Citations

432 Background: In clinical trials and clinical practice, tumor response assessment with computed tomography (CT) defines critical end points in patients with metastatic disease treated with systemic agents. Methods to reduce errors and improve efficiency in tumor response assessment could improve patient care. Methods: Eleven readers from 10 different institutions independently categorized tumor response according to three different therapeutic response criteria using paired baseline and initial post-therapy CT studies from 20 randomly selected patients with metastatic renal cell carcinoma treated with sunitinib as part of a completed phase III multi-institutional study. Images were evaluated with a manual tumor response evaluation method (standard-of-care) and with computer-assisted response evaluation (CARE) that included stepwise guidance, interactive error-identification and correction methods, automated tumor metric extraction, calculations, response categorization, and data/image archival. A cross-over design, patient randomization, and two-week washout period were used to reduce recall bias. Comparative effectiveness metrics included error rate and mean patient evaluation time. Results: The standard-of-care method was on average associated with one or more errors in 30.5% (6.1/20) of patients while CARE had a 0.0% (0.0/20) error rate (p<0.001). The most common errors were related to data transfer and arithmetic calculation. In patients with errors, the median number of error types was 1 (range 1-3). Mean patient evaluation time with CARE was twice as fast as the standard-of-care method (6.4 vs. 13.1 minutes, p<0.001). Conclusions: Computer-assisted tumor response evaluation reduced errors and time of evaluation, indicating better overall effectiveness than manual tumor response evaluation methods that are the current standard-of-care.

Abstract Introduction A classification framework for posterior cortical atrophy (PCA) is proposed to improve the uniformity of definition of the syndrome in a variety of research settings. Methods Consensus statements about PCA were developed through a detailed literature review, the formation of an international multidisciplinary working party which convened on four occasions, and a Web‐based quantitative survey regarding symptom frequency and the conceptualization of PCA. Results A three‐level classification framework for PCA is described comprising both syndrome‐ and disease‐level descriptions. Classification level 1 (PCA) defines the core clinical, cognitive, and neuroimaging features and exclusion criteria of the clinico‐radiological syndrome. Classification level 2 (PCA‐pure, PCA‐plus) establishes whether, in addition to the core PCA syndrome, the core features of any other neurodegenerative syndromes are present. Classification level 3 (PCA attributable to AD [PCA‐AD], Lewy body disease [PCA‐LBD], corticobasal degeneration [PCA‐CBD], prion disease [PCA‐prion]) provides a more formal determination of the underlying cause of the PCA syndrome, based on available pathophysiological biomarker evidence. The issue of additional syndrome‐level descriptors is discussed in relation to the challenges of defining stages of syndrome severity and characterizing phenotypic heterogeneity within the PCA spectrum. Discussion There was strong agreement regarding the definition of the core clinico‐radiological syndrome, meaning that the current consensus statement should be regarded as a refinement, development, and extension of previous single‐center PCA criteria rather than any wholesale alteration or redescription of the syndrome. The framework and terminology may facilitate the interpretation of research data across studies, be applicable across a broad range of research scenarios (e.g., behavioral interventions, pharmacological trials), and provide a foundation for future collaborative work.

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.

