Enhancing Cancer Care of Rural Dwellers through Telehealth and Engagement (ENCORE): Protocol to Evaluate Effectiveness of a Multi-Level Telehealth-Based Intervention to Improve Rural Cancer Care Delivery

BACKGROUND: Despite lower cancer incidence rates, cancer mortality is higher among rural compared to urban dwellers. Patient, provider, and institutional level factors contribute to these disparities. The overarching objective of this study is to leverage the multidisciplinary, multispecialty oncology team from an academic cancer center in order to provide comprehensive cancer care at both the patient and provider levels in rural healthcare centers. Our specific aims are to: 1) evaluate the clinical effectiveness of a multi-level telehealth-based intervention consisting of provider access to molecular tumor board expertise along with patient access to a supportive care intervention to improve cancer care delivery; and 2) identify the facilitators and barriers to future larger scale dissemination and implementation of the multi-level intervention. METHODS: Coordinated by a National Cancer Institute-designated comprehensive cancer center, this study will include providers and patients across several clinics in two large healthcare systems serving rural communities. Using a telehealth-based molecular tumor board, sequencing results are reviewed, predictive and prognostic markers are discussed, and treatment plans are formulated between expert oncologists and rural providers. Simultaneously, the rural patients will be randomized to receive an evidence-based 6-week self-management supportive care program, Cancer Thriving and Surviving, versus an education attention control. Primary outcomes will be provider uptake of the molecular tumor board recommendation and patient treatment adherence. A mixed methods approach guided by the Consolidated Framework for Implementation Research that combines qualitative key informant interviews and quantitative surveys will be collected from both the patient and provider in order to identify facilitators and barriers to implementing the multi-level intervention. DISCUSSION: The proposed study will leverage information technology-enabled, team-based care delivery models in order to deliver comprehensive, coordinated, and high-quality cancer care to rural and/or underserved populations. Simultaneous attention to institutional, provider, and patient level barriers to quality care will afford the opportunity for us to broadly share oncology expertise and develop dissemination and implementation strategies that will enhance the cancer care delivered to patients residing within underserved rural communities. TRIAL REGISTRATION: Clinicaltrials.gov , NCT04758338 . Registered 17 February 2021 - Retrospectively registered, http://www.clinicaltrials.gov/

Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging Data to Constrain a Positron Emission Tomography Kinetic Model: Theory and Simulations

We show how dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data can constrain a compartmental model for analyzing dynamic positron emission tomography (PET) data. We first develop the theory that enables the use of DCE-MRI data to separate whole tissue time activity curves (TACs) available from dynamic PET data into individual TACs associated with the blood space, the extravascular-extracellular space (EES), and the extravascular-intracellular space (EIS). Then we simulate whole tissue TACs over a range of physiologically relevant kinetic parameter values and show that using appropriate DCE-MRI data can separate the PET TAC into the three components with accuracy that is noise dependent. The simulations show that accurate blood, EES, and EIS TACs can be obtained as evidenced by concordance correlation coefficients >0.9 between the true and estimated TACs. Additionally, provided that the estimated DCE-MRI parameters are within 10% of their true values, the errors in the PET kinetic parameters are within approximately 20% of their true values. The parameters returned by this approach may provide new information on the transport of a tracer in a variety of dynamic PET studies

COVID-19 in patients with thoracic malignancies (TERAVOLT): first results of an international, registry-based, cohort study

Background: Early reports on patients with cancer and COVID-19 have suggested a high mortality rate compared with the general population. Patients with thoracic malignancies are thought to be particularly susceptible to COVID-19 given their older age, smoking habits, and pre-existing cardiopulmonary comorbidities, in addition to cancer treatments. We aimed to study the effect of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection on patients with thoracic malignancies. Methods: The Thoracic Cancers International COVID-19 Collaboration (TERAVOLT) registry is a multicentre observational study composed of a cross-sectional component and a longitudinal cohort component. Eligibility criteria were the presence of any thoracic cancer (non-small-cell lung cancer [NSCLC], small-cell lung cancer, mesothelioma, thymic epithelial tumours, and other pulmonary neuroendocrine neoplasms) and a COVID-19 diagnosis, either laboratory confirmed with RT-PCR, suspected with symptoms and contacts, or radiologically suspected cases with lung imaging features consistent with COVID-19 pneumonia and symptoms. Patients of any age, sex, histology, or stage were considered eligible, including those in active treatment and clinical follow-up. Clinical data were extracted from medical records of consecutive patients from Jan 1, 2020, and will be collected until the end of pandemic declared by WHO. Data on demographics, oncological history and comorbidities, COVID-19 diagnosis, and course of illness and clinical outcomes were collected. Associations between demographic or clinical characteristics and outcomes were measured with odds ratios (ORs) with 95% CIs using univariable and multivariable logistic regression, with sex, age, smoking status, hypertension, and chronic obstructive pulmonary disease included in multivariable analysis. This is a preliminary analysis of the first 200 patients. The registry continues to accept new sites and patient data. Findings: Between March 26 and April 12, 2020, 200 patients with COVID-19 and thoracic cancers from eight countries were identified and included in the TERAVOLT registry; median age was 68·0 years (61·8-75·0) and the majority had an Eastern Cooperative Oncology Group performance status of 0-1 (142 [72%] of 196 patients), were current or former smokers (159 [81%] of 196), had non-small-cell lung cancer (151 [76%] of 200), and were on therapy at the time of COVID-19 diagnosis (147 [74%] of 199), with 112 (57%) of 197 on first-line treatment. 152 (76%) patients were hospitalised and 66 (33%) died. 13 (10%) of 134 patients who met criteria for ICU admission were admitted to ICU; the remaining 121 were hospitalised, but were not admitted to ICU. Univariable analyses revealed that being older than 65 years (OR 1·88, 95% 1·00-3·62), being a current or former smoker (4·24, 1·70-12·95), receiving treatment with chemotherapy alone (2·54, 1·09-6·11), and the presence of any comorbidities (2·65, 1·09-7·46) were associated with increased risk of death. However, in multivariable analysis, only smoking history (OR 3·18, 95% CI 1·11-9·06) was associated with increased risk of death. Interpretation: With an ongoing global pandemic of COVID-19, our data suggest high mortality and low admission to intensive care in patients with thoracic cancer. Whether mortality could be reduced with treatment in intensive care remains to be determined. With improved cancer therapeutic options, access to intensive care should be discussed in a multidisciplinary setting based on cancer specific mortality and patients' preference

