Application of STREAM-URO and APPRAISE-AI reporting standards for artificial intelligence studies in pediatric urology: A case example with pediatric hydronephrosis.

INTRODUCTION: Artificial intelligence (AI) and machine learning (ML) in pediatric urology is gaining increased popularity and credibility. However, the literature lacks standardization in reporting and there are areas for methodological improvement, which incurs difficulty in comparison between studies and may ultimately hurt clinical implementation of these models. The "STandardized REporting of Applications of Machine learning in UROlogy" (STREAM-URO) framework provides methodological instructions to improve transparent reporting in urology and APPRAISE-AI in a critical appraisal tool which provides quantitative measures for the quality of AI studies. The adoption of these will allow urologists and developers to ensure consistency in reporting, improve comparison, develop better models, and hopefully inspire clinical translation. METHODS: In this article, we have applied STREAM-URO framework and APPRAISE-AI tool to the pediatric hydronephrosis literature. By doing this, we aim to describe best practices on ML reporting in urology with STREAM-URO and provide readers with a critical appraisal tool for ML quality with APPRAISE-AI. By applying these to the pediatric hydronephrosis literature, we provide some tutorial for other readers to employ these in developing and appraising ML models. We also present itemized recommendations for adequate reporting, and critically appraise the quality of ML in pediatric hydronephrosis insofar. We provide examples of strong reporting and highlight areas for improvement. RESULTS: There were 8 ML models applied to pediatric hydronephrosis. The 26-item STREAM-URO framework is provided in Appendix A and 24-item APPRAISE-AI tool is provided in Appendix B. Across the 8 studies, the median compliance with STREAM-URO was 67 % and overall study quality was moderate. The highest scoring APPRAISE-AI domains in pediatric hydronephrosis were clinical relevance and reporting quality, while the worst were methodological conduct, robustness of results, and reproducibility. CONCLUSIONS: If properly conducted and reported, ML has the potential to impact the care we provide to patients in pediatric urology. While AI is exciting, the paucity of strong evidence limits our ability to translate models to practice. The first step toward this goal is adequate reporting and ensuring high quality models, and STREAM-URO and APPRAISE-AI can facilitate better reporting and critical appraisal, respectively.

Trends in Management of Fetuses with Suspected Lower Urinary Tract Obstruction (LUTO): A High-Risk Fetal and Pediatric Center Experience in a Universal-Access-to-Care System.

INTRODUCTION: Neonates with lower urinary tract obstruction (LUTO) experience high morbidity and mortality associated with the development of chronic kidney disease. The prenatal detection rate for LUTO is less than 50%, with late or missed diagnosis leading to delayed management and long-term sequelae in the remainder. We aimed to explore the trends in prenatal detection and management at a high-risk fetal center and determine if similar trends of postnatal presentations were noted for the same period. METHODS: Prenatal and postnatal LUTO databases from a tertiary fetal center and its associated pediatric center between 2009 and 2021 were reviewed, capturing maternal age, gestational age (GA) at diagnosis, and rates of termination of pregnancy (TOP). Time series analysis using autocorrelation was performed to investigate time trend changes for prenatally suspected and postnatally confirmed LUTO cases. RESULTS: A total of 161 fetuses with prenatally suspected LUTO were identified, including 78 terminations. No significant time trend was found when evaluating the correlation between time periods, prenatal suspicion, and postnatal confirmation of LUTO cases (Durbin-Watson [DW] = 1.99, p = 0.3641 and DW = 2.86, p = 0.9113, respectively). GA at referral was 20.0 weeks (interquartile range [IQR] 12, 35) and 22.0 weeks (IQR 13, 37) for TOP and continued pregnancies (p < 0.0001). GA at initial ultrasound was earlier in terminated fetuses compared to continued (20.0 [IQR 12, 35] weeks vs. 22.5 [IQR 13, 39] weeks, p < 0.0001). While prenatal LUTO suspicion remained consistently higher than postnatal presentations, the rates of postnatal presentations and terminations remained stable during the study years (p = 0.7913 and 0.2338), as were GA at TOP and maternal age at diagnosis (p = 0.1710 and 0.1921). CONCLUSION: This study demonstrated that more severe cases of LUTO are referred earlier and are more likely to undergo TOP. No significant trend was detected between time and prenatally suspected or postnatally confirmed LUTO, highlighting the need for further studies to better delineate factors that can increase prenatal detection.

