS: RSF revealed that five parameters, namely size of the tumor, BCLC-B sub-classification, AFP level, ALB level, and number of lesions, were strong predictors of survival. These were thereafter used for model development. The established model had a c-index of 0.69, whereas AUROC for predicting survival outcomes of the first three years reached 0.72, 0.71, and 0.73, respectively. Additionally, the model had better performance relative to other eight Cox proportional-hazards models, and excellent performance in the subgroup of BCLC-B sub-classification B I and B II stages. CONCLUSION: The RSF-based model, established herein, can effectively predict survival of patients with BCLC stage B HCC, with better performance than previous Cox proportional hazards models.
Authors: Jin Hyoung Kim; Ju Hyun Shim; Han Chu Lee; Kyu-Bo Sung; Heung-Kyu Ko; Gi-Young Ko; Dong Il Gwon; Jong Woo Kim; Young-Suk Lim; Seong Ho Park Journal: Liver Int Date: 2017-06-26 Impact factor: 5.828
Authors: L Kadalayil; R Benini; L Pallan; J O'Beirne; L Marelli; D Yu; A Hackshaw; R Fox; P Johnson; A K Burroughs; D H Palmer; T Meyer Journal: Ann Oncol Date: 2013-07-14 Impact factor: 32.976
Authors: Young Eun Chon; Hana Park; Hye Kyung Hyun; Yeonjung Ha; Mi Na Kim; Beom Kyung Kim; Joo Ho Lee; Seung Up Kim; Do Young Kim; Sang Hoon Ahn; Seong Gyu Hwang; Kwang-Hyub Han; Kyu Sung Rim; Jun Yong Park Journal: Cancers (Basel) Date: 2019-04-10 Impact factor: 6.639