A Deep Learning Workflow for Mass-Forming Intrahepatic Cholangiocarcinoma and Hepatocellular Carcinoma Classification Based on MRI

Article Radiology, Nuclear Medicine & Medical Imaging

Added-value of ancillary imaging features for differentiating hepatocellular carcinoma from intrahepatic mass-forming cholangiocarcinoma on Gd-BOPTA-enhanced MRI in LI-RADS M

Wanjing Zheng et al.

Summary: Adding ancillary imaging features improved sensitivity and accuracy for HCC diagnosis with differentiation from IMCC in LR-M, with enhancing capsule identified as a reliable imaging feature to help identify HCC.

ABDOMINAL RADIOLOGY (2022)

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Article Radiology, Nuclear Medicine & Medical Imaging

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

Paula M. Oestmann et al.

Summary: This study successfully trained a deep learning model to differentiate between pathologically confirmed HCC and non-HCC lesions on MRI, with good overall accuracy but lower accuracy for lesions with more atypical imaging features.

EUROPEAN RADIOLOGY (2021)

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Article Multidisciplinary Sciences

Value of perfusion parameters histogram analysis of triphasic CT in differentiating intrahepatic mass forming cholangiocarcinoma from hepatocellular carcinoma

Fang Zhao et al.

Summary: This study aimed to identify differential perfusion parameters and corresponding histogram parameters of IMCC and HCC from triphasic CT scans. The results showed that AEF and HAP were significantly higher in HCC than in IMCC, indicating a more distinct difference in arterial perfusion between the tumor and normal liver in HCC. The relative AEF provided useful value in discriminating between IMCC and HCC.

SCIENTIFIC REPORTS (2021)

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Article Gastroenterology & Hepatology

Artificial intelligence in the diagnosis and management of hepatocellular carcinoma

Masaya Sato et al.

Summary: Machine learning approach has shown potential in integrating multiple factors to improve the accuracy of diagnosis and management of liver diseases, particularly hepatocellular carcinoma.

JOURNAL OF GASTROENTEROLOGY AND HEPATOLOGY (2021)

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Article Engineering, Biomedical

A New optimized sequential method for lung tumor diagnosis based on deep learning and converged search and rescue algorithm

Qingji Tian et al.

Summary: This study aimed to achieve higher classification accuracy of lung cancer glands using deep learning and metaheuristics, with the final suggested method outperforming three state-of-the-art methods in terms of precision, recall, F1-score, and accuracy.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2021)

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Article Engineering, Electrical & Electronic

Novel computer-aided lung cancer detection based on convolutional neural network-based and feature-based classifiers using metaheuristics

Zhiqiang Guo et al.

Summary: This study presents a lung cancer diagnosis system based on CT scan images, utilizing a sequential approach with CNN and feature-based classifiers. The CNN classifier is optimized with a new method called improved Harris hawk optimizer, and if cancer is not detected, the feature-based method is applied for further classification. Based on the results, the system determines whether the case study is healthy or cancerous.

INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY (2021)

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Article Oncology