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Gallbladder

Gallbladder

The gallbladder is a small, pear-shaped organ that plays a crucial role in our digestive system. Measuring about 10 cm in length, it is comparable in size to a kiwi fruit and is located in a hollow area on the lower surface of the liver. The gallbladder's primary function is to store and concentrate bile, a fluid produced by the liver that aids in digestion.
The gallbladder's anatomy consists of three regions: the fundus, body, and neck. Extending from the neck, the cystic duct joins...

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Updated: Aug 22, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Differential diagnoses of gallbladder tumors using CT-based deep learning.

Hiroaki Fujita¹, Taiichi Wakiya¹, Keinosuke Ishido¹

¹Department of Gastroenterological Surgery Hirosaki University Graduate School of Medicine Hirosaki Japan.

Annals of Gastroenterological Surgery

|November 7, 2022

Summary

A deep learning model using CT scans accurately distinguishes gallbladder cancer from xanthogranulomatous cholecystitis. This AI tool aids in selecting appropriate surgical procedures, improving patient outcomes.

Keywords:

deep learning gallbladder cancer neural network precision medicine xanthogranulomatous cholecystitis

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Area of Science:

Radiology
Artificial Intelligence
Oncology

Background:

Differentiating gallbladder cancer (GBC) from xanthogranulomatous cholecystitis (XGC) is diagnostically challenging.
Accurate differential diagnosis is crucial for appropriate surgical planning and patient management.

Purpose of the Study:

To develop and validate a deep learning (DL) model for distinguishing GBC from XGC using computed tomography (CT) images.
To improve surgical decision-making and therapeutic success rates.

Main Methods:

A dataset of preoperative CT images from 28 GBC and 21 XGC patients was compiled.
A CT patch-based discriminating model using a residual convolutional neural network was developed and validated with 5-fold cross-validation.

Main Results:

The DL model achieved high performance in the validation dataset, with 98.8% sensitivity, 98.0% specificity, and 98.5% accuracy (AUC=0.9985).
The model demonstrated strong discriminating accuracy (98.2%, AUC=0.9893) in the test dataset, excluding cases that received neoadjuvant chemotherapy.
Discriminating accuracy was lower (61.8%) for GBC patients who received neoadjuvant chemotherapy.

Conclusions:

The CT-based DL model shows high discriminating performance for GBC and XGC.
This AI approach offers a novel strategy for procedure selection, potentially avoiding unnecessary invasive interventions.