A neural network - based algorithm for predicting stone -free status after ESWL therapy

ABSTRACT Objective: The prototype artificial neural network (ANN) model was developed using data from patients with renal stone, in order to predict stone-free status and to help in planning treatment with Extracorporeal Shock Wave Lithotripsy (ESWL) for kidney stones. Materials and Methods: Data were collected from the 203 patients including gender, single or multiple nature of the stone, location of the stone, infundibulopelvic angle primary or secondary nature of the stone, status of hydronephrosis, stone size after ESWL, age, size, skin to stone distance, stone density and creatinine, for eleven variables. Regression analysis and the ANN method were applied to predict treatment success using the same series of data. Results: Subsequently, patients were divided into three groups by neural network software, in order to implement the ANN: training group (n=139), validation group (n=32), and the test group (n=32). ANN analysis demonstrated that the prediction accuracy of the stone-free rate was 99.25% in the training group, 85.48% in the validation group, and 88.70% in the test group. Conclusions: Successful results were obtained to predict the stone-free rate, with the help of the ANN model designed by using a series of data collected from real patients in whom ESWL was implemented to help in planning treatment for kidney stones

A neural network - based algorithm for predicting stone -free status after ESWL therapy

<div><p>ABSTRACT Objective: The prototype artificial neural network (ANN) model was developed using data from patients with renal stone, in order to predict stone-free status and to help in planning treatment with Extracorporeal Shock Wave Lithotripsy (ESWL) for kidney stones. Materials and Methods: Data were collected from the 203 patients including gender, single or multiple nature of the stone, location of the stone, infundibulopelvic angle primary or secondary nature of the stone, status of hydronephrosis, stone size after ESWL, age, size, skin to stone distance, stone density and creatinine, for eleven variables. Regression analysis and the ANN method were applied to predict treatment success using the same series of data. Results: Subsequently, patients were divided into three groups by neural network software, in order to implement the ANN: training group (n=139), validation group (n=32), and the test group (n=32). ANN analysis demonstrated that the prediction accuracy of the stone-free rate was 99.25% in the training group, 85.48% in the validation group, and 88.70% in the test group. Conclusions: Successful results were obtained to predict the stone-free rate, with the help of the ANN model designed by using a series of data collected from real patients in whom ESWL was implemented to help in planning treatment for kidney stones.</p></div

Brain diffusion tensor imaging in children with tuberous sclerosis

Purpose: To evaluate diffusion characteristics of tubers and white matter lesions in children with tuberous sclerosis (TS) using diffusion tensor imaging (DTI)

A Multicenter Study of Genotype Variation/Demographic Patterns in 2475 Individuals Including 1444 Cases With Breast Cancer in Turkey

Objective: Breast cancer (BC) is the most common cancer type in women and may be inherited, mostly in an autosomal dominant pattern. The clinical diagnosis of BC relies on the published diagnostic criteria, and analysis of two genes, BRCA1 and BRCA2, which are strongly associated with BC, are included in these criteria. The aim of this study was to compare BC index cases with non-BC individuals in terms of genotype and diagnostic features to investigate the genotype/demographic information association. Materials and Methods: Mutational analyses for the BRCA1/BRCA2 genes was performed in 2475 individuals between 2013-2022 from collaborative centers across Turkey, of whom 1444 with BC were designated as index cases. Results: Overall, mutations were identified in 17% (421/2475), while the percentage of mutation carriers in cases of BC was similar, 16.6% (239/1444). BRCA1/BRCA2 gene mutations were detected in 17.8% (131/737) of familial cases and 12% (78/549) of sporadic cases. Mutations in BRCA1 were found in 4.9%, whereas 12% were in BRCA2 (p<0.05). Meta-analyses were performed to compare these results with other studies of Mediterranean-region populations. Conclusion: Patients with BRCA2 mutations were significantly more common than those with BRCA1 mutations. In sporadic cases, there was a lower proportion with BRCA1/BRCA2 variants, as expected, and these results were consistent with the data of Mediterranean-region populations. However, the present study, because of the large sample size, revealed more robust findings than previous studies. These findings may be helpful in facilitating the clinical management of BC for both familial and non-familial cases