Lo, Ying‐Chun

Context.— Studies of lungs in patients with COVID-19 have focused on early findings. Objective.— To systematically study histopathologic and imaging features and presence of SARS-CoV-2 RNA in lung tissue from patients in later stages of COVID-19. Design.— Autopsies, explants, surgical lung biopsies, transbronchial biopsies, cryobiopsies, and needle biopsies from patients with COVID-19 whose onset of symptoms/confirmed diagnosis was more than 28 days before the procedure were studied. Available images were reviewed. Reverse transcription droplet digital polymerase chain reaction for SARS-CoV-2 RNA was performed on lung tissue. Results.— Of 44 specimens (43 patients; median age, 59.3 years; 26 [60.5%] male) features of acute lung injury (ALI) were seen in 39 (88.6%), predominantly organizing pneumonia and diffuse alveolar damage, up to 298 days after onset of COVID-19. Fibrotic changes were found in 33 specimens (75%), most commonly fibrotic diffuse alveolar damage (n = 22) and cicatricial organizing pneumonia (n = 12). Time between acquiring COVID-19 and specimen was shorter in patients with diffuse ALI (median, 61.5 days) compared with patients with focal (140 days) or no ALI (130 days) (P = .009). Sixteen (of 20; 80%) SARS-CoV-2 reverse transcription droplet digital polymerase chain reaction tests were positive, up to 174 days after COVID-19 onset. Time between COVID-19 onset and most recent computed tomography in patients with consolidation on imaging was shorter (median, 43.0 days) versus in patients without consolidation (87.5 days; P = .02). Reticulations were associated with longer time to computed tomography after COVID-19 onset (median, 82 versus 23.5 days; P = .006). Conclusions.— ALI and SARS-CoV-2 RNA can be detected in patients with COVID-19 for many months. ALI may evolve into fibrotic interstitial lung disease.

Abstract Precision oncology emphasizes genotyping as one of the mainstays of oncological decision-making. The core information element exchanged between the laboratory and the oncologist is the precise mutation. Specifically, it is the written representation typically in the form of a variant description at the DNA or protein level. These annotations can be confusing, and many commercial laboratories have abandoned DNA-level annotations. Here we present a complex double-point mutation to illustrate a situation where a formally “correct” reporting nomenclature can obscure clinically relevant and potentially clinically actionable information. Key Points

AbstractBackgroundIn recent years, the fibroblast growth factor receptor (FGFR) pathway has been proven to be an important therapeutic target in bladder cancer. FGFR‐targeted therapies are effective for patients with FGFR mutation, which can be discovered through genetic sequencing. However, genetic sequencing is not commonly performed at diagnosis, whereas a histologic assessment of the tumor is. We aim to computationally extract imaging biomarkers from existing tumor diagnostic slides in order to predict FGFR alterations in bladder cancer.MethodsThis study analyzed genomic profiles and H&E‐stained tumor diagnostic slides of bladder cancer cases from The Cancer Genome Atlas (n = 418 cases). A convolutional neural network (CNN) identified tumor‐infiltrating lymphocytes (TIL). The percentage of the tissue containing TIL (“TIL percentage”) was then used to predict FGFR activation status with a logistic regression model.ResultsThis predictive model could proficiently identify patients with any type of FGFR gene aberration using the CNN‐based TIL percentage (sensitivity = 0.89, specificity = 0.42, AUROC = 0.76). A similar model which focused on predicting patients with only FGFR2/FGFR3 mutation was also found to be highly sensitive, but also specific (sensitivity = 0.82, specificity = 0.85, AUROC = 0.86).ConclusionTIL percentage is a computationally derived image biomarker from routine tumor histology that can predict whether a tumor has FGFR mutations. CNNs and other digital pathology methods may complement genome sequencing and provide earlier screening options for candidates of targeted therapies.