A quantitative analysis of voiding cystourethrogram features confirms the association between high-grade vesicoureteral reflux with male sex, younger age, and hydronephrosis.

INTRODUCTION: Vesicoureteral reflux (VUR) is commonly diagnosed in the workup of urinary tract infections or hydronephrosis in children. Traditionally, VUR severity is graded subjectively based on voiding cystourethrogram (VCUG) imaging. Herein, we characterized the association between age, sex, and indication for VCUG, by employing standardized quantitative features. METHODS: We included renal units with a high certainty in VUR grade (>80% consensus) from the qVUR model validation study at our institution between 2013 and 2019. We abstracted the following variables: age, sex, laterality, indication for VCUG, and qVUR parameters (tortuosity, ureter widths on VCUG). High-grade VUR was defined as grade 4 or 5 The association between each variable and VUR grade was assessed. RESULTS: A total of 443 patients (523 renal units) were included, consisting of a 48:52 male/female ratio. The median age at VCUG was 13 months. Younger age at VCUG (<6 months) was associated with greater odds of severe VUR (odds ratio [OR] 2.0), and there was a weak correlation between age and VUR grade (ρ=-0.17). Male sex was associated with increased odds of high-grade VUR (OR 2.7). VCUGs indicated for hydronephrosis were associated with high-grade VUR (OR 4.1) compared to those indicated for UTI only. Ureter tortuosity and width were significantly associated with each clinical variable and VUR severity. CONCLUSIONS: Male sex, younger age (<6 months), and history of hydronephrosis are associated with both high-grade VUR and standardized quantitative measures, including greater ureter tortuosity and increased ureteral width. This lends support to quantitative assessment to improve reliability in VUR grading.

Differential Expression Enrichment Tool (DEET): an interactive atlas of human differential gene expression.

Differential gene expression analysis using RNA sequencing (RNA-seq) data is a standard approach for making biological discoveries. Ongoing large-scale efforts to process and normalize publicly available gene expression data enable rapid and systematic reanalysis. While several powerful tools systematically process RNA-seq data, enabling their reanalysis, few resources systematically recompute differentially expressed genes (DEGs) generated from individual studies. We developed a robust differential expression analysis pipeline to recompute 3162 human DEG lists from The Cancer Genome Atlas, Genotype-Tissue Expression Consortium, and 142 studies within the Sequence Read Archive. After measuring the accuracy of the recomputed DEG lists, we built the Differential Expression Enrichment Tool (DEET), which enables users to interact with the recomputed DEG lists. DEET, available through CRAN and RShiny, systematically queries which of the recomputed DEG lists share similar genes, pathways, and TF targets to their own gene lists. DEET identifies relevant studies based on shared results with the user's gene lists, aiding in hypothesis generation and data-driven literature review.

Using a machine learning algorithm to predict outcome of primary cytoreductive surgery in advanced ovarian cancer.

OBJECTIVE: To develop a machine learning (ML) algorithm to predict outcome of primary cytoreductive surgery (PCS) in patients with advanced ovarian cancer (AOC) METHODS: This retrospective cohort study included patients with AOC undergoing PCS between January 2017 and February 2021. Using radiologic criteria, patient factors (age, CA-125, performance status, BRCA) and surgical complexity scores, we trained a random forest model to predict the dichotomous outcome of optimal cytoreduction (<1 cm) and no gross residual (RD = 0 mm) using JMP-Pro 15 (SAS). This model is available at https://ipm-ml.ccm.sickkids.ca. RESULTS: One hundred and fifty-one patients underwent PCS and randomly assigned to train (n = 92), validate (n = 30), or test (n = 29) the model. The median age was 58 (27-83). Patients with suboptimal cytoreduction were more likely to have an Eastern Cooperative Oncology Group 3-4 (11% vs. 0.75%, p = 0.004), lower albumin (38 vs. 41, p = 0.02), and higher CA125 (1126 vs. 388, p = 0.012) than patients with optimal cytoreduction (n = 133). There were no significant differences in age, histology, stage, or BRCA status between groups. The bootstrap random forest model had AUCs of 99.8% (training), 89.6%(validation), and 89.0% (test). The top five contributors were CA125, albumin, diaphragmatic disease, age, and ascites. For RD = 0 mm, the AUCs were 94.4%, 52%, and 84%, respectively. CONCLUSION: Our ML algorithm demonstrated high accuracy in predicting optimal cytoreduction in patients with AOC selected for PCS and may assist decision-making.

