Treatment response and clinical event-free survival in autoimmune hepatitis: A Canadian multicentre cohort study.

BACKGROUND & AIMS: Treatment outcomes for people living with autoimmune hepatitis (AIH) are limited by a lack of specific therapies, as well as limited well-validated prognostic tools and clinical trial endpoints. We sought to identify predictors of outcome for people living with AIH. METHODS: We evaluated the clinical course of people with AIH across 11 Canadian centres. Biochemical changes were analysed using linear mixed-effect and logistic regression. Clinical outcome was dynamically modelled using time-varying Cox proportional hazard modelling and landmark analysis. RESULTS: In 691 patients (median age 49 years, 75.4% female), with a median follow-up of 6 years (25th-75th percentile, 2.5-11), 118 clinical events occurred. Alanine aminotransferase (ALT) normalisation occurred in 63.8% of the cohort by 12 months. Older age at diagnosis (odd ratio [OR] 1.19, 95% CI 1.06-1.35) and female sex (OR 1.94, 95% CI 1.18-3.19) were associated with ALT normalisation at 6 months, whilst baseline cirrhosis status was associated with reduced chance of normalisation at 12 months (OR 0.52, 95% CI 0.33-0.82). Baseline total bilirubin, aminotransferases, and IgG values, as well as initial prednisone dose, did not predict average ALT reduction. At baseline, older age (hazard ratio [HR] 1.25, 95% CI 1.12-1.40), cirrhosis at diagnosis (HR 3.67, 95% CI 2.48-5.43), and elevated baseline total bilirubin (HR 1.36, 95% CI 1.17-1.58) increased the risk of clinical events. Prolonged elevations in ALT (HR 1.07, 95% CI 1.00-1.13) and aspartate aminotransferase (HR 1.13, 95% CI 1.06-1.21), but not IgG (HR 1.01, 95% CI 0.95-1.07), were associated with higher risk of clinical events. Higher ALT at 6 months was associated with worse clinical event-free survival. CONCLUSION: In people living with AIH, sustained elevated aminotransferase values, but not IgG, are associated with poorer long-term outcomes. Biochemical response and long-term survival are not associated with starting prednisone dose. IMPACT AND IMPLICATIONS: Using clinical data from multiple Canadian liver clinics treating autoimmune hepatitis (AIH), we evaluate treatment response and clinical outcomes. For the first time, we apply mixed-effect and time-varying survival statistical methods to rigorously examine treatment response and the impact of fluctuating liver biochemistry on clinical event-free survival. Key to the study impact, our data is 'real-world', represents a diverse population across Canada, and uses continuous measurements over follow-up. Our results challenge the role of IgG as a marker of treatment response and if normalisation of IgG should remain an important part of the definition of biochemical remission. Our analysis further highlights that baseline markers of disease severity may not prognosticate early treatment response. Additionally, the initial prednisone dose may be less relevant for achieving aminotransferase normalisation. This is important for patients and treating clinicians given the relevance and importance of side effects.

Evaluation of quantitative MRCP (MRCP+) for risk stratification of primary sclerosing cholangitis: comparison with morphological MRCP, MR elastography, and biochemical risk scores.

OBJECTIVES: To study the association of MRCP+ parameters with biochemical scoring systems and MR elastography (MRE) in primary sclerosing cholangitis (PSC). To evaluate the incremental value of combining MRCP+ with morphological scores in associating with biochemical scores. METHODS AND MATERIALS: MRI images, liver stiffness measurements by MRE, and biochemical testing of 65 patients with PSC that were retrospectively enrolled between January 2014 and December 2015 were obtained. MRCP+ was used to post-process MRCP images to obtain quantitative measurements of the bile ducts and biliary tree. Linear regression analysis was used to test the associations. Bootstrapping was used as a validation method. RESULTS: The total number of segmental strictures had the strongest association with Mayo Risk Score (R2 = 0.14), minimum stricture diameter had the highest association with Amsterdam Oxford Prognostic Index (R2 = 0.12), and the percentage of duct nodes with width 0-3 mm had the strongest association with PSC Risk Estimate Tool (R2 = 0.09). The presence of Ducts with medians > 9 mm had the highest association with MRE (R2= 0.21). The strength of association of MRCP+ to Mayo Risk Score was similar to ANALI2 and weaker than MRE (R2 = 0.23, 0.24, 0.38 respectively). MRCP+ enhanced the association of ANALI 2 and MRE with the Mayo Risk Score. CONCLUSIONS: MRCP+ demonstrated a significant association with biochemical scores and MRE. The association of MRCP+ with the biochemical scores was generally comparable to ANALI scores. MRCP+ enhanced the association of ANALI2 and MRE with the Mayo Risk Score. KEY POINTS: • MRCP+ has the potential to act as a risk stratfier in PSC. • MRE outperformed MRCP+ for risk stratifcation. • Combination of MRCP+ with MRE and ANALI scores improved overall performace as risk stratifiers.

