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To develop nomograms based on diffusion-weighted imaging (DWI) histogram parameters and clinical features to preoperatively predict pathogen type and extended-spectrum β-lactamase (ESBL) infection in perianal abscesses.

We retrospectively analyzed 157 surgically confirmed patients, stratified by pathogen type (Escherichia coli, n = 110; Klebsiella pneumoniae, n = 47) and ESBL test results (ESBL-negative, n = 91; ESBL-positive, n = 30). Ninety-seven apparent diffusion coefficient (ADC) histogram parameters were extracted. Histogram features selected using least absolute shrinkage and selection operator (LASSO) regression, together with clinical variables identified by univariate logistic regression, were incorporated into multivariate logistic regression models to construct nomograms. Internal validation used 1,000 bootstrap resamples. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), the Hosmer-Lemeshow test, calibration curves, and decision curve analysis (DCA).

The pathogen discrimination model, integrating an ADC-derived composite score (ADC_Score) based on 20 retained histogram features with age, diabetes, and hypertension, achieved an AUC of 0.897, sensitivity of 0.872, and specificity of 0.809. The ESBL prediction model, incorporating ADC_Score based on 13 retained features together with white blood cell count (WBC) and age, yielded an AUC of 0.823, sensitivity of 0.867, and specificity of 0.659. Calibration curves and the Hosmer-Lemeshow test indicated good agreement between predicted and observed probabilities, and DCA suggested potential net benefit for both models within the internally validated cohort.

DWI histogram-based nomograms demonstrated promising performance for pathogen prediction in perianal abscesses, while the incremental value for ESBL prediction was limited. These models represent an internally validated development study and require external validation before clinical application.