Prediction of left lobe hypertrophy after right lobe radioembolization of the liver using a clinical data model with external validation

In cirrhotic patients with hepatocellular carcinoma (HCC), right-sided radioembolization (RE) with Yttrium-90-loaded microspheres is an established palliative therapy and can be considered a “curative intention” treatment when aiming for sequential tumor resection. To become surgical candidate, hypertrophy of the left liver lobe to > 40% (future liver remnant, FLR) is mandatory, which can develop after RE. The amount of radiation-induced shrinkage of the right lobe and compensatory hypertrophy of the left lobe is difficult for clinicians to predict. This study aimed to utilize machine learning to predict left lobe liver hypertrophy in patients with HCC and cirrhosis scheduled for right lobe RE, with external validation. The results revealed that machine learning can accurately predict relative and absolute volume changes of the left liver lobe after right lobe RE. This prediction algorithm could help to estimate the chances of conversion from palliative RE to curative major hepatectomy following significant FLR hypertrophy

METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII

Purpose: To propose a new quality scoring tool, METhodological RadiomICs Score (METRICS), to assess and improve research quality of radiomics studies. Methods: We conducted an online modified Delphi study with a group of international experts. It was performed in three consecutive stages: Stage#1, item preparation; Stage#2, panel discussion among EuSoMII Auditing Group members to identify the items to be voted; and Stage#3, four rounds of the modified Delphi exercise by panelists to determine the items eligible for the METRICS and their weights. The consensus threshold was 75%. Based on the median ranks derived from expert panel opinion and their rank-sum based conversion to importance scores, the category and item weights were calculated. Result: In total, 59 panelists from 19 countries participated in selection and ranking of the items and categories. Final METRICS tool included 30 items within 9 categories. According to their weights, the categories were in descending order of importance: study design, imaging data, image processing and feature extraction, metrics and comparison, testing, feature processing, preparation for modeling, segmentation, and open science. A web application and a repository were developed to streamline the calculation of the METRICS score and to collect feedback from the radiomics community. Conclusion: In this work, we developed a scoring tool for assessing the methodological quality of the radiomics research, with a large international panel and a modified Delphi protocol. With its conditional format to cover methodological variations, it provides a well-constructed framework for the key methodological concepts to assess the quality of radiomic research papers. Critical relevance statement: A quality assessment tool, METhodological RadiomICs Score (METRICS), is made available by a large group of international domain experts, with transparent methodology, aiming at evaluating and improving research quality in radiomics and machine learning. Key points: • A methodological scoring tool, METRICS, was developed for assessing the quality of radiomics research, with a large international expert panel and a modified Delphi protocol. • The proposed scoring tool presents expert opinion-based importance weights of categories and items with a transparent methodology for the first time. • METRICS accounts for varying use cases, from handcrafted radiomics to entirely deep learning-based pipelines. • A web application has been developed to help with the calculation of the METRICS score (https://metricsscore.github.io/metrics/METRICS.html) and a repository created to collect feedback from the radiomics community (https://github.com/metricsscore/metrics). Graphical Abstract: [Figure not available: see fulltext.

Measuring the bias of incorrect application of feature selection when using cross-validation in radiomics

Background!#!Many studies in radiomics are using feature selection methods to identify the most predictive features. At the same time, they employ cross-validation to estimate the performance of the developed models. However, if the feature selection is performed before the cross-validation, data leakage can occur, and the results can be biased. To measure the extent of this bias, we collected ten publicly available radiomics datasets and conducted two experiments. First, the models were developed by incorrectly applying the feature selection prior to cross-validation. Then, the same experiment was conducted by applying feature selection correctly within cross-validation to each fold. The resulting models were then evaluated against each other in terms of AUC-ROC, AUC-F1, and Accuracy.!##!Results!#!Applying the feature selection incorrectly prior to the cross-validation showed a bias of up to 0.15 in AUC-ROC, 0.29 in AUC-F1, and 0.17 in Accuracy.!##!Conclusions!#!Incorrect application of feature selection and cross-validation can lead to highly biased results for radiomic datasets

