Improved diagnostic accuracy in differentiating malignant and benign lesions using single-voxel proton MRS of the breast at 3 T MRI

AIM: To investigate the diagnostic accuracy of single-voxel proton magnetic resonance spectroscopy (SV (1)H MRS) by quantifying total choline-containing compounds (tCho) in differentiating malignant from benign lesions, and subsequently, to analyse the relationship of tCho levels in malignant breast lesions with their histopathological subtypes. MATERIALS AND METHODS: A prospective study of SV 1H MRS was performed following dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in 61 women using a 3 T MR system. All lesions (n = 57) were analysed for characteristics of morphology, contrast-enhancement kinetics, and tCho peak heights at SV (1)H MRS that were two-times above baseline. Subsequently, the tCho in selected lesions (n = 32) was quantified by calculating the area under the curve, and a tCho concentration equal to or greater than the cut-off value was considered to represent malignancy. The relationship between tCho in invasive ductal carcinomas (IDCs) and their Bloom & Richardson grading of malignancy was assessed. RESULTS: Fifty-two patients (57 lesions; 42 malignant and 15 benign) were analysed. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), of predicting malignancy were 100, 73.3, 91.3, and 100%, respectively, using DCE-MRI and 95.2, 93.3, 97.6, and 87.5%, respectively, using SV (1)H MRS. The tCho cut-off for receiver operating characteristic (ROC) curve was 0.33 mmol/l. The relationship between tCho levels in malignant breast lesions with their histopathological subtypes was not statistically significant (p = 0.3). CONCLUSION: Good correlation between tCho peaks and malignancy, enables SV (1)H MRS to be used as a clinically applicable, simple, yet non-invasive tool for improved specificity and diagnostic accuracy in detecting breast cancer

European polygenic risk score for prediction of breast cancer shows similar performance in Asian women

Abstract: Polygenic risk scores (PRS) have been shown to predict breast cancer risk in European women, but their utility in Asian women is unclear. Here we evaluate the best performing PRSs for European-ancestry women using data from 17,262 breast cancer cases and 17,695 controls of Asian ancestry from 13 case-control studies, and 10,255 Chinese women from a prospective cohort (413 incident breast cancers). Compared to women in the middle quintile of the risk distribution, women in the highest 1% of PRS distribution have a ~2.7-fold risk and women in the lowest 1% of PRS distribution has ~0.4-fold risk of developing breast cancer. There is no evidence of heterogeneity in PRS performance in Chinese, Malay and Indian women. A PRS developed for European-ancestry women is also predictive of breast cancer risk in Asian women and can help in developing risk-stratified screening programmes in Asia

Tunnelled peripherally inserted central catherer-how we do them

In the current study, we report a new technique to place a tunnelled peripherally inserted central catheter (PICC) at the upper arm of patient under real-time ultrasound-guided venipuncture using disposal equipment provided within a standard PICC set. The tunnelling of the PICC required an extra time of 5 minutes but was well tolerated by all patients involved in the study. The tunnelled PICC was applied on 50 patients and the infection rate as well its catheter dwell time were compared to another 50 patients with conventional PICC. The rate of patients who developed infection decreased from 34% for conventional PICC to 16% in tunnelled PICC patients. The central line-associated blood stream infections rate was also decreased from 4.4 per 1000 catheter-days for conventional PICC to 1.3 per 1000 catheter-days for tunnelled PICC. The mean time to infection development for tunnelled PICC (24 days) was longer than those observed with conventional PICC (19 days). Tunnelled PICC has also increased the mean catheter dwell time from 27 days (for conventional PICC) to 47 days. Tunnelling a PICC has the potential to reduce the infection rate while increase the catheter dwell time. © 2018, Faculty of Medicine, University of Malaya. All rights reserved

