Deep Learning Radiomics Based on Contrast-Enhanced Ultrasound Might Optimize Curative Treatments for Very-Early or Early-Stage Hepatocellular Carcinoma Patients

Liu, Fei; Liú, Dan; Wang, Kun; Xie, Xiaohua; Su, Liya; Kuang, Ming; Huang, Guangliang; Peng, Baogang; Wang, Yuqi; Lin, Manxia; Tian, Jie

doi:10.1159/000505694

Cited by 100 publications

(63 citation statements)

References 34 publications

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“…A more recent study, performed in 419 patients examined by CEUS within 1 week before receiving radiofrequency ablation or surgical resection (radiofrequency ablation: 214, surgical resection: 205), analyzed the CEUS findings and found that 17.3% of radiofrequency ablation patients and 27.3% of surgical resection patients should swap their treatment; as a result, their average probability of 2-year progression-free survival would increase by 12% and 15%, respectively [ 82 ]. Moreover, dynamic CEUS radiomics performed well (AUC 0.84) in predicting the post-ablation early recurrence of HCC [ 69 ].…”

Section: Radiomics-based Us For the Diagnosis Of Hccmentioning

confidence: 99%

AI-Based Radiological Imaging for HCC: Current Status and Future of Ultrasound

et al. 2021

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Hepatocellular carcinoma (HCC) is a common cancer worldwide. Recent international guidelines request an identification of the stage and patient background/condition for an appropriate decision for the management direction. Radiomics is a technology based on the quantitative extraction of image characteristics from radiological imaging modalities. Artificial intelligence (AI) algorithms are the principal axis of the radiomics procedure and may provide various results from large data sets beyond conventional techniques. This review article focused on the application of the radiomics-related diagnosis of HCC using radiological imaging (computed tomography, magnetic resonance imaging, and ultrasound (B-mode, contrast-enhanced ultrasound, and elastography)), and discussed the current role, limitation and future of ultrasound. Although the evidence has shown the positive effect of AI-based ultrasound in the prediction of tumor characteristics and malignant potential, posttreatment response and prognosis, there are still a number of issues in the practical management of patients with HCC. It is highly expected that the wide range of applications of AI for ultrasound will support the further improvement of the diagnostic ability of HCC and provide a great benefit to the patients.

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Section: Radiomics-based Us For the Diagnosis Of Hccmentioning

confidence: 99%

AI-Based Radiological Imaging for HCC: Current Status and Future of Ultrasound

et al. 2021

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“…For the development of AI that determine the stage of liver fibrosis more accurately, Gatos et al reported a detection algorithm that excludes unreliable regions on SWE images, which contributes to a reduction in interobserver variability (45). Applying these AI models may be an alternative to invasive liver biopsy for (38) SVM, support vector machine; ANN, artificial neural network; CNN, convolutional neural network; CEUS, contrast-enhanced ultrasonography; HCC, hepatocellular carcinoma; AUC, area under the receiver operating characteristic curve; TACE, transarterial chemoembolization; RFA, radiofrequency ablation; C-index, concordance index.…”

Section: Ai-aided Diagnosis For Liver Tumors In Ultrasonographymentioning

confidence: 99%

“…They reported AUCs of 0.93 and 0.81 for the AI based on CEUS and B-mode US images, respectively, indicating a higher performance of the model pre-trained with CEUS images than that with B-mode US images. They also reported AI models for predicting outcomes in patients with HCC after two types of treatment—radiofrequency ablation (RFA) and liver resection—from radiomics information based on CEUS images ( 38 ). For the prediction of two-year progression-free survival (PFS), both models provided high prediction accuracy.…”

Section: Current Ai Models For Medical Imaging Of Liver Lesionsmentioning

confidence: 99%

Artificial Intelligence in Medical Imaging and Its Application in Sonography for the Management of Liver Tumor

Nishida

Kudo

2020

Front. Oncol.

