Comparison of Deep-Learning and Conventional Machine-Learning Methods for the Automatic Recognition of the Hepatocellular Carcinoma Areas from Ultrasound Images

Brehar, Raluca; Mitrea, Delia-Alexandrina; Vancea, Flaviu; Mariţa, Tiberiu; Nedevschi, Sergiu; Lupșor-Platon, Monica; Rotaru, Magda; Badea, Radu

doi:10.3390/s20113085

Cited by 69 publications

(42 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Brehar et al . investigated the performance in differentiating HCC from cirrhotic parenchyma using B‐mode US and reported a higher performance of the DL approach as compared with that of classical ML classifiers, such as GB, SVM, MLP, or RF‐based classifications 25 . The current results are potentially applicable to the definitive diagnosis of liver tumors using B‐mode US.…”

Section: Machine Learning Approach For Hepatocellular Carcinoma Diagnmentioning

confidence: 79%

Artificial intelligence in the diagnosis and management of hepatocellular carcinoma

Sato

Tateishi

Yatomi

et al. 2021

J of Gastro and Hepatol

View full text Add to dashboard Cite

Despite recent improvements in therapeutic interventions, hepatocellular carcinoma is still associated with a poor prognosis in patients with an advanced disease at diagnosis. Recently, significant progress has been made in image recognition through advances in the field of artificial intelligence (AI) (or machine learning), especially deep learning. AI is a multidisciplinary field that draws on the fields of computer science and mathematics for developing and implementing computer algorithms capable of maximizing the predictive accuracy from static or dynamic data sources using analytic or probabilistic models. Because of the multifactorial and complex nature of liver diseases, the machine learning approach to integrate multiple factors would appear to be an advantageous approach to improve the likelihood of making a precise diagnosis and predicting the response of treatment and prognosis of liver diseases. In this review, we attempted to summarize the potential use of AI in the diagnosis and management of liver diseases, especially hepatocellular carcinoma.

show abstract

Section: Machine Learning Approach For Hepatocellular Carcinoma Diagnmentioning

confidence: 79%

Artificial intelligence in the diagnosis and management of hepatocellular carcinoma

Sato

Tateishi

Yatomi

et al. 2021

J of Gastro and Hepatol

View full text Add to dashboard Cite

show abstract

“…The analysis through deep learning has been demonstrated to be superior in image recognition compared to conventional radiological techniques and handcrafted radiomics ( 51 , 52 ). The methods applied in our study, suppress some cumbersome steps such as tumor segmentation, which implies a significant limitation in this type of work and may bias the selection of variables of interest, such as texture features.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Intraoperative Ultrasound B-Mode and Strain Elastography for the Differentiation of Glioblastomas From Solitary Brain Metastases. An Automated Deep Learning Approach for Image Analysis

et al. 2021

View full text Add to dashboard Cite

BackgroundThe differential diagnosis of glioblastomas (GBM) from solitary brain metastases (SBM) is essential because the surgical strategy varies according to the histopathological diagnosis. Intraoperative ultrasound elastography (IOUS-E) is a relatively novel technique implemented in the surgical management of brain tumors that provides additional information about the elasticity of tissues. This study compares the discriminative capacity of intraoperative ultrasound B-mode and strain elastography to differentiate GBM from SBM.MethodsWe performed a retrospective analysis of patients who underwent craniotomy between March 2018 to June 2020 with glioblastoma (GBM) and solitary brain metastases (SBM) diagnoses. Cases with an intraoperative ultrasound study were included. Images were acquired before dural opening, first in B-mode, and then using the strain elastography module. After image pre-processing, an analysis based on deep learning was conducted using the open-source software Orange. We have trained an existing neural network to classify tumors into GBM and SBM via the transfer learning method using Inception V3. Then, logistic regression (LR) with LASSO (least absolute shrinkage and selection operator) regularization, support vector machine (SVM), random forest (RF), neural network (NN), and k-nearest neighbor (kNN) were used as classification algorithms. After the models’ training, ten-fold stratified cross-validation was performed. The models were evaluated using the area under the curve (AUC), classification accuracy, and precision.ResultsA total of 36 patients were included in the analysis, 26 GBM and 10 SBM. Models were built using a total of 812 ultrasound images, 435 of B-mode, 265 (60.92%) corresponded to GBM and 170 (39.8%) to metastases. In addition, 377 elastograms, 232 (61.54%) GBM and 145 (38.46%) metastases were analyzed. For B-mode, AUC and accuracy values of the classification algorithms ranged from 0.790 to 0.943 and from 72 to 89%, respectively. For elastography, AUC and accuracy values ranged from 0.847 to 0.985 and from 79% to 95%, respectively.ConclusionAutomated processing of ultrasound images through deep learning can generate high-precision classification algorithms that differentiate glioblastomas from metastases using intraoperative ultrasound. The best performance regarding AUC was achieved by the elastography-based model supporting the additional diagnostic value that this technique provides.

show abstract

“…With the development of deep learning, DNNs are widely used in establishing models [ 27 ]. Convolutional neural networks (CNNs) [ 28 , 29 ] are one of the most common DNNs that can automatically identify and segment medical imaging. Another type of DNN is the recurrent neural network (RNN).…”

Section: Introductionmentioning

confidence: 99%

Current updates in machine learning in the prediction of therapeutic outcome of hepatocellular carcinoma: what should we know?

et al. 2021

View full text Add to dashboard Cite

With the development of machine learning (ML) algorithms, a growing number of predictive models have been established for predicting the therapeutic outcome of patients with hepatocellular carcinoma (HCC) after various treatment modalities. By using the different combinations of clinical and radiological variables, ML algorithms can simulate human learning to detect hidden patterns within the data and play a critical role in artificial intelligence techniques. Compared to traditional statistical methods, ML methods have greater predictive effects. ML algorithms are widely applied in nearly all steps of model establishment, such as imaging feature extraction, predictive factor classification, and model development. Therefore, this review presents the literature pertaining to ML algorithms and aims to summarize the strengths and limitations of ML, as well as its potential value in prognostic prediction, after various treatment modalities for HCC.

show abstract

Comparison of Deep-Learning and Conventional Machine-Learning Methods for the Automatic Recognition of the Hepatocellular Carcinoma Areas from Ultrasound Images

Cited by 69 publications

References 37 publications

Artificial intelligence in the diagnosis and management of hepatocellular carcinoma

Artificial intelligence in the diagnosis and management of hepatocellular carcinoma

Comparison of Intraoperative Ultrasound B-Mode and Strain Elastography for the Differentiation of Glioblastomas From Solitary Brain Metastases. An Automated Deep Learning Approach for Image Analysis

Current updates in machine learning in the prediction of therapeutic outcome of hepatocellular carcinoma: what should we know?

Contact Info

Product

Resources

About