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Gallbladder Neoplasms

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Diagnostic performance of endoscopic ultrasound-artificial intelligence using deep learning analysis of gallbladder polypoid lesions.

Journal of gastroenterology and hepatology
BACKGROUND AND AIM: Endoscopic ultrasound (EUS) is the most accurate diagnostic modality for polypoid lesions of the gallbladder (GB), but is limited by subjective interpretation. Deep learning-based artificial intelligence (AI) algorithms are under ...

Analysis of ultrasonographic images using a deep learning-based model as ancillary diagnostic tool for diagnosing gallbladder polyps.

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
BACKGROUND: Accurately diagnosing gallbladder polyps (GBPs) is important to avoid misdiagnosis and overtreatment.

A deep learning model based on contrast-enhanced computed tomography for differential diagnosis of gallbladder carcinoma.

Hepatobiliary & pancreatic diseases international : HBPD INT
BACKGROUND: Gallbladder carcinoma (GBC) is highly malignant, and its early diagnosis remains difficult. This study aimed to develop a deep learning model based on contrast-enhanced computed tomography (CT) images to assist radiologists in identifying...

Radiomics-based machine learning and deep learning to predict serosal involvement in gallbladder cancer.

Abdominal radiology (New York)
OBJECTIVE: Our study aimed to determine whether radiomics models based on contrast-enhanced computed tomography (CECT) have considerable ability to predict serosal involvement in gallbladder cancer (GBC) patients.

Deep-learning models for differentiation of xanthogranulomatous cholecystitis and gallbladder cancer on ultrasound.

Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology
BACKGROUND: The radiological differentiation of xanthogranulomatous cholecystitis (XGC) and gallbladder cancer (GBC) is challenging yet critical. We aimed at utilizing the deep learning (DL)-based approach for differentiating XGC and GBC on ultrasoun...

Contrast-Enhanced CT-Based Deep Learning Radiomics Nomogram for the Survival Prediction in Gallbladder Cancer.

Academic radiology
RATIONALE AND OBJECTIVES: An accurate prognostic model is essential for the development of treatment strategies for gallbladder cancer (GBC). This study proposes an integrated model using clinical features, radiomics, and deep learning based on contr...

Deep learning nomogram for preoperative distinction between Xanthogranulomatous cholecystitis and gallbladder carcinoma: A novel approach for surgical decision.

Computers in biology and medicine
The distinction between Xanthogranulomatous Cholecystitis (XGC) and Gallbladder Carcinoma (GBC) is challenging due to their similar imaging features. This study aimed to differentiate between XGC and GBC using a deep learning nomogram model built fro...

Comparing robotic and open surgical techniques in gallbladder cancer management: a detailed systematic review and meta-analysis.

Journal of robotic surgery
This meta-analysis aims to evaluate the safety and oncological outcomes of robotic surgery compared to open surgery in treating gallbladder cancer (GBC). In October 2023, we performed a literature search across major global databases such as PubMed, ...

Applications of artificial intelligence in biliary tract cancers.

Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology
Biliary tract cancers are malignant neoplasms arising from bile duct epithelial cells. They include cholangiocarcinomas and gallbladder cancer. Gallbladder cancer has a marked geographical preference and is one of the most common cancers in women in ...

The value of CT radiomics combined with deep transfer learning in predicting the nature of gallbladder polypoid lesions.

Acta radiologica (Stockholm, Sweden : 1987)
BACKGROUND: Computed tomography (CT) radiomics combined with deep transfer learning was used to identify cholesterol and adenomatous gallbladder polyps that have not been well evaluated before surgery.