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Table 5 Radiology-based HCC diagnosis/prediction

From: The power of deep learning in simplifying feature selection for hepatocellular carcinoma: a review

Study

Data source

Used algorithms

Input

Output

Key findings

Jin, Yao [42]2021

One center in China

Deep learning-based radiomics

Ultrasound images

Predict HCC development

The DL radiomics model successfully predicted the risk of developing hepatocellular carcinoma (HCC) over five years in the test set, achieving a high AUC of 0.900

Brehar, Mitrea [43]

Center in

Romania

convolution neural network

Ultrasound images

HCC detection

The CNN model achieved outstanding results in HCC detection, with an AUC of 0.95, accuracy of 91%, 94.4%, and sensitivity of 88.4%

Shi, Kuang [44]

One center in China

convolution neural network

Computed Tomography images

Focal liver lesion type classification

Applying a convolution neural network to three-phase CT images resulted in a noteworthy performance in differentiating hepatocellular carcinoma from other focal liver lesions (FLLs), with an Area Under the Curve (AUC) of 0.925

Wu, White [45]

center in

United States

convolution neural network

Magnetic Resonance images

Classification of liver lesions using LI-RADS

The convolution neural network model delivered exceptional results in LI-RADS grading of liver tumors, exhibiting an AUC of 0.95. With an accuracy of 90%, the model demonstrated a high sensitivity of 100% and a positive predictive value (PPV) of 83.5%

Zhen, Cheng [46]

center in China

convolution neural network

Magnetic Resonance images

Liver tumor type classification

The integration of clinical data with a convolution neural network resulted in a highly accurate classification of hepatocellular carcinoma, with an AUC of 0.985 and a strong agreement rate of 91.9% compared to pathology

Wang, Jian [47]

center in China

convolution neural network

Magnetic Resonance images

Microvascular invasion in hepatocellular carcinoma

The combination of deep features from MRI images achieved an AUC of 0.79 when predicting macrovascular invasion (MVI) in HCC patients

Jiang, Cao [48]

center in China

convolution neural network

Computed Tomography images

Microvascular invasion in hepatocellular carcinoma

convolution neural network achieved an AUC of 0.906 for the prediction of MVI. Mean

survival was better in the group without MVI

An, Jiang [49]

center in China

convolution neural network

Magnetic Resonance images

detects the amount of normal tissue that is destroyed around a tumor during cancer ablation therapy (ablative margin)

The deep learning model provided accurate estimations of ablative margins and effectively assessed the risk of tumor recurrence at the original site

Liu, Xu [50]

center in China

convolution neural network

Computed Tomography images

Survival rate after transarterial chemoembolization (TACE)

A higher DL score served as an independent prognostic factor, accurately predicting overall survival with AUC values ranging from 0.85 to 0.90

Liu, Liu [51]

center in China

convolution neural network

Ultrasound images

The outcome of Transarterial Chemoembolisation

The model successfully predicted the tumor Outcome of transarterial chemoembolization (TACE) with a remarkable area under the curve of 0.93

Peng, Kang [52]

three centers in China

convolution neural network

Computed Tomography images

The outcome of Transarterial Chemoembolisation

In two separate validation cohorts, the deep learning model exhibited accuracies of 85.1% and 82.8% when predicting the outcome of transarterial chemoembolization (TACE)

Zhang, Xia [53]

three centers in China

convolution neural network

Computed Tomography images

Overall survival of patients treated with transarterial chemoembolization (TACE) and sorafenib

The deep learning signature demonstrated a C-index of 0.714 in accurately predicting overall survival in hepatocellular carcinoma patients who underwent treatment with TACE and sorafenib

Mitrea, Brehar [54]

GE7 dataset and

GE9 dataset

fusion between the convolution neural network and CML methods

ultrasound image

enhancing the HCC automatic recognition performance

The integration of CNN-based techniques with traditional machine learning methods, leveraging advanced texture analysis, has demonstrated remarkable effectiveness, yielding classification accuracies surpassing 95% in numerous scenarios

Lai, Wu [55]

China Medical University Hospital

ResNet-18 convolutional neural network

Computed Tomography images and FDG PET-CT images

Overall Survival Prediction in Patients with Hepatocellular Carcinoma Based on 18F-FDG PET-CT Images

The developed prognostic model combined FDG PET-CT images and FDG CT images, Leading to better performance than using CT images alone (0.807 AUC vs. 0.743 AUC). The model utilizing FDG PET-CT images exhibited slightly higher sensitivity than the model relying solely on CT images (0.571 SEN vs. 0.432 SEN)

Sun, Shi [56]

Hospital of Harbin Medical University (Harbin, China)

(LASSO) regression for feature selection

Deep learning and radiomic

CECT images

Predicting Treatment Response to Transarterial Chemoembolization (TACE) in Patients with Hepatocellular Carcinoma

The DLRC model was created by incorporating 19 quantitative radiomic features, 10 deep learning features, and 3 clinical factors. In the training cohort, the DLRC model achieved an AUC of 0.937 (95% confidence interval [CI], 0.912–0.962), while in the validation cohort, it achieved an AUC of 0.909 (95% CI, 0.850–0.968)