# Diagnostic predictive evaluation of pneumocystis jirovecii pneumonia using digital chest CT analysis combined with clinical features

## TL;DR

This study develops a model combining chest CT analysis and clinical data to improve early diagnosis of Pneumocystis jirovecii pneumonia.

## Contribution

A novel diagnostic model integrating AI-based CT analysis with clinical features to distinguish PJP from bacterial pneumonia.

## Key findings

- The model achieved 89.8% AUC in training and 82.0% in validation cohorts.
- Key predictors included CT lesion volumes and procalcitonin levels.
- The model showed 74.5% sensitivity and 90.4% specificity in training data.

## Abstract

Pneumocystis jirovecii pneumonia (PJP) is a serious form of pneumonia characterized by non-specific symptoms. Diagnosis is challenging due to overlapping clinical and laboratory features with bacterial pneumonia (BP). This study aimed to develop a diagnostic prediction model integrating digital chest CT analysis with clinical and laboratory parameters to enable early identification of PJP.

A retrospective analysis was performed on patients with confirmed PJP or BP at two medical centers between May 2020 and June 2024. Patient history, clinical symptoms, and laboratory test results were compared between cohorts. Chest CT images were analyzed using AI-assisted tools. Predictive factors were identified through univariate and multivariate logistic regression analyses, and a diagnostic nomogram was constructed. External validation was conducted using an independent cohort.

Multivariate analysis identified previous immunomodulator use, procalcitonin levels, inflammatory lesion volume/total lung volume, whole lung −700 to −450 HU pneumonia lesion volume, and whole lung −450 to −300 HU pneumonia lesion volume as independent predictors of PJP. The constructed nomogram achieved AUCs of 0.898 and 0.820 in the training and validation cohorts, respectively, with sensitivity of 74.5% and specificity of 90.4% in the training cohort, and sensitivity of 73.5% and specificity of 79.4% in the validation cohort. Calibration curves and decision curve analyses confirmed the model’s predictive accuracy and clinical utility.

The model provides a valuable tool for differentiating PJP from BP, demonstrating that AI-assisted recognition of chest CT images can effectively support pathogen identification. Its application has the potential to improve early diagnosis of PJP and enhance patient outcomes.