Abstract Background: It is documented that inequities in research participation by minority and underrepresented populations limits the progression of personalized medicine for cancer treatment for these groups. A lack of understanding essential information during the consent process and poor physician-patient communication have been noted as two overarching barriers to patients participating in translational, clinical and biospecimen research. To address these major patient barriers, we developed a plain language research literacy support (RLS) tool to improve the consent process and facilitate dialogue between physicians/research staff and patients. We describe the tool's iterative development process and testing of the developed culturally appropriate RLS tool. Methods: We tested the tool among: 1) patient participants ≥18 years of age, who were able to read and speak English from communities associated with five academic centers in the Midwest and Northeast United States; and 2) eligible recruiters who were employed at an academic center and actively recruiting patients into research. The development process for the RLS tool consisted of two phases: cognitive and beta testing. During cognitive testing, we solicited feedback from patient and recruiter participants about the content and format of the tool including low literacy language, pictures and recommendations for additional content. The tool consisted of concept cards to provide information to assist patients in making an informed decision about participating in research. During beta testing, a condensed version of the tool was tested to assess acceptability and change in knowledge about research participation. Results: Participants included 155 patients (52: cognitive testing phase; 103: beta testing phase) and 20 recruiter participants. Patient participants were mostly females (71%). Self-identified race of patients included: Whites (43%), African Americans (32%), Native Americans (20%), and other (6%). In addition, 23% of patient participants self-identified as Hispanic. During the cognitive testing phase, patient participants reported that the information was clearly laid out and the tool explained the different steps in the recruitment and enrollment process. Patient participants indicated they had learned new information related to randomization, standard of care, coded-information, and biobanking. Although some patient participants acknowledged the cards contained a great deal of information, most reported that the information was essential. Several participants suggested cards be tailored for the Native American population. Recruiters indicated that the cards provided useful information and would assist patients and research staff. During beta testing phase, 98% of participants agreed the information on the cards helped them understand clinical research. After reviewing the cards, 49% of patient participants reported that they would be very likely to participate in research and 32% reported being somewhat likely to participate in research. The majority (61%) of participants indicated that if invited to participate in a research study, they would prefer that the cards would be mailed to them in advance while 34% wanted to review the cards at their medical appointment. Half of the participants preferred accessing the cards on a computer if administered in a video format. Conclusion: The RLS tool was well received among patient and recruiter participants, making it a potentially valuable resource for enrollment of minority and underrepresented patients into health research. The next step will be to test the RLS tool for effectiveness in a randomized controlled trial. Citation Format: Erika E. de la Riva, Rodney Haring, Elisa M. Rodriguez, Evelyn Gonzalez, Mira Katz, Nikia Clark, Whitney Ann E. Henry, Rosa O. Ortiz, Barret J. Zimmermann, Marla L. Clayman, Deborah O. Erwin, Judith S. Kaur, Melissa A. Simon. Development and beta testing of a culturally appropriate research literacy support tool to increase research participation among minority and underrepresented populations. [abstract]. In: Proceedings of the Ninth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2016 Sep 25-28; Fort Lauderdale, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(2 Suppl):Abstract nr A11.

54 Background: After diagnosis and treatment, lung cancer survivors do not return to a health-related quality of life (QOL) and function comparable to pre-diagnosis levels as observed in other adult cancer survivors. We hypothesize that inadequate attention has been paid to addressing and improving QOL and function among lung cancer survivors. Our primary goal is to evaluate existing symptoms and QOL deficits among lung cancer survivors. Methods: We are evaluating patterns of QOL deficits (scored 5 or less on the scale from 0 [worst] to 10 [best]) in relation to time of lung cancer diagnosis, treatments, progression and/or recurrence, and health-related conditions. We are collecting and analyzing relevant health care utilization for selected QOL deficits, and identifying availability, accessibility, and effectiveness of evidence-based interventions. The expected outcomes will be new knowledge on patterns of QOL deficits, quality-adjusted life years in relation to the availability, accessibility, and adequacy of needed care and the effectiveness of interventions targeting improvement in QOL and symptoms. Results: In our initial analysis, among 3,707 lung cancer survivors who were diagnosed in 1999 to 2010, post-cancer treatment, and followed through 2011, 39% (1,442) reported deficits in overall QOL, 71% fatigue, 60% dyspnea, 41% being sedentary, 17% low spiritual well-being (SWB), and 10% currently smoking cigarettes. Among the 1,442 lung cancer survivors with a deficit in overall QOL, 97.5% reported severe fatigue or dyspnea, being sedentary or smoking, or having low SWB. In the remaining 36 of the 1,442, 24 had pain, cough, poor appetite, or other symptoms; only 12 (<1%) did not report deficit in any of the measured QOL domains. Analyses are ongoing. Conclusions: Based on evidence gained to date, we are developing and testing interventions targeting symptoms and QOL deficits. We expect to gain strong evidence for recommendations in redefining and delivering care and support that are necessary for improving the health and QOL of lung cancer survivors.