Deep learning–assisted differentiation of pathologically proven atypical and typical hepatocellular carcinoma (HCC) versus non-HCC on contrast-enhanced MRI of the liver

Oestmann, Paula M; Wang, Clinton J.; Savic, Lynn Jeanette; Hamm, Charlie Alexander; Stark, Sophie; Schobert, Isabel; Gebauer, Bernhard; Schlachter, Todd; Lin, MingDe; Weinreb, Jeffrey C.; Batra, Ramesh; Mulligan, David C.; Zhang, Xuchen; Duncan, James S.; Chapiro, Julius

doi:10.1007/s00330-020-07559-1

Cited by 41 publications

(27 citation statements)

References 18 publications

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“…Survival and recurrence rates in NCHCC have been improving and expected to reach HCC patients with efficacy of antiviral treatment options, living donor liver transplantation, parenchymal sparing liver resection and two stage liver resections. Use of artificial intelligence, deep learning models (convolutional neural network) are being utilized for identification of NCHCC[ 118 ]. Messenger RNA (mRNA) is a family of RNA molecules which are involved in coding proteins and convey genetic information.…”

Section: Future Research Directionmentioning

confidence: 99%

Non-cirrhotic hepatocellular carcinoma in chronic viral hepatitis: Current insights and advancements

Perisetti

Goyal

Thandassery

et al. 2021

WJG

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Primary liver cancers carry significant morbidity and mortality. Hepatocellular carcinoma (HCC) develops within the hepatic parenchyma and is the most common malignancy originating from the liver. Although 80% of HCCs develop within background cirrhosis, 20% may arise in a non-cirrhotic milieu and are referred to non-cirrhotic-HCC (NCHCC). NCHCC is often diagnosed late due to lack of surveillance. In addition, the rising prevalence of non-alcoholic fatty liver disease and diabetes mellitus have increased the risk of developing HCC on non-cirrhotic patients. Viral infections such as chronic Hepatitis B and less often chronic hepatitis C with advance fibrosis are associated with NCHCC. NCHCC individuals may have Hepatitis B core antibodies and occult HBV infection, signifying the role of Hepatitis B infection in NCHCC. Given the effectiveness of current antiviral therapies, surgical techniques and locoregional treatment options, nowadays such patients have more options and potential for cure. However, these lesions need early identification with diagnostic models and multiple surveillance strategies to improve overall outcomes. Better understanding of the NCHCC risk factors, tumorigenesis, diagnostic tools and treatment options are critical to improving prognosis and overall outcomes on these patients. In this review, we aim to discuss NCHCC epidemiology, risk factors, and pathogenesis, and elaborate on NCHCC diagnosis and treatment strategies.

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Section: Future Research Directionmentioning

confidence: 99%

Non-cirrhotic hepatocellular carcinoma in chronic viral hepatitis: Current insights and advancements

Perisetti

Goyal

Thandassery

et al. 2021

WJG

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“…DP is slowly gaining acceptance in routine diagnostics and can now be utilized both for frozen sections and paraffin tissue blocks 24,36,37 . The utility of deep neural networks is approaching a level of accuracy similar to or even higher than analysis made by dermatologists and dermatopathologists 38–40 . To further characterize the study tumor samples, a method for visualizing the heterogeneous melanoma morphology by DP was created, utilizing recent developments in machine learning.…”

Section: Resultsmentioning

confidence: 99%

The human melanoma proteome atlas—Defining the molecular pathology

Betancourt

Gil

Kim

et al. 2021

Clinical & Translational Med

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“…Focusing on HCC diagnosis, a recent retrospective study developed a CNN that employed MRI images of patients with HCC. The model was trained with a combination of images that met the Liver Imaging Reporting and Data System (typical) and with images that did not (atypical)[ 127 ]. In a multicenter, retrospective study, a CNN was developed that employed MRI scans to identify HCC lesions[ 128 ].…”

Section: Applications Of Ai In Hepatologymentioning

confidence: 99%

Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology

Christou

Tsoulfas

2021

WJG

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Artificial intelligence (AI) is an umbrella term used to describe a cluster of interrelated fields. Machine learning (ML) refers to a model that learns from past data to predict future data. Medicine and particularly gastroenterology and hepatology, are data-rich fields with extensive data repositories, and therefore fruitful ground for AI/ML-based software applications. In this study, we comprehensively review the current applications of AI/ML-based models in these fields and the opportunities that arise from their application. Specifically, we refer to the applications of AI/ML-based models in prevention, diagnosis, management, and prognosis of gastrointestinal bleeding, inflammatory bowel diseases, gastrointestinal premalignant and malignant lesions, other nonmalignant gastrointestinal lesions and diseases, hepatitis B and C infection, chronic liver diseases, hepatocellular carcinoma, cholangiocarcinoma, and primary sclerosing cholangitis. At the same time, we identify the major challenges that restrain the widespread use of these models in healthcare in an effort to explore ways to overcome them. Notably, we elaborate on the concerns regarding intrinsic biases, data protection, cybersecurity, intellectual property, liability, ethical challenges, and transparency. Even at a slower pace than anticipated, AI is infiltrating the healthcare industry. AI in healthcare will become a reality, and every physician will have to engage with it by necessity.

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Deep learning–assisted differentiation of pathologically proven atypical and typical hepatocellular carcinoma (HCC) versus non-HCC on contrast-enhanced MRI of the liver

Cited by 41 publications

References 18 publications

Non-cirrhotic hepatocellular carcinoma in chronic viral hepatitis: Current insights and advancements

Non-cirrhotic hepatocellular carcinoma in chronic viral hepatitis: Current insights and advancements

The human melanoma proteome atlas—Defining the molecular pathology

Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology

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