A Geographic Analysis Of The Radiation Oncology Workforce: Assessing The Impact On Prostate Cancer Management And Outcomes

Previous analyses of the radiation oncology (RO) workforce have focused on gross numbers and not geographic distribution. We investigated trends in the geographic distribution of the radiation oncology workforce across the United States. Additionally, we assessed the impact of geographic variations in the RO workforce on prostate cancer management and outcomes. We hypothesized that geographic variations in the workforce would be associated with prostate cancer management and prostate-cancer mortality. We used the Area Resource File to calculate and map the ratio of radiation oncologists to the population aged 65 or older (ROR) within different health service areas (HSA) across the United States from 1995-2007. Multivariate regression models were built to test the association between ROR and socioeconomic variables (income, minority population, unemployment rate, population education). Using patient data from the Surveillance Epidemiology End Results Program (SEER) we built multivariate logistic regression models to test associations between variations in the RO workforce and patient decisions to observe, undergo a radical prostatectomy, or undergo radiation therapy. Using mortality data from the State Cancer Profiles dataset, we built multivariate linear regression to test the association between RO workforce and count-level age-adjusted prostate cancer mortality. Despite a 24% increase in the workforce from 1995 to 2007, there remained consistent geographic maldistribution of radiation oncologists, specifically affecting the rural HSAs. Regression analysis found higher ROR associated with more educated (p=.001), affluent (p\u3c.001) HSAs with lower unemployment rates (p\u3c.001), and higher minority populations (p=.022). Of the 108,612 prostate cancer patients queried from the SEER dataset, patients with low-risk disease (p\u3c.001) residing in HSAs with fewer radiation oncologists (p=.001-.041), fewer urologists (p\u3c.001), and more primary care physicians (p\u3c.001) were most likely to observed in lieu of curative treatment. Of the 91,643 patients who underwent some form of curative treatment, older, single (p\u3c.001), African American patients (p\u3c.001) with low-risk disease (p\u3c.001) residing in HSAs with more radiation oncologists (p=.007-.001) and primary care physicians (p\u3c.001) were more likely to receive radiation therapy. The presence of at least one radiation oncologist was associated with between 5.74% and 1.48% reduction in prostate cancer mortality (p=.001-.045) even when adjusting for county-level prostate cancer incidence. Despite a modest growth in the radiation oncology workforce, there exists persistent geographic maldistribution of radiation oncologists allocated along socioeconomic and racial lines. Regional variations in the RO workforce are associated with variations in the management of prostate cancer. The presence of at least one radiation oncologist is associated with a reduction in county-level prostate cancer mortality. There is a need for geographically aware policy in order to optimize the RO workforce and improve prostate cancer outcomes

CUTS: A Fully Unsupervised Framework for Medical Image Segmentation

In this work we introduce CUTS (Contrastive and Unsupervised Training for Segmentation) the first fully unsupervised deep learning framework for medical image segmentation, facilitating the use of the vast majority of imaging data that is not labeled or annotated. Segmenting medical images into regions of interest is a critical task for facilitating both patient diagnoses and quantitative research. A major limiting factor in this segmentation is the lack of labeled data, as getting expert annotations for each new set of imaging data or task can be expensive, labor intensive, and inconsistent across annotators: thus, we utilize self-supervision based on pixel-centered patches from the images themselves. Our unsupervised approach is based on a training objective with both contrastive learning and autoencoding aspects. Previous contrastive learning approaches for medical image segmentation have focused on image-level contrastive training, rather than our intra-image patch-level approach or have used this as a pre-training task where the network needed further supervised training afterwards. By contrast, we build the first entirely unsupervised framework that operates at the pixel-centered-patch level. Specifically, we add novel augmentations, a patch reconstruction loss, and introduce a new pixel clustering and identification framework. Our model achieves improved results on several key medical imaging tasks, as verified by held-out expert annotations on the task of segmenting geographic atrophy (GA) regions of images of the retina

