Deep learning-based image quantification of CTPA to phenotype pulmonary arterial hypertension

<bold>Introduction:</bold> Pulmonary arterial hypertension (PAH) patients are increasingly older with comorbidities, leading to management and prognostic implications. Deep learning-based segmentation of CTPA with quantification of pulmonary blood volumes and parenchymal abnormalities may help these patients. <bold>Methods:</bold> We used a deep learning model to quantify pulmonary blood and ground glass (GGO) volumes normalised to lung volumes on CTPA in a secondary analysis of the prospective 2000-2024 CAPHTURE (Cambridge PH Registry) PAH cohort. Volumes Z-scores were separated into tertiles, data reported in medians and survival analysed with Kaplan-Meier and Cox regression. <bold>Results:</bold> 120 PAH patients (median age 50, 80% female) were recruited. Patients in the higher GGO/lung volume tertile had higher number of cardiac comorbidities (0 vs 1 vs 1, p=0.01), reduced 6-minute walk distance improvement after PAH medication (54 vs 12 vs 12m, p=0.002), poorer tolerability of optimal PAH medications (p<0.001), required more diuretics (p<0.001), and had poorer survival (Figure 1). Patients in the highest vein/lung volume tertile had more cardiac comorbidities (0 vs 1 vs 1, p=0.048) and poorer survival. <bold>Conclusion:</bold> Automated quantification of GGO and pulmonary vein volume from CTPA can help management decisions by phenotyping PAH patients with cardiometabolic comorbidities who are less likely to benefit from current PAH medications and have worse survival. <fig><object-id>erj;66/suppl_69/PA6175/F1</object-id><object-id>F1</object-id><object-id>F1</object-id><graphic></graphic></fig>