Can machine learning radiomics provide pre-operative differentiation of combined hepatocellular cholangiocarcinoma from hepatocellular carcinoma and cholangiocarcinoma to inform optimal treatment planning?

Liu, Xiaoyang; Khalvati, Farzad; Namdar, Khashayar; Fischer, Sandra E.; Lewis, Sara; Taouli, Bachir; Haider, Masoom A.; Jhaveri, Kartik

doi:10.1007/s00330-020-07119-7

Cited by 81 publications

(58 citation statements)

References 36 publications

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“…Another study also stressed the effect of using radiomics: contrast-enhanced MRI and precontrast and portal-phase CT exhibited good performance in the differentiation of HCC from non-HCC (AUC of 0.79 to 0.81 for MRI and AUC of 0.81 and 0.71 for CT). The rates of the misdiagnosis of combined hepatocellular CC as HCC or CC using radiologists' readings were 69% by CT and 58% by MRI [24]. These data may ground strong recommendations for the application of radiomics analysis, with future validation, for the preoperative diagnosis of liver cancer and for optimal treatment decisions regarding liver resection and transplantation.…”

Section: Characterizationmentioning

confidence: 77%

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: Characterizationmentioning

confidence: 77%

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

et al. 2021

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“…In this study, the LASSO penalized LR model showed an AUC value of 0.810, which was comparable with that of “Liver Imaging Reporting and Data System” 37 (AUC, 0.841) and European Association for the Study of the Liver criteria (AUC, 0.811) 38 . In another study, the AUC (reflecting the performance) of ML (SVM) on a single phase of contrast‐enhanced MRI (and CT) radiomic features to differentiate conventional HCC from ICC and combined HCC was reported to be 0.79–0.81 39 …”

Section: Machine Learning Approach For Hepatocellular Carcinoma Diagnmentioning

confidence: 99%

Artificial intelligence in the diagnosis and management of hepatocellular carcinoma

Sato

Tateishi

Yatomi

et al. 2021

J of Gastro and Hepatol

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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.

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“…Liu et al . analyzed CT and MRI radiomic features with support vector machine for classifying hepatic malignancies 41 . Prediction based on MRI radiomic features achieved the best performance in differentiating CHC from non‐CHC (AUC 0.77).…”

Section: Biliary Diseasesmentioning

confidence: 99%

Applications of artificial intelligence in pancreatic and biliary diseases

Chen

Chang

et al. 2021

J of Gastro and Hepatol

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The application of artificial intelligence (AI) in medicine has increased rapidly with respect to tasks including disease detection/diagnosis, risk stratification, and prognosis prediction. With recent advances in computing power and algorithms, AI has shown promise in taking advantage of vast electronic health data and imaging studies to supplement clinicians. Machine learning and deep learning are the most widely used AI methodologies for medical research and have been applied in pancreatobiliary diseases for which diagnosis and treatment selection are often complicated and require joint consideration of data from multiple sources. The aim of this review is to provide a concise introduction of the major AI methodologies and the current landscape of AI research in pancreatobiliary diseases.

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Can machine learning radiomics provide pre-operative differentiation of combined hepatocellular cholangiocarcinoma from hepatocellular carcinoma and cholangiocarcinoma to inform optimal treatment planning?

Cited by 81 publications

References 36 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 the diagnosis and management of hepatocellular carcinoma

Applications of artificial intelligence in pancreatic and biliary diseases

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