Lo, Ying-Chun

AimsGiant cell arteritis (GCA) is a systemic vasculitis affecting medium and large arteries in patients aged over 50 years. Involvement of temporal arteries (TA) can lead to complications such as blindness and stroke. While the diagnostic gold standard is temporal artery biopsy (TAB), comorbidities and age‐related changes can make interpretation of such specimens difficult. This study aims to establish a baseline of TA changes in subjects without GCA to facilitate the interpretation of TAB.Methods and resultsBilateral TA specimens were collected from 100 consecutive eligible postmortem examinations. Subjects were divided into four age groups and specimens semiquantitatively evaluated for eccentric intimal fibroplasia, disruption and calcification of the internal elastic lamina (IEL), medial attenuation and degree of lymphocytic inflammation of the peri‐adventitia, adventitia, media and intima. The individual scores of intimal fibroplasia, IEL disruption and medial attenuation were added to yield a ‘combined score (CS)’. Seventy‐eight 78 decedents were included in the final analysis following exclusion of 22 individuals for either lack of clinical information or inability to collect TA tissue. A total of 128 temporal artery specimens (50 bilateral from individual decedents, 28 unilateral) were available for examination. Intimal proliferation, IEL loss, IEL calcification and CS increased with age in a statistically significant fashion. Comparison of the oldest age group with the others showed statistically significant differences, although this was not uniformly preserved in comparison between the three youngest groups.ConclusionSenescent arterial changes and healed GCA exhibit histological similarity and such changes increase proportionally with age. The CS demonstrates significant association with age overall and represents a potential avenue for development to ‘normalise’ TA biopsies from older individuals.

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.