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

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

A rapid volume of interest-based approach of radiomics analysis of breast MRI for tumor decoding and phenotyping of breast cancer.

BackgroundRecently, radiomics has emerged as a non-invasive, imaging-based tissue characterization method in multiple cancer types. One limitation for robust and reproducible analysis lies in the inter-reader variability of the tumor annotations, which can potentially cause differences in the extracted feature sets and results. In this study, the diagnostic potential of a rapid and clinically feasible VOI (Volume of Interest)-based approach to radiomics is investigated to assess MR-derived parameters for predicting molecular subtype, hormonal receptor status, Ki67- and HER2-Expression, metastasis of lymph nodes and lymph vessel involvement as well as grading in patients with breast cancer.MethodsA total of 98 treatment-naïve patients (mean 59.7 years, range 28.0-89.4) with BI-RADS 5 and 6 lesions who underwent a dedicated breast MRI prior to therapy were retrospectively included in this study. The imaging protocol comprised dynamic contrast-enhanced T1-weighted imaging and T2-weighted imaging. Tumor annotations were obtained by drawing VOIs around the primary tumor lesions followed by thresholding. From each segmentation, 13.118 quantitative imaging features were extracted and analyzed with machine learning methods. Validation was performed by 5-fold cross-validation with 25 repeats.ResultsPredictions for molecular subtypes obtained AUCs of 0.75 (HER2-enriched), 0.73 (triple-negative), 0.65 (luminal A) and 0.69 (luminal B). Differentiating subtypes from one another was highest for HER2-enriched vs triple-negative (AUC 0.97), followed by luminal B vs triple-negative (0.86). Receptor status predictions for Estrogen Receptor (ER), Progesterone Receptor (PR) and Hormone receptor positivity yielded AUCs of 0.67, 0.69 and 0.69, while Ki67 and HER2 Expressions achieved 0.81 and 0.62. Involvement of the lymph vessels could be predicted with an AUC of 0.8, while lymph node metastasis yielded an AUC of 0.71. Models for grading performed similar with an AUC of 0.71 for Elston-Ellis grading and 0.74 for the histological grading.ConclusionOur preliminary results of a rapid approach to VOI-based tumor-annotations for radiomics provides comparable results to current publications with the perks of clinical suitability, enabling a comprehensive non-invasive platform for breast tumor decoding and phenotyping