AIMC Topic: Carcinoma, Hepatocellular

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The Value of Machine Learning-based Radiomics Model Characterized by PET Imaging with Ga-FAPI in Assessing Microvascular Invasion of Hepatocellular Carcinoma.

Academic radiology
RATIONALE AND OBJECTIVES: This study aimed to develop a radiomics model characterized by Ga-fibroblast activation protein inhibitors (FAPI) positron emission tomography (PET) imaging to predict microvascular invasion (MVI) of hepatocellular carcinoma...

Machine learning approach identifies inflammatory gene signature for predicting survival outcomes in hepatocellular carcinoma.

Scientific reports
BACKGROUND: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths worldwide, often linked to chronic inflammation. Our study aimed to probe inflammation pathways at the genetic level and pinpoint biomarkers linked to HCC patient ...

Multimodal multiphasic preoperative image-based deep-learning predicts HCC outcomes after curative surgery.

Hepatology (Baltimore, Md.)
BACKGROUND AND AIMS: HCC recurrence frequently occurs after curative surgery. Histological microvascular invasion (MVI) predicts recurrence but cannot provide preoperative prognostication, whereas clinical prediction scores have variable performances...

Analysis of four long non-coding RNAs for hepatocellular carcinoma screening and prognosis by the aid of machine learning techniques.

Scientific reports
Hepatocellular carcinoma (HCC) represents a significant health burden in Egypt, largely attributable to the endemic prevalence of hepatitis B and C viruses. Early identification of HCC remains a challenge due to the lack of widespread screening among...

F-FDG PET/CT-based habitat radiomics combining stacking ensemble learning for predicting prognosis in hepatocellular carcinoma: a multi-center study.

BMC cancer
BACKGROUND: This study aims to develop habitat radiomic models to predict overall survival (OS) for hepatocellular carcinoma (HCC), based on the characterization of the intratumoral heterogeneity reflected in F-FDG PET/CT images.

Preoperative prediction of post hepatectomy liver failure after surgery for hepatocellular carcinoma on CT-scan by machine learning and radiomics analyses.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
INTRODUCTION: No instruments are available to predict preoperatively the risk of posthepatectomy liver failure (PHLF) in HCC patients. The aim was to predict the occurrence of PHLF preoperatively by radiomics and clinical data through machine-learnin...