Chest Computed Tomography-Based Radiomics for the Diagnosis and Prognosis of Pulmonary Hypertension.
Authors
Affiliations (7)
Affiliations (7)
- Department of Cardiology, Renji Hospital, School of Medicine Shanghai Jiao Tong University Shanghai China.
- Department of Cardiology, Shanghai Pudong Hospital Fudan University Pudong Medical Center Shanghai China.
- Department of Cardiology, Sun Yat-sen Memorial Hospital Sun Yat-sen University Guangzhou Guangdong China.
- Department of Cardiology, Zhongshan Hospital Fudan University, Shanghai Institute of Cardiovascular Disease Shanghai China.
- Department of Respiratory and Critical Care Medicine The First Affiliated Hospital of Nanjing Medical University Nanjing Jiangsu China.
- Department of Cardio-Pulmonary Circulation, Shanghai Pulmonary Hospital Tongji University Shanghai China.
- Department of Cardiology, the Second Affiliated Hospital Third Military Medical University (Army Medical University) Chongqing China.
Abstract
Imaging technique has emerged as an innovative tool for diagnosing and monitoring patients with pulmonary hypertension (PH). Current studies in radiomics primarily focus on hemodynamics and cardiac function, whereas the assessment of pulmonary vessels is often neglected. This study aims to investigate the diagnostic and prognostic value of computed tomography pulmonary vascular radiomics in PH. This multicenter study enrolled 193 patients with PH and 193 controls (102 symptomatic non-PH cases and 91 healthy volunteers) for diagnostic analysis, with external validation in 38 patients with PH and 38 controls. For prognostic analysis, 166 patients with PH were prospectively followed (median follow-up: 16 months; 96 clinical deterioration events), with external validation in 32 patients with PH (median follow-up: 7 months; 11 events). Pulmonary vascular radiomics features extracted from chest computed tomography were used to develop predictive models for PH diagnosis and prognosis. The diagnostic model integrating 7 radiomic and 3 clinical features achieved an area under the curve of 0.984 (95% CI, 0.959-0.995) in the derivation cohort and 0.980 (0.901-1.000) in external validation. For exploratory pulmonary arterial hypertension subtyping, the model incorporating 8 radiomic and 2 clinical features yielded an area under the curve of 0.898 (0.825-0.972) internally and 0.877 (0.352-1.000) externally. The prognostic radiomic-clinical model outperformed the European Society of Cardiology 4-strata risk assessment, with a 2-year area under the curve of 0.866 (0.8-0.942) versus 0.709 (0.648-0.789). The radiomics-based models have strong diagnostic and prognostic capabilities for PH and can also successfully differentiate pulmonary arterial hypertension. This suggests the potential of radiomics to discern PH with different clinical risks, which may facilitate personalized drug therapy.