US-Based Deep Learning Model for Differentiating Hepatocellular Carcinoma (HCC) From Other Malignancy in Cirrhotic Patients

The aim was to build a predictive model based on ultrasonography (US)-based deep learning model (US-DLM) and clinical features (Clin) for differentiating hepatocellular carcinoma (HCC) from other malignancy (OM) in cirrhotic patients. 112 patients with 120 HCCs and 60 patients with 61 OMs were inclu...

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Main Authors: Hang Zhou, Tao Jiang, Qunying Li, Chao Zhang, Cong Zhang, Yajing Liu, Jing Cao, Yu Sun, Peile Jin, Jiali Luo, Minqiang Pan, Pintong Huang
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-06-01
Series:Frontiers in Oncology
Subjects:
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2021.672055/full
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spelling doaj-e2f67fa83482483a9e757d4351ff652a2021-06-08T05:19:55ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2021-06-011110.3389/fonc.2021.672055672055US-Based Deep Learning Model for Differentiating Hepatocellular Carcinoma (HCC) From Other Malignancy in Cirrhotic PatientsHang ZhouTao JiangQunying LiChao ZhangCong ZhangYajing LiuJing CaoYu SunPeile JinJiali LuoMinqiang PanPintong HuangThe aim was to build a predictive model based on ultrasonography (US)-based deep learning model (US-DLM) and clinical features (Clin) for differentiating hepatocellular carcinoma (HCC) from other malignancy (OM) in cirrhotic patients. 112 patients with 120 HCCs and 60 patients with 61 OMs were included. They were randomly divided into training and test cohorts with a 4:1 ratio for developing and evaluating US-DLM model, respectively. Significant Clin predictors of OM in the training cohort were combined with US-DLM to build a nomogram predictive model (US-DLM+Clin). The diagnostic performance of US-DLM and US-DLM+Clin were compared with that of contrast enhanced magnetic resonance imaging (MRI) liver imaging and reporting system category M (MRI LR-M). US-DLM was the best independent predictor for evaluating OMs, followed by clinical information, including high cancer antigen 199 (CA199) level and female. The US-DLM achieved an AUC of 0.74 in the test cohort, which was comparable with that of MRI LR-M (AUC=0.84, p=0.232). The US-DLM+Clin for predicting OMs also had similar AUC value (0.81) compared with that of LR-M+Clin (0.83, p>0.05). US-DLM+Clin obtained a higher specificity, but a lower sensitivity, compared to that of LR-M +Clin (Specificity: 82.6% vs. 73.9%, p=0.007; Sensitivity: 78.6% vs. 92.9%, p=0.006) for evaluating OMs in the test set. The US-DLM+Clin model is valuable in differentiating HCC from OM in the setting of cirrhosis.https://www.frontiersin.org/articles/10.3389/fonc.2021.672055/fullhepatocellular carcinomacirrhosisdeep learning—artificial neural networkultrasonographycontrast enhanced magnetic resonance imaging
collection DOAJ
language English
format Article
sources DOAJ
author Hang Zhou
Tao Jiang
Qunying Li
Chao Zhang
Cong Zhang
Yajing Liu
Jing Cao
Yu Sun
Peile Jin
Jiali Luo
Minqiang Pan
Pintong Huang
spellingShingle Hang Zhou
Tao Jiang
Qunying Li
Chao Zhang
Cong Zhang
Yajing Liu
Jing Cao
Yu Sun
Peile Jin
Jiali Luo
Minqiang Pan
Pintong Huang
US-Based Deep Learning Model for Differentiating Hepatocellular Carcinoma (HCC) From Other Malignancy in Cirrhotic Patients
Frontiers in Oncology
hepatocellular carcinoma
cirrhosis
deep learning—artificial neural network
ultrasonography
contrast enhanced magnetic resonance imaging
author_facet Hang Zhou
Tao Jiang
Qunying Li
Chao Zhang
Cong Zhang
Yajing Liu
Jing Cao
Yu Sun
Peile Jin
Jiali Luo
Minqiang Pan
Pintong Huang
author_sort Hang Zhou
title US-Based Deep Learning Model for Differentiating Hepatocellular Carcinoma (HCC) From Other Malignancy in Cirrhotic Patients
title_short US-Based Deep Learning Model for Differentiating Hepatocellular Carcinoma (HCC) From Other Malignancy in Cirrhotic Patients
title_full US-Based Deep Learning Model for Differentiating Hepatocellular Carcinoma (HCC) From Other Malignancy in Cirrhotic Patients
title_fullStr US-Based Deep Learning Model for Differentiating Hepatocellular Carcinoma (HCC) From Other Malignancy in Cirrhotic Patients
title_full_unstemmed US-Based Deep Learning Model for Differentiating Hepatocellular Carcinoma (HCC) From Other Malignancy in Cirrhotic Patients
title_sort us-based deep learning model for differentiating hepatocellular carcinoma (hcc) from other malignancy in cirrhotic patients
publisher Frontiers Media S.A.
series Frontiers in Oncology
issn 2234-943X
publishDate 2021-06-01
description The aim was to build a predictive model based on ultrasonography (US)-based deep learning model (US-DLM) and clinical features (Clin) for differentiating hepatocellular carcinoma (HCC) from other malignancy (OM) in cirrhotic patients. 112 patients with 120 HCCs and 60 patients with 61 OMs were included. They were randomly divided into training and test cohorts with a 4:1 ratio for developing and evaluating US-DLM model, respectively. Significant Clin predictors of OM in the training cohort were combined with US-DLM to build a nomogram predictive model (US-DLM+Clin). The diagnostic performance of US-DLM and US-DLM+Clin were compared with that of contrast enhanced magnetic resonance imaging (MRI) liver imaging and reporting system category M (MRI LR-M). US-DLM was the best independent predictor for evaluating OMs, followed by clinical information, including high cancer antigen 199 (CA199) level and female. The US-DLM achieved an AUC of 0.74 in the test cohort, which was comparable with that of MRI LR-M (AUC=0.84, p=0.232). The US-DLM+Clin for predicting OMs also had similar AUC value (0.81) compared with that of LR-M+Clin (0.83, p>0.05). US-DLM+Clin obtained a higher specificity, but a lower sensitivity, compared to that of LR-M +Clin (Specificity: 82.6% vs. 73.9%, p=0.007; Sensitivity: 78.6% vs. 92.9%, p=0.006) for evaluating OMs in the test set. The US-DLM+Clin model is valuable in differentiating HCC from OM in the setting of cirrhosis.
topic hepatocellular carcinoma
cirrhosis
deep learning—artificial neural network
ultrasonography
contrast enhanced magnetic resonance imaging
url https://www.frontiersin.org/articles/10.3389/fonc.2021.672055/full
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