VERGARA, ALEJANDRO DIAZ ARUMIR

Abstract Rationale In fungal endemic regions, differentiating benign from malignant pulmonary nodules on CT scans remains a major diagnostic challenge. Coccidioides nodules frequently resemble malignant lesions on CT and PET imaging, presenting significant challenges for differentiation, even among radiologists. Consequently, the benign biopsy and resection rate, especially in the Southwestern US, remains high. We previously developed [DOI:10.1371/journal.pone.0196910] and externally validated [DOI:10.1183/13993003.02485-2020] the Benign versus aggRessive nODule Evaluation using Radiomic Stratification (BRODERS) machine learning (ML) classifier to non-invasively distinguish between benign and malignant pulmonary nodules with an AUC of 0.87. However, its performance in certain benign nodules with malignant surface and texture characteristics like in coccidioidomycosis had a suboptimal AUC of 0.65. In this study, we redesigned our radiomics model by including numerous clinical features. Methods In this retrospective study, 113 pulmonary nodules (66 Coccidioides and 47 adenocarcinoma spectrum) detected on CTs with ground truth pathology diagnosis were identified by screening for key terms like “nodule”, “cocci”, and “adenocarcinoma” from both CT and pathology reports using a large language model trained on clinical textual data (Illuminate®). Clinical features such as demographics, smoking history, and laboratory studies were extracted by trained clinicians from the electronic medical record. All nodules were segmented and radiomic features extracted using a semi-automated seed growing algorithm previously described [DOI: 10.1097/JTO.0b013e3182843721]. The database was randomly split into an 80%-20% development and hold-out set prior to ML modeling. Feature selection for variables was conducted utilizing Elastic Net Regression, which provides a balanced approach between Lasso and Ridge regression. A logistic regression model was developed using the selected features and ground truth information of the training cohort. The true performance of the model was tested on the hold-out set. Results 160 radiomics and 22 clinical features were extracted. Elastic Net regression identified 44 features that contributed the most. Some of these clinical features include presence of satellite lesions, smoking history, and presence of positive IgG coccidiomycosis serology. The AUC of the ROC curve for the hold-out test set was 0.86. Conclusions The clinical features of significance that emerged during multimodal modeling are explainable. Designing explainable ML models is important, especially in the Southwestern US where Coccidioides nodules can appear to be malignant to both human experts and ML models. Although coccidioidomycosis serologies alone lack sensitivity and specificity to diagnose Coccidioides nodules, integrating them with a multimodal ML model can improve differentiation between benign Coccidioides and malignant adenocarcinoma nodules. This abstract is funded by: None

Background: Pleuroparenchymal fibroelastosis (PPFE) is a rare interstitial lung disease (ILD) associated with systemic autoimmune rheumatic diseases (SARDs). Its clinical characteristics and outcomes in SARDs remain poorly defined. Objectives: To describe demographics, imaging patterns, and outcomes in SARD-associated PPFE. Methods: Patients diagnosed with PPFE and SARDs at Mayo Clinic (2000-2024) were included. PPFE diagnosis was confirmed by high-resolution computed tomography, and SARD diagnoses were established by rheumatologists. Results: Fifteen of 62 patients with PPFE had SARDs with majority having systemic sclerosis (SSc)(n= 7, 46%). Median age at PPFE diagnosis was 57.2 years; 80% were female; 40% were ever-smokers; 40% required supplemental oxygen; 5 had pulmonary hypertension. Coexisting ILD patterns included UIP (26.7%) and NSIP (20%). Coexisting ILD patterns included usual interstitial pneumonia (26.7%) and nonspecific interstitial pneumonia (20%). Three patients died, including two with SSc. Conclusion: Systemic sclerosis was the most frequently observed SARD among patients with PPFE, and may coexist with other ILD patterns; recognition may inform risk stratification, multidisciplinary evaluation, and longitudinal follow-up.