LiverNet: efficient and robust deep learning model for automatic diagnosis of sub-types of liver hepatocellular carcinoma cancer from H&E stained liver histopathology images

Background Hepatocellular carcinoma (HCC), usually known as hepatoma, is the third leading cause of cancer mortality globally. Early detection of HCC helps in its treatment and increases survival rates. Objective The aim of this study is to develop a deep learning model, using the trend and severity of each medical event from the electronic health record to accurately predict the patients who will be diagnosed with HCC in 1 year. Methods Patients with HCC were screened out from the National Health Insurance Research Database of Taiwan between 1999 and 2013. To be included, the patients with HCC had to register as patients with cancer in the catastrophic illness file and had to be diagnosed as a patient with HCC in an inpatient admission. The control cases (non-HCC patients) were randomly sampled from the same database. We used age, gender, diagnosis code, drug code, and time information as the input variables of a convolution neural network model to predict those patients with HCC. We also inspected the highly weighted variables in the model and compared them to their odds ratio at HCC to understand how the predictive model works Results We included 47,945 individuals, 9553 of whom were patients with HCC. The area under the receiver operating curve (AUROC) of the model for predicting HCC risk 1 year in advance was 0.94 (95% CI 0.937-0.943), with a sensitivity of 0.869 and a specificity 0.865. The AUROC for predicting HCC patients 7 days, 6 months, 1 year, 2 years, and 3 years early were 0.96, 0.94, 0.94, 0.91, and 0.91, respectively. Conclusions The findings of this study show that the convolutional neural network model has immense potential to predict the risk of HCC 1 year in advance with minimal features available in the electronic health records.

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Automatic Diagnosis of Glaucoma using Ensemble based Deep Learning Model

2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS) ◽

10.1109/icaccs51430.2021.9441817 ◽

2021 ◽

Author(s):

N. Deepa ◽

S. Esakkirajan ◽

B. Keerthiveena ◽

S. Bala Dhanalakshmi

Keyword(s):

Deep Learning ◽

Learning Model ◽

Automatic Diagnosis ◽

Deep Learning Model

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

Frontiers in Oncology ◽

10.3389/fonc.2021.672055 ◽

2021 ◽

Vol 11 ◽

Author(s):

Hang Zhou ◽

Tao Jiang ◽

Qunying Li ◽

Chao Zhang ◽

Cong Zhang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Deep Learning ◽

Predictive Model ◽

Clinical Information ◽

Learning Model ◽

Lower Sensitivity ◽

Liver Imaging ◽

The Us ◽

Cirrhotic Patients ◽

Deep Learning Model

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.

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A transferrable and interpretable multiple instance learning model for microsatellite instability prediction based on histopathology images

10.1101/2020.02.29.971150 ◽

2020 ◽

Author(s):

Rui Cao ◽

Fan Yang ◽

Si-Cong Ma ◽

Li Liu ◽

Yan Li ◽

...

Keyword(s):

Deep Learning ◽

Microsatellite Instability ◽

Transfer Learning ◽

Area Under The Curve ◽

Learning Model ◽

Multiple Instance Learning ◽

The Cancer Genome Atlas ◽

Data Set ◽

Histopathology Images ◽

Deep Learning Model

ABSTRACTBackgroundMicrosatellite instability (MSI) is a negative prognostic factor for colorectal cancer (CRC) and can be used as a predictor of success for immunotherapy in pan-cancer. However, current MSI identification methods are not available for all patients. We propose an ensemble multiple instance learning (MIL)-based deep learning model to predict MSI status directly from histopathology images.DesignTwo cohorts of patients were collected, including 429 from The Cancer Genome Atlas (TCGA-COAD) and 785 from a self-collected Asian data set (Asian-CRC). The initial model was developed and validated in TCGA-COAD, and then generalized in Asian-CRC through transfer learning. The pathological signatures extracted from the model are associated with genotypes for model interpretation.ResultsA model called Ensembled Patch Likelihood Aggregation (EPLA) was developed in the TCGA-COAD training set based on two consecutive stages: patch-level prediction and WSI-level prediction. The EPLA model achieved an area-under-the -curve (AUC) of 0.8848 in the TCGA-COAD test set, which outperformed the state-of-the-art approach, and an AUC of 0.8504 in the Asian-CRC after transfer learning. Furthermore, the five pathological imaging signatures identified using the model are associated with genomic and transcriptomic profiles, which makes the MIL model interpretable. Results show that our model recognizes pathological signatures related to mutation burden, DNA repair pathways, and immunity.ConclusionOur MIL-based deep learning model can effectively predict MSI from histopathology images and are transferable to a new patient cohort. The interpretability of our model by association with genomic and transcriptomic biomarkers lays the foundation for prospective clinical research.

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Deep learning model for prediction of hepatocellular carcinoma in patients with HBV-related cirrhosis on antiviral therapy

JHEP Reports ◽

10.1016/j.jhepr.2020.100175 ◽

2020 ◽

Vol 2 (6) ◽

pp. 100175 ◽

Cited By ~ 1

Author(s):

Joon Yeul Nam ◽

Dong Hyun Sinn ◽

Junho Bae ◽

Eun Sun Jang ◽

Jin-Wook Kim ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Deep Learning ◽

Antiviral Therapy ◽

Learning Model ◽

Deep Learning Model

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Predicting Hepatocellular Carcinoma With Minimal Features From Electronic Health Records: Development of a Deep Learning Model (Preprint)

10.2196/preprints.19812 ◽

2020 ◽

Author(s):

Chia-Wei Liang ◽

Hsuan-Chia Yang ◽

Md Mohaimenul Islam ◽

Phung Anh Alex Nguyen ◽

Yi-Ting Feng ◽

...

Keyword(s):

Neural Network ◽

Hepatocellular Carcinoma ◽

Deep Learning ◽

Electronic Health Records ◽

Network Model ◽

Neural Network Model ◽

Learning Model ◽

Health Records ◽

Electronic Health ◽

Deep Learning Model

BACKGROUND Hepatocellular carcinoma (HCC), usually known as hepatoma, is the third leading cause of cancer mortality globally. Early detection of HCC helps in its treatment and increases survival rates. OBJECTIVE The aim of this study is to develop a deep learning model, using the trend and severity of each medical event from the electronic health record to accurately predict the patients who will be diagnosed with HCC in 1 year. METHODS Patients with HCC were screened out from the National Health Insurance Research Database of Taiwan between 1999 and 2013. To be included, the patients with HCC had to register as patients with cancer in the catastrophic illness file and had to be diagnosed as a patient with HCC in an inpatient admission. The control cases (non-HCC patients) were randomly sampled from the same database. We used age, gender, diagnosis code, drug code, and time information as the input variables of a convolution neural network model to predict those patients with HCC. We also inspected the highly weighted variables in the model and compared them to their odds ratio at HCC to understand how the predictive model works RESULTS We included 47,945 individuals, 9553 of whom were patients with HCC. The area under the receiver operating curve (AUROC) of the model for predicting HCC risk 1 year in advance was 0.94 (95% CI 0.937-0.943), with a sensitivity of 0.869 and a specificity 0.865. The AUROC for predicting HCC patients 7 days, 6 months, 1 year, 2 years, and 3 years early were 0.96, 0.94, 0.94, 0.91, and 0.91, respectively. CONCLUSIONS The findings of this study show that the convolutional neural network model has immense potential to predict the risk of HCC 1 year in advance with minimal features available in the electronic health records.

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