A Radiomic Approach for Evaluating Intra-Subgroup Heterogeneity in SHH and Group 4 Pediatric Medulloblastoma: A Preliminary Multi-Institutional Study.

Medulloblastoma (MB) is the most frequent malignant brain tumor in children with extensive heterogeneity that results in varied clinical outcomes. Recently, MB was categorized into four molecular subgroups, WNT, SHH, Group 3, and Group 4. While SHH and Group 4 are known for their intermediate prognosis, studies have reported wide disparities in patient outcomes within these subgroups. This study aims to create a radiomic prognostic signature, medulloblastoma radiomics risk (mRRisk), to identify the risk levels within the SHH and Group 4 subgroups, individually, for reliable risk stratification. Our hypothesis is that this signature can comprehensively capture tumor characteristics that enable the accurate identification of the risk level. In total, 70 MB studies (48 Group 4, and 22 SHH) were retrospectively curated from three institutions. For each subgroup, 232 hand-crafted features that capture the entropy, surface changes, and contour characteristics of the tumor were extracted. Features were concatenated and fed into regression models for risk stratification. Contrasted with Chang stratification that did not yield any significant differences within subgroups, significant differences were observed between two risk groups in Group 4 (p = 0.04, Concordance Index (CI) = 0.82) on the cystic core and non-enhancing tumor, and SHH (p = 0.03, CI = 0.74) on the enhancing tumor. Our results indicate that radiomics may serve as a prognostic tool for refining MB risk stratification, towards improved patient care.

Development and external validation of tools for categorizing diagnosis codes in international hospital data.

BACKGROUND: The Clinical Classification Software Refined (CCSR) is a tool that groups many thousands of International Classification of Diseases 10th Revision (ICD-10) diagnosis codes into approximately 500 clinically meaningful categories, simplifying analyses. However, CCSR was developed for use in the United States and may not work well with other country-specific ICD-10 coding systems. METHOD: We developed an algorithm for semi-automated matching of Canadian ICD-10 codes (ICD-10-CA) to CCSR categories using discharge diagnoses from adult admissions at 7 hospitals between Apr 1, 2010 and Dec 31, 2020, and manually validated the results. We then externally validated our approach using inpatient hospital encounters in Denmark from 2017 to 2018. KEY RESULTS: There were 383,972 Canadian hospital admissions with 5,186 distinct ICD-10-CA diagnosis codes and 1,855,837 Danish encounters with 4,612 ICD-10 diagnosis codes. Only 46.6% of Canadian codes and 49.4% of Danish codes could be directly categorized using the official CCSR tool. Our algorithm facilitated the mapping of 98.5% of all Canadian codes and 97.7% of Danish codes. Validation of our algorithm by clinicians demonstrated excellent accuracy (97.1% and 97.0% in Canadian and Danish data, respectively). Without our algorithm, many common conditions did not match directly to a CCSR category, such as 96.6% of hospital admissions for heart failure. CONCLUSION: The GEMINI CCSR matching algorithm (available as an open-source package at https://github.com/GEMINI-Medicine/gemini-ccsr) improves the categorization of Canadian and Danish ICD-10 codes into clinically coherent categories compared to the original CCSR tool. We expect this approach to generalize well to other countries and enable a wide range of research and quality measurement applications.

Opportunities and Advances in Radiomics and Radiogenomics for Pediatric Medulloblastoma Tumors.

