Noninvasive Autopsy-Validated Tumor Probability Maps Identify Glioma Invasion Beyond Contrast Enhancement.

BACKGROUND AND OBJECTIVES: This study identified a clinically significant subset of patients with glioma with tumor outside of contrast enhancement present at autopsy and subsequently developed a method for detecting nonenhancing tumor using radio-pathomic mapping. We tested the hypothesis that autopsy-based radio-pathomic tumor probability maps would be able to noninvasively identify areas of infiltrative tumor beyond traditional imaging signatures. METHODS: A total of 159 tissue samples from 65 subjects were aligned to MRI acquired nearest to death for this retrospective study. Demographic and survival characteristics for patients with and without tumor beyond the contrast-enhancing margin were computed. An ensemble algorithm was used to predict pixelwise tumor presence from pathological annotations using segmented cellularity (Cell), extracellular fluid, and cytoplasm density as input (6 train/3 test subjects). A second level of ensemble algorithms was used to predict voxelwise Cell, extracellular fluid, and cytoplasm on the full data set (43 train/22 test subjects) using 5-by-5 voxel tiles from T1, T1 + C, fluid-attenuated inversion recovery, and apparent diffusion coefficient as input. The models were then combined to generate noninvasive whole brain maps of tumor probability. RESULTS: Tumor outside of contrast was identified in 41.5% of patients, who showed worse survival outcomes (hazard ratio = 3.90, P < .001). Tumor probability maps reliably tracked nonenhancing tumor on a range of local and external unseen data, identifying tumor outside of contrast in 69% of presurgical cases that also showed reduced survival outcomes (hazard ratio = 1.67, P = .027). CONCLUSION: This study developed a multistage model for mapping gliomas using autopsy tissue samples as ground truth, which was able to identify regions of tumor beyond traditional imaging signatures.

Morphologic features of prostate cancer-encased native vessels: An image analysis study.

BACKGROUND: Vasculature plays a crucial role in the progression of prostate cancer (PC). Changes to the prostatic native vessels have not been studied since 2000 when Garcia et al. demonstrated marked media hypercellularity and increased artery thickness in prostatic native arteries within PC. We aim to further evaluate and characterize prostatic native vessels with a more accurate method with the use of virtual slides and digital analysis. DESIGN: Pathologist-annotated whole-mount digital slides from 96 entirely submitted prostatectomies were annotated for PC (color-coded by Gleason) using Omero platform. A subset of 44 cases met criteria for further analysis of media thickness, cellularity, and wall thickness to lumen ratio. Cases were included based on containing ≥5 native arteries (≥100 µm diameter) encased on at least 3 sides by PC, with vessels (≥100 µm diameter) designated as controls if they were ≥ 1000 µm away from PC. Annotated vessels were segmented and processed using Matlab 2023b. Mean media thickness (corrected for oblique sections), media: lumen ratio (based on numbers of pixels), and media cellularity (nuclei count) were studied by analysis with SPSS by linear mixed model with nested random effects for subject and slide to account for repeated measures. RESULTS: Vessels encased by PC showed greater media thickness (p=0.02), cellularity (p=0.02) and wall thickness/lumen ratio (p= <0.001) compared to vessels away from PC. These values showed an increasing trend according to stage in cellularity (p=0.14), media thickness (p=0.12) and wall thickness/ lumen ratio (p= 0.33) with higher stage (pT3). A Gleason group comparison showed a borderline-significant gradewise trend when analyzing wall thickness/lumen ratio (p=0.06). Grade 5 emerged as significantly different (p=0.02) from grades 3 or 4 non-cribriform. CONCLUSIONS: Similar to the 2000 study, increased media thickness and hypercellularity of vessels encased by PC were evident compared to controls. Borderline grade-dependent increased vessel cellularity changes were seen, suggesting a possible role in PC progression; the predictive value of these changes for outcome is uncertain. Whether the etiology of changes reflects locally increased intravascular pressure of vessels within tumor should be investigated.

Radio-pathomic maps of glioblastoma identify phenotypes of non-enhancing tumor infiltration associated with bevacizumab treatment response.

