Multimodal radiomics integrating deep learning and clinical features for diagnosing multidrug-resistant tuberculosis in HIV/AIDS patients.

BACKGROUND

This study aimed to develop and validate a predictive model based on multimodal data, including clinical features, radiomics features, and deep learning features, to distinguish multidrug-resistant tuberculosis (MDR-TB) in HIV/AIDS patients, thereby improving diagnostic accuracy.

METHODS

A retrospective cohort of HIV/AIDS patients with drug-sensitive tuberculosis (n = 164) and MDR-TB (n = 63) admitted to the Fourth People's Hospital of Nanning between January 2016 and July 2024 was included. The dataset was randomly divided into training and validation sets at a 7:3 ratio. A multimodal model was constructed by integrating a clinical model, a radiomics model, and a 2.5D multi-instance learning (MIL) approach.

RESULTS

Key predictors-platelet count and C-reactive protein-were identified through univariate and multivariate logistic regression analysis. The integrated model achieved the highest performance in both the training and validation set (AUC=0.943 and 0.899, respectively), significantly outperforming individual models. Grad-CAM effectively localized key image regions influencing decision-making, while a nomogram quantified the contribution weights of each predictor, enhancing model transparency. The Hosmer-Lemeshow (HL) test confirmed good model calibration, and the decision curve analysis (DCA) curve demonstrated the optimal clinical net benefit of the integrated model.

CONCLUSION

The multimodal integrated model developed in this study significantly improved the diagnostic efficacy of MDR-TB in HIV/AIDS patients by combining clinical, radiomics, and deep learning features, providing a reliable tool for individualized precision diagnosis and treatment.