Recent advances in artificial intelligence have greatly impacted the field of medical imaging and vastly improved the development of computational algorithms for data analysis. In the field of pediatric neuro-oncology, radiomics, the process of obtaining high-dimensional data from radiographic images, has been recently utilized in applications including survival prognostication, molecular classification, and tumor type classification. Similarly, radiogenomics, or the integration of radiomic and genomic data, has allowed for building comprehensive computational models to better understand disease etiology. While there exist excellent review articles on radiomics and radiogenomic pipelines and their applications in adult solid tumors, in this review article, we specifically review these computational approaches in the context of pediatric medulloblastoma tumors. Based on our systematic literature research via PubMed and Google Scholar, we provide a detailed summary of a total of 15 articles that have utilized radiomic and radiogenomic analysis for survival prognostication, tumor segmentation, and molecular subgroup classification in the context of pediatric medulloblastoma. Lastly, we shed light on the current challenges with the existing approaches as well as future directions and opportunities with using these computational radiomic and radiogenomic approaches for pediatric medulloblastoma tumors.

Domiciliation of Trichoderma asperellum Suppresses Globiosporangium ultimum and Promotes Pea Growth, Ultrastructure, and Metabolic Features.

The beneficial microorganisms represent a new and hopeful solution for a sustainable environment and development. In this investigation, Trichoderma asperellum ZNW, isolated from seeds, was domiciliated within the pea plant for improving growth, disease management, and enhancement of productivity. Globisporangium ultimum NZW was isolated from deformed pea seeds, representing the first record of the pathogen caused by pea damping-off. Both fungi were molecularly identified. T. asperellum ZNW produced several lytic enzymes and bioactive metabolites as detected by GC-MC. The SEM illustrated the mycoparasitic behavior of T. asperellum ZNW on G. ultimum NZW mycelia. In the pot experiment, T. asperellum domiciliated the root and grew as an endophytic fungus, leading to root vessel lignification. Under soil infection, T. asperellum reduced damping-off, by enhancing peroxidase, polyphenol, total phenols, and photosynthetic pigments content. The vegetative growth, yield, and soil dehydrogenase activity were improved, with an enhancement in the numerical diversity of the microbial rhizosphere. This work may enable more understanding of the plant-fungal interaction, yet, working on domiciliation is recommended as a new approach to plant protection and growth promotion under various ecological setups.

RADIomic Spatial TexturAl Descriptor (RADISTAT): Quantifying Spatial Organization of Imaging Heterogeneity Associated With Tumor Response to Treatment.

Localized disease heterogeneity on imaging extracted via radiomics approaches have recently been associated with disease prognosis and treatment response. Traditionally, radiomics analyses leverage texture operators to derive voxel- or region-wise feature values towards quantifying subtle variations in image appearance within a region-of-interest (ROI). With the goal of mining additional voxel-wise texture patterns from radiomic "expression maps", we introduce a new RADIomic Spatial TexturAl descripTor (RADISTAT). This was driven by the hypothesis that quantifying spatial organization of texture patterns within an ROI could allow for better capturing interactions between different tissue classes present in a given region; thus enabling more accurate characterization of disease or response phenotypes. RADISTAT involves: (a) robustly identifying sub-compartments of low, intermediate, and high radiomic expression (i.e. heterogeneity) in a feature map and (b) quantifying spatial organization of sub-compartments via graph interactions. RADISTAT was evaluated in two clinically challenging problems: (1) discriminating nodal/distant metastasis from metastasis-free rectal cancer patients on post-chemoradiation T2w MRI, and (2) distinguishing tumor progression from pseudo-progression in glioblastoma multiforme using post-chemoradiation T1w MRI. Across over 800 experiments, RADISTAT yielded a consistent discriminatory signature for tumor progression (GBM) and disease metastasis (RCa); where its sub-compartments were associated with pathologic tissue types (fibrosis or tumor, determined via fusion of MRI and pathology). In a multi-institutional setting for both clinical problems, RADISTAT resulted in higher classifier performance (11% improvement in AUC, on average) compared to radiomic descriptors. Furthermore, combining RADISTAT with radiomic descriptors resulted in significantly improved performance compared to using radiomic descriptors alone.

Radiomic Deformation and Textural Heterogeneity (R-DepTH) Descriptor to Characterize Tumor Field Effect: Application to Survival Prediction in Glioblastoma.