BACKGROUND: Autopsy-based radio-pathomic maps of glioma pathology have shown substantial promise inidentifying areas of non-enhancing tumor presence, which may be able to differentiate subsets of patients that respond favorably to treatments such as bevacizumab that have shown mixed efficacy evidence. We tested the hypthesis that phenotypes of non-enhancing tumor fronts can distinguish between glioblastoma patients that will respond favorably to bevacizumab and will visually capture treatment response. METHODS: T1, T1C, FLAIR, and ADC images were used to generate radio-pathomic maps of tumor characteristics for 79 pre-treatment patients with a primary GBM or high-grade IDH1-mutant astrocytoma for this study. Novel phenotyping (hypercellular, hypocellular, hybrid, or well-circumscribed front) of the non-enhancing tumor front was performed on each case. Kaplan Meier analyses were then used to assess differences in survival and bevacizumab efficacy between phenotypes. Phenotype compartment segmentations generated longitudinally for a subset of 26 patients over the course of bevacizumab treatment, where a mixed effect model was used to detect longitudinal changes. RESULTS: Well-Circumscribed patients showed significant/trending increases in survival compared to Hypercellular Front (HR = 2.0, p = 0.05), Hypocellular Front (HR = 2.02, p = 0.03), and Hybrid Front tumors (HR = 1.75, p = 0.09). Only patients with hypocellular or hybrid fronts showed significant survival benefits from bevacizumab treatment (HR = 2.35, p = 0.02; and HR = 2.45, p = 0.03, respectively). Hypocellular volumes decreased by an average 50.52 mm3 per day of bevacizumab treatment (p = 0.002). CONCLUSION: Patients with a hypocellular tumor front identified by radio-pathomic maps showed improved treatment efficacy when treated with bevacizumab, and reducing hypocellular volumes over the course of treatment may indicate treatment response.

Radio-pathomic maps of glioblastoma identify phenotypes of non-enhancing tumor infiltration associated with bevacizumab treatment response.

Background: Autopsy-based radio-pathomic maps of glioma pathology have shown substantial promise inidentifying areas of non-enhancing tumor presence, which may be able to differentiate subsets of patients that respond favorably to treatments such as bevacizumab that have shown mixed efficacy evidence. We tested the hypthesis that phenotypes of non-enhancing tumor fronts can distinguish between glioblastoma patients that will respond favorably to bevacizumab and will visually capture treatment response. Methods: T1, T1C, FLAIR, and ADC images were used to generate radio-pathomic maps of tumor characteristics for 79 pre-treatment patients with a primary GBM or high-grade IDH1-mutant astrocytoma for this study. Novel phenotyping (hypercellular, hypocellular, hybrid, or well-circumscribed front) of the non-enhancing tumor front was performed on each case. Kaplan Meier analyses were then used to assess differences in survival and bevacizumab efficacy between phenotypes. Phenotype compartment segmentations generated longitudinally for a subset of 26 patients over the course of bevacizumab treatment, where a mixed effect model was used to detect longitudinal changes. Results: Well-Circumscribed patients showed significant/trending increases in survival compared to Hypercellular Front (HR = 2.0, p = 0.05), Hypocellular Front (HR = 2.02, p = 0.03), and Hybrid Front tumors (HR = 1.75, p = 0.09). Only patients with hypocellular or hybrid fronts showed significant survival benefits from bevacizumab treatment (HR = 2.35, p = 0.02; and HR = 2.45, p = 0.03, respectively). Hypocellular volumes decreased by an average 50.52 mm3 per day of bevacizumab treatment (p = 0.002). Conclusion: Patients with a hypocellular tumor front identified by radio-pathomic maps showed improved treatment efficacy when treated with bevacizumab, and reducing hypocellular volumes over the course of treatment may indicate treatment response.

The ISMRM Open Science Initiative for Perfusion Imaging (OSIPI): Results from the OSIPI-Dynamic Contrast-Enhanced challenge.

PURPOSE: K trans $$ {K}^{\mathrm{trans}} $$ has often been proposed as a quantitative imaging biomarker for diagnosis, prognosis, and treatment response assessment for various tumors. None of the many software tools for K trans $$ {K}^{\mathrm{trans}} $$ quantification are standardized. The ISMRM Open Science Initiative for Perfusion Imaging-Dynamic Contrast-Enhanced (OSIPI-DCE) challenge was designed to benchmark methods to better help the efforts to standardize K trans $$ {K}^{\mathrm{trans}} $$ measurement. METHODS: A framework was created to evaluate K trans $$ {K}^{\mathrm{trans}} $$ values produced by DCE-MRI analysis pipelines to enable benchmarking. The perfusion MRI community was invited to apply their pipelines for K trans $$ {K}^{\mathrm{trans}} $$ quantification in glioblastoma from clinical and synthetic patients. Submissions were required to include the entrants' K trans $$ {K}^{\mathrm{trans}} $$ values, the applied software, and a standard operating procedure. These were evaluated using the proposed OSIP I gold $$ \mathrm{OSIP}{\mathrm{I}}_{\mathrm{gold}} $$ score defined with accuracy, repeatability, and reproducibility components. RESULTS: Across the 10 received submissions, the OSIP I gold $$ \mathrm{OSIP}{\mathrm{I}}_{\mathrm{gold}} $$ score ranged from 28% to 78% with a 59% median. The accuracy, repeatability, and reproducibility scores ranged from 0.54 to 0.92, 0.64 to 0.86, and 0.65 to 1.00, respectively (0-1 = lowest-highest). Manual arterial input function selection markedly affected the reproducibility and showed greater variability in K trans $$ {K}^{\mathrm{trans}} $$ analysis than automated methods. Furthermore, provision of a detailed standard operating procedure was critical for higher reproducibility. CONCLUSIONS: This study reports results from the OSIPI-DCE challenge and highlights the high inter-software variability within K trans $$ {K}^{\mathrm{trans}} $$ estimation, providing a framework for ongoing benchmarking against the scores presented. Through this challenge, the participating teams were ranked based on the performance of their software tools in the particular setting of this challenge. In a real-world clinical setting, many of these tools may perform differently with different benchmarking methodology.

