Rationale: 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, Dl_CO, 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.

Background and objective: Prediction of disease course in patients with progressive pulmonary fibrosis remains challenging. The purpose of this study was to assess the prognostic value of lung fibrosis extent quantified at computed tomography (CT) using data‐driven texture analysis (DTA) in a large cohort of well‐characterized patients with idiopathic pulmonary fibrosis (IPF) enrolled in a national registry.Methods: This retrospective analysis included participants in the Australian IPF Registry with available CT between 2007 and 2016. CT scans were analysed using the DTA method to quantify the extent of lung fibrosis. Demographics, longitudinal pulmonary function and quantitative CT metrics were compared using descriptive statistics. Linear mixed models, and Cox analyses adjusted for age, gender, BMI, smoking history and treatment with anti‐fibrotics were performed to assess the relationships between baseline DTA, pulmonary function metrics and outcomes.Results: CT scans of 393 participants were analysed, 221 of which had available pulmonary function testing obtained within 90 days of CT. Linear mixed‐effect modelling showed that baseline DTA score was significantly associated with annual rate of decline in forced vital capacity and diffusing capacity of carbon monoxide. In multivariable Cox proportional hazard models, greater extent of lung fibrosis was associated with poorer transplant‐free survival (hazard ratio [HR] 1.20, p < 0.0001) and progression‐free survival (HR 1.14, p < 0.0001).Conclusion: In a multi‐centre observational registry of patients with IPF, the extent of fibrotic abnormality on baseline CT quantified using DTA is associated with outcomes independent of pulmonary function.

Idiopathic pulmonary fibrosis (IPF) is a progressive disease leading to significant morbidity and mortality. In 2017 the Thoracic Society of Australia and New Zealand (TSANZ) and Lung Foundation Australia (LFA) published a position statement on the treatment of IPF. Since that time, subsidized anti‐fibrotic therapy in the form of pirfenidone and nintedanib is now available in both Australia and New Zealand. More recently, evidence has been published in support of nintedanib for non‐IPF progressive pulmonary fibrosis (PPF). Additionally, there have been numerous publications relating to the non‐pharmacologic management of IPF and PPF. This 2023 update to the position statement for treatment of IPF summarizes developments since 2017 and reaffirms the importance of a multi‐faceted approach to the management of IPF and progressive pulmonary fibrosis.

The presence of clinical, serological and/or radiological features suggestive, but not confirmatory, of a defined connective tissue disease in patients with interstitial lung disease is a relatively frequent occurrence. In 2015, the European Respiratory Society and the American Thoracic Society proposed classification criteria for the interstitial pneumonia with autoimmune features (IPAF) research entity to capture such patients in a standardised manner, with the intention of nurturing clinical research. This initiative resulted in the publication of several series of IPAF patients, with significant variation between cohorts in clinical characteristics, outcome and the application of IPAF criteria in patient selection. From this increasing body of published work, it has become apparent that revision of IPAF criteria is now required in order to justify the eventual designation of IPAF as a standalone diagnostic term, as opposed to a provisional entity put forward as a basis for clinical research. This review covers the current state of IPAF, conclusions that can and cannot be drawn from the IPAF evidence base, and ongoing uncertainties that require further expert group consideration.