The concept of tumor field effect implies that cancer is a systemic disease with its impact way beyond the visible tumor confines. For instance, in Glioblastoma (GBM), an aggressive brain tumor, the increase in intracranial pressure due to tumor burden often leads to brain herniation and poor outcomes. Our work is based on the rationale that highly aggressive tumors tend to grow uncontrollably, leading to pronounced biomechanical tissue deformations in the normal parenchyma, which when combined with local morphological differences in the tumor confines on MRI scans, will comprehensively capture tumor field effect. Specifically, we present an integrated MRI-based descriptor, radiomic-Deformation and Textural Heterogeneity (r-DepTH). This descriptor comprises measurements of the subtle perturbations in tissue deformations throughout the surrounding normal parenchyma due to mass effect. This involves non-rigidly aligning the patients' MRI scans to a healthy atlas via diffeomorphic registration. The resulting inverse mapping is used to obtain the deformation field magnitudes in the normal parenchyma. These measurements are then combined with a 3D texture descriptor, Co-occurrence of Local Anisotropic Gradient Orientations (COLLAGE), which captures the morphological heterogeneity and infiltration within the tumor confines, on MRI scans. In this work, we extensively evaluated r-DepTH for survival risk-stratification on a total of 207 GBM cases from 3 different cohorts (Cohort 1 ( n1 = 53 ), Cohort 2 ( n2 = 75 ), and Cohort 3 ( n3 = 79 )), where each of these three cohorts was used as a training set for our model separately, and the other two cohorts were used for testing, independently, for each training experiment. When employing Cohort 1 for training, r-DepTH yielded Concordance indices (C-indices) of 0.7 and 0.65, hazard ratios (HR) and Confidence Intervals (CI) of 10 (6 - 19) and 5 (3 - 8) on Cohorts 2 and 3, respectively. Similarly, training on Cohort 2 yielded C-indices of 0.6 and 0.7, HR and CI of 1 (0.7 - 2) and 3 (2 - 5) on Cohorts 1 and 3, respectively. Finally, training on Cohort 3 yielded C-indices of 0.75 and 0.63, HR and CI of 24 (10 - 57) and 12 (6 - 21) on Cohorts 1 and 2, respectively. Our results show that r-DepTH descriptor may serve as a comprehensive and a robust MRI-based prognostic marker of disease aggressiveness and survival in solid tumors.

Novel MRI deformation-heterogeneity radiomic features are associated with molecular subgroups and overall survival in pediatric medulloblastoma: Preliminary findings from a multi-institutional study.

Introduction: Medulloblastoma (MB) is a malignant, heterogenous brain tumor. Advances in molecular profiling have led to identifying four molecular subgroups of MB (WNT, SHH, Group 3, Group 4), each with distinct clinical behaviors. We hypothesize that (1) aggressive MB tumors, growing heterogeneously, induce pronounced local structural deformations in the surrounding parenchyma, and (b) these local deformations as captured on Gadolinium (Gd)-enhanced-T1w MRI are independently associated with molecular subgroups, as well as overall survival in MB patients. Methods: In this work, a total of 88 MB studies from 2 institutions were analyzed. Following tumor delineation, Gd-T1w scan for every patient was registered to a normal age-specific T1w-MRI template via deformable registration. Following patient-atlas registration, local structural deformations in the brain parenchyma were obtained for every patient by computing statistics from deformation magnitudes obtained from every 5mm annular region, 0 < d < 60 mm, where d is the distance from the tumor infiltrating edge. Results: Multi-class comparison via ANOVA yielded significant differences between deformation magnitudes obtained for Group 3, Group 4, and SHH molecular subgroups, observed up to 60-mm outside the tumor edge. Additionally, Kaplan-Meier survival analysis showed that the local deformation statistics, combined with the current clinical risk-stratification approaches (molecular subgroup information and Chang's classification), could identify significant differences between high-risk and low-risk survival groups, achieving better performance results than using any of these approaches individually. Discussion: These preliminary findings suggest there exists significant association of our tumor-induced deformation descriptor with overall survival in MB, and that there could be an added value in using the proposed radiomic descriptor along with the current risk classification approaches, towards more reliable risk assessment in pediatric MB.

Pattern of infants' feeding and weaning in Suez Governorate, Egypt: an exploratory study.

