Stratification of portal vein-invaded hepatocellular carcinoma treated with transarterial chemoembolization monotherapy

Purpose: The study aimed to establish a prognostic prediction model and an artificial neural network (ANN) model to determine who will benefit from transarterial chemoembolization (TACE) monotherapy for patients with hepatocellular carcinoma (HCC) invading portal vein. Methods: Treatment-naïve patie...

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Main Authors: Lei Zhang, BinYan Zhong, Bo Hu, Wei Li, Peng Huang, Shen Zhang, JinJin Song, JianSong Ji, CaiFang Ni
Format: Article
Language:English
Published: KeAi Communications Co., Ltd. 2020-12-01
Series:Journal of Interventional Medicine
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2096360220300491
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spelling doaj-87cdd6045e724382bfa860bbb977747c2020-11-29T04:15:36ZengKeAi Communications Co., Ltd.Journal of Interventional Medicine2096-36022020-12-0134201207Stratification of portal vein-invaded hepatocellular carcinoma treated with transarterial chemoembolization monotherapyLei Zhang0BinYan Zhong1Bo Hu2Wei Li3Peng Huang4Shen Zhang5JinJin Song6JianSong Ji7CaiFang Ni8Department of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, ChinaDepartment of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, ChinaDepartment of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, ChinaDepartment of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, ChinaDepartment of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, ChinaDepartment of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, ChinaDepartment of Interventional Radiology, Zhejiang University Lishui Hospital, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Central Hospital, Lishui, ChinaDepartment of Interventional Radiology, Zhejiang University Lishui Hospital, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Central Hospital, Lishui, China; Corresponding author. Department of Interventional Radiology, Zhejiang University Lishui Hospital, No. 289 Kuocang Road, Lishui, 323000, China.Department of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China; Corresponding author. Department of Interventional, The First Affiliated Hospital of Soochow University, No. 188, Shizi Street, Suzhou, 215006, China.Purpose: The study aimed to establish a prognostic prediction model and an artificial neural network (ANN) model to determine who will benefit from transarterial chemoembolization (TACE) monotherapy for patients with hepatocellular carcinoma (HCC) invading portal vein. Methods: Treatment-naïve patients with HCC and portal vein invasion who were treated with TACE monotherapy at hospital A as training cohort and hospital B as validation cohort were included. The primary endpoint was overall survival (OS). In training cohort, independent risk factors associated with OS were identified by univariate and multivariate analysis. The prognostic prediction (PP) and ANN models based on the independent risk factors were established to find out who will benefit most from TACE monotherapy. The type of portal vein tumor thrombosis was classified based on the Cheng’s Classification. The accuracy of the models was validated in validation cohort. Results: Totally, 242 patients (training cohort: n ​= ​159; validation cohort: n ​= ​83) were included. The median OS was 7.1 and 8.5 months in training and validation cohort, respectively. In training cohort, the PP model was established based on the following five independent risk factors: Cheng’s Classification, Eastern Cooperative Oncology Group score, maximum tumor size, number of HCC nodules, and Child-Pugh stage. PP score of 17.5 was identified as cut-off point and patients were divided into two groups by PP score <17.5 and >17.5 in survival benefit and prognostication (8.8 vs. 5.5 months; P ​< ​0.001). These five factors were included and ranked based on the importance associated with OS in the ANN model. Both of the two models received high accuracy after validation. Conclusions: Portal vein invaded HCC patients with PP score <17.5 may benefit most from TACE monotherapy. For these patients, TACE monotherapy should be considered.http://www.sciencedirect.com/science/article/pii/S2096360220300491Hepatocellular carcinomaTransarterial chemoembolizationPortal vein tumor thrombosisPrognosticArtificial neural network
collection DOAJ
language English
format Article
sources DOAJ
author Lei Zhang
BinYan Zhong
Bo Hu
Wei Li
Peng Huang
Shen Zhang
JinJin Song
JianSong Ji
CaiFang Ni
spellingShingle Lei Zhang
BinYan Zhong
Bo Hu
Wei Li
Peng Huang
Shen Zhang
JinJin Song
JianSong Ji
CaiFang Ni
Stratification of portal vein-invaded hepatocellular carcinoma treated with transarterial chemoembolization monotherapy
Journal of Interventional Medicine
Hepatocellular carcinoma
Transarterial chemoembolization
Portal vein tumor thrombosis
Prognostic
Artificial neural network
author_facet Lei Zhang
BinYan Zhong
Bo Hu
Wei Li
Peng Huang
Shen Zhang
JinJin Song
JianSong Ji
CaiFang Ni
author_sort Lei Zhang
title Stratification of portal vein-invaded hepatocellular carcinoma treated with transarterial chemoembolization monotherapy
title_short Stratification of portal vein-invaded hepatocellular carcinoma treated with transarterial chemoembolization monotherapy
title_full Stratification of portal vein-invaded hepatocellular carcinoma treated with transarterial chemoembolization monotherapy
title_fullStr Stratification of portal vein-invaded hepatocellular carcinoma treated with transarterial chemoembolization monotherapy
title_full_unstemmed Stratification of portal vein-invaded hepatocellular carcinoma treated with transarterial chemoembolization monotherapy
title_sort stratification of portal vein-invaded hepatocellular carcinoma treated with transarterial chemoembolization monotherapy
publisher KeAi Communications Co., Ltd.
series Journal of Interventional Medicine
issn 2096-3602
publishDate 2020-12-01
description Purpose: The study aimed to establish a prognostic prediction model and an artificial neural network (ANN) model to determine who will benefit from transarterial chemoembolization (TACE) monotherapy for patients with hepatocellular carcinoma (HCC) invading portal vein. Methods: Treatment-naïve patients with HCC and portal vein invasion who were treated with TACE monotherapy at hospital A as training cohort and hospital B as validation cohort were included. The primary endpoint was overall survival (OS). In training cohort, independent risk factors associated with OS were identified by univariate and multivariate analysis. The prognostic prediction (PP) and ANN models based on the independent risk factors were established to find out who will benefit most from TACE monotherapy. The type of portal vein tumor thrombosis was classified based on the Cheng’s Classification. The accuracy of the models was validated in validation cohort. Results: Totally, 242 patients (training cohort: n ​= ​159; validation cohort: n ​= ​83) were included. The median OS was 7.1 and 8.5 months in training and validation cohort, respectively. In training cohort, the PP model was established based on the following five independent risk factors: Cheng’s Classification, Eastern Cooperative Oncology Group score, maximum tumor size, number of HCC nodules, and Child-Pugh stage. PP score of 17.5 was identified as cut-off point and patients were divided into two groups by PP score <17.5 and >17.5 in survival benefit and prognostication (8.8 vs. 5.5 months; P ​< ​0.001). These five factors were included and ranked based on the importance associated with OS in the ANN model. Both of the two models received high accuracy after validation. Conclusions: Portal vein invaded HCC patients with PP score <17.5 may benefit most from TACE monotherapy. For these patients, TACE monotherapy should be considered.
topic Hepatocellular carcinoma
Transarterial chemoembolization
Portal vein tumor thrombosis
Prognostic
Artificial neural network
url http://www.sciencedirect.com/science/article/pii/S2096360220300491
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