BACKGROUND AND PURPOSE: Currently, contrast-enhancing margins on T1WI are used to guide treatment of gliomas, yet tumor invasion beyond the contrast-enhancing region is a known confounding factor. Therefore, this study used postmortem tissue samples aligned with clinically acquired MRIs to quantify the relationship between intensity values and cellularity as well as to develop a radio-pathomic model to predict cellularity using MR imaging data. MATERIALS AND METHODS: This single-institution study used 93 samples collected at postmortem examination from 44 patients with brain cancer. Tissue samples were processed, stained with H&E, and digitized for nuclei segmentation and cell density calculation. Pre- and postgadolinium contrast T1WI, T2 FLAIR, and ADC images were collected from each patient’s final acquisition before death. In-house software was used to align tissue samples to the FLAIR image via manually defined control points. Mixed-effects models were used to assess the relationship between single-image intensity and cellularity for each image. An ensemble learner was trained to predict cellularity using 5 × 5 voxel tiles from each image, with a two-thirds to one-third train-test split for validation. RESULTS: Single-image analyses found subtle associations between image intensity and cellularity, with a less pronounced relationship in patients with glioblastoma. The radio-pathomic model accurately predicted cellularity in the test set (root mean squared error = 1015 cells/mm²) and identified regions of hypercellularity beyond the contrast-enhancing region. CONCLUSIONS: A radio-pathomic model for cellularity trained with tissue samples acquired at postmortem examination is able to identify regions of hypercellular tumor beyond traditional imaging signatures.

Despite being the most commonly diagnosed noncutaneous cancer in men, improved prostate cancer screening and aggressive therapies have led to a high five-year overall survival rate.¹ There is still substitutional morbidity associated with overtreatment of indolent disease due to the semi-invasive nature of the current diagnostic paradigm, the 12-core transrectal ultrasound-guided biopsy.² As such, there has been a growing interest in recent years for developing improved strategies for differentiating aggressive from indolent prostate cancer noninvasively.

Head contact-induced loads can result in skull fractures and/or brain injuries. While skull fractures have been produced from post mortem human cadaver surrogates (PMHS), injury probability curves describing their structural responses have not been developed. The objectives of this study were to develop skull fracture-based injury risk curves and describe human tolerances using survival analysis. Published PMHS data in this journal were used. Mean age, stature, and weight of 12 PMHS were: 66.6±2.3 years, 1.71±2.9 m, and 76.4±4.6 kg. A testing device applied contact loading to the head. Failure force, deflection, energy, and linear and secant stiffness variables were used to develop probability curves. Parametric survival analysis included identifying most optimal distribution, ensuring that the chosen distribution is not significantly different from the nonparametric model, determining ±95% confidence interval bounds and Normalized CI Sizes (NICS), obtaining quality indices for each risk curve, and determining their hierarchical sequence using the Brier score (TBS). Lognormal distribution was the most optimal distribution for all variables, except failure force, for which Weibull distribution was optimal. Tightness-of-fit of risk curves for failure force, energy, and deflection were better than linear and secant stiffness variables. Force best represented skull fracture response based on TBS and NCIS, followed by deflection and energy, while two stiffness variables were least preferred metrics. These structural response-based set of risk curves, hitherto not reported, form a fundamental dataset for validating/assessing accuracy of outputs from computational models and serve as hierarchical skull fracture injury criteria under head contact loads.

It is estimated that one in seven men will develop prostate cancer (PCa) in their lifetime and that PCa itself accounts for one in five new cancer diagnoses.¹ Invasive removal of prostate tissue is currently required to confirm cancer diagnosis and drive treatment. From these samples, pathologists use the well-characterized Gleason criteria^2,3 to interpret histomorphometric features and grade the tissues,⁴ which can then be used to assign a grade group (GG).⁵ While these grading criteria have been shown to hold great prognostic value, they are inherently subjective, relying on a pathologist’s interpretation.⁶

Androgen receptor (AR) drives prostate cancer (PC) growth and progression, and targeting AR signaling is the mainstay of pharmacological therapies for PC. Resistance develops relatively fast as a result of refueled AR activity. A major gap in the field is the lack of understanding of targetable mechanisms that induce persistent AR expression in castrate-resistant PC (CRPC). This study uncovers an unexpected function of active Stat5 signaling, a known promoter of PC growth and clinical progression, as a potent inducer of AR gene transcription. Stat5 suppression inhibited AR gene transcription in preclinical PC models and reduced the levels of wild-type, mutated, and truncated AR proteins. Pharmacological Stat5 inhibition by a specific small-molecule Stat5 inhibitor down-regulated Stat5-inducible genes as well as AR and AR-regulated genes and suppressed PC growth. This work introduces the concept of Stat5 as an inducer of AR gene transcription in PC. Pharmacological Stat5 inhibitors may represent a new strategy for suppressing AR and CRPC growth.