BACKGROUND: Breastfeeding and proper weaning contribute to achievement of the Sustainable Development Goals. In Egypt, by age 4-5 months, only 13% of infants are exclusively breastfed. A survey conducted in Egyptian hospitals concluded that many of the 10 steps to support successful breastfeeding were not executed correctly and other steps were not executed at all. AIMS: To explore the patterns of feeding and weaning among infants in Egypt, and identify their determinants, to improve practice and promote children's nutritional status. METHODS: A cross-sectional analytical study of 333 mother-infant pairs attending two primary healthcare (PHC) centres for vaccination sessions between April 2017 and June 2018. Mothers were interviewed using a structured questionnaire. RESULTS: Almost all infants were born in hospitals. Exclusive breastfeeding was not widely practiced. Prelacteal feeding was a common malpractice. The majority of mothers initiated artificial feeding during the first month of life. Rural mothers tended to introduce different foods earlier than urban mothers did. Minimum dietary diversity was achieved by 50.9% of urban infants aged ≥ 6 months (≥ 4 food groups), compared with 25.9% of rural infants. Minimum recommended meal frequency for age was fulfilled for 51.9% of urban and 29.6% of rural infants. More than 85% of mothers expressed their need for additional knowledge, and more than half identified the PHC centre as the appropriate source for information. CONCLUSIONS: Our study reflects deficiency in maternal practice regarding breastfeeding and weaning, despite being regular visitors to the PHC centre.

Study of the relationship between urinary level of uromodulin, renal involvement and disease activity in patients with systemic lupus erythrematosus.

Systemic lupus erythematosus (SLE) is a multifactorial chronic inflammatory autoimmune connective tissue disease. Lupus nephritis (LN) is a common and serious complication of SLE which can progress to end-stage renal disease. Renal biopsy is the gold standard in the diagnosis and classification of LN, but since it is an invasive procedure, it is neither desirable nor applicable for all cases. This has led to the search for an alternative, noninvasive, site-specific, and immune process-related biomarkers. Uromodulin (Tamm-Horsfall glycoprotein) is the most abundant urinary protein expressed exclusively by the thick ascending limb cells and released into urine of healthy controls. Studies showed that it may act as a danger signaling molecule eliciting an inflammatory response following conditions that damage the nephron integrity and leading to uromodulin release into the interstitial space. This study aimed to assess uromodulin as a screening biomarker of tubulointerstitial involvement in patients with SLE and to elucidate its correlation with disease activity and progression. The study was conducted on 70 patients divided into two groups: control group (Group I) consisted of 20 apparently healthy volunteers of comparable age and sex to the patients' group, and 50 SLE patients (Group II) diagnosed according to the 2012 Systemic Lupus Collaborating Clinics (SLICC) classification criteria. Group II was further subdivided into 23 patients without manifestations of LN (Group II A) and 27 patients with manifestations of LN (Group II B). Urinary uromodulin level showed statistically significant difference among the studied groups, being lowest among the LN patients with a mean value 5.6 ± 3.4, in SLE patients without nephritis 9.9 ± 5.2 and 12.9 ± 4.6 in the control group. Urinary uromodulin also correlated positively with estimated glome- rular filtration rate. A negative correlation was found between urinary uromodulin and serum creatinine, 24 h urinary proteins and SLICC renal activity score. No statistically significant correlation was found between urinary uromo- dulin and SLE disease activity index. Thus, decreasing urinary uromodulin levels can be a marker for renal involvement and tubulo- interstitial nephritis in active SLE patients and a marker for chronic kidney disease and nephron loss in the absence of activity markers.

Can Tumor Location on Pre-treatment MRI Predict Likelihood of Pseudo-Progression vs. Tumor Recurrence in Glioblastoma?-A Feasibility Study.