Quantitative Histomorphometric Features of Prostate Cancer Predict Patients Who Biochemically Recur Following Prostatectomy.

Prostate cancer is the most commonly diagnosed cancer in men, accounting for 27% of the new male cancer diagnoses in 2022. If organ-confined, removal of the prostate through radical prostatectomy is considered curative; however, distant metastases may occur, resulting in a poor patient prognosis. This study sought to determine whether quantitative pathomic features of prostate cancer differ in patients who biochemically experience biological recurrence after surgery. Whole-mount prostate histology from 78 patients was analyzed for this study. In total, 614 slides were hematoxylin and eosin stained and digitized to produce whole slide images (WSI). Regions of differing Gleason patterns were digitally annotated by a genitourinary fellowship-trained pathologist, and high-resolution tiles were extracted from each annotated region of interest for further analysis. Individual glands within the prostate were identified using automated image processing algorithms, and histomorphometric features were calculated on a per-tile basis and across WSI and averaged by patients. Tiles were organized into cancer and benign tissues. Logistic regression models were fit to assess the predictive value of the calculated pathomic features across tile groups and WSI; additionally, models using clinical information were used for comparisons. Logistic regression classified each pathomic feature model at accuracies >80% with areas under the curve of 0.82, 0.76, 0.75, and 0.72 for all tiles, cancer only, noncancer only, and across WSI. This was comparable with standard clinical information, Gleason Grade Groups, and CAPRA score, which achieved similar accuracies but areas under the curve of 0.80, 0.77, and 0.70, respectively. This study demonstrates that the use of quantitative pathomic features calculated from digital histology of prostate cancer may provide clinicians with additional information beyond the traditional qualitative pathologist assessment. Further research is warranted to determine possible inclusion in treatment guidance.

T2-Weighted MRI Radiomic Features Predict Prostate Cancer Presence and Eventual Biochemical Recurrence.

Prostate cancer (PCa) is the most diagnosed non-cutaneous cancer in men. Despite therapies such as radical prostatectomy, which is considered curative, distant metastases may form, resulting in biochemical recurrence (BCR). This study used radiomic features calculated from multi-parametric magnetic resonance imaging (MP-MRI) to evaluate their ability to predict BCR and PCa presence. Data from a total of 279 patients, of which 46 experienced BCR, undergoing MP-MRI prior to surgery were assessed for this study. After surgery, the prostate was sectioned using patient-specific 3D-printed slicing jigs modeled using the T2-weighted imaging (T2WI). Sectioned tissue was stained, digitized, and annotated by a GU-fellowship trained pathologist for cancer presence. Digitized slides and annotations were co-registered to the T2WI and radiomic features were calculated across the whole prostate and cancerous lesions. A tree regression model was fitted to assess the ability of radiomic features to predict BCR, and a tree classification model was fitted with the same radiomic features to classify regions of cancer. We found that 10 radiomic features predicted eventual BCR with an AUC of 0.97 and classified cancer at an accuracy of 89.9%. This study showcases the application of a radiomic feature-based tool to screen for the presence of prostate cancer and assess patient prognosis, as determined by biochemical recurrence.

Whole slide imaging (WSI) scanner differences influence optical and computed properties of digitized prostate cancer histology.