Evolution and implementation of radiographic response criteria in neuro-oncology

Radiographic response assessment in neuro-oncology is critical in clinical practice and trials. Conventional criteria, such as the MacDonald and response assessment in neuro-oncology (RANO) criteria, rely on bidimensional (2D) measurements of a single tumor cross-section. Although RANO criteria are established for response assessment in clinical trials, there is a critical need to address the complexity of brain tumor treatment response with multiple new approaches being proposed. These include volumetric analysis of tumor compartments, structured MRI reporting systems like the Brain Tumor Reporting and Data System, and standardized approaches to advanced imaging techniques to distinguish tumor response from treatment effects. In this review, we discuss the strengths and limitations of different neuro-oncology response criteria and summarize current research findings on the role of novel response methods in neuro-oncology clinical trials and practice

Comparison of radiomic feature aggregation methods for patients with multiple tumors.

Radiomic feature analysis has been shown to be effective at analyzing diagnostic images to model cancer outcomes. It has not yet been established how to best combine radiomic features in cancer patients with multifocal tumors. As the number of patients with multifocal metastatic cancer continues to rise, there is a need for improving personalized patient-level prognosis to better inform treatment. We compared six mathematical methods of combining radiomic features of 3,596 tumors in 831 patients with multiple brain metastases and evaluated the performance of these aggregation methods using three survival models: a standard Cox proportional hazards model, a Cox proportional hazards model with LASSO regression, and a random survival forest. Across all three survival models, the weighted average of the largest three metastases had the highest concordance index (95% confidence interval) of 0.627 (0.595-0.661) for the Cox proportional hazards model, 0.628 (0.591-0.666) for the Cox proportional hazards model with LASSO regression, and 0.652 (0.565-0.727) for the random survival forest model. This finding was consistent when evaluating patients with different numbers of brain metastases and different tumor volumes. Radiomic features can be effectively combined to estimate patient-level outcomes in patients with multifocal brain metastases. Future studies are needed to confirm that the volume-weighted average of the largest three tumors is an effective method for combining radiomic features across other imaging modalities and tumor types

Neurodevelopmental disorders in children aged 2-9 years: Population-based burden estimates across five regions in India.

BACKGROUND: Neurodevelopmental disorders (NDDs) compromise the development and attainment of full social and economic potential at individual, family, community, and country levels. Paucity of data on NDDs slows down policy and programmatic action in most developing countries despite perceived high burden. METHODS AND FINDINGS: We assessed 3,964 children (with almost equal number of boys and girls distributed in 2-<6 and 6-9 year age categories) identified from five geographically diverse populations in India using cluster sampling technique (probability proportionate to population size). These were from the North-Central, i.e., Palwal (N = 998; all rural, 16.4% non-Hindu, 25.3% from scheduled caste/tribe [SC-ST] [these are considered underserved communities who are eligible for affirmative action]); North, i.e., Kangra (N = 997; 91.6% rural, 3.7% non-Hindu, 25.3% SC-ST); East, i.e., Dhenkanal (N = 981; 89.8% rural, 1.2% non-Hindu, 38.0% SC-ST); South, i.e., Hyderabad (N = 495; all urban, 25.7% non-Hindu, 27.3% SC-ST) and West, i.e., North Goa (N = 493; 68.0% rural, 11.4% non-Hindu, 18.5% SC-ST). All children were assessed for vision impairment (VI), epilepsy (Epi), neuromotor impairments including cerebral palsy (NMI-CP), hearing impairment (HI), speech and language disorders, autism spectrum disorders (ASDs), and intellectual disability (ID). Furthermore, 6-9-year-old children were also assessed for attention deficit hyperactivity disorder (ADHD) and learning disorders (LDs). We standardized sample characteristics as per Census of India 2011 to arrive at district level and all-sites-pooled estimates. Site-specific prevalence of any of seven NDDs in 2-<6 year olds ranged from 2.9% (95% CI 1.6-5.5) to 18.7% (95% CI 14.7-23.6), and for any of nine NDDs in the 6-9-year-old children, from 6.5% (95% CI 4.6-9.1) to 18.5% (95% CI 15.3-22.3). Two or more NDDs were present in 0.4% (95% CI 0.1-1.7) to 4.3% (95% CI 2.2-8.2) in the younger age category and 0.7% (95% CI 0.2-2.0) to 5.3% (95% CI 3.3-8.2) in the older age category. All-site-pooled estimates for NDDs were 9.2% (95% CI 7.5-11.2) and 13.6% (95% CI 11.3-16.2) in children of 2-<6 and 6-9 year age categories, respectively, without significant difference according to gender, rural/urban residence, or religion; almost one-fifth of these children had more than one NDD. The pooled estimates for prevalence increased by up to three percentage points when these were adjusted for national rates of stunting or low birth weight (LBW). HI, ID, speech and language disorders, Epi, and LDs were the common NDDs across sites. Upon risk modelling, noninstitutional delivery, history of perinatal asphyxia, neonatal illness, postnatal neurological/brain infections, stunting, LBW/prematurity, and older age category (6-9 year) were significantly associated with NDDs. The study sample was underrepresentative of stunting and LBW and had a 15.6% refusal. These factors could be contributing to underestimation of the true NDD burden in our population. CONCLUSIONS: The study identifies NDDs in children aged 2-9 years as a significant public health burden for India. HI was higher than and ASD prevalence comparable to the published global literature. Most risk factors of NDDs were modifiable and amenable to public health interventions