Multi-institutional Validation of Improved Vesicoureteral Reflux Assessment With Simple and Machine Learning Approaches.

PURPOSE: Vesicoureteral reflux grading from voiding cystourethrograms is highly subjective with low reliability. We aimed to demonstrate improved reliability for vesicoureteral reflux grading with simple and machine learning approaches using ureteral tortuosity and dilatation on voiding cystourethrograms. MATERIALS AND METHODS: Voiding cystourethrograms were collected from our institution for training and 5 external data sets for validation. Each voiding cystourethrogram was graded by 5-7 raters to determine a consensus vesicoureteral reflux grade label and inter- and intra-rater reliability was assessed. Each voiding cystourethrogram was assessed for 4 features: ureteral tortuosity, proximal, distal, and maximum ureteral dilatation. The labels were then assigned to the combination of the 4 features. A machine learning-based model, qVUR, was trained to predict vesicoureteral reflux grade from these features and model performance was assessed by AUROC (area under the receiver-operator-characteristic). RESULTS: A total of 1,492 kidneys and ureters were collected from voiding cystourethrograms resulting in a total of 8,230 independent gradings. The internal inter-rater reliability for vesicoureteral reflux grading was 0.44 with a median percent agreement of 0.71 and low intra-rater reliability. Higher values for each feature were associated with higher vesicoureteral reflux grade. qVUR performed with an accuracy of 0.62 (AUROC=0.84) with stable performance across all external data sets. The model improved vesicoureteral reflux grade reliability by 3.6-fold compared to traditional grading (P < .001). CONCLUSIONS: In a large pediatric population from multiple institutions, we show that machine learning-based assessment for vesicoureteral reflux improves reliability compared to current grading methods. qVUR is generalizable and robust with similar accuracy to clinicians but the added prognostic value of quantitative measures warrants further study.

Large scale genotype- and phenotype-driven machine learning in Von Hippel-Lindau disease.

Von Hippel-Lindau (VHL) disease is a hereditary cancer syndrome where individuals are predisposed to tumor development in the brain, adrenal gland, kidney, and other organs. It is caused by pathogenic variants in the VHL tumor suppressor gene. Standardized disease information has been difficult to collect due to the rarity and diversity of VHL patients. Over 4100 unique articles published until October 2019 were screened for germline genotype-phenotype data. Patient data were translated into standardized descriptions using Human Genome Variation Society gene variant nomenclature and Human Phenotype Ontology terms and has been manually curated into an open-access knowledgebase called Clinical Interpretation of Variants in Cancer. In total, 634 unique VHL variants, 2882 patients, and 1991 families from 427 papers were captured. We identified relationship trends between phenotype and genotype data using classic statistical methods and spectral clustering unsupervised learning. Our analyses reveal earlier onset of pheochromocytoma/paraganglioma and retinal angiomas, phenotype co-occurrences and genotype-phenotype correlations including hotspots. It confirms existing VHL associations and can be used to identify new patterns and associations in VHL disease. Our database serves as an aggregate knowledge translation tool to facilitate sharing information about the pathogenicity of VHL variants.

Dynamic risk profiling of HCC recurrence after curative intent liver resection.

BACKGROUND AND AIMS: Following liver resection (LR) for HCC, the likelihood of survival is dynamic, in that multiple recurrences and/or metastases are possible, each having variable impacts on outcomes. We sought to evaluate the natural progression, pattern, and timing of various disease states after LR for HCC using multistate modeling and to create a practical calculator to provide prognostic information for patients and clinicians. APPROACH AND RESULTS: Adult patients undergoing LR for HCC between January 2000 and December 2018 were retrospectively identified at a single center. Multistate analysis modeled post-LR tumor progression by describing transitions between distinct disease states. In this model, the states included surgery, intrahepatic recurrence (first, second, third, fourth, fifth), distant metastasis with or without intrahepatic recurrence, and death. Of the 486 patients included, 169 (34.8%) remained recurrence-free, 205 (42.2%) developed intrahepatic recurrence, 80 (16.5%) developed distant metastasis, and 32 (7%) died. For an average patient having undergone LR, there was a 33.1% chance of remaining disease-free, a 31.0% chance of at least one intrahepatic recurrence, a 16.3% chance of distant metastasis, and a 19.8% chance of death within the first 60 months post-LR. The transition probability from surgery to first intrahepatic recurrence, without a subsequent state transition, increased from 3% (3 months) to 17.4% (30 months) and 17.2% (60 months). Factors that could modify these probabilities included tumor size, satellite lesions, and microvascular invasion. The online multistate model calculator can be found on https://multistatehcc.shinyapps.io/home/. CONCLUSIONS: In contrast to standard single time-to-event estimates, multistate modeling provides more realistic prognostication of outcomes after LR for HCC by taking into account many postoperative disease states and transitions between them. Our multistate modeling calculator can provide meaningful data to guide the management of patients undergoing postoperative surveillance and therapy.

