Towards the adoption of quantitative computed tomography in the management of interstitial lung disease.

The shortcomings of qualitative visual assessment have led to the development of computer-based tools to characterise and quantify disease on high-resolution computed tomography (HRCT) in patients with interstitial lung diseases (ILDs). Quantitative CT (QCT) software enables quantification of patterns on HRCT with results that are objective, reproducible, sensitive to change and predictive of disease progression. Applications developed to provide a diagnosis or pattern classification are mainly based on artificial intelligence. Deep learning, which identifies patterns in high-dimensional data and maps them to segmentations or outcomes, can be used to identify the imaging patterns that most accurately predict disease progression. Optimisation of QCT software will require the implementation of protocol standards to generate data of sufficient quality for use in computerised applications and the identification of diagnostic, imaging and physiological features that are robustly associated with mortality for use as anchors in the development of algorithms. Consortia such as the Open Source Imaging Consortium have a key role to play in the collation of imaging and clinical data that can be used to identify digital imaging biomarkers that inform diagnosis, prognosis and response to therapy.

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Conflicts of interest: All the authors are members of OSIC. In addition, S.L.F. Walsh reports relationships with Boehringer Ingelheim (BI), Bracco, FLUIDDA, Galapagos, OncoArendi, Roche and Sanofi-Genzyme. J. De Backer owns shares in FLUIDDA. H. Prosch reports grants from BI; payment for presentations from AstraZeneca, Bristol Myers Squibb (BMS), BI, Janssen, Merck Sharp & Dohme (MSD), Novartis, Roche/InterMune, Sanofi, Siemens and Takeda; support for travel from BI; and has served on an Advisory Board for BMS, BI, MSD, Roche/InterMune and Sanofi. G. Langs reports payment for presentations from Novartis; research support from Novartis and NVIDIA; and is co-founder, shareholder and Chief Scientist of contextflow. L. Calandriello has served on an Advisory Board for and received payment for presentations from BI. V. Cottin reports grants from BI; consulting fees from BI, FibroGen, Galapagos, Galecto, PureTech, RedX, Roche and Shionogi; payment for presentations and support for attending meetings from BI and Roche; and has served on a Data Safety Monitoring Board or Advisory Board for Celgene, BMS, Galapagos and Roche/Promedior. K.K. Brown reports grants from NHLBI; consultancy fees, speaker fees, support for travel and/or has served as an advisor or on the data monitoring committee for AbbVie, Biogen, Blade Therapeutics, BI, BMS, CSL Behring, DevPro Biopharma, Dispersol, Eleven P15, Galapagos, Galecto, Huitai Biomedicine, Humanetics, Pliant, Redx Pharma, Sanofi, Third Pole and Translate Bio; and he holds a leadership role with the Fleischner Society. Y. Inoue reports grants from the Japanese Ministry of Health, Labour, and Welfare and the Japan Agency for Medical Research and Development; payment for presentations from BI, Kyorin, Shionogi, GlaxoSmithKline, and ThermoFisher; and has served as a consultant or steering committee member for BI, Galapagos, Roche, Taiho, CSL Behring, Vicore Pharma and Savara. V. Tzilas reports no disclosures. E. Estes reports no disclosures.