Prediction Of Microvascular Invasion Of Hepatocellar Carcinoma With Contrast-Enhanced MR Using 3D CNN And LSTM

Men, Shaoyang; Ju, Hanqiu; Zhang, Lijuan; Zhou, Wu

doi:10.1109/isbi.2019.8759582

Cited by 7 publications

(2 citation statements)

References 8 publications

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“…Prediction of microvascular invasion (MVI) before surgery is valuable for liver cancer patients’ treatment planning since MVI is an adverse prognostic factor for these patients [ 101 ]. Men et al proposed 3D CNNs with LSTM to predict MVI on enhanced MRI images receiving an AUC score of 89% [ 102 ]. Jiang et al [ 103 ] also reported a 3D CNN-based one with enhanced CT images achieving an AUC score of 90.6%.…”

Section: Clinical Applicationsmentioning

confidence: 99%

Advances in Deep Learning-Based Medical Image Analysis

et al. 2021

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Importance. With the booming growth of artificial intelligence (AI), especially the recent advancements of deep learning, utilizing advanced deep learning-based methods for medical image analysis has become an active research area both in medical industry and academia. This paper reviewed the recent progress of deep learning research in medical image analysis and clinical applications. It also discussed the existing problems in the field and provided possible solutions and future directions. Highlights. This paper reviewed the advancement of convolutional neural network-based techniques in clinical applications. More specifically, state-of-the-art clinical applications include four major human body systems: the nervous system, the cardiovascular system, the digestive system, and the skeletal system. Overall, according to the best available evidence, deep learning models performed well in medical image analysis, but what cannot be ignored are the algorithms derived from small-scale medical datasets impeding the clinical applicability. Future direction could include federated learning, benchmark dataset collection, and utilizing domain subject knowledge as priors. Conclusion. Recent advanced deep learning technologies have achieved great success in medical image analysis with high accuracy, efficiency, stability, and scalability. Technological advancements that can alleviate the high demands on high-quality large-scale datasets could be one of the future developments in this area.

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Section: Clinical Applicationsmentioning

confidence: 99%

Advances in Deep Learning-Based Medical Image Analysis

et al. 2021

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“…Deep learning is a new direction for MVI prediction. Deep features based on the Conventional Neutral Network (CNN) and Recurrent Neural Network (RNN) take advantage of combining multiphases information from MR image, which makes it get high accuracy in the prediction the presence of MVI and the diagnosis of malignant tumor [8,9]. But the black box pattern of deep learning makes it hard to do medical analysis based on the black-box features.…”

Section: Introductionmentioning

confidence: 99%

Predicting Microvascular Invasion of Hepatocellular Carcinoma by texture analysis of multi-phase MR image

Yu¹,

Chen²,

et al.

EPiC Series in Computing

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Microvascular invasion (MVI) diagnosis is of vital importance in the curative treatment of hepatocellular carcinoma patients due to its close relationship with prognostic analysis. Currently, MVI detection often bases on surgical specimen, which is invasive for patients. This study, we extracted texture features of multi-phase MR image to predict the presence of MVI. Feature extraction employed neighboring gray level dependency emphasis (NGLDM) method, which is a common texture feature analysis method. Next, we built a SVM classifier to predict the presence of MVI using extracted features. Especially, multi-phase features were designed to enhance the precision of prediction. Enhanced MR images of pre-contrast phase and portal vein phase were used to extracting features. The method was tested by 5-fold cross- validation on the dataset. The precision of prediction was 91.31%, compared with the baseline method of 70.71%. To make the prediction more interpretable, the relationship between NGLDM texture features and the presence of MVI was discussed in the end.

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Radiomics models for preoperative prediction of microvascular invasion in hepatocellular carcinoma: a systematic review and meta-analysis

Zhong

Long

et al. 2022

Abdom Radiol

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Prediction Of Microvascular Invasion Of Hepatocellar Carcinoma With Contrast-Enhanced MR Using 3D CNN And LSTM

Cited by 7 publications

References 8 publications

Advances in Deep Learning-Based Medical Image Analysis

Advances in Deep Learning-Based Medical Image Analysis

Predicting Microvascular Invasion of Hepatocellular Carcinoma by texture analysis of multi-phase MR image

Radiomics models for preoperative prediction of microvascular invasion in hepatocellular carcinoma: a systematic review and meta-analysis

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