Posterior Urethral Valves Outcomes Prediction (PUVOP): a machine learning tool to predict clinically relevant outcomes in boys with posterior urethral valves.

BACKGROUND: Early kidney and anatomic features may be predictive of future progression and need for additional procedures in patients with posterior urethral valve (PUV). The objective of this study was to use machine learning (ML) to predict clinically relevant outcomes in these patients. METHODS: Patients diagnosed with PUV with kidney function measurements at our institution between 2000 and 2020 were included. Pertinent clinical measures were abstracted, including estimated glomerular filtration rate (eGFR) at each visit, initial vesicoureteral reflux grade, and renal dysplasia at presentation. ML models were developed to predict clinically relevant outcomes: progression in CKD stage, initiation of kidney replacement therapy (KRT), and need for clean-intermittent catheterization (CIC). Model performance was assessed by concordance index (c-index) and the model was externally validated. RESULTS: A total of 103 patients were included with a median follow-up of 5.7 years. Of these patients, 26 (25%) had CKD progression, 18 (17%) required KRT, and 32 (31%) were prescribed CIC. Additionally, 22 patients were included for external validation. The ML model predicted CKD progression (c-index = 0.77; external C-index = 0.78), KRT (c-index = 0.95; external C-index = 0.89) and indicated CIC (c-index = 0.70; external C-index = 0.64), and all performed better than Cox proportional-hazards regression. The models have been packaged into a simple easy-to-use tool, available at https://share.streamlit.io/jcckwong/puvop/main/app.py CONCLUSION: ML-based approaches for predicting clinically relevant outcomes in PUV are feasible. Further validation is warranted, but this implementable model can act as a decision-making aid. A higher resolution version of the Graphical abstract is available as Supplementary information.

Personalized application of machine learning algorithms to identify pediatric patients at risk for recurrent ureteropelvic junction obstruction after dismembered pyeloplasty.

PURPOSE: To develop a model that predicts whether a child will develop a recurrent obstruction after pyeloplasty, determine their survival risk score, and expected time to re-intervention using machine learning (ML). METHODS: We reviewed patients undergoing pyeloplasty from 2008 to 2020 at our institution, including all children and adolescents younger than 18 years. We developed a two-stage machine learning model from 34 clinical fields, which included patient characteristics, ultrasound findings, and anatomical variation. We fit and trained with a logistic lasso model for binary cure model and subsequent survival model. Feature importance on the model was determined with post-selection inference. Performance metrics included area under the receiver-operating-characteristic (AUROC), concordance, and leave-one-out cross validation. RESULTS: A total of 543 patients were identified, with a median preoperative and postoperative anteroposterior diameter of 23 and 10 mm, respectively. 39 of 232 patients included in the survival model required re-intervention. The cure and survival models performed well with a leave-one-out cross validation AUROC and concordance of 0.86 and 0.78, respectively. Post-selective inference showed that larger anteroposterior diameter at the second post-op follow-up, and anatomical variation in the form of concurrent anomalies were significant model features predicting negative outcomes. The model can be used at https://sickkidsurology.shinyapps.io/PyeloplastyReOpRisk/ . CONCLUSION: Our ML-based model performed well in predicting the risk of and time to re-intervention after pyeloplasty. The implementation of this ML-based approach is novel in pediatric urology and will likely help achieve personalized risk stratification for patients undergoing pyeloplasty. Further real-world validation is warranted.

The Toronto Postliver Transplantation Hepatocellular Carcinoma Recurrence Calculator: A Machine Learning Approach.