Purpose: Digital pathology is becoming an increasingly popular area of advancement in both research and clinically. Pathologists are now able to manage and interpret slides digitally, as well as collaborate with external pathologists with digital copies of slides. Differences in slide scanners include variation in resolution, image contrast, and optical properties, which may influence downstream image processing. This study tested the hypothesis that varying slide scanners would result in differences in computed pathomic features on prostate cancer whole mount slides. Design: This study collected 192 unique tissue slides from 30 patients following prostatectomy. Tissue samples were paraffin-embedded, stained for hematoxylin and eosin (H&E), and digitized using 3 different scanning microscopes at the highest available magnification rate, for a total of 3 digitized slides per tissue slide. These scanners included a (S1) Nikon microscope equipped with an automated sliding stage, an (S2) Olympus VS120 slide scanner, and a (S3) Huron TissueScope LE scanner. A color deconvolution algorithm was then used to optimize contrast by projecting the RGB image into color channels representing optical stain density. The resulting intensity standardized images were then computationally processed to segment tissue and calculate pathomic features including lumen, stroma, epithelium, and epithelial cell density, as well as second-order features including lumen area and roundness; epithelial area, roundness, and wall thickness; and cell fraction. For each tested feature, mean values of that feature per digitized slide were collected and compared across slide scanners using mixed effect models, fit to compare differences in the tested feature associated with all slide scanners for each slide, including a random effect of subject with a nested random effect of slide to account for repeated measures. Similar models were also computed for tissue densities to examine how differences in scanner impact downstream processing. Results: Each mean color channel intensity (i.e., Red, Green, Blue) differed between slide scanners (all P<.001). Of the color deconvolved images, only the hematoxylin channel was similar in all 3 scanners (all P>.05). Lumen and stroma densities between S3 and S1 slides, and epithelial cell density between S3 and S2 (P>.05) were comparable but all other comparisons were significantly different (P<.05). The second-order features were found to be comparable for all scanner comparisons, except for lumen area and epithelium area. Conclusion: This study demonstrates that both optical and computed properties of digitized histological samples are impacted by slide scanner differences. Future research is warranted to better understand which scanner properties influence the tissue segmentation process and to develop harmonization techniques for comparing data across multiple slide scanners.

Comparison of a machine and deep learning model for automated tumor annotation on digitized whole slide prostate cancer histology.

One in eight men will be affected by prostate cancer (PCa) in their lives. While the current clinical standard prognostic marker for PCa is the Gleason score, it is subject to inter-reviewer variability. This study compares two machine learning methods for discriminating between cancerous regions on digitized histology from 47 PCa patients. Whole-slide images were annotated by a GU fellowship-trained pathologist for each Gleason pattern. High-resolution tiles were extracted from annotated and unlabeled tissue. Patients were separated into a training set of 31 patients (Cohort A, n = 9345 tiles) and a testing cohort of 16 patients (Cohort B, n = 4375 tiles). Tiles from Cohort A were used to train a ResNet model, and glands from these tiles were segmented to calculate pathomic features to train a bagged ensemble model to discriminate tumors as (1) cancer and noncancer, (2) high- and low-grade cancer from noncancer, and (3) all Gleason patterns. The outputs of these models were compared to ground-truth pathologist annotations. The ensemble and ResNet models had overall accuracies of 89% and 88%, respectively, at predicting cancer from noncancer. The ResNet model was additionally able to differentiate Gleason patterns on data from Cohort B while the ensemble model was not. Our results suggest that quantitative pathomic features calculated from PCa histology can distinguish regions of cancer; however, texture features captured by deep learning frameworks better differentiate unique Gleason patterns.

Image Quality Evaluation in Dual-Energy CT of the Chest, Abdomen, and Pelvis in Obese Patients With Deep Learning Image Reconstruction.

OBJECTIVE: The aim of this study was to evaluate image quality in vascular and oncologic dual-energy computed tomography (CT) imaging studies performed with a deep learning (DL)-based image reconstruction algorithm in patients with body mass index of ≥30. METHODS: Vascular and multiphase oncologic staging dual-energy CT examinations were evaluated. Two image reconstruction algorithms were applied to the dual-energy CT data sets: standard of care Adaptive Statistical Iterative Reconstruction (ASiR-V) and TrueFidelity DL image reconstruction at 2 levels (medium and high). Subjective quality criteria were independently evaluated by 4 abdominal radiologists, and interreader agreement was assessed. Signal-to-noise ratio (SNR) and contrast-to-noise ratio were compared between image reconstruction methods. RESULTS: Forty-eight patients were included in this study, and the mean patient body mass index was 39.5 (SD, 7.36). TrueFidelity-High (DL-High) and TrueFidelity-Medium (DL-Med) image reconstructions showed statistically significant higher Likert scores compared with ASiR-V across all subjective image quality criteria ( P < 0.001 for DL-High vs ASiR-V; P < 0.05 for DL-Med vs ASiR-V), and SNRs for aorta and liver were significantly higher for DL-High versus ASiR-V ( P < 0.001). Contrast-to-noise ratio for aorta and SNR for aorta and liver were significantly higher for DL-Med versus ASiR-V ( P < 0.05). CONCLUSIONS: TrueFidelity DL image reconstruction provides improved image quality compared with ASiR-V in dual-energy CTs obtained in obese patients.

