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Cholecystitis

Cholecystitis

Cholecystitis is inflammation of the gallbladder, most commonly caused by obstruction of the cystic duct. This blockage prevents bile from draining, leading to gallbladder distension, inflammation, and potentially serious complications. This condition may present acutely or chronically and can happen with or without gallstones.EtiologyAbout 95% of cholecystitis cases are calculous, caused by gallstones blocking the cystic duct, leading to bile accumulation and inflammation of the gallbladder...

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Determination of Malignancy Risk Factors Using Gallstone Data and Comparing Machine Learning Methods to Predict

Sirin Cetin¹, Ayse Ulgen^2,3, Ozge Pasin⁴

¹Department of Biostatistics, Faculty of Medicine, Amasya University, Amasya 05100, Türkiye.

Journal of Clinical Medicine

|September 13, 2025

Summary

Gallstone disease is linked to malignancy risk. Advanced machine learning models, particularly Tabular Prior-data Fitted Network (TabPFN), effectively identified key predictors like age, BMI, and HRT history for malignancy.

Keywords:

clinical insights gallstone disease machine learning malignancy predictive modeling

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Area of Science:

Digestive system disorders
Oncology
Computational biology

Background:

Gallstone disease is a common condition with multifactorial risk factors.
Some risk factors for gallstones may also increase cancer risk.
Understanding these links is crucial for early detection and prevention.

Purpose of the Study:

To investigate the association between gallstone disease and malignancy.
To apply advanced machine learning (ML) algorithms for this analysis.
To identify significant predictors of malignancy in patients with gallstones.

Main Methods:

Analysis of a dataset of approximately 1000 patients.
Utilized six ML algorithms: random forests, SVMs, MLP, MLP_PT, naive Bayes, and TabPFN.
Performance evaluated using metrics like AUC, accuracy, sensitivity, and specificity.

Main Results:

Age, body mass index (BMI), and hormone replacement therapy (HRT) history were identified as key predictors of malignancy.
The Tabular Prior-data Fitted Network (TabPFN) model demonstrated superior performance.
TabPFN achieved high accuracy and effectiveness in predicting malignancy.

Conclusions:

Cutting-edge ML methods can uncover complex clinical data relationships.
TabPFN shows significant promise for predictive modeling in gallstone-related malignancy.
Findings offer insights for personalized patient management and risk assessment.