Citations

The ability of bone to respond to increased loading as a function of age was tested by use of three-point bending and histomorphometry. The hindlimbs of male Fischer 344 rats of three age groups (young = 4 mo, adult = 12 mo, and old = 22 mo; n = 10 per age group) were progressively overloaded by training the rats to depress a lever high on the side of a cage while wearing a weighted backpack. This squatlike movement required full extension of the hindlimbs. Exercised (Exer) rats performed 50 repetitions three times per week for 9 wk. Pack weight was gradually increased to 65% of body weight. Controls ( n = 10 per age group) performed the same exercise without additional weight. Neither the mechanical properties of the femur nor histomorphometry in the proximal tibia was significantly affected in young or adult rats. However, old Exer rats were found to have significantly smaller medullary areas and a decreased trabecular spacing than their age-matched controls. These results suggest a greater sensitivity to increased loading in aged rats.

BACKGROUND13‐Cis retinoic acid (13‐CRA) is a synthetic vitamin A derivative. High‐dose 13‐CRA in patients with squamous cell cancers of the head and neck (SCCHNs) reduces the incidence of second primary tumors (SPTs). The authors report long‐term results from a phase 3 randomized trial that compared treatment with low‐dose 13‐CRA versus placebo for patients who had early stage SCCHN, with a focus on the development of SPTs and overall survival (OS).METHODSIn total, 176 patients who received treatment for stage I/II SCCHN were randomized to receive either low‐dose 13‐CRA (weight‐based dose of 7.5 mg or 10 mg) or placebo for 2 years. A competing‐risk approach and the log‐rank test were used to compare the time to SPT and OS, respectively, between groups.RESULTS13‐CRA neither significantly reduced the cumulative incidence of SPT (P = .61) nor improved the time to SPT (hazard ratio [HR] for 13‐CRA/placebo; 0.86; P = .61). Despite limited power, there was a trend toward improved OS for the 13‐CRA arm (HR, 0.75; P = .14), particularly among patients whose index tumor was surgically excised (N = 26; HR, 0.50; P = .057) and among women (N = 39; HR, 0.44; P = .065) and never/former smokers (N = 129; HR, 0.61; P = .055), with a median follow‐up of 16 years. The main 13‐CRA related toxicities were dry skin and cheilitis.CONCLUSIONSTreatment with low‐dose 13‐CRA for 2 years did not decrease the incidence of SPT; subset analysis indicates a potential survival advantage among patients who are women and never/former smokers. More targeted interventions based on clinical risk factors and molecular characterization of tumors may yield greater success in future prevention trials. Cancer 2017;123:4653‐4662. © 2017 American Cancer Society.

Abstract With mean survival from glioblastoma multiforme (GBM) barely edging over 15 months, it become imperative to develop novel tools informing on both progression and treatment efficacy. As the aggressive growth of GBM outstrips available resources, regions of hypoxia develop. Hypoxia can be clinically assessed through [18F]-flouromisonidazole (FMISO) PET. However, PET imaging suffers from low resolution and reconstruction artifact. We present the comparative and predictive results of a multi-stage model utilizing the mathematical biology of cancer and the physics of PET imaging. Our mathematical model of GBM characterizes cancer cells into the interacting phenotypes of normoxic, hypoxic, and necrotic cells supported by vasculature. The pharmacokinetic activity of FMISO is induced in the simulated tissue then acquired and reconstructed by an analytic simulator of the PET process. This multi-stage process creates a patient-specific virtual PET images with characteristic of the clinical PET scan. Extending previous work, we model the full three-dimensional dynamics of tumor progression, pharmacokinetic activity, and FMISO PET for the tumor kinetics of six GBM patients. Virtual and clinical FMISO PET images are compared. Overall hypoxic burden and spatial distribution show strong correspondence between virtual and clinical FMISO PET images for all six patients. Simulated FMISO PET dynamics, with tumor kinetics derived from routine clinical MRI scans, provide a unique and evolving tool giving insight into the biological connections between magnetic resonance and molecular imaging. Citation Format: Joshua Jacobs, Andrea Hawkins-Darrud, Robert Harrison, Sandra Johnston, Paul Kinahan, Kristin Swanson. Correlating magnetic resonance and molecular imaging using three dimensional untreated virtual control. [abstract]. In: Proceedings of the AACR Special Conference on Engineering and Physical Sciences in Oncology; 2016 Jun 25-28; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2017;77(2 Suppl):Abstract nr A10.