Diffusion Restriction Comparison between Gleason 4 Fused Glands and Cribriform Glands within Patient Using Whole-Mount Prostate Pathology as Ground Truth.

The presence and extent of cribriform patterned Gleason 4 (G4) glands are associated with poor prognosis following radical prostatectomy. This study used whole-mount prostate histology and multiparametric magnetic resonance imaging (MP-MRI) to evaluate diffusion differences in G4 gland morphology. Fourty-eight patients underwent MP-MRI prior to prostatectomy, of whom 22 patients had regions of both G4 cribriform glands and G4 fused glands (G4CG and G4FG, respectively). After surgery, the prostate was sliced using custom, patient-specific 3D-printed slicing jigs modeled according to the T2-weighted MR image, processed, and embedded in paraffin. Whole-mount hematoxylin and eosin-stained slides were annotated by our urologic pathologist and digitally contoured to differentiate the lumen, epithelium, and stroma. Digitized slides were co-registered to the T2-weighted MRI scan. Linear mixed models were fitted to the MP-MRI data to consider the different hierarchical structures at the patient and slide level. We found that Gleason 4 cribriform glands were more diffusion-restricted than fused glands.

Multi-Site Concordance of Diffusion-Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness.

BACKGROUND: Diffusion-weighted imaging (DWI) is commonly used to detect prostate cancer, and a major clinical challenge is differentiating aggressive from indolent disease. PURPOSE: To compare 14 site-specific parametric fitting implementations applied to the same dataset of whole-mount pathologically validated DWI to test the hypothesis that cancer differentiation varies with different fitting algorithms. STUDY TYPE: Prospective. POPULATION: Thirty-three patients prospectively imaged prior to prostatectomy. FIELD STRENGTH/SEQUENCE: 3 T, field-of-view optimized and constrained undistorted single-shot DWI sequence. ASSESSMENT: Datasets, including a noise-free digital reference object (DRO), were distributed to the 14 teams, where locally implemented DWI parameter maps were calculated, including mono-exponential apparent diffusion coefficient (MEADC), kurtosis (K), diffusion kurtosis (DK), bi-exponential diffusion (BID), pseudo-diffusion (BID*), and perfusion fraction (F). The resulting parametric maps were centrally analyzed, where differentiation of benign from cancerous tissue was compared between DWI parameters and the fitting algorithms with a receiver operating characteristic area under the curve (ROC AUC). STATISTICAL TEST: Levene's test, P < 0.05 corrected for multiple comparisons was considered statistically significant. RESULTS: The DRO results indicated minimal discordance between sites. Comparison across sites indicated that K, DK, and MEADC had significantly higher prostate cancer detection capability (AUC range = 0.72-0.76, 0.76-0.81, and 0.76-0.80 respectively) as compared to bi-exponential parameters (BID, BID*, F) which had lower AUC and greater between site variation (AUC range = 0.53-0.80, 0.51-0.81, and 0.52-0.80 respectively). Post-processing parameters also affected the resulting AUC, moving from, for example, 0.75 to 0.87 for MEADC varying cluster size. DATA CONCLUSION: We found that conventional diffusion models had consistent performance at differentiating prostate cancer from benign tissue. Our results also indicated that post-processing decisions on DWI data can affect sensitivity and specificity when applied to radiological-pathological studies in prostate cancer. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 3.

Stakeholder views of the practical and cultural barriers to epilepsy care in Uganda.