Liver transplantation (LT) listing criteria for hepatocellular carcinoma (HCC) remain controversial. To optimize the utility of limited donor organs, this study aims to leverage machine learning to develop an accurate posttransplantation HCC recurrence prediction calculator. Patients with HCC listed for LT from 2000 to 2016 were identified, with 739 patients who underwent LT used for modeling. Data included serial imaging, alpha-fetoprotein (AFP), locoregional therapies, treatment response, and posttransplantation outcomes. We compared the CoxNet (regularized Cox regression), survival random forest, survival support vector machine, and DeepSurv machine learning algorithms via the mean cross-validated concordance index. We validated the selected CoxNet model by comparing it with other currently available recurrence risk algorithms on a held-out test set (AFP, Model of Recurrence After Liver Transplant [MORAL], and Hazard Associated with liver Transplantation for Hepatocellular Carcinoma [HALT-HCC score]). The developed CoxNet-based recurrence prediction model showed a satisfying overall concordance score of 0.75 (95% confidence interval [CI], 0.64-0.84). In comparison, the recalibrated risk algorithms' concordance scores were as follows: AFP score 0.64 (outperformed by the CoxNet model, 1-sided 95% CI, >0.01; P = 0.04) and MORAL score 0.64 (outperformed by the CoxNet model 1-sided 95% CI, >0.02; P = 0.03). The recalibrated HALT-HCC score performed well with a concordance of 0.72 (95% CI, 0.63-0.81) and was not significantly outperformed (1-sided 95% CI, ≥0.05; P = 0.29). Developing a comprehensive posttransplantation HCC recurrence risk calculator using machine learning is feasible and can yield higher accuracy than other available risk scores. Further research is needed to confirm the utility of machine learning in this setting.

A machine learning-based approach for quantitative grading of vesicoureteral reflux from voiding cystourethrograms: Methods and proof of concept.

INTRODUCTION: The objectivity of vesicoureteral reflux (VUR) grading has come into question for low inter-rater reliability. Using quantitative image features to aid in VUR grading may make it more consistent. OBJECTIVE: To develop a novel quantitative approach to the assignment of VUR from voiding cystourethrograms (VCUG) alone. STUDY DESIGN: An online dataset of VCUGs was abstracted and individual renal units were graded as low-grade (I-III) or high-grade (IV-V). We developed an image analysis and machine learning workflow to automatically calculate and normalize the ureteropelvic junction (UPJ) width, ureterovesical junction (UVJ) width, maximum ureter width, and tortuosity of the ureter based on three simple user annotations. A random forest classifier was trained to distinguish between low-vs high-grade VUR. An external validation cohort was generated from the institutional imaging repository. Discriminative capability was quantified using receiver-operating-characteristic and precision-recall curve analysis. We used Shapley Additive exPlanations to interpret the model's predictions. RESULTS: 41 renal units were abstracted from an online dataset, and 44 renal units were collected from the institutional imaging repository. Significant differences observed in UVJ width, UPJ width, maximum ureter width, and tortuosity between low- and high-grade VUR. A random-forest classifier performed favourably with an accuracy of 0.83, AUROC of 0.90 and AUPRC of 0.89 on leave-one-out cross-validation, and accuracy of 0.84, AUROC of 0.88 and AUPRC of 0.89 on external validation. Tortuosity had the highest feature importance, followed by maximum ureter width, UVJ width, and UPJ width. We deployed this tool as a web-application, qVUR (quantitative VUR), where users are able to upload any VCUG for automated grading using the model generated here (https://akhondker.shinyapps.io/qVUR/). DISCUSSION: This study provides the first step towards creating an automated and more objective standard for determining the significance of VUR features. Our findings suggest that tortuosity and ureter dilatation are predictors of high-grade VUR. Moreover, this proof-of-concept model was deployed in a simple-to-use web application. CONCLUSION: Grading of VUR using quantitative metrics is possible, even in non-standardized datasets of VCUG. Machine learning methods can be applied to objectively grade VUR in the future.

A Pre-TACE Radiomics Model to Predict HCC Progression and Recurrence in Liver Transplantation: A Pilot Study on a Novel Biomarker.

