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Machine learning-based diagnostic model for preoperative differentiation between xanthogranulomatous cholecystitis and gallbladder carcinoma: a multicenter retrospective cohort study.
Fu, Tianwei; Bao, Yating; Zhong, Zhihan; Gao, Zhenyu; Ye, Taiwei; Zhang, Chengwu; Jing, Huang; Xiao, Zunqiang.
Afiliação
  • Fu T; General Surgery, Cancer Center, Department of Hepatobiliary & Pancreatic Surgery and Minimally Invasive Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China.
  • Bao Y; Department of Hepatopancreatobiliary Surgery, The Affiliated Lihuili Hospital of Ningbo University, Ningbo University, Ningbo, Zhejiang, China.
  • Zhong Z; General Surgery, Cancer Center, Department of Hepatobiliary & Pancreatic Surgery and Minimally Invasive Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China.
  • Gao Z; General Surgery, Cancer Center, Department of Hepatobiliary & Pancreatic Surgery and Minimally Invasive Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China.
  • Ye T; General Surgery, Cancer Center, Department of Hepatobiliary & Pancreatic Surgery and Minimally Invasive Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China.
  • Zhang C; General Surgery, Cancer Center, Department of Hepatobiliary & Pancreatic Surgery and Minimally Invasive Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China.
  • Jing H; Department of Hepatopancreatobiliary Surgery, The Affiliated Lihuili Hospital of Ningbo University, Ningbo University, Ningbo, Zhejiang, China.
  • Xiao Z; General Surgery, Cancer Center, Department of Hepatobiliary & Pancreatic Surgery and Minimally Invasive Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China.
Front Oncol ; 14: 1355927, 2024.
Article em En | MEDLINE | ID: mdl-38476361
ABSTRACT

Background:

Xanthogranulomatous cholecystitis (XGC) and gallbladder carcinoma (GBC) share similar imaging and serological profiles, posing significant challenges in accurate preoperative diagnosis. This study aimed to identify reliable indicators and develop a predictive model to differentiate between XGC and GBC.

Methods:

This retrospective study involved 436 patients from Zhejiang Provincial People's Hospital and The Affiliated Lihuili Hospital of Ningbo University. Comprehensive preoperative imaging, including ultrasound, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and blood tests, were analyzed. Machine learning (Random Forest method) was employed for variable selection, and a multivariate logistic regression analysis was used to construct a nomogram for predicting GBC. Statistical analyses were performed using SPSS and RStudio software.

Results:

The study identified gender, Murphy's sign, absolute neutrophil count, glutamyl transpeptidase level, carcinoembryonic antigen level, and comprehensive imaging diagnosis as potential risk factors for GBC. A nomogram incorporating these factors demonstrated high predictive accuracy for GBC, outperforming individual or combined traditional diagnostic methods. External validation of the nomogram showed consistent results.

Conclusion:

The study successfully developed a predictive nomogram for distinguishing GBC from XGC with high accuracy. This model, integrating multiple clinical and imaging indicators, offers a valuable tool for clinicians in making informed diagnostic decisions. The findings advocate for the use of comprehensive preoperative evaluations combined with advanced analytical tools to improve diagnostic accuracy in complex medical conditions.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Front Oncol Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China País de publicação: Suíça

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Front Oncol Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China País de publicação: Suíça