OBJECTIVE: Epilepsy is one of the most prevalent, treatable neurological diseases globally. In sub-Saharan Africa, people with epilepsy (PWE) frequently seek treatment from traditional or pastoral healers, who are more accessible than biomedical care providers. This often contributes to the substantial time delay in obtaining adequate biomedical care for these patients. In Uganda, the few biomedical providers who can treat epilepsy cannot meet the great need for epilepsy care. Additionally, patients are often hesitant to seek biomedical care, often preferring the easily accessible and trusted sociocultural treatment options. This study sought to elucidate the barriers to biomedical care for PWE as well as identify potential solutions to overcome these barriers from various stakeholder perspectives. METHODS: This study used qualitative research methods. Semistructured interviews and focus group discussions were conducted with four major stakeholder groups: PWE or family members of PWE, neurologists and psychiatrists, traditional healers, and pastoral healers. All interviews and focus group discussions that were in English were audio recorded and transcribed verbatim. Those that were not in English were translated live and audio recorded. A translator later translated the non-English portion of audio recording to ensure proper interpretation. Two independent coders coded the dataset and conducted an inter-rater reliability (IRR) assessment to ensure reliable coding of the data. Thematic analysis was then performed to discern themes from the data and compare nuances between each of the study design groups. RESULTS: Participants discussed several different causes of epilepsy ranging from spiritual to biological causes, often incorporating elements of both. Commonly endorsed spiritual causes of epilepsy included witchcraft and ancestral spirits. Commonly endorsed biological causes included genetics, fever, malaria, and brain injury. For patients and families, beliefs about the cause of epilepsy often played a role in whom they chose to seek treatment from. Three major barriers to biomedical care were discussed: practical barriers, medical infrastructure barriers, and barriers related to stigma. Practical barriers related to issues such as transportation, cost of medical care, and distance to the nearest healthcare facility. Under medical infrastructure, drug stock-outs and lack of access to antiepileptic drugs (AEDs) were the most consistent problems stated among patients. Stigma was heavily discussed and brought up by nearly every participant. Additionally, three significant solutions to improving epilepsy care in Uganda were highlighted by participants: collaboration among treatment providers, community sensitization efforts to address stigma, and building medical infrastructure. Within building infrastructure, all participant types, except traditional healers, proposed the development of an epilepsy clinic designed to specifically treat epilepsy. CONCLUSIONS: Based on these findings, there are four critical interventions that should be considered for improving epilepsy care in Uganda: the creation of dedicated epilepsy clinics, infrastructure strengthening to address medication stock-outs, community outreach programs for sensitization, and collaboration between biomedical providers and traditional healers. This article is part of the Special Issue "The Intersection of Culture, Resources, and Disease: Epilepsy Care in Uganda".

Epilepsy beliefs and misconceptions among patient and community samples in Uganda.

OBJECTIVE: The objective of the study was to characterize and compare the attitudes, beliefs, and perceptions about epilepsy across community and patient cohorts in Uganda. METHODS: This was a descriptive study utilizing two samples: a randomly selected, national survey community sample and a hospital-based patient sample of people with epilepsy (PWE) and their caregivers attending clinic settings in Kampala and Mbarara, Uganda for epilepsy care. Both samples were surveyed about their beliefs about epilepsy, its treatment, and people who have the illness. Multivariate linear regression was used to examine group differences and variables associated with specific beliefs. RESULTS: Among the 15,818 community survey participants who participated in this study, 435 study participants reported symptoms suggestive of recurrent seizures, and all 626 subjects in the hospital-based sample had confirmed epilepsy. Results revealed significant differences across groups in their endorsement of epilepsy as a contagion; 37% of people in the community unaffected by epilepsy, and 39% of people with suspected epilepsy who did not seek care believed that epilepsy was contagious by touch, in contrast to 8% of PWE or their caregivers attending regular hospital-based care. Higher educational attainment and income, and seeking regular hospital-based medical care were associated with less endorsement of epilepsy as a contagion, while age, education, income, area of residence, and presence of seizure symptoms, were significant predictors of support or belief in the basic rights of PWE. Study participants within the community who screened negative for seizures placed the most restrictions on rights for PWE. To varying degrees, the samples all endorsed the effectiveness of allopathic, traditional, and religious providers, and the use of pharmaceutical drugs, traditional rituals and herbs, and prayer. CONCLUSIONS: People with epilepsy who are attending biomedical care for routine epilepsy care think differently about epilepsy, its treatment, and the rights of those with the disease than the general population. Within the community setting, more erroneous beliefs and negative attitudes about epilepsy and PWE persist, and they not only contribute to stigma but also interfere with the patients' health-seeking behavior. Further confounding the care of PWE, the pluralistic healthcare system in Uganda is evident in endorsements spanning biomedical, traditional, and religious treatment methods. Focused awareness campaigns utilizing local epilepsy societies are needed to promote epilepsy health literacy, to favorably impact acceptance and opportunities for PWE in Uganda, and to facilitate efficient uptake of biomedical care. This article is part of the Special Issue "The Intersection of Culture, Resources, and Disease: Epilepsy Care in Uganda".

Barriers to biomedical care for people with epilepsy in Uganda: A cross-sectional study.