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Recent advancement in artificial intelligence (AI) facilitate the development of AI-powered medical imaging including ultrasonography (US). However, overlooking or misdiagnosis of malignant lesions may result in serious consequences; the introduction of AI to the imaging modalities may be an ideal solution to prevent human error. For the development of AI for medical imaging, it is necessary to understand the characteristics of modalities on the context of task setting, required data sets, suitable AI algorism, and expected performance with clinical impact. Regarding the AI-aided US diagnosis, several attempts have been made to construct an image database and develop an AI-aided diagnosis system in the field of oncology. Regarding the diagnosis of liver tumors using US images, 4- or 5-class classifications, including the discrimination of hepatocellular carcinoma (HCC), metastatic tumors, hemangiomas, liver cysts, and focal nodular hyperplasia, have been reported using AI. Combination of radiomic approach with AI is also becoming a powerful tool for predicting the outcome in patients with HCC after treatment, indicating the potential of AI for applying personalized medical care. However, US images show high heterogeneity because of differences in conditions during the examination, and a variety of imaging parameters may affect the quality of images; such conditions may hamper the development of US-based AI. In this review, we summarized the development of AI in medical images with challenges to task setting, data curation, and focus on the application of AI for the managements of liver tumor, especially for US diagnosis.

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“…This system achieved an average accuracy of 93.1%, when the 10-fold cross-validation strategy was employed. In [ 41 ], the authors assessed a radiomics methodology based on deep learning techniques to derive features in order to evaluate the Progression-Free Survival (PFS), as well as the Surgical Resection (SR) of the Radiofrequency Ablation (RFA) procedure. Another objective was that of optimizing the selection of patients with incipient HCC, supposed to undergo specific treatments.…”

Section: Introductionmentioning

confidence: 99%

Hepatocellular Carcinoma Automatic Diagnosis within CEUS and B-Mode Ultrasound Images Using Advanced Machine Learning Methods

Mitrea

Badea

Mitrea

et al. 2021

Sensors

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Hepatocellular Carcinoma (HCC) is the most common malignant liver tumor, being present in 70% of liver cancer cases. It usually evolves on the top of the cirrhotic parenchyma. The most reliable method for HCC diagnosis is the needle biopsy, which is an invasive, dangerous method. In our research, specific techniques for non-invasive, computerized HCC diagnosis are developed, by exploiting the information from ultrasound images. In this work, the possibility of performing the automatic diagnosis of HCC within B-mode ultrasound and Contrast-Enhanced Ultrasound (CEUS) images, using advanced machine learning methods based on Convolutional Neural Networks (CNN), was assessed. The recognition performance was evaluated separately on B-mode ultrasound images and on CEUS images, respectively, as well as on combined B-mode ultrasound and CEUS images. For this purpose, we considered the possibility of combining the input images directly, performing feature level fusion, then providing the resulted data at the entrances of representative CNN classifiers. In addition, several multimodal combined classifiers were experimented, resulted by the fusion, at classifier, respectively, at the decision levels of two different branches based on the same CNN architecture, as well as on different CNN architectures. Various combination methods, and also the dimensionality reduction method of Kernel Principal Component Analysis (KPCA), were involved in this process. These results were compared with those obtained on the same dataset, when employing advanced texture analysis techniques in conjunction with conventional classification methods and also with equivalent state-of-the-art approaches. An accuracy above 97% was achieved when our new methodology was applied.

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Deep Learning Radiomics Based on Contrast-Enhanced Ultrasound Might Optimize Curative Treatments for Very-Early or Early-Stage Hepatocellular Carcinoma Patients

Cited by 100 publications

References 34 publications

AI-Based Radiological Imaging for HCC: Current Status and Future of Ultrasound

AI-Based Radiological Imaging for HCC: Current Status and Future of Ultrasound

Artificial Intelligence in Medical Imaging and Its Application in Sonography for the Management of Liver Tumor

Hepatocellular Carcinoma Automatic Diagnosis within CEUS and B-Mode Ultrasound Images Using Advanced Machine Learning Methods

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