A significant challenge in Glioblastoma (GBM) management is identifying pseudo-progression (PsP), a benign radiation-induced effect, from tumor recurrence, on routine imaging following conventional treatment. Previous studies have linked tumor lobar presence and laterality to GBM outcomes, suggesting that disease etiology and progression in GBM may be impacted by tumor location. Hence, in this feasibility study, we seek to investigate the following question: Can tumor location on treatment-naïve MRI provide early cues regarding likelihood of a patient developing pseudo-progression vs. tumor recurrence? In this study, 74 pre-treatment Glioblastoma MRI scans with PsP (33) and tumor recurrence (41) were analyzed. First, enhancing lesion on Gd-T1w MRI and peri-lesional hyperintensities on T2w/FLAIR were segmented by experts and then registered to a brain atlas. Using patients from the two phenotypes, we construct two atlases by quantifying frequency of occurrence of enhancing lesion and peri-lesion hyperintensities, by averaging voxel intensities across the population. Analysis of differential involvement was then performed to compute voxel-wise significant differences (p-value < 0.05) across the atlases. Statistically significant clusters were finally mapped to a structural atlas to provide anatomic localization of their location. Our results demonstrate that patients with tumor recurrence showed prominence of their initial tumor in the parietal lobe, while patients with PsP showed a multi-focal distribution of the initial tumor in the frontal and temporal lobes, insula, and putamen. These preliminary results suggest that lateralization of pre-treatment lesions toward certain anatomical areas of the brain may allow to provide early cues regarding assessing likelihood of occurrence of pseudo-progression from tumor recurrence on MRI scans.

Real-World Effectiveness of Obeticholic Acid in Patients with Primary Biliary Cholangitis.

Patients with primary biliary cholangitis (PBC) with incomplete response to ursodeoxycholic acid are at risk of disease progression and need additional therapy. Obeticholic acid (OCA) was approved in Canada in May 2017, but its effectiveness in a real-world setting has not been described. We sought to describe our experience with OCA in a Canadian cohort. OCA-naive patients treated at two Canadian centers were included. Clinical and biochemical data were collected at OCA initiation and during follow-up. Primary outcomes were changes in serum alkaline phosphatase (ALP), gamma-glutamyl transferase (GGT), and total bilirubin (TB) over the duration of therapy. Secondary outcomes were changes in alanine aminotransferase (ALT), aspartate aminotransferase (AST), immunoglobulin M (IgM), platelets, and albumin; and achievement of the primary endpoint of the original phase 3 study that led to OCA approval (A Placebo-Controlled Trial of Obeticholic Acid in Primary Biliary Cholangitis [POISE]), dose reductions, discontinuations, and tolerability. Repeated-measures models were used to assess changes in biochemistry over time. Sixty-four patients were included; 4 carried a diagnosis of overlap with autoimmune hepatitis. Mean age was 54.6 years, median ALP was 250 U/L, TB was 13 µmol/L, platelet count was 225 × 109/L, and 24% had liver stiffness measurements ≥16.9 kPa. There was a significant reduction in mean ALP of 55 U/L (P < 0.001), GGT of 138 U/L (P < 0.001), ALT of 11.9 U/L (P < 0.001), AST of 5.7 U/L (P < 0.05), and IgM of 0.70 g/L (P < 0.001) over 12 months; TB remained stable (P = 0.98). Forty-four patients met POISE-inclusion criteria, 39% (n = 17) of whom had 12-month biochemical measurements. In this subset, 18% (n = 3/17) met the 12-month POISE primary endpoint, but considering follow-up to 19 months, 43% achieved this target (n = 9/21). Pruritus was the most commonly reported complaint. Conclusion: Use of OCA was associated with improvement in biochemical surrogates of outcome in PBC in a real-world setting.

Radiogenomic-Based Survival Risk Stratification of Tumor Habitat on Gd-T1w MRI Is Associated with Biological Processes in Glioblastoma.

PURPOSE: To (i) create a survival risk score using radiomic features from the tumor habitat on routine MRI to predict progression-free survival (PFS) in glioblastoma and (ii) obtain a biological basis for these prognostic radiomic features, by studying their radiogenomic associations with molecular signaling pathways. EXPERIMENTAL DESIGN: Two hundred three patients with pretreatment Gd-T1w, T2w, T2w-FLAIR MRI were obtained from 3 cohorts: The Cancer Imaging Archive (TCIA; n = 130), Ivy GAP (n = 32), and Cleveland Clinic (n = 41). Gene-expression profiles of corresponding patients were obtained for TCIA cohort. For every study, following expert segmentation of tumor subcompartments (necrotic core, enhancing tumor, peritumoral edema), 936 3D radiomic features were extracted from each subcompartment across all MRI protocols. Using Cox regression model, radiomic risk score (RRS) was developed for every protocol to predict PFS on the training cohort (n = 130) and evaluated on the holdout cohort (n = 73). Further, Gene Ontology and single-sample gene set enrichment analysis were used to identify specific molecular signaling pathway networks associated with RRS features. RESULTS: Twenty-five radiomic features from the tumor habitat yielded the RRS. A combination of RRS with clinical (age and gender) and molecular features (MGMT and IDH status) resulted in a concordance index of 0.81 (P < 0.0001) on training and 0.84 (P = 0.03) on the test set. Radiogenomic analysis revealed associations of RRS features with signaling pathways for cell differentiation, cell adhesion, and angiogenesis, which contribute to chemoresistance in GBM. CONCLUSIONS: Our findings suggest that prognostic radiomic features from routine Gd-T1w MRI may also be significantly associated with key biological processes that affect response to chemotherapy in GBM.