BACKGROUND: Despite transarterial chemoembolization (TACE) for hepatocellular carcinoma (HCC), a significant number of patients will develop progression on the liver transplant (LT) waiting list or disease recurrence post-LT. We sought to evaluate the feasibility of a pre-TACE radiomics model, an imaging-based tool to predict these adverse outcomes. METHODS: We analyzed the pre-TACE computed tomography images of patients waiting for a LT. The primary endpoint was a combined event that included waitlist dropout for tumor progression or tumor recurrence post-LT. The radiomic features were extracted from the largest HCC volume from the arterial and portal venous phase. A third set of features was created, combining the features from these 2 contrast phases. We applied a least absolute shrinkage and selection operator feature selection method and a support vector machine classifier. Three prognostic models were built using each feature set. The models' performance was compared using 5-fold cross-validated area under the receiver operating characteristic curves. RESULTS: . Eighty-eight patients were included, of whom 33 experienced the combined event (37.5%). The median time to dropout was 5.6 mo (interquartile range: 3.6-9.3), and the median time for post-LT recurrence was 19.2 mo (interquartile range: 6.1-34.0). Twenty-four patients (27.3%) dropped out and 64 (72.7%) patients were transplanted. Of these, 14 (21.9%) had recurrence post-LT. Model performance yielded a mean area under the receiver operating characteristic curves of 0.70 (±0.07), 0.87 (±0.06), and 0.81 (±0.06) for the arterial, venous, and the combined models, respectively. CONCLUSIONS: A pre-TACE radiomics model for HCC patients undergoing LT may be a useful tool for outcome prediction. Further external model validation with a larger sample size is required.

Accurate Classification of Pediatric Colonic Inflammatory Bowel Disease Subtype Using a Random Forest Machine Learning Classifier.

BACKGROUND: The pediatric inflammatory bowel disease (PIBD) classes algorithm was developed to bring consistency to labelling of colonic IBD, but labels are exclusively based on features atypical for ulcerative colitis (UC). AIM: The aim of the study was to develop an algorithm and identify features that discriminate between pediatric UC and colonic Crohn disease (CD). METHODS: Baseline clinical, endoscopic, radiologic, and histologic data, including the PIBD class features in 74 colonic IBD (56: UC, 18: colonic CD) patients were collected. The PIBD class features and additional features common to UC were used to perform initial clustering, using similarity network fusion (SNF). We trained a Random Forest (RF) classifier on the full dataset and used a leave-one-out approach to evaluate model accuracy. The top-features were used to build a new classifier, which we tested on 15 previously unused patients. We then performed clustering with SNF on the top RF features and assessed ability to discriminate between UC and colonic-CD independent of a supervised model. RESULTS: The initial SNF clustering with 58 patients demonstrated 2 groups: group 1 (n = 39, 90% UC) and group 2 (n = 19, 68% colonic-CD). Our RF classifier correctly labelled 97% of the 58 patients based on leave-one-out cross validation and identified the 7 most important features (3 histological and 4 endoscopic) to clinically distinguish these groups. We trained a new RF classifier with the top 7 features and found 100% accuracy in a set of 15 held-out patients. Finally, post hoc clustering with these 7 features revealed 2 groups of patients: group 1 (n = 55, 98% UC) and group 2 (n = 18, 94% colonic-CD). CONCLUSIONS: A combination of supervised and unsupervised analyses identified a short list of features, which consistently distinguish UC from colonic CD. Future directions include validation in other populations.

Artificial intelligence in transplantation (machine-learning classifiers and transplant oncology).

PURPOSE OF REVIEW: To highlight recent efforts in the development and implementation of machine learning in transplant oncology - a field that uses liver transplantation for the treatment of hepatobiliary malignancies - and particularly in hepatocellular carcinoma, the most commonly treated diagnosis in transplant oncology. RECENT FINDINGS: The development of machine learning has occurred within three domains related to hepatocellular carcinoma: identification of key clinicopathological variables, genomics, and image processing. SUMMARY: Machine-learning classifiers can be effectively applied for more accurate clinical prediction and handling of data, such as genetics and imaging in transplant oncology. This has allowed for the identification of factors that most significantly influence recurrence and survival in disease, such as hepatocellular carcinoma, and thus help in prognosticating patients who may benefit from a liver transplant. Although progress has been made in using these methods to analyse clinicopathological information, genomic profiles, and image processed data (both histopathological and radiomic), future progress relies on integrating data across these domains.

