Nomogram models integrating multiregion radiomics, quantitative computed tomography, and clinical indicators for distinguishing connective tissue disease-associated interstitial lung disease from idiopathic pulmonary fibrosis and improving the prediction of gender-age-physiology stage

Background: Accurate diagnosis of idiopathic pulmonary fibrosis (IPF) and connective tissue disease-associated interstitial lung disease (CTD-ILD), along with assessment of the interstitial lung disease gender-age-physiology (ILD-GAP) index, is crucial for formulating effective therapeutic interventions. The aim of this study was to examine whether multiregion radiomics combined with clinical indicators and quantitative computed tomography (QCT) can enhance the diagnostic accuracy for IPF and CTD-ILD. We also assessed the potential of radiomics as a noninvasive alternative to pulmonary function testing (PFT) for evaluating ILD-GAP staging, a prognostic marker.

Methods: This retrospective study included patients diagnosed with IPF and CTD-ILD at two centers. Honeycombing (H), reticulation (R), and ground-glass opacity (GGO) were outlined in representative regions, and radiomics features were then extracted. Multidimensional data included multiregional radiomics, QCT (lesion volume), and clinical indicators. The most appropriate machine learning algorithms were selected to develop models. Ultimately, the logistic regression algorithm was employed to establish a nomogram model for distinguishing IPF from CTD-ILD and a nomogram gender-age-physiology (nomogram-GAP) model for predicting ILD-GAP staging. Model performance was evaluated via the area under the receiver operating characteristic curve (AUC), calibration, and decision curve analysis (DCA).

Results: To differentiate IPF from CTD-ILD, we enrolled patients from Center A into training (n=153), test (n=64), and internal validation (n=42) sets, as well as 40 patients from Center B into an external validation set. The nomogram model yielded an AUC of 0.854 in the internal validation set and 0.823 in the external validation set. For ILD-GAP staging prediction, Center A patients were split into training (n=155) and internal validation sets (n=31). The nomogram-GAP model had an AUC of 0.881 in the internal validation set. Calibration and DCA demonstrated that the nomogram model and nomogram-GAP model exhibit strong consistency and clinical utility.

Conclusions: Two types of nomograms integrating multiregion radiomics, QCT, and clinical indicators demonstrated promising performance in distinguishing IPF from CTD-ILD and predicting ILD-GAP staging. When PFTs are unreliable or impracticable, the nomogram-GAP model may serve as a reliable marker for assessing ILD-GAP staging.