Griscelli Syndrome Presented with Status Epilepticus and Hemophagocytic Lymphohistiocytosis

WOS: 000398059500029PubMed ID: 2709528

Predictive value of the "Blood Pressure To Height Ratio" in diagnosis of prehypertension and hypertension during childhood in Southeastern Turkey

###EgeUn###Recently, a simple, accurate and non-age-related index "Systolic/Diastolic Blood Pressure to Height Ratio (SBPHR/DBPHR)" is started to try for diagnosing hypertension in childhood. The aim of this study was to investigate the possible cut-off points and diagnostic value of BPHR for identifying prehypertension/hypertension in children and adolescent, and evaluation of the relationship between body fat composition and BP. The community-based descriptive cross-sectional study was carried out with 2730 students in 17 elementary and high school. Total body fat composition was analyzed with bioelectrical impedance analysis method. The ROC curve analysis indicated that SBPHR/DBPHR was a good predictor for identifying hypertension (AUC = 0.937, p < 0.0001; AUC = 0.880, p < 0.0001, respectively). The optimal cut-off values of SBPHR/DBPHR for hypertension were detected as 0.7767, 0.4688; respectively. Although, optimal cut-off points of SBPHR/DBPHR were statistically significant for discriminating prehypertension (0.6849, p < 0.0001; 0.4425, p < 0.0001, respectively), but the diagnostic value was lower (AUC = 0.738; AUC = 0.751, respectively). An increase of 1 unit in total body fat (%) leads to an average 0.38/0.26 mmHg increase in SBP/DBP values (p < 0.001). The results suggest that BPHR may be a useful diagnostic marker for screening elevated BP in childhood, and SBP/DBP values affected by the increase in total body fat percentage in obese and non-obese children

Waist to height ratio as a screening tool for identifying childhood obesity and associated factors

Kilic, Beltinge Demircioglu/0000-0001-9408-2139;WOS: 000502819400003PubMed: 31777510Objective: To investigate the prevalence of obesity and associated factors during childhood in Southeastern Turkey. Another objective was to determine the cut-off points of Waist to Height Ratio (WHtR) values for defining obesity/abdominal obesity. Methods: the community-based descriptive cross-sectional study was conducted in Gaziantep Turkey between November 2011 and December 2011 with 2718 primary school/high schools students aged 6-17 years. the SPSS 22.00 was used for the analysis of data. Results: the prevalence of overweight, obesity, abdominal obesity, was 13.2%, 4.2%, 26.4%, respectively. There was a reverse relationship between BMI/WC values and sleep durations (p= 1 hours in a day (p<0.05). Parental obesity status has an effective role on the WC/BMI values of children (p<0.05). the WHtR was a good predictor of diagnosis on obesity and abdominal obesity (AUC=0.928, p<0.0001; AUC=0.920, p<0.0001; respectively). the optimal cut-off values for obesity and abdominal obesity were detected as 0.5077, 0.4741, respectively. Conclusions: the WHtR can be used for diagnosis of obesity/abdominal obesity. Parental obesity, short sleep duration and computer use more than one hour per day are risk factors for the development of obesity in children and adolescents

Evaluation of serum lipids and carotid artery intima media thickness in epileptic children treated with valproic acid

The aim of this study is to evaluate the carotid artery intima media thickness and serum lipids in pediatric patients with epilepsy treated with valproic acid. The study included 44 pediatric epileptic and 40 healthy children. Intima media thickness of left common carotid artery and fasting lipid profile (total cholesterol, triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol) were assessed. Although we did not observe any differences regarding serum lipid profiles, intima media thickness of common carotid artery was significantly higher in epileptic patients treated with valproic acid. We suggest that this increase in intima media thickness of common carotid artery may be due to epilepsy and/or valproic acid treatment. (C) 2008 Elsevier B.V. All rights reserved