Interstitial lung disease (ILD) patients are often misdiagnosed or diagnosed late. Earlier diagnosis allows earlier access to correct treatment, consequently improved survival and quality of life. We investigated if AI software can detect patterns of ILD on spirometry earlier to formal diagnosis. All subjects from UK Biobank with ILD as cause of death but without clinical ILD diagnosis at the time of spirometry, were selected (N=109). Spirometry data and subject demographics (gender, age, BMI, race, smoking) were input to an AI clinical decision support software (ArtiQ.PFT), which calculates a probability of having present one of lung diseases. Time between last recorded spirometry and clinical ILD diagnosis was 3.8±1.9 years, with 1.1±1.2y more to ILD death. AI detected ILD in 27% of subjects (N=29), 3.8±2.1 years prior to ILD diagnosis through standard care. 66% (N=19) of these subjects had normal lung function, 34% (N=10) were restrictive. In remaining 73% (N=80), AI most commonly pointed to no disease (39%) or was uncertain (18%). Spirometry in the group where AI identified ILD were significantly different from those where ILD was not detected [FVC%pred 76±12 vs. 87±17 (p=0.003), FEV1%pred 80±13 vs. 84±21 (p=0.36), PEF%pred 103±25 vs. 87±29 (p<0.0001), FEV1/FVC 0.82±0.05 vs. 0.75±0.09 (p=0.007)]. There was no difference in time to mortality, nor in time between spirometry and clinical diagnosis among groups. In one-quarter of patients, AI detected ILD many years before clinical diagnosis. Most had lung function within normal ranges, suggesting that AI may detect ILD prior to standard interpretation. With further validation, merging AI-support and regular spirometry testing may lead to earlier ILD diagnosis.

The tumour microenvironment (TME) forms a major obstacle in effective cancer treatment and for clinical success of immunotherapy. Conventional co-cultures have shed light onto multiple aspects of cancer immunobiology, but they are limited by the lack of physiological complexity. We develop a human organotypic skin melanoma culture (OMC) that allows real-time study of host-malignant cell interactions within a multicellular tissue architecture. By co-culturing decellularized dermis with keratinocytes, fibroblasts and immune cells in the presence of melanoma cells, we generate a reconstructed TME that closely resembles tumour growth as observed in human lesions and supports cell survival and function. We demonstrate that the OMC is suitable and outperforms conventional 2D co-cultures for the study of TME-imprinting mechanisms. Within the OMC, we observe the tumour-driven conversion of cDC2s into CD14 + DCs, characterized by an immunosuppressive phenotype. The OMC provides a valuable approach to study how a TME affects the immune system.