Protocols for Mitigating Blackhole Attacks in Delay Tolerant Networks

High node mobility and infrequent connectivity in delay tolerant networks (DTNs) makes it challenging to implement traditional security algorithms for detecting malicious nodes. In DTN, most of the routing algorithms are based on the announcement of routing metrics like probability of delivery, contact strength or social group strength by the nodes in contact. Blackhole in DTN exploits these characteristics of routing protocols and either announces a high value of these metrics or tries to attain a high value for them by following fast, repeated movement patterns. Dynamic social grouping (DSG) based routing algorithm shows that social behavior of nodes helps to make better forwarding decisions and to achieve highest message delivery ratio amongst other existing routing algorithms. We examine the impact of blackholes, intermittent blackholes and tailgating attack on DSG. We propose a suit of three solutions. Our first solution detects blackholes and tailgating malicious nodes in the network, however, is not suitable for intermittent blackholes. Second solution handles intermittent blackholes and performs well when the nodes are well connected. The third and final solution handles intermittent blackholes in sparsely connected as well as in well-connected networks. In all proposed solutions, blackholes are not able to degrade the performance of the protocols by changing their geographical locations. We demonstrate through simulation that our protocols improve upon the message delivery ratio over the existing solutions. An appropriate protocol from the suit may be used depending upon an application

Comparing stress testing and fractional flow reserve to evaluate presence, location and extent of ischemia in coronary artery disease

Background: FFR provides an accurate and reproducible assessment of the functional severity of coronary stenosis. Whereas stress testing remains the preferred initial modality for assessment of ischemia, there is limited data comparing it with FFR. We sought to determine the correlation between cardiac stress testing and coronary fractional flow reserve (FFR) measurement for assessing the presence, location, and burden of myocardial ischemia in patients referred for evaluation of coronary artery disease (CAD). Methods: Over 5-year study period, of the 5420 consecutive coronary angiograms that were screened, 326 patients had FFR measurements. Of these, 96 patients with FFR measurements who had a preceding stress test (stress echocardiography [SE] or myocardial perfusion imaging [MPI]) within a year were included. Results: Of the 96 patients, there were 46 (48%) men and 50 (52%) women with a mean age of 61 ± 10 years. SE was performed in 57 (59.3%) and MPI in 32 (40.7%) of patients. FFR was ≤0.79 in 54 (56%) patients. Stress testing had low sensitivity (55%) and specificity (47%) compared to FFR. The concordance between FFR and stress testing was low for both presence (k=0.03) and location (k=0.05) of the ischemic territory. The number of ischemic vascular territories was correctly estimated in only 39% of the stress tests. SE was more likely to overestimate and MPI more likely to underestimate extent of ischemia. Conclusions: In patients referred for evaluation of CAD, there was poor correlation between stress testing and FFR. A prospective study comparing these two modalities with FFR is needed