• WalshSimon L FSLFNational Heart and Lung Institute, Imperial College London, London, United Kingdom., John A Mackintosh, Lucio Calandriello, Mario Silva, Nicola Sverzellati, LariciAnna RitaAR0000-0002-1882-6244Dipartimento di Diagnostica per immagini, Radioterapia, Oncologia ed Ematologia, Fondazione Policlinico Universitario A. Gemelli, Istituto di Ricovero e Cura a Carattere Scientifico, Rome, Italy., Stephen M Humphries, David A Lynch, Helen E Jo, Ian Glaspole, Christopher Grainge, Nicole Goh, HopkinsPeter M APMAQueensland Lung Transplant Service, The Prince Charles Hospital, Brisbane, Queensland, Australia.Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia., Yuben Moodley, Paul N Reynolds, Christopher Zappala, Gregory Keir, Wendy A Cooper, Annabelle M Mahar, Samantha Ellis, Athol U Wells, and Tamera J Corte.
• National Heart and Lung Institute, Imperial College London, London, United Kingdom.
• Am. J. Respir. Crit. Care Med. 2022 Oct 1; 206 (7): 883891883-891.

AbstractRationale: Reliable outcome prediction in patients with fibrotic lung disease using baseline high-resolution computed tomography (HRCT) data remains challenging. Objectives: To evaluate the prognostic accuracy of a deep learning algorithm (SOFIA [Systematic Objective Fibrotic Imaging Analysis Algorithm]), trained and validated in the identification of usual interstitial pneumonia (UIP)-like features on HRCT (UIP probability), in a large cohort of well-characterized patients with progressive fibrotic lung disease drawn from a national registry. Methods: SOFIA and radiologist UIP probabilities were converted to Prospective Investigation of Pulmonary Embolism Diagnosis (PIOPED)-based UIP probability categories (UIP not included in the differential, 0-4%; low probability of UIP, 5-29%; intermediate probability of UIP, 30-69%; high probability of UIP, 70-94%; and pathognomonic for UIP, 95-100%), and their prognostic utility was assessed using Cox proportional hazards modeling. Measurements and Main Results: In multivariable analysis adjusting for age, sex, guideline-based radiologic diagnosis, anddisease severity (using total interstitial lung disease [ILD] extent on HRCT, percent predicted FVC, DlCO, or the composite physiologic index), only SOFIA UIP probability PIOPED categories predicted survival. SOFIA-PIOPED UIP probability categories remained prognostically significant in patients considered indeterminate (n = 83) by expert radiologist consensus (hazard ratio, 1.73; P < 0.0001; 95% confidence interval, 1.40-2.14). In patients undergoing surgical lung biopsy (n = 86), after adjusting for guideline-based histologic pattern and total ILD extent on HRCT, only SOFIA-PIOPED probabilities were predictive of mortality (hazard ratio, 1.75; P < 0.0001; 95% confidence interval, 1.37-2.25). Conclusions: Deep learning-based UIP probability on HRCT provides enhanced outcome prediction in patients with progressive fibrotic lung disease when compared with expert radiologist evaluation or guideline-based histologic pattern. In principle, this tool may be useful in multidisciplinary characterization of fibrotic lung disease. The utility of this technology as a decision support system when ILD expertise is unavailable requires further investigation.

17 keywords...

hide…