Radiomics-based convolutional neural network for brain tumor segmentation on multiparametric magnetic resonance imaging.

Accurate segmentation of gliomas on routine magnetic resonance image (MRI) scans plays an important role in disease diagnosis, prognosis, and patient treatment planning. We present a fully automated approach, radiomics-based convolutional neural network (RadCNN), for segmenting both high- and low-grade gliomas using multimodal MRI volumes (T1c, T2w, and FLAIR). RadCNN incorporates radiomic texture features (i.e., Haralick, Gabor, and Laws) within DeepMedic [a deep 3-D convolutional neural network (CNN) segmentation framework that uses image intensities; a top performing method in the BraTS 2016 challenge] to further augment the performance of brain tumor subcompartment segmentation. We first identify textural radiomic representations that best separate the different subcompartments [enhancing tumor (ET), whole tumor (WT), and tumor core (TC)] on the training set, and then feed these representations as inputs to the CNN classifier for prediction of different subcompartments. We hypothesize that textural radiomic representations of lesion subcompartments will enhance the separation of subcompartment boundaries, and hence providing these features as inputs to the deep CNN, over and above raw intensity values alone, will improve the subcompartment segmentation. Using a training set of N = 241 patients, validation set of N = 44 , and test set of N = 46 patients, RadCNN method achieved Dice similarity coefficient (DSC) scores of 0.71, 0.89, and 0.73 for ET, WT, and TC, respectively. Compared to the DeepMedic model, RadCNN showed improvement in DSC scores for both ET and WT and demonstrated comparable results in segmenting the TC. Similarly, smaller Hausdorff distance measures were obtained with RadCNN as compared to the DeepMedic model across all the subcompartments. Following the segmentation of the different subcompartments, we extracted a set of subcompartment specific radiomic descriptors that capture lesion disorder and assessed their ability in separating patients into different survival cohorts (short-, mid- and long-term survival) based on their overall survival from the date of baseline diagnosis. Using a multilinear regression approach, we achieved accuracies of 0.57, 0.63, and 0.45 for the training, validation, and test cases, respectively.

A fast stochastic framework for automatic MR brain images segmentation.

This paper introduces a new framework for the segmentation of different brain structures (white matter, gray matter, and cerebrospinal fluid) from 3D MR brain images at different life stages. The proposed segmentation framework is based on a shape prior built using a subset of co-aligned training images that is adapted during the segmentation process based on first- and second-order visual appearance characteristics of MR images. These characteristics are described using voxel-wise image intensities and their spatial interaction features. To more accurately model the empirical grey level distribution of the brain signals, we use a linear combination of discrete Gaussians (LCDG) model having positive and negative components. To accurately account for the large inhomogeneity in infant MRIs, a higher-order Markov-Gibbs Random Field (MGRF) spatial interaction model that integrates third- and fourth- order families with a traditional second-order model is proposed. The proposed approach was tested and evaluated on 102 3D MR brain scans using three metrics: the Dice coefficient, the 95-percentile modified Hausdorff distance, and the absolute brain volume difference. Experimental results show better segmentation of MR brain images compared to current open source segmentation tools.

A computer-aided diagnostic system for detecting diabetic retinopathy in optical coherence tomography images.

