Sung-Jong Eun

Noninvasive identification of viable cell populations in docetaxel-treated breast tumors using ferritin-based magnetic resonance imaging.

Background Cancer stem cells (CSCs) are highly tumorigenic and are responsible for tumor progression and chemoresistance. Noninvasive imaging methods for the visualization of CSC populations within tumors in vivo will have a considerable impact on the development of new CSC-targeting therapeutics. Methodology/Principal Findings In this study, human breast cancer stem cells (BCSCs) transduced with dual reporter genes (human ferritin heavy chain [FTH] and enhanced green fluorescence protein [EGFP]) were transplanted into NOD/SCID mice to allow noninvasive tracking of BCSC-derived populations. No changes in the properties of the BCSCs were observed due to ferritin overexpression. Magnetic resonance imaging (MRI) revealed significantly different signal intensities (R2* values) between BCSCs and FTH-BCSCs in vitro and in vivo. In addition, distinct populations of pixels with high R2* values were detected in docetaxel-treated FTH-BCSC tumors compared with control tumors, even before the tumor sizes changed. Histological analysis revealed that areas showing high R2* values in docetaxel-treated FTH-BCSC tumors by MRI contained EGFP+/FTH+ viable cell populations with high percentages of CD44+/CD24− cells. Conclusions/Significance These findings suggest that ferritin-based MRI, which provides high spatial resolution and tissue contrast, can be used as a reliable method to identify viable cell populations derived from BCSCs after chemotherapy and may serve as a new tool to monitor the efficacy of CSC-targeting therapies in vivo.

Application of Virtual, Augmented, and Mixed Reality to Urology

Recent developments in virtual, augmented, and mixed reality have introduced a considerable number of new devices into the consumer market. This momentum is also affecting the medical and health care sector. Although many of the theoretical and practical foundations of virtual reality (VR) were already researched and experienced in the 1980s, the vastly improved features of displays, sensors, interactivity, and computing power currently available in devices offer a new field of applications to the medical sector and also to urology in particular. The purpose of this review article is to review the extent to which VR technology has already influenced certain aspects of medicine, the applications that are currently in use in urology, and the future development trends that could be expected.

Evidence Is Enough?: A Systematic Review and Network Meta-Analysis of the Efficacy of Tamsulosin 0.2 mg and Tamsulosin 0.4 mg as an Initial Therapeutic Dose in Asian Benign Prostatic Hyperplasia Patients

Purpose We compared the efficacy of tamsulosin between 0.2 mg and 0.4 mg in Asian prostatic hyperplasia (BPH) patients using network meta-analysis due to lack of studies with direct comparison. Methods The literature search was conducted using the MEDLINE, Embase, and Cochrane Library. Keywords used were “BPH,” “tamsulosin,” “placebo.” Experimental groups were defined as tamsulosin 0.2 mg (Tam 0.2) and 0.4 mg (Tam 0.4) and common control group was defined as placebo for indirect treatment comparison. Mixed treatment comparison was performed including one direct comparison study. Results Seven studies met the eligible criteria. Indirect treatment comparison revealed that total International Prostate Symptoms Score (IPSS) and quality of life score of IPSS were not significantly different in Tam 0.2 and Tam 0.4 (P>0.05). There was no significant difference of maximal flow rate and postvoid residual urine volume in Tam 0.2 and Tam 0.4 (P>0.05). Mixed treatment comparison including one direct comparison study showed inconsistency (P<0.001). Therefore, analysis using direct treatment comparison effect sizes of Tam 0.2 vs. placebo and Tam 0.4 vs. placebo was done and there was no significant difference. Conclusions Network meta-analysis showed no difference of efficacy between tamsulosin 0.2 mg and 0.4 mg and the evidence of tamsulosin 0.4 mg as initial dose for Asian BPH patient seems to be insufficient. Therefore, initial dose of tamsulosin for Asian BPH patient should be 0.2 mg.

