Symptoms of drug-associated interstitial lung disease (ILD) are nonspecific and can be difficult to distinguish from a number of illnesses that commonly occur in patients with non-small-cell lung cancer (NSCLC) on therapy. Identification of drug involvement and differentiation from other illnesses is problematic, although radiological manifestations and clinical tests enable many of the alternative causes of symptoms in advanced NSCLC to be excluded. In lung cancer patients, high-resolution computed tomography (HRCT) is more sensitive than a chest radiograph in evaluating the severity and progression of parenchymal lung disease. Indeed, the use of HRCT imaging has led to the recognition of many distinct patterns of lung involvement and, along with clinical signs and symptoms, helps to predict both outcome and response to treatment. This manuscript outlines the radiology of drug-associated ILD and its differential diagnosis in NSCLC. An algorithm that uses clinical tests to exclude alternative diagnoses is also described.

BackgroundClinical guidelines specify that diagnosis of interstitial pulmonary fibrosis (IPF) requires identification of usual interstitial pneumonia (UIP) pattern. While UIP can be identified by high resolution CT of the chest, the results are often inconclusive, making surgical lung biopsy necessary to reach a definitive diagnosis (Raghu et al., Am J Respir Crit Care Med 183(6):788–824, 2011). The Envisia genomic classifier differentiates UIP from non-UIP pathology in transbronchial biopsies (TBB), potentially allowing patients to avoid an invasive procedure (Brown et al., Am J Respir Crit Care Med 195:A6792, 2017). To ensure patient safety and efficacy, a laboratory developed test (LDT) must meet strict regulatory requirements for accuracy, reproducibility and robustness. The analytical characteristics of the Envisia test are assessed and reported here.MethodsThe Envisia test utilizes total RNA extracted from TBB samples to perform Next Generation RNA Sequencing. The gene count data from 190 genes are then input to the Envisia genomic classifier, a machine learning algorithm, to output either a UIP or non-UIP classification result. We characterized the stability of RNA in TBBs during collection and shipment, and evaluated input RNA mass and proportions on the limit of detection of UIP. We evaluated potentially interfering substances such as blood and genomic DNA. Intra-run, inter-run, and inter-laboratory reproducibility of test results were also characterized.ResultsRNA content within TBBs preserved in RNAprotect is stable for up to 14 days with no detectable change in RNA quality. The Envisia test is tolerant to variation in RNA input (5 to 30 ng), with no impact on classifier results. The Envisia test can tolerate dilution of non-UIP and UIP classification signals at the RNA level by up to 60% and 20%, respectively. Analytical specificity studies utilizing UIP and non-UIP samples mixed with genomic DNA (up to 30% relative input) demonstrated no impact to classifier results. The Envisia test tolerates up to 22% of blood contamination, well beyond the level observed in TBBs. The test is reproducible from RNA extraction through to Envisia test result (standard deviation of 0.20 for Envisia classification scores on > 7-unit scale).ConclusionsThe Envisia test demonstrates the robust analytical performance required of an LDT. Envisia can be used to inform the diagnoses of patients with suspected IPF.Electronic supplementary materialThe online version of this article (doi:10.1186/s12890-017-0485-4) contains supplementary material, which is available to authorized users.