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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2021-06-01
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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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