The Design of the Self-diagnosis Algorithm for the Efficient Control of Sudden Cancer Pain

Painisoneofthemostcommonandpainfulsymptomsthatcancerpatientssufferfrom.Pain seriouslyaffects30-50% ofthepatientsattheearlycancerdiagnosisstageorwhoreceiveactive anticancertreatments,60-70% ofthepatientswithprogressivecancer,and80-90% ofthe patientsatthelatestageofcancer.However,thereisnosystematicandeasypaincontrol program forthecancerpatients.Inthisstudy,analgorithm isproposedtoprovidequickpain reliefserviceupontheoccurrenceofsuddenpain,forthepurposeofcontrolingthesuddenpain thatcanceroperation survivors experience.In developing the algorithm,questionnaires, evaluation forms and NationalComprehensive CancerNetwork (NCCN)guideline were considered,andatrialservicewasprovidedtoagroupof20cancerpatientsforamonthto evaluatethedesignedalgorithm.Theresultsofthetrialservicewereexaminedbyexpertmedical workerstoevaluatetheproposedalgorithm,anda90% compatibilitydecisionwasderived,which verifiedtheeffectivenessoftheproposedalgorithm.Inthecaseofincompatibilitydecision,the managementofthepaindiarydidnothavecompatibleresults.Therefore,thefurtherstudywil additionalyaddressthecustomizedpaindiaryalgorithm. ■ keyword : Sudden Cancer Pain Cancer Patient Self-Control Algorithm of Cancer Pain Cancer Pain Guideline *본 연구는 보건복지부 보건의료연구개발사업의 지원에 의하여 이루어진 것임(A112020) *본 연구는 산업통상자원부 및 한국산업기술평가관리원의 산업융합원천기술개발사업의 일환으로 수행하였음 [과제번호 :10037283,과제명 :만성질환자를 위한 멀티 플랫폼 기반 건강관리 및 증진서비스 개발] 접수일자 :2014년 03월 27일 수정일자 :2014년 05월 07일 심사완료일 :2014년 05월 14일 교신저자 :박동균,e-mail:pdk66@gilhospital.com 효율적인 돌발성 암 통증 관리를 위한 자가 진단 알고리즘 설계 459

Personalized Urination Activity Recognition Based on a Recurrent Neural Network Using Smart Band

Purpose Though it is very important obtaining exact data about patients’ voiding patterns for managing voiding dysfunction, actual practice is very difficult and cumbersome. In this study, data about urination time and interval measured by smart band device on patients’ wrist were collected and analyzed to resolve the clinical arguments about the efficacy of voiding diary. By developing a smart band based algorithm for recognition of complex and serial pattern of motion, this study aimed to explore the feasibility of measurement the urination time and intervals for voiding dysfunction management. Methods We designed a device capable of recognizing urination time and intervals based on specific postures of the patient and consistent changes in posture. These motion data were obtained by a smart band worn on the wrist. An algorithm that recognizes the repetitive and common 3-step behavior for urination (forward movement, urination, backward movement) was devised based on the movement and tilt angle data collected from a 3-axis accelerometer. The sequence of body movements during voiding has consistent temporal characteristics, so we used a recurrent neural network and long short-term memory based framework to analyze the sequential data and to recognize urination time. Real-time data were acquired from the smart band, and for data corresponding to a certain duration, the value of the signals was calculated and then compared with the set analysis model to calculate the time of urination. A comparative study was conducted between real voiding and device-detected voiding to assess the performance of the proposed recognition technology. Results The accuracy of the algorithm was calculated based on clinical guidelines established by urologists. The accuracy of this detecting device was high (up to 94.2%), proving the robustness of the proposed algorithm. Conclusions This urination behavior recognition technology showed high accuracy and could be applied in clinical settings to characterize patients’ voiding patterns. As wearable devices are developed and generalized, algorithms detecting consistent sequential body movement patterns reflecting specific physiologic behavior might be a new methodology for studying human physiologic behavior.

