The promise of quantitative approaches to computed tomography imaging in pulmonary sarcoidosis.

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Abstract

Pulmonary sarcoidosis is characterized by marked radiologic heterogeneity and limited reproducibility of visual high-resolution computed tomography (HRCT) assessment, which together restrict standardization, phenotyping, and prognostication. This review examines the potential that quantitative and artificial intelligence-based approaches offer in solving these challenges. It then offers key insights into the solutions needed to fully realize the value of HRCT imaging in pulmonary sarcoidosis. Early quantitative and artificial intelligence-driven studies demonstrate that HRCT images can be transformed into objective, reproducible numerical representations that capture disease patterning beyond conventional visual interpretation. These approaches show promise for clinically relevant and standardized assessment of pulmonary involvement. However, in sarcoidosis, existing work remains largely preliminary and limited by small sample sizes, single center designs, technical heterogeneity, and unreliable ground truth imaging labels. Recent studies also highlight opportunities for alternative quantitative strategies that may be better suited to the data constraints of rare diseases. Quantitative HRCT analysis offers a compelling framework for advancing imaging-based assessment in pulmonary sarcoidosis, but meaningful progress will require large multicenter cohorts and more objective nonimaging outcomes that move beyond subjective visual assessment. Quantitative imaging may then help reposition HRCT as a reproducible biomarker platform for research and clinical care.

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