Shear wave elastography in the evaluation of renal parenchymal stiffness in patients with chronic kidney disease

objective: To investigate the use of shear wave elastography (SWE)-derived estimates of Young's modulus (YM) as an indicator to detect abnormal renal tissue diagnosed by estimated glomerular filtration rate (eGFR). methods: The study comprised 106 chronic kidney disease (CKD) patients and 203 control subjects. Conventional ultrasound was performed to measure the kidney length and cortical thickness. SWE imaging was performed to measure renal parenchymal stiffness. Diagnostic performance of SWE and conventional ultrasound were correlated with serum creatinine, urea levels and eGFR. results: Pearson's correlation coefficient revealed a negative correlation between YM measurements and eGFR (r = −0.576, p < 0.0001). Positive correlations between YM measurements and age (r = 0.321, p < 0.05), serum creatinine (r = 0.375, p < 0.0001) and urea (r = 0.287, p < 0.0001) were also observed. The area under the receiver operating characteristic curve for SWE (0.87) was superior to conventional ultrasound alone (0.35-0.37). The cut-off value of less or equal to 4.31 kPa suggested a non-diseased kidney (80.3% sensitivity, 79.5% specificity). conclusion: SWE was superior to renal length and cortical thickness in detecting CKD. A value of 4.31 kPa or less showed good accuracy in determining whether a kidney was diseased or not

Students’ performance in the different clinical skills assessed in OSCE: what does it reveal?

Introduction: The purpose of this study was to compare students’ performance in the different clinical skills (CSs) assessed in the objective structured clinical examination. Methods: Data for this study were obtained from final year medical students’ exit examination (n=185). Retrospective analysis of data was conducted using SPSS. Means for the six CSs assessed across the 16 stations were computed and compared. Results: Means for history taking, physical examination, communication skills, clinical reasoning skills (CRSs), procedural skills (PSs), and professionalism were 6.25±1.29, 6.39±1.36, 6.34±0.98, 5.86±0.99, 6.59±1.08, and 6.28±1.02, respectively. Repeated measures ANOVA showed there was a significant difference in the means of the six CSs assessed [F(2.980, 548.332)=20.253, p<0.001]. Pairwise multiple comparisons revealed significant differences between the means of the eight pairs of CSs assessed, at p<0.05. Conclusions: CRSs appeared to be the weakest while PSs were the strongest, among the six CSs assessed. Students’ unsatisfactory performance in CRS needs to be addressed as CRS is one of the core competencies in medical education and a critical skill to be acquired by medical students before entering the workplace. Despite its challenges, students must learn the skills of clinical reasoning, while clinical teachers should facilitate the clinical reasoning process and guide students’ clinical reasoning development

Decision support system for fatty liver disease using GIST descriptors extracted from ultrasound images

Steatosis or fatty liver disease (FLD) is characterized by the abnormal retention of large vacuoles of neutral fat in the liver cells, either due to alcoholism or metabolic syndrome. Succession of FLD can lead to severe liver diseases such as hepatocellular carcinoma, cirrhosis and hepatic inflammation but it is a reversible disease if diagnosed early. Thus, computer-aided diagnostic tools play a very important role in the automated diagnosis of FLD. This paper focuses on the detection of steatosis and classification of steatotic livers from the normal using ultrasound images. The significant information from the image is extracted using GIST descriptor models. Marginal Fisher Analysis (MFA) integrated with Wilcoxon signed-rank test helps to eliminate the trivial features and provides the distinctive features for qualitative classification. Finally the clinically significant features are fused using classifiers such as decision tree (DT), support vector machine (SVM), adaBoost, k-nearest neighbor (kNN), probabilistic neural network (PNN), naïve Bayes (NB), fuzzy Sugeno (FS), linear and quadratic discriminant analysis classification of normal and abnormal liver images. Results portray that PNN classifier can diagnose FLD with an average classification accuracy of 98%, 96% sensitivity, 100% specificity and Area Under Curve (AUC) of 0.9674 correctly

Are first year medical students ready for OSCE?

