Multidimensional Early Prediction Score for Drug-Resistant Epilepsy.

BACKGROUND AND PURPOSE: Achieving favorable postoperative outcomes in patients with drug-resistant epilepsy (DRE) requires early referrals for preoperative examinations. The purpose of this study was to investigate the possibility of a user-friendly early DRE prediction model that is easy for nonexperts to utilize. METHODS: A two-step genotype analysis was performed, by applying 1) whole-exome sequencing (WES) to the initial test set (n=243) and 2) target sequencing to the validation set (n=311). Based on a multicenter case-control study design using the WES data set, 11 genetic and 2 clinical predictors were selected to develop the DRE risk prediction model. The early prediction scores for DRE (EPS-DRE) was calculated for each group of the selected genetic predictors (EPS-DREgen), clinical predictors (EPS-DREcln), and two types of predictor mix (EPS-DREmix) in both the initial test set and the validation set. RESULTS: The multidimensional EPS-DREmix of the predictor mix group provided a better match to the outcome data than did the unidimensional EPS-DREgen or EPS-DREcln. Unlike previous studies, the EPS-DREmix model was developed using only 11 genetic and 2 clinical predictors, but it exhibited good discrimination ability in distinguishing DRE from drug-responsive epilepsy. These results were verified using an unrelated validation set. CONCLUSIONS: Our results suggest that EPS-DREmix has good performance in early DRE prediction and is a user-friendly tool that is easy to apply in real clinical trials, especially by nonexperts who do not have detailed knowledge or equipment for assessing DRE. Further studies are needed to improve the performance of the EPS-DREmix model.

An algorithm for candidate sequencing in non-dystrophic skeletal muscle channelopathies.

Human skeletal muscle channelopathies (HSMCs) are a group of heritable conditions with ion channel-related etiology and similar presentation. To create a comprehensive picture of the phenotypic spectrum for each condition and to devise a strategy that facilitates the differential diagnosis, we collected the genotype and phenotype information from more than 500 previously published HSMC studies. Using these records, we were able to identify clear correlations between particular clinical features and the underlying alteration(s) in the genes SCN4A, CACNA1S, KCNJ2, and CLCN1. This allowed us to develop a clinical, symptom-based, binary decision flow algorithm that predicts the proper genetic origin with high accuracy (0.88-0.93). The algorithm was implemented in a stand-alone online tool ("CGPS"- http://cgps.ddd.co.kr ) to assist with HSCM diagnosis in the clinical practice. The CGPS provides simple, symptom-oriented navigation that guides the user to the most likely molecular basis of the presentation, which permits highly targeted genetic screens and, upon confirmation, tailored pharmacotherapy based on the molecular origin.

Separation of left and right lungs using 3-dimensional information of sequential computed tomography images and a guided dynamic programming algorithm.

OBJECTIVE: This article presents a new computerized scheme that aims to accurately and robustly separate left and right lungs on computed tomography (CT) examinations. METHODS: We developed and tested a method to separate the left and right lungs using sequential CT information and a guided dynamic programming algorithm using adaptively and automatically selected start point and end point with especially severe and multiple connections. RESULTS: The scheme successfully identified and separated all 827 connections on the total 4034 CT images in an independent testing data set of CT examinations. The proposed scheme separated multiple connections regardless of their locations, and the guided dynamic programming algorithm reduced the computation time to approximately 4.6% in comparison with the traditional dynamic programming and avoided the permeation of the separation boundary into normal lung tissue. CONCLUSIONS: The proposed method is able to robustly and accurately disconnect all connections between left and right lungs, and the guided dynamic programming algorithm is able to remove redundant processing.