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Glioblastoma is the most common aggressive adult brain tumor. Numerous studies have reported results from either private institutional data or publicly available datasets. However, current public datasets are limited in terms of: a) number of subjects, b) lack of consistent acquisition protocol, c) data quality, or d) accompanying clinical, demographic, and molecular information. Toward alleviating these limitations, we contribute the "University of Pennsylvania Glioblastoma Imaging, Genomics, and Radiomics" (UPenn-GBM) dataset, which describes the currently largest publicly available comprehensive collection of 630 patients diagnosed with de novo glioblastoma. The UPenn-GBM dataset includes (a) advanced multi-parametric magnetic resonance imaging scans acquired during routine clinical practice, at the University of Pennsylvania Health System, (b) accompanying clinical, demographic, and molecular information, (d) perfusion and diffusion derivative volumes, (e) computationally-derived and manually-revised expert annotations of tumor sub-regions, as well as (f) quantitative imaging (also known as radiomic) features corresponding to each of these regions. This collection describes our contribution towards repeatable, reproducible, and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments.

Quantitative 3′-deoxy-3′-18F-fluorothymidine (18F-FLT) PET has potential as a noninvasive tumor biomarker for the objective assessment of response to treatment. To guide interpretation of these quantitative data, we evaluated the repeatability of 18F-FLT PET as part of a multicenter trial involving patients with high-grade glioma. Methods: 18F-FLT PET was performed on 10 patients with recurrent high-grade glioma at 5 different institutions within the Adult Brain Tumor Consortium trial ABTC1101. Data were acquired according to a double baseline protocol in which PET examinations were repeated within 2 d of each other with no intervening treatment. On each of the 2 imaging days, dedicated brain PET was performed at 2 time points, 1 and 3 h after 18F-FLT administration. Tumor SUVs and related parameters were measured at a central laboratory using various volumes of interest: isocontour at 30% of the maximum pixel (SUVmean_30%), gradient-based segmentation (SUVmean_gradient), the maximum pixel (SUVmax), and a 1-mL sphere at the region of highest uptake (SUVpeak). Repeatability coefficients (RCs) were calculated from the relative differences between corresponding SUV measurements obtained on the 2 d. Results: RCs for tumor SUVs were 22.5% (SUVmean_30%), 23.8% (SUVmean_gradient), 23.2% (SUVmax), and 18.5% (SUVpeak) at 1 h after injection. Corresponding data at 3 h were 22.4%, 25.0%, 27.3%, and 23.6%. Normalizing the tumor SUV data with reference to a background region improved repeatability, and the most stable parameter was the tumor-to-background ratio derived using SUVpeak (RC, 16.5%). Conclusion: SUV quantification of 18F-FLT uptake in glioma had an RC in the range of 18%–24% when imaging began 1 h after 18F-FLT administration. The volume-of-interest methodology had a small but not negligible influence on repeatability, with the best performance obtained using SUVpeak. Although changes in 18F-FLT SUV after treatment cannot be directly interpreted as a change in tumor proliferation, we have established ranges beyond which SUV differences are likely due to legitimate biologic effects.

Being overweight or obese is associated with increased breast cancer risk and disease severity among postmenopausal women, but whether extent of mammography use and accuracy modify this association and further contribute to increases in disease severity at diagnosis among overweight and obese women is unclear.We prospectively collected data during 1996-2005 on 287,115 postmenopausal women not using hormone therapy (HT) who underwent 614,562 mammography examinations; 4,446 women were diagnosed with breast cancer within 12 months of a mammography examination. We calculated rates per 1,000 mammography examinations of large (15 mm), advanced-stage (IIb, III, or IV), high-grade (3 or 4), estrogen receptor (ER)-positive and -negative, and screen-detected and non-screen-detected breast cancer across body mass index (BMI, kg/m(2)) groups defined as normal (18.5-24.9), overweight (25.0-29.9), obese class I (30.0-34.9), and obese class II/III (or =35.0), adjusting for age, race/ethnicity, and mammography registry and use. All statistical tests were two-sided.Adjusted rates per 1000 mammography examinations of overall breast cancer increased across BMI groups (6.6 normal, 7.4 overweight, 7.9 obese I, 8.5 obese II/III; P(trend).001), as did rates of advanced disease, including large invasive (2.3 normal, 2.6 overweight, 2.9 obese I, 3.2 obese II/III; P(trend).001), advanced-stage (0.8 normal, 0.9 overweight, 1.3 obese I, 1.5 obese II/III; P(trend).001), and high nuclear grade (1.5 normal, 1.7 overweight, 1.7 obese I, 1.9 obese II/III; P(trend) = .10) tumors. Rates of ER-positive tumors increased across BMI groups (P(trend).001); rates of ER-negative tumors did not. Rates of screen-detected cancers were higher among overweight and obese women than normal and underweight women, but rates of non-screen-detected (false-negative) cancers were similar. Rates of advanced breast cancer increased across BMI groups regardless of extent of mammography use.Patterns of mammography use and mammography accuracy are not the primary reasons for higher rates of advanced breast cancer among overweight and obese postmenopausal women not using HT; thus, biologic differences in breast tumor development and/or progression may be important.