Practical Dynamic Contrast Enhanced MRI in Small Animal Models of Cancer: Data Acquisition, Data Analysis, and Interpretation

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) consists of the continuous acquisition of images before, during, and after the injection of a contrast agent. DCE-MRI allows for noninvasive evaluation of tumor parameters related to vascular perfusion and permeability and tissue volume fractions, and is frequently employed in both preclinical and clinical investigations. However, the experimental and analytical subtleties of the technique are not frequently discussed in the literature, nor are its relationships to other commonly used quantitative imaging techniques. This review aims to provide practical information on the development, implementation, and validation of a DCE-MRI study in the context of a preclinical study (though we do frequently refer to clinical studies that are related to these topics)

Characterizing Trastuzumab-Induced Alterations in Intratumoral Heterogeneity with Quantitative Imaging and Immunohistochemistry in HER2+ Breast Cancer

The purpose of this study is to investigate imaging and histology-based measurements of intratumoral heterogeneity to evaluate early treatment response to targeted therapy in a murine model of HER2+ breast cancer. BT474 tumor-bearing mice (N = 30) were treated with trastuzumab or saline and imaged longitudinally with either dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) or 18F-fluoromisonidazole (FMISO) positron emission tomography (PET). At the imaging study end point (day 4 for MRI or 7 for PET), each tumor was excised for immunohistochemistry analysis. Voxel-based histogram analysis was performed on imaging-derived parametric maps (i.e., Ktrans and ve from DCE-MRI, SUV from 18F-FMISO-PET) of the tumor region of interest to measure heterogeneity. Image processing and histogram analysis of whole tumor slice immunohistochemistry data were performed to validate the in vivo imaging findings. Trastuzumab-treated tumors had increased heterogeneity in quantitative imaging measures of cellularity (ve), with a mean Kolmogorov-Smirnov (K-S) distance of 0.32 (P = .05) between baseline and end point distributions. Trastuzumab-treated tumors had increased vascular heterogeneity (Ktrans) and decreased hypoxic heterogeneity (SUV), with a mean K-S distance of 0.42 (P < .01) and 0.46 (P = .047), respectively, between baseline and study end points. These observations were validated by whole-slice immunohistochemistry analysis with mean interquartile range of CD31 distributions of 1.72 for treated and 0.95 for control groups (P = .02). Quantitative longitudinal changes in tumor cellular and vascular heterogeneity in response to therapy may provide evidence for early prediction of response and guide therapy for patients with HER2+ breast cancer

Multiparametric Analysis of Longitudinal Quantitative MRI Data to Identify Distinct Tumor Habitats in Preclinical Models of Breast Cancer

This study identifies physiological tumor habitats from quantitative magnetic resonance imaging (MRI) data and evaluates their alterations in response to therapy. Two models of breast cancer (BT-474 and MDA-MB-231) were imaged longitudinally with diffusion-weighted MRI and dynamic contrast-enhanced MRI to quantify tumor cellularity and vascularity, respectively, during treatment with trastuzumab or albumin-bound paclitaxel. Tumors were stained for anti-CD31, anti-Ki-67, and H&amp;E. Imaging and histology data were clustered to identify tumor habitats and percent tumor volume (MRI) or area (histology) of each habitat was quantified. Histological habitats were correlated with MRI habitats. Clustering of both the MRI and histology data yielded three clusters: high-vascularity high-cellularity (HV-HC), low-vascularity high-cellularity (LV-HC), and low-vascularity low-cellularity (LV-LC). At day 4, BT-474 tumors treated with trastuzumab showed a decrease in LV-HC (p = 0.03) and increase in HV-HC (p = 0.03) percent tumor volume compared to control. MDA-MB-231 tumors treated with low-dose albumin-bound paclitaxel showed a longitudinal decrease in LV-HC percent tumor volume at day 3 (p = 0.01). Positive correlations were found between histological and imaging-derived habitats: HV-HC (BT-474: p = 0.03), LV-HC (MDA-MB-231: p = 0.04), LV-LC (BT-474: p = 0.04; MDA-MB-231: p &lt; 0.01). Physiologically distinct tumor habitats associated with therapeutic response were identified with MRI and histology data in preclinical models of breast cancer