OBJECTIVE: Epilepsy, a neurological disorder with effective biomedical treatment, remains largely untreated in Uganda. Potential reasons for this treatment gap (TG) include limited access to trained providers and clinics, social stigmata of seizures, cultural beliefs, or lack of public understanding of epilepsy as a treatable condition. The current study aimed to formally evaluate barriers faced by people with epilepsy (PWE) in Uganda when seeking biomedical care. METHODS: In a cross-sectional study, 435 participants drawn from a community prevalence study were enrolled. We included participants reporting a history of recurrent seizures suggestive of epilepsy, who completed a survey about barriers to obtaining care for their symptoms. Principal axis factor analysis (PFA) using a promax rotation was conducted for data reduction. Frequencies of barrier factors were compared across those who did not seek care for epilepsy (n = 228), those who sought care from biomedical facilities (n = 166), and those who sought care from a traditional or pastoral healer (n = 41). RESULTS: The PFA yielded a five-factor solution: 1) logistical and actual costs; 2) treatment effectiveness; 3) influence of the opinion of others; 4) doctors' care; and 5) contextual factors impacting decision-making. Variables related to logistical and actual costs were most endorsed. Comparison of groups by care sought did not reveal a difference in endorsement of factors, with the exception that those who sought biomedical care were more likely to endorse factors related to doctors' care compared with those that sought care from traditional or pastoral healers (P = .005). CONCLUSIONS: People with repetitive seizures in Uganda report several barriers to obtaining biomedical care in Uganda, with those related to practical and actual costs endorsed the most. It is imperative that interventions developed to reduce the TG in Uganda consider these practical issues to improve access to effective epilepsy care. This article is part of the Special Issue "The Intersection of Culture, Resources, and Disease: Epilepsy Care in Uganda.

Prospective study of the association between sport-related concussion and brain morphometry (3T-MRI) in collegiate athletes: study from the NCAA-DoD CARE Consortium.

OBJECTIVES: To determine the acute and early long-term associations of sport-related concussion (SRC) and subcortical and cortical structures in collegiate contact sport athletes. METHODS: Athletes with a recent SRC (n=99) and matched contact (n=91) and non-contact sport controls (n=95) completed up to four neuroimaging sessions from 24 to 48 hours to 6 months postinjury. Subcortical volumes (amygdala, hippocampus, thalamus and dorsal striatum) and vertex-wise measurements of cortical thickness/volume were computed using FreeSurfer. Linear mixed-effects models examined the acute and longitudinal associations between concussion and structural metrics, controlling for intracranial volume (or mean thickness) and demographic variables (including prior concussions and sport exposure). RESULTS: There were significant group-dependent changes in amygdala volumes across visits (p=0.041); this effect was driven by a trend for increased amygdala volume at 6 months relative to subacute visits in contact controls, with no differences in athletes with SRC. No differences were observed in any cortical metric (ie, thickness or volume) for primary or secondary analyses. CONCLUSION: A single SRC had minimal associations with grey matter structure across a 6-month time frame.

Pluralistic and singular causal attributions for epilepsy in Uganda.

OBJECTIVE: In Uganda, causal attributions for epilepsy reflect a variety of beliefs and impact care-seeking behavior, perpetuate stigma, and undermine the effectiveness of interventions to narrow the epilepsy treatment gap. The objective of this study was to characterize beliefs about seizure etiology to gain a better understanding of how epilepsy is conceptualized in the community in order to inform culturally appropriate educational policies and interventions. METHODS: In a community-based study, 15,383 participants were surveyed about beliefs related to 15 potential causes for epilepsy. Principal axis factor analysis (PFA) was performed to identify causative factors and then utilized to classify singular versus pluralistic belief systems related to epilepsy etiology. Analysis of variance (ANOVA) and Mann-Whitney U-tests were conducted to examine the differences in background characteristics across the etiology belief groups. RESULTS: Three main causative factors emerged from the PFA: biological, sociospiritual, and biospiritual. Among those endorsing at least one factor (n = 13,036), the biological factor was endorsed most frequently as a potential cause for epilepsy (88.0%), followed by the sociospiritual (63.4%), then biospiritual (47.6%). Review of the patterns of endorsement found that only 22.2% endorsed the biological factor alone, 6.7% the sociospiritual factor alone, and 2.8% the biospiritual factor alone (total 31.7%). The remainder endorsed a combination of two or all three factors as being potentially causal, and most (65.7%) endorsed a pluralistic combination inclusive of a biological etiology. Group comparisons showed that endorsing only the biological factor was associated with the highest levels of education (p < 0.01), the pluralistic group had the highest ratio of people in the household who needed assistance to those that could provide aid (p < 0.01), and there were significant differences in income across specific groups (p < 0.01). CONCLUSIONS: Pluralistic attributions for epilepsy are common in Uganda, with the majority of community members drawing from biomedical and traditional concepts to construct complex explanations for seizures that transcend discrete belief categories traditionally depicted in the literature. These findings emphasize the need to understand cultural beliefs about epilepsy in order to design contextually specific interventions and education programs, which respect the fundamental beliefs and values of the community. This article is part of the Special Issue "The Intersection of Culture, Resources, and Disease: Epilepsy Care in Uganda".

Sociocultural determinants and patterns of healthcare utilization for epilepsy care in Uganda.