Objective: This study aimed to assess First Year medical students' readiness for OSCE. Design: This is a retrospective study where secondary data comprising both quantitative and qualitative data, were analysed. Materials and Methods: Three cohorts of First Year medical students (n = 454) took a 5-station OSCE. Two categories of tasks were assessed. Category A assessed patient and doctor interaction while Category B assessed clinical skills. A student must be scored as satisfactory in at least four out of five stations for a pass in Category A and at least three out of five stations for a pass in Category B. A pass in both Categories A and B is required to pass the OSCE. For each cohort, overall passing percentage, as well as passing percentage for Category A and Category B of each station, was computed. Examiners' feedback on students' performance during OSCE for each station was examined. Feedback from students regarding the OSCE was also sought. Results: For Cohort 2013, Cohort 2014 and Cohort 2015, 174/179 (97.21%), 118/129 (91.47%) and 140/147 (95.24%) of students passed the OSCE respectively. Cohort 2013, Cohort 2014 and Cohort 2015 recorded mean percent pass of (95.31%, 88.83%), (89.15%, 83.10%) and (98.36%, 84.52%) for Category A and Category B respectively. Examiners' feedback was generally favourable. Feedback from students was mixed but constructive and generally encouraging. Conclusions: Based on students' performance in the OSCE as well as feedback from both examiners and students, First Year medical students appeared to be ready for OSCE assessment

Microstructural integrity of white matter tracts amongst older fallers: A DTI study

Objectives: This study assesses the whole brain microstructural integrity of white matter tracts (WMT) among older individuals with a history of falls compared to non-fallers. Methods: 85 participants (43 fallers, 42 non-fallers) were evaluated with conventional MRI and diffusion tensor imaging (DTI) sequences of the brain. DTI metrics were obtained from selected WMT using tract-based spatial statistics (TBSS) method. This was followed by binary logistic regression to investigate the clinical variables that could act as confounding elements on the outcomes. The TBSS analysis was then repeated, but this time including all significant predictor variables from the regression analysis as TBSS covariates. Results: The mean diffusivity (MD) and axial diffusivity (AD) and to a lesser extent radial diffusivity (RD) values of the projection fibers and commissural bundles were significantly different in fallers (p < 0.05) compared to non-fallers. However, the final logistic regression model obtained showed that only functional reach, white matter lesion volume, hypertension and orthostatic hypotension demonstrated statistical significant differences between fallers and non-fallers. No significant differences were found in the DTI metrics when taking into account age and the four variables as covariates in the repeated analysis. Conclusion: This DTI study of 85 subjects, do not support DTI metrics as a singular factor that contributes independently to the fall outcomes. Other clinical and imaging factors have to be taken into account

An integrated index for identification of fatty liver disease using radon transform and discrete cosine transform features in ultrasound images

Alcoholic and non-alcoholic fatty liver disease is one of the leading causes of chronic liver diseases and mortality in Western countries and Asia. Ultrasound image assessment is most commonly and widely used to identify the Non-Alcoholic Fatty Liver Disease (NAFLD). It is one of the faster and safer non-invasive methods of NAFLD diagnosis available in imaging modalities. The diagnosis of NAFLD using biopsies is expensive, invasive, and causes anxiety to the patients. The advent of advanced image processing and data mining techniques have helped to develop faster, efficient, objective, and accurate decision support system for fatty liver disease using ultrasound images. This paper proposes a novel feature extraction models based on Radon Transform (RT) and Discrete Cosine Transform (DCT). First, Radon Transform (RT) is performed on the ultrasound images for every 1 degree to capture the low frequency details. Then 2D-DCT is applied on the Radon transformed image to obtain the frequency features (DCT coefficients). Further the 2D-DCT frequency coefficients (features) obtained are converted to 1D coefficients vector in zigzag fashion. This 1D array of DCT coefficients are subjected to Locality Sensitive Discriminant Analysis (LSDA) to reduce the number of features. Then these features are ranked using minimum Redundancy and Maximum Relevance (mRMR) ranking method. Finally, highly ranked minimum numbers of features are fused using Decision Tree (DT), k-Nearest Neighbour (k-NN), Probabilistic Neural Network (PNN), Support Vector Machine (SVM), Fuzzy Sugeno (FS) and AdaBoost classifiers to get the highest classification performance. In this work