Phase I/II study of nivolumab plus vorolanib in patients with thoracic malignancies: Interim efficacy of the SCLC and primary refractory NSCLC cohorts, and safety data across all cohorts.

Research Funding: Bristol Myers Squibb, Xcovery Background:Combination strategies to improve the efficacy of single agent immune checkpoint inhibitors (ICIs) are increasingly being explored, with one strategy being the addition of vascular endothelial growth factor (VEGF) inhibition. Having shown promise in the treatment of hepatocellular carcinoma and renal cell carcinoma, NCT03583086 is a multi-institutional, phase I/II study of combination vorolanib and nivolumab in both naïve and refractory thoracic tumors that progressed on at least one prior line of platinum-based chemotherapy. Though structurally similar to the tyrosine kinase inhibitor, sunitinib, vorolanib was designed to have a more favorable safety profile with comparable efficacy. Here we present safety data across all cohorts and interim efficacy analyses of the SCLC and NSCLC with primary resistance to ICI-based therapy cohorts, both of which have now completed enrolment.Methods:The maximum tolerated dose determined in phase I was vorolanib 200mg daily and nivolumab 240mg q2 weeks. Phase II uses a two-stage MinMax design across 5 cohorts with objective response rate (ORR) as the primary endpoint: NSCLC (ICI naïve, primary refractory, and acquired resistance), SCLC, and thymic carcinoma. Primary refractory is defined as radiographic progression of disease within 12 weeks of ICI initiation.Results:As of January 2021, 75 patients have been enrolled across all cohorts. Stage 1 of the SCLC and primary refractory NSCLC cohorts have completed accrual at 18 and 15 patients, respectively. In the SCLC cohort, disease-control rate (DCR) was 7% and no objective responses were achieved among 14 evaluable patients. In the primary refractory NSCLC cohort, DCR was 57% and ORR 7% (1 partial response) among 14 evaluable patients. A total of 140 treatment-related adverse events (TRAEs) have been reported, 13 (9%) were grade 3 and there were no grade 4/5 events. Fatigue (9%), nausea (6%), diarrhea (6%), ALT elevation (5%), and AST elevation (5%) were the most common all grade TRAEs. The most common grade 3 TRAEs were ALT elevation and hypertension.Conclusions:This therapeutic strategy of nivolumab plus vorolanib appears to be a well-tolerated combination with a manageable safety profile. Adding VEGF inhibition may offer additional disease control in the setting of NSCLC with primary resistance to ICIs, but neither the SCLC or primary refractory NSCLC cohorts achieved the pre-determined target number of objective responses for progression to stage 2 of the study. Enrolment in the other 3 cohorts as well as exploratory correlatives are ongoing. Clinical trial information: NCT0358308

Bloch–Siegert B1-Mapping Improves Accuracy and Precision of Longitudinal Relaxation Measurements in the Breast at 3 T

Variable flip angle (VFA) sequences are a popular method of calculating T1 values, which are required in a quantitative analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI). B1 inhomogeneities are substantial in the breast at 3 T, and these errors negatively impact the accuracy of the VFA approach, thus leading to large errors in the DCE-MRI parameters that could limit clinical adoption of the technique. This study evaluated the ability of Bloch–Siegert B1 mapping to improve the accuracy and precision of VFA-derived T1 measurements in the breast. Test–retest MRI sessions were performed on 16 women with no history of breast disease. T1 was calculated using the VFA sequence, and B1 field variations were measured using the Bloch–Siegert methodology. As a gold standard, inversion recovery (IR) measurements of T1 were performed. Fibroglandular tissue and adipose tissue from each breast were segmented using the IR images, and the mean T1 was calculated for each tissue. Accuracy was evaluated by percent error (%err). Reproducibility was assessed via the 95% confidence interval (CI) of the mean difference and repeatability coefficient (r). After B1 correction, %err significantly (P &lt; 0.001) decreased from 17% to 8.6%, and the 95% CI and r decreased from ±94 to ±38 milliseconds and from 276 to 111 milliseconds, respectively. Similar accuracy and reproducibility results were observed in the adipose tissue of the right breast and in both tissues of the left breast. Our data show that Bloch–Siegert B1 mapping improves accuracy and precision of VFA-derived T1 measurements in the breast