PPAR and GST polymorphisms may predict changes in intellectual functioning in medulloblastoma survivors.

PURPOSE: Advances in the treatment of pediatric medulloblastoma have led to improved survival rates, though treatment-related toxicity leaves children with significant long-term deficits. There is significant variability in the cognitive outcome of medulloblastoma survivors, and it has been suggested that this variability may be attributable to genetic factors. The aim of this study was to explore the contributions of single nucleotide polymorphisms (SNPs) in two genes, peroxisome proliferator activated receptor (PPAR) and glutathione-S-transferase (GST), to changes in general intellectual functioning in medulloblastoma survivors. METHODS: Patients (n = 44, meanage = 6.71 years, 61.3% males) were selected on the basis of available tissue samples and neurocognitive measures. Patients received surgical tumor resection, craniospinal radiation, radiation boost to the tumor site, and multiagent chemotherapy. Genotyping analyses were completed using the Illumina Human Omni2.5 BeadChip, and 41 single nucleotide polymorphisms (SNPs) were assessed across both genes. We used a machine learning algorithm to identify polymorphisms that were significantly associated with declines in general intellectual functioning following treatment for medulloblastoma. RESULTS: We identified age at diagnosis, radiation therapy, chemotherapy, and eight SNPs associated with PPARs as predictors of general intellectual functioning. Of the eight SNPs identified, PPARα (rs6008197), PPARγ (rs13306747), and PPARδ (rs3734254) were most significantly associated with long-term changes in general intellectual functioning in medulloblastoma survivors. CONCLUSIONS: PPAR polymorphisms may predict intellectual outcome changes in children treated for medulloblastoma. Importantly, emerging evidence suggests that PPAR agonists may provide an opportunity to minimize the effects of treatment-related cognitive sequelae in these children.

Integration of DNA methylation & health scores identifies subtypes in myalgic encephalomyelitis/chronic fatigue syndrome.

AIM: To identify subtypes in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) based on DNA methylation profiles and health scores. METHODS: DNA methylome profiles in immune cells were integrated with symptomatology from 70 women with ME/CFS using similarity network fusion to identify subtypes. RESULTS: We discovered four ME/CFS subtypes associated with DNA methylation modifications in 1939 CpG sites, three RAND-36 categories and five DePaul Symptom Questionnaire measures. Methylation patterns of immune response genes and differences in physical functioning and postexertional malaise differentiated the subtypes. CONCLUSION: ME/CFS subtypes are associated with specific DNA methylation differences and health symptomatology and provide additional evidence of the potential relevance of metabolic and immune differences in ME/CFS with respect to specific symptoms.

Characterizing neurocognitive late effects in childhood leukemia survivors using a combination of neuropsychological and cognitive neuroscience measures.

Knowledge about cognitive late effects in survivors of childhood acute lymphoblastic leukemia (ALL) is largely based on standardized neuropsychological measures and parent reports. To examine whether cognitive neuroscience paradigms provided additional insights into neurocognitive and behavioral late effects in ALL survivors, we assessed cognition and behavior using a selection of cognitive neuroscience tasks and standardized measures probing domains previously demonstrated to be affected by chemotherapy. 130 ALL survivors and 158 control subjects, between 8 and 18 years old at time of testing, completed the n-back (working memory) and stop-signal (response inhibition) tasks. ALL survivors also completed standardized measures of intelligence (Wechsler Intelligence Scales [WISC-IV]), motor skills (Grooved Pegboard), math abilities (WIAT-III), and executive functions (Delis-Kaplan Executive Function System). Parents completed behavioral measures of executive functions (Behavior Rating Inventory of Executive Function [BRIEF]) and attention (Conners-3). ALL survivors exhibited deficiencies in working memory and response inhibition compared with controls. ALL survivors also exhibited deficits on WISC-IV working memory and processing speed, Grooved Pegboard, WIAT-III addition and subtraction fluency, and numerical operations, as well as DKEFS number-letter switching. Parent reports suggested more attention deficits (Conners-3) and behavioral difficulties (BRIEF) in ALL survivors compared with referenced norms. Low correspondence between standardized and experimental measures of working memory and response inhibition was noted. The use of cognitive neuroscience paradigms complements our understanding of the cognitive deficits evident after treatment of ALL. These measures could further delineate cognitive processes involved in neurocognitive late effects, providing opportunities to explore their underlying mechanisms.