Abstract Purpose: Investigate the utility of patient-specific spatial predictions of tumor cell density from a bio-mathematical model. Introduction: Glioblastomas (GBMs) are the most malignant of all primary brain tumors. While it is known there is always a non-detectable portion of the tumor, current techniques of monitoring GBM progression, imaging and initial histological assessment, are not able to reliably estimate the tumor invasion past the enhancing region on T2-Weighted (T2W) imaging. Over the last two decades, a large effort has been made to create a simple patient-specific mathematical model of gliomas. The resulting model, referred to as the Proliferation-Invasion (PI) model, is based on two key parameters, the net growth rate, ρ, and the dispersal coefficient, D. In this model, the ratio of D/ρ is related to degree of invasion and the product D*ρ, is related to the speed of growth. The intuitive understanding provided by this model has been able to provide patient-specific understanding of disease kinetics enabling prediction of outcomes following surgical resection, radiation and the development of a prognostic response metric. Previous literature utilizing this model has been based on the assumption that what is seen on the pretreatment T1-Weighted contrast-enhanced (T1Gd) and T2W, images correspond to an 80% and 16% tumor cell density threshold respectively. This assumption allows for an estimate of D/ρ from a single time point of imaging. While these values were based on extensive experience, for ethical and technical reasons, they have never been rigorously investigated histologically. Recent technological advances have made it possible for surgeons to use an MRI to guide the acquisition of tissue making it possible to know with a good degree of accuracy where on the MR image the histological specimen comes from. Methods: Model Calibration : To estimate D/ρ for each patient, we assume abnormalities on the T1Gd and T2W images correspond to an 80% and 16% tumor cell density threshold respectively. We then utilize a Bayesian calibration approach based on adaptive grid refinement while holding the velocity constant to find the most likely value of D/ρ to match the observed radial measurements. Three-Dimensional Density Maps : Given a gray/white segmentation and an estimate for D/ρ, we can build a tumor cell density prediction in the patient's anatomy using the Eikonal equations and the modified Fast Marching Method (FMM) algorithm presented by Konukoglu et al. Patient Cohort : Eighteen patients were recruited with clinically suspected GBM undergoing preoperative stereotactic MRI for surgical resection with IRB approval Barrow Neurological Institute and Mayo Clinic in Arizona. Surgical Biopsy : Pre-operative conventional MRI, including T1Gd and T2W, was utilized to guide stereotactic biopsies. An average of 5–6 tissue specimens were acquired from each tumor by using stereotactic surgical localization, following the smallest possible diameter craniotomies to minimize brain shift. Histological Analysis : 4 μm tissue sections were stained with hematoxylin and eosin (H&E) for neuropathology review. H&E slides were reviewed by a neuropathologist. The percent tumor nuclei in the field was estimated. Results and Conclusion: Our analysis showed that the intuitive ordering of diffuse to nodular tumor profiles from the PI model is consistent with the histologic data. Further, utilizing the histologic data, limitations to a universal threshold cutoff of the T2/FLAIR region are specified and a better threshold cutoff value is suggested. We hope that this paper will lead to more studies of this kind resulting in more accurate ways to predict the spatial distribution of GBM tumor cells from medical imaging. Such advances could revolutionize surgical procedures and radiation therapy and may enable additional insights into tumor kinetic differences meaningful for treatment. Citation Format: Andrea Hawkins-Daarud, Lauren DeGirolamo, Joshua Jacobs, Kamala Clark-Swanson, Jennifer M. Eschbacher, Kris A. Smith, Peter Nakaji, Leslie C. Baxter, John P. Karis, Teresa Wu, J. Ross Mitchell, Jing Li, Leland Hu, Kristin R. Swanson. Histologic evidence for a bio-mathematical model of glioblastoma invasion. [abstract]. In: Proceedings of the AACR Special Conference on Engineering and Physical Sciences in Oncology; 2016 Jun 25-28; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2017;77(2 Suppl):Abstract nr A08.