Development of Personalized Urination Recognition Technology Using Smart Bands

Purpose This study collected and analyzed activity data sensed through smart bands worn by patients in order to resolve the clinical issues posed by using voiding charts. By developing a smart band-based algorithm for recognizing urination activity in patients, this study aimed to explore the feasibility of urination monitoring systems. Methods This study aimed to develop an algorithm that recognizes urination based on a patient’s posture and changes in posture. Motion data was obtained from a smart band on the arm. An algorithm that recognizes the 3 stages of urination (forward movement, urination, backward movement) was developed based on data collected from a 3-axis accelerometer and from tilt angle data. Real-time data were acquired from the smart band, and for data corresponding to a certain duration, the absolute value of the signals was calculated and then compared with the set threshold value to determine the occurrence of vibration signals. In feature extraction, the most essential information describing each pattern was identified after analyzing the characteristics of the data. The results of the feature extraction process were sorted using a classifier to detect urination. Results An experiment was carried out to assess the performance of the recognition technology proposed in this study. The final accuracy of the algorithm was calculated based on clinical guidelines for urologists. The experiment showed a high average accuracy of 90.4%, proving the robustness of the proposed algorithm. Conclusions The proposed urination recognition technology draws on acceleration data and tilt angle data collected via a smart band; these data were then analyzed using a classifier after comparative analyses with standardized feature patterns.

Effective Object Recognition based on Physical Theory in Medical Image Processing

In medical image processing field, object recognition is usually processed based on region segmentation algorithm. Region segmentation in the computing field is carried out by computerized processing of various input information such as brightness, shape, and pattern analysis. If the information mentioned does not make sense, however, many limitations could occur with region segmentation during computer processing. Therefore, this paper suggests effective region segmentation method based on R2-map information within the magnetic resonance (MR) theory. In this study, the experiment had been conducted using images including the liver region and by setting up feature points of R2-map as seed points for 2D region growing and final boundary correction to enable region segmentation even when the border line was not clear. As a result, an average area difference of 7.5%, which was higher than the accuracy of conventional exist region segmentation algorithm, was obtained.

Extraction Method of Geometry Information for Effective Analysis in Tongue Diagnosis

In Oriental medicine, the status of a tongue is the important indicator to diagnose the condition of internal organs in a body. A tongue diagnosis is not only convenient but also non-invasive, and therefore widely used in Oriental medicine. But tongue diagnosis has some problems that should be objective and standardized, it also exhaust the diagnosis tool that can help for oriental medicine doctor`s decision-making. In this paper, to solve the this problem we propose a method that calculates the tongue geometry information for effective tongue diagnosis analysis. Our method is to extract the tongue region for using improved snake algorithm, and calculates the geometry information by using convex hull and In-painting. In experiment, our method has stable performance as 7.2% by tooth plate and 8.5% by crack in region difference ratio.

Brain Segmentation Using Susceptibility Weighted Imaging Method

Object recognition is usually processed based on region segmentation algorithm. Region segmentation in the IT field is carried out by computerized processing of various input information such as brightness, shape, and pattern analysis. If the information mentioned does not make sense, however, many limitations could occur with region segmentation during computer processing. Therefore, this paper suggests effective region segmentation method based on Susceptibility Weighted Imaging (SWI) within the magnetic resonance (MR) theory. When we do pre-processing, proposed method was composed of SWI process. And then we do the Gray-white matter segmentation by improved region growing. In this study, the experiment had been conducted using images including the brain region and by getting up contrast enhancement image of SWI for segmentation to extract region (white matter) segmentation even when the border line was not clear. As a result, an average area difference of 8.8%, which was higher than the accuracy of conventional region segmentation algorithm, was obtained.

Effective Fat Quantification Using Improved Least-Square Fit at High-Field MRI

In high-field magnetic resonance imaging (MRI), water-fat separation in the presence of B0 field inhomogeneity is important research. Various field map estimation techniques that use three-point multi-echo acquisitions have been developed for reliable water fat separation. Among the numerous techniques, iterative decomposition of water and fat with echo asymmetry and least squares estimation (IDEAL) has gained considerable popularity as an iterative method for acquiring high-quality water and fat images. However, due to the worsened B0 inhomogeneity at high-field, IDEAL cannot adjust for meaningful field map estimation, particularly for a large field of view. Previously, to improve the robustness of this estimation, a region-growing (RG) technique was developed to take advantage of the 2D linear extrapolation procedure through the seed point set by the median value in the target object. There are some limitations with this approach, such as the dependence on the initial seed point, such as a number, intensity, and position of the seed point. In this work, we introduce a effective method called the improved least square fit method that does not need to consider parameters related with accuracy. As a result of the proposed method, we obtained a effective fat quantification result that can be applied in high-fields, with an average water residual rate of 7.2% higher than the existing method.