OBJECTIVE: Epilepsy is a global public health concern, with the majority of cases occurring in lower- and middle-income countries where the treatment gap remains formidable. In this study, we simultaneously explore how beliefs about epilepsy causation, perceived barriers to care, seizure disorder characteristics, and demographics influence the initial choice of healthcare for epilepsy and its impact on attaining biomedical care (BMC). METHODS: This study utilized the baseline sample (n = 626) from a prospective cohort study of people with epilepsy (PWE) attending three public hospitals in Uganda (Mulago National Referral Hospital, Butabika National Referral Mental Hospital, and Mbarara Regional Referral Hospital) for epilepsy care. Patient and household demographics, clinical seizure disorder characteristics, and sociocultural questionnaires were administered. Logistic regression and principal component analyses (PCA) were conducted to examine associations with the choice of primary seizure treatment. RESULTS: The sample was 49% female, and 24% lived in rural settings. A biomedical health facility was the first point of care for 355 (56.7%) participants, while 229 (36.6%) first sought care from a traditional healer and 42 (6.7%) from a pastoral healer. Preliminary inspection of candidate predictors using relaxed criteria for significance (p < 0.20) identified several factors potentially associated with a greater odds of seeking BMC first. Demographic predictors included older caredriver (decision-maker for the participant) age (odds ratio [OR]: 1.01, 95% confidence interval [CI]: [0.99, 1.02], p-value: 0.09), greater caredriver education level (OR = 1.21, 95% CI: [1.07, 1.37], p-value = 0.003), and lower ratio of sick to healthy family members (OR = 0.77 [0.56, 1.05], P = 0.097). For clinical predictors, none of the proposed predictors associated significantly with seeking BMC first. Self-report causation predictors associated with a greater odds of seeking BMC first included higher belief in biological causes of epilepsy (OR = 1.31 [0.92, 1.88], P = 0.133) and lower belief in socio-spiritual causes of epilepsy (OR = 0.68 [0.56, 0.84], P < 0.001). In the multivariate model, only higher caredriver education (OR = 1.19 [1.04, 1.36], P = 0.009) and lower belief in socio-spiritual causes of epilepsy (OR = 0.69 [0.56, 0.86], P < 0.01) remained as predictors of seeking BMC first. Additionally, PCA revealed a pattern which included high income with low beliefs in nonbiological causes of epilepsy as being associated with seeking BMC first (OR = 1.32 [1.12, 1.55], p = 0.001). Despite reaching some form of care faster, individuals seeking care from traditional or pastoral healers experienced a significant delay to eventual BMC (P < 0.001), with an average delay of more than two years (traditional healer: 2.53 years [1.98, 3.24]; pastoral care: 2.18 [1.21, 3.91]). CONCLUSIONS: Coupled with low economic and educational status, belief in spiritual causation of epilepsy is a dominant determinant of opting for traditional or pastoral healing over BMC, regardless of concurrent belief in biological etiologies. There is a prolonged delay to eventual BMC for PWE who begin their treatment seeking with nonallopathic providers, and although nonallopathic healers provide PWE with benefits not provided by BMC, this notable delay likely prevents earlier administration of evidence-based care with known efficacy. Based on these findings, initiatives to increase public awareness of neurobiological causes of epilepsy and effectiveness of biomedical drug treatments may be effective in preventing delays to care, as would programs designed to facilitate cooperation and referral among traditional, faith-based, and biomedical providers. This article is part of the Special Issue "The Intersection of Culture, Resources, and Disease: Epilepsy Care in Uganda".

Accurate segmentation of prostate cancer histomorphometric features using a weakly supervised convolutional neural network.

Purpose: Prostate cancer primarily arises from the glandular epithelium. Histomophometric techniques have been used to assess the glandular epithelium in automated detection and classification pipelines; however, they are often rigid in their implementation, and their performance suffers on large datasets where variation in staining, imaging, and preparation is difficult to control. The purpose of this study is to quantify performance of a pixelwise segmentation algorithm that was trained using different combinations of weak and strong stroma, epithelium, and lumen labels in a prostate histology dataset. Approach: We have combined weakly labeled datasets generated using simple morphometric techniques and high-quality labeled datasets from human observers in prostate biopsy cores to train a convolutional neural network for use in whole mount prostate labeling pipelines. With trained networks, we characterize pixelwise segmentation of stromal, epithelium, and lumen (SEL) regions on both biopsy core and whole-mount H&E-stained tissue. Results: We provide evidence that by simply training a deep learning algorithm on weakly labeled data generated from rigid morphometric methods, we can improve the robustness of classification over the morphometric methods used to train the classifier. Conclusions: We show that not only does our approach of combining weak and strong labels for training the CNN improve qualitative SEL labeling within tissue but also the deep learning generated labels are superior for cancer classification in a higher-order algorithm over the morphometrically derived labels it was trained on.