Citations

Background: Texturing of breast implants is done to decrease the risk of associated complications. Each manufacturer utilizes unique and at times proprietary techniques to texture the surface of their implants. Little is known about the integrity of this surface structure texturing or the propensity for the surfaces to shed particulate matter. This study aimed to determine the extent of surface particulate shedding from 3 textured implants approved by the US Food and Drug Administration (FDA), which are manufactured by Allergan, Mentor, and Sientra. Methods: Control images of each of the 3 textured breast implants were obtained with scanning electron microscopy (SEM). A liquid adhesive, ethylene vinyl acetate (EVA) copolymer was then applied to the external shell of the implants, allowed to cool, and peeled from the surface. Images of the EVA copolymer were taken with SEM to qualitatively analyze displacement of surface particulate debris. Scanning electron microscopy imaging of the implants was repeated for qualitative comparisons with the control images. Results: The peeled copolymer of the 3 implants exhibited surface shedding. Comparison of the 3 breast implants showed the shedding to be greatest for the Allergan implant. Conclusions: This study highlights the dynamic surface material properties of the 3 FDA-approved breast implants. Shedding of particulate matter from the implant surfaces can be precipitated by moderate adhesion. Our qualitative examination of SEM findings showed more debris shed from the Allergan breast implants than from the Mentor or Sientra implants.

PurposeDespite the intra- and intertumoral heterogeneity seen in glioblastoma multiforme (GBM), there is little definitive data on the underlying cause of the differences in patient survivals. Serial imaging assessment of tumor growth allows quantification of tumor growth kinetics (TGK) measured in terms of changes in the velocity of radial expansion seen on imaging. Because a systematic study of this entire TGK phenotype—growth before treatment and during each treatment to recurrence —has never been coordinately studied in GBMs, we sought to identify whether patients cluster into discrete groups on the basis of their TGK.Patients and MethodsFrom our multi-institutional database, we identified 48 patients who underwent maximally safe resection followed by radiotherapy with imaging follow-up through the time of recurrence. The patients were then clustered into two groups through a k-means algorithm taking as input only the TGK before and during treatment.ResultsThere was a significant survival difference between the clusters ( P = .003). Paradoxically, patients among the long-lived cluster had significantly larger tumors at diagnosis ( P = .027) and faster growth before treatment ( P = .003) but demonstrated a better response to adjuvant chemotherapy ( P = .048). A predictive model was built to identify which cluster patients would likely fall into on the basis of information that would be available to clinicians immediately after radiotherapy (accuracy, 90.3%).ConclusionDichotomizing the heterogeneity of GBMs into two populations—one faster growing yet more responsive with increased survival and one slower growing yet less responsive with shorter survival—suggests that many patients who receive standard-of-care treatments may get better benefit from select alternative treatments.

ObjectiveTo develop response criteria for adult dermatomyositis (DM) and polymyositis (PM).MethodsExpert surveys, logistic regression, and conjoint analysis were used to develop 287 definitions using core set measures. Myositis experts rated greater improvement among multiple pairwise scenarios in conjoint analysis surveys, where different levels of improvement in 2 core set measures were presented. The PAPRIKA (Potentially All Pairwise Rankings of All Possible Alternatives) method determined the relative weights of core set measures and conjoint analysis definitions. The performance characteristics of the definitions were evaluated on patient profiles using expert consensus (gold standard) and were validated using data from a clinical trial. The nominal group technique was used to reach consensus.ResultsConsensus was reached for a conjoint analysis–based continuous model using absolute percent change in core set measures (physician, patient, and extramuscular global activity, muscle strength, Health Assessment Questionnaire, and muscle enzyme levels). A total improvement score (range 0–100), determined by summing scores for each core set measure, was based on improvement in and relative weight of each core set measure. Thresholds for minimal, moderate, and major improvement were ≥20, ≥40, and ≥60 points in the total improvement score. The same criteria were chosen for juvenile DM, with different improvement thresholds. Sensitivity and specificity in DM/PM patient cohorts were 85% and 92%, 90% and 96%, and 92% and 98% for minimal, moderate, and major improvement, respectively. Definitions were validated in the clinical trial analysis for differentiating the physician rating of improvement (P < 0.001).ConclusionThe response criteria for adult DM/PM consisted of the conjoint analysis model based on absolute percent change in 6 core set measures, with thresholds for minimal, moderate, and major improvement.

402 Background: AGS-003 is an autologous tumor RNA-loaded dendritic cell-based immunotherapy being tested in advanced renal cell carcinoma (RCC) patients. The immunologic specificity for the patient’s own tumor is achieved using amplified tumor RNA electroporated into monocyte derived dendritic cells. To produce AGS-003 for RCC patients the RNA is amplified from a 100 mg primary tumor sample obtained via nephrectomy. In this non treatment study we evaluated the feasibility of RNA amplification from MIBC tissue acquired by transurethral resection of bladder tumor (TURBT). Methods: MIBC tissue was obtained via TURBT. Following draining of the irrigant used during the TURBT procedure, tissue was placed into an RNA preservative solution to prevent RNA degradation until processing. Methods developed for the amplification of RCC tumor RNA and RNA quality testing were applied to the RNA extracted from the MIBC specimens. Results: This work demonstrated that the methods developed for the amplification and testing of RNA from RCC can be successfully employed for the amplification of RNA from MIBC specimens collected by TURBT. Results demonstrated that the total RNA yields obtained from bladder tissue exceed that of similar masses of primary RCC tumors. This finding enabled the reduction in the amount of starting material from 100 mg to 50 mg of bladder tissue. The amount of amplified RNA produced from the lower bladder cancer masses were sufficient to support the production of immunotherapy at full scale (17 doses average). Conclusions: These data demonstrate the feasibility of RNA extraction and amplification from muscle invasive bladder cancer tissue and supports our planned phase II clinical study. [Table: see text]

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.

