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Prediction of Benign and Malignant Gallbladder Space-Occupying Lesions Using Interpretable Machine Learning Models: A Multicenter Retrospective Study

Prediction of Benign and Malignant Gallbladder Space-Occupying Lesions Using Interpretable Machine Learning Models: A Multicenter Retrospective Study

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2600121723
Enrollment
Unknown
Registered
2026-04-02
Start date
2026-04-10
Completion date
Unknown
Last updated
2026-04-14

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Gallbladder space-occupying lesions and Gallbladder cancer

Interventions

Gold Standard:The final diagnosis was confirmed by postoperative pathology.
Index test:The interpretable machine learning models developed in this study—namely, C5.0?GP?LR?MLP?NB?NN?XGB—were evaluated as diagnostic tools.

Sponsors

Qilu Hospital of Shandong University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to No maximum

Inclusion criteria

Inclusion criteria: 1.aAged >=18 years; 2.Patients who visited the corresponding hospitals within the specified study period and were found to have gallbladder space-occupying lesions on preoperative imaging. 3.Underwent cholecystectomy at the corresponding hospitals and had complete postoperative pathological reports.

Exclusion criteria

Exclusion criteria: 1.Patients with severely missing clinical data, where the missing rate of key predictor variables exceeded 50%, were excluded. 2.Postoperative pathological diagnosis was unclear / inconclusive. 3.History of previous gallbladder surgery. 4.Presence of other malignant diseases prior to presentation.

Design outcomes

Primary

MeasureTime frame
Area under the receiver operating characteristic curve (AUC);

Secondary

MeasureTime frame
Decision Curve Analysis(DCA);Sensitivity;Specificity;F1 score;Accuracy;Positive predictive value(PPV);Negative predictive value(NPV);

Countries

China

Contacts

Public ContactPeng Cheng

Qilu Hospital of Shandong University

Dr.Peng@email.sdu.edu.cn+86 531 82166651

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Apr 17, 2026