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Abstract INTRODUCTION DSC-MRI perfusion methods are commonly used to evaluate both primary and metastatic brain cancer with the creation of maps of relative cerebral blood volume (rCBV). Recently, guidelines were established to ensure a standard DSC-MRI acquisition protocol that reduces inter-site variability. The purpose of this study was to initiate the determination of rCBV benchmark values using the DSC-MRI consensus protocol in treatment-naïve brain metastases. METHODS Patients from three sites with treatment-naïve, contrast-enhancing brain metastases on MRI were considered for inclusion in this retrospective study. The MRIs included pre- and post-contrast T1w(T1+C) images obtained after administration of gadolinium-based contrast agent (GBCA) (0.1 mmol/kg), which serves as the recommended preload for the DSC-MRI data collection. A 2nd GBCA dose (0.1 mmol/kg) was administered 40-60sec after the collection of baseline GRE-EPI images using recommended settings (FA=60o, TE/TR=30ms/1100-1250ms) for 120s. Calibrated pre/post T1w difference maps (dT1) were used for delineation of enhancing lesion, and standardized (calibrated) rCBV (sRCBV) created. Mean sRCBV for metastases were compared to normal appearing white matter (NAWM) and treatment-naive glioblastoma (GBM) from a previous study. Pairwise comparisons were performed using the Mann-Whitney nonparametric test. RESULTS N=52 patients with primary histology of lung (n=27); breast (n=6); skin (n=7); gastrointestinal (GI: n=3) and genitourinary (GU: n=9) cancers were included in comparison to GBM (n=31). The mean sRCBV for all metastases (1.77+/-1.05) is significantly lower (p=0.0003) than for GBM (2.67+/-1.34) but with both statistically greater (p< 0.0001) than NAWM (0.706+/-0.163). Individually, lung (1.47+/- 0.61), breast (2.275+/-0.87), skin (2.10+/-1.22), GI (1.91+/- 0.64) and GU (2.01+/-0.63) mean sRCBV are statistically greater than for NAWM. CONCLUSION Using the consensus DSC-MRI acquisition protocol confirms use of sRCBV to identify biologically active, treatment-naive brain metastases setting benchmark values for future applications.

AbstractObjectiveHigh BMI is associated with many comorbidities and mortality. This study aimed to elucidate the overall clinical risk of obesity using a genome‐ and phenome‐wide approach.MethodsThis study performed a phenome‐wide association study of BMI using a clinical cohort of 736,726 adults. This was followed by genetic association studies using two separate cohorts: one consisting of 65,174 adults in the Electronic Medical Records and Genomics (eMERGE) Network and another with 405,432 participants in the UK Biobank.ResultsClass 3 obesity was associated with 433 phenotypes, representing 59.3% of all billing codes in individuals with severe obesity. A genome‐wide polygenic risk score for BMI, accounting for 7.5% of variance in BMI, was associated with 296 clinical diseases, including strong associations with type 2 diabetes, sleep apnea, hypertension, and chronic liver disease. In all three cohorts, 199 phenotypes were associated with class 3 obesity and polygenic risk for obesity, including novel associations such as increased risk of renal failure, venous insufficiency, and gastroesophageal reflux.ConclusionsThis combined genomic and phenomic systematic approach demonstrated that obesity has a strong genetic predisposition and is associated with a considerable burden of disease across all disease classes.

Adult-type diffusely infiltrating gliomas, of which glioblastoma is the most common and aggressive, almost always recur after treatment and are fatal. Improved understanding of therapy-driven tumor evolution and acquired therapy resistance in gliomas is essential for improving patient outcomes, yet the majority of the models currently used in preclinical research are of therapy-naïve tumors. Here, we describe the development of therapy-resistant IDH-wildtype glioblastoma patient-derived xenografts (PDX) through orthotopic engraftment of therapy naïve PDX in athymic nude mice, and repeated in vivo exposure to the therapeutic modalities most often used in treating glioblastoma patients: radiotherapy and temozolomide chemotherapy. Post-temozolomide PDX became enriched for C>T transition mutations, acquired inactivating mutations in DNA mismatch repair genes (especially MSH6), and developed hypermutation. Such post-temozolomide PDX were resistant to additional temozolomide (median survival decrease from 80 days in parental PDX to 42 days in a temozolomide-resistant derivative). However, temozolomide-resistant PDX were sensitive to lomustine (also known as CCNU), a nitrosourea which induces tumor cell apoptosis by a different mechanism than temozolomide. These PDX models mimic changes observed in recurrent GBM in patients, including critical features of therapy-driven tumor evolution. These models can therefore serve as valuable tools for improving our understanding and treatment of recurrent glioma.

Context.— The accurate identification of different lung adenocarcinoma histologic subtypes is important for determining prognosis but can be challenging because of overlaps in the diagnostic features, leading to considerable interobserver variability. Objective.— To provide an overview of the diagnostic agreement for lung adenocarcinoma subtypes among pathologists and to create a ground truth using the clustering approach for downstream computational applications. Design.— Three sets of lung adenocarcinoma histologic images with different evaluation levels (small patches, areas with relatively uniform histology, and whole slide images) were reviewed by 17 international expert lung pathologists and 1 pathologist in training. Each image was classified into one or several lung adenocarcinoma subtypes. Results.— Among the 4702 patches of the first set, 1742 (37%) had an overall consensus among all pathologists. The overall Fleiss κ score for the agreement of all subtypes was 0.58. Using cluster analysis, pathologists were hierarchically grouped into 2 clusters, with κ scores of 0.588 and 0.563 in clusters 1 and 2, respectively. Similar results were obtained for the second and third sets, with fair-to-moderate agreements. Patches from the first 2 sets that obtained the consensus of the 18 pathologists were retrieved to form consensus patches and were regarded as the ground truth of lung adenocarcinoma subtypes. Conclusions.— Our observations highlight discrepancies among experts when assessing lung adenocarcinoma subtypes. However, a subsequent number of consensus patches could be retrieved from each cluster, which can be used as ground truth for the downstream computational pathology applications, with minimal influence from interobserver variability.