Objective This study sought to estimate the prevalence of median lingual lymph node (MLLN) metastases from oral cavity squamous cell carcinoma (OCSCC) and determine the frequency with which MLLNs can be identified with magnetic resonance imaging (MRI) in control subjects. Methods Pathology reports were used to identify patients with surgically treated OCSCC who underwent preoperative positron emission tomography–computed tomography to define the prevalence of MLLN metastases. As a control group, 500 consecutive face-neck MRIs from noncancer patients were reviewed for structures consistent with MLLNs. Results In the study group, 1 (0.95%) of 105 OCSCC cases demonstrated a single MLLN metastasis from a lateral tongue tumor (T4aN2c). The MLLN exceeded 1 cm in all planes and was abnormal in morphology. The frequency of suspected MLLNs in controls was 1.0%, with a maximum measurement of 0.9 cm. Conclusions Median lingual lymph nodes are infrequently identified with MRI in controls, concordant with the low prevalence of metastases from OCSCC to this inconstant nodal group.

AbstractBackgroundGlioblastoma (GBM) exhibits profound intratumoral genetic heterogeneity. Each tumor comprises multiple genetically distinct clonal populations with different therapeutic sensitivities. This has implications for targeted therapy and genetically informed paradigms. Contrast-enhanced (CE)-MRI and conventional sampling techniques have failed to resolve this heterogeneity, particularly for nonenhancing tumor populations. This study explores the feasibility of using multiparametric MRI and texture analysis to characterize regional genetic heterogeneity throughout MRI-enhancing and nonenhancing tumor segments.MethodsWe collected multiple image-guided biopsies from primary GBM patients throughout regions of enhancement (ENH) and nonenhancing parenchyma (so called brain-around-tumor, [BAT]). For each biopsy, we analyzed DNA copy number variants for core GBM driver genes reported by The Cancer Genome Atlas. We co-registered biopsy locations with MRI and texture maps to correlate regional genetic status with spatially matched imaging measurements. We also built multivariate predictive decision-tree models for each GBM driver gene and validated accuracies using leave-one-out-cross-validation (LOOCV).ResultsWe collected 48 biopsies (13 tumors) and identified significant imaging correlations (univariate analysis) for 6 driver genes: EGFR, PDGFRA, PTEN, CDKN2A, RB1, and TP53. Predictive model accuracies (on LOOCV) varied by driver gene of interest. Highest accuracies were observed for PDGFRA (77.1%), EGFR (75%), CDKN2A (87.5%), and RB1 (87.5%), while lowest accuracy was observed in TP53 (37.5%). Models for 4 driver genes (EGFR, RB1, CDKN2A, and PTEN) showed higher accuracy in BAT samples (n = 16) compared with those from ENH segments (n = 32).ConclusionMRI and texture analysis can help characterize regional genetic heterogeneity, which offers potential diagnostic value under the paradigm of individualized oncology.

BACKGROUNDEfficacy trials have shown that antiviral therapy improves the outcomes of patients with chronic hepatitis B virus (HBV) infection. However, prospective data regarding the effect of antiviral therapy on the incidence of hepatocellular carcinoma (HCC), especially among patients without cirrhosis, are limited. The authors examined the impact of tenofovir disoproxil fumarate (TDF) on the incidence of HCC using a validated prediction model.METHODSThe incidence of HCC in patients treated with TDF was obtained in the pivotal TDF registration studies after 384 weeks of follow‐up. The predicted risk of HCC in individual patients was calculated using the Risk Estimation for Hepatocellular Carcinoma in Chronic Hepatitis B (REACH‐B) model, which estimates HCC incidence for up to 10 years based on age, sex, alanine aminotransferase level, hepatitis B e antigen status, and HBV‐DNA. Standardized incidence ratios (SIRs) were calculated comparing the observed and predicted numbers of HCC cases in the study cohort.RESULTSAmong 634 patients with evaluable baseline biopsies, 152 had cirrhosis (Ishak fibrosis score of 5 or 6) and 482 did not. During the 384 weeks of study, 14 cases of HCC were reported, with 4 occurring within the first year. The incidence of HCC was 0.37% per year in the study as a whole (0.28% among patients without cirrhosis and 0.65% among patients with cirrhosis). Among patients without cirrhosis, the observed incidence of HCC was significantly lower than predicted (SIR, 0.40; 95% confidence interval, 0.199‐0.795). The last HCC case in a patient with cirrhosis occurred around week 192 with an SIR of 0.51 (95% confidence interval, 0.231‐1.144) reported at week 384.CONCLUSIONSBased on the REACH‐B risk calculator, long‐term therapy with TDF was associated with a reduced incidence of HCC among patients without cirrhosis who met treatment criteria. Cancer 2015;121:3631–3638. © 2015 American Cancer Society.