PURPOSE: Detection (diagnosis) of diabetic retinopathy (DR) in optical coherence tomography (OCT) images for patients with type 2 diabetes, but almost clinically normal retina appearances. METHODS: The proposed computer-aided diagnostic (CAD) system detects the DR in three steps: (a) localizing and segmenting 12 distinct retinal layers on the OCT image; (b) deriving features of the segmented layers, and (c) learning most discriminative features and classifying each subject as normal or diabetic. To localise and segment the retinal layers, signals (intensities) of the OCT image are described with a joint Markov-Gibbs random field (MGRF) model of intensities and shape descriptors. Each segmented layer is characterized with cumulative probability distribution functions (CDF) of its locally extracted features, such as reflectivity, curvature, and thickness. A multistage deep fusion classification network (DFCN) with a stack of non-negativity-constrained autoencoders (NCAE) is trained to select the most discriminative retinal layers' features and use their CDFs for detecting the DR. A training atlas was built using the OCT scans for 12 normal subjects and their maps of layers hand-drawn by retina experts. RESULTS: Preliminary experiments on 52 clinical OCT scans (26 normal and 26 with early-stage DR, balanced between 40-79 yr old males and females; 40 training and 12 test subjects) gave the DR detection accuracy, sensitivity, and specificity of 92%; 83%, and 100%, respectively. The 100% accuracy, sensitivity, and specificity have been obtained in the leave-one-out cross-validation test for all the 52 subjects. CONCLUSION: Both the quantitative and visual assessments confirmed the high accuracy of the proposed computer-assisted diagnostic system for early DR detection using the OCT retinal images.

Studying Autism Spectrum Disorder with Structural and Diffusion Magnetic Resonance Imaging: A Survey.

Magnetic resonance imaging (MRI) modalities have emerged as powerful means that facilitate non-invasive clinical diagnostics of various diseases and abnormalities since their inception in the 1980s. Multiple MRI modalities, such as different types of the sMRI and DTI, have been employed to investigate facets of ASD in order to better understand this complex syndrome. This paper reviews recent applications of structural magnetic resonance imaging (sMRI) and diffusion tensor imaging (DTI), to study autism spectrum disorder (ASD). Main reported findings are sometimes contradictory due to different age ranges, hardware protocols, population types, numbers of participants, and image analysis parameters. The primary anatomical structures, such as amygdalae, cerebrum, and cerebellum, associated with clinical-pathological correlates of ASD are highlighted through successive life stages, from infancy to adulthood. This survey demonstrates the absence of consistent pathology in the brains of autistic children and lack of research investigations in patients under 2 years of age in the literature. The known publications also emphasize advances in data acquisition and analysis, as well as significance of multimodal approaches that combine resting-state, task-evoked, and sMRI measures. Initial results obtained with the sMRI and DTI show good promise toward the early and non-invasive ASD diagnostics.

Infant Brain Extraction in T1-Weighted MR Images Using BET and Refinement Using LCDG and MGRF Models.

In this paper, we propose a novel framework for the automated extraction of the brain from T1-weighted MR images. The proposed approach is primarily based on the integration of a stochastic model [a two-level Markov-Gibbs random field (MGRF)] that serves to learn the visual appearance of the brain texture, and a geometric model (the brain isosurfaces) that preserves the brain geometry during the extraction process. The proposed framework consists of three main steps: 1) Following bias correction of the brain, a new three-dimensional (3-D) MGRF having a 26-pairwise interaction model is applied to enhance the homogeneity of MR images and preserve the 3-D edges between different brain tissues. 2) The nonbrain tissue found in the MR images is initially removed using the brain extraction tool (BET), and then the brain is parceled to nested isosurfaces using a fast marching level set method. 3) Finally, a classification step is applied in order to accurately remove the remaining parts of the skull without distorting the brain geometry. The classification of each voxel found on the isosurfaces is made based on the first- and second-order visual appearance features. The first-order visual appearance is estimated using a linear combination of discrete Gaussians (LCDG) to model the intensity distribution of the brain signals. The second-order visual appearance is constructed using an MGRF model with analytically estimated parameters. The fusion of the LCDG and MGRF, along with their analytical estimation, allows the approach to be fast and accurate for use in clinical applications. The proposed approach was tested on in vivo data using 300 infant 3-D MR brain scans, which were qualitatively validated by an MR expert. In addition, it was quantitatively validated using 30 datasets based on three metrics: the Dice coefficient, the 95% modified Hausdorff distance, and absolute brain volume difference. Results showed the capability of the proposed approach, outperforming four widely used BETs: BET, BET2, brain surface extractor, and infant brain extraction and analysis toolbox. Experiments conducted also proved that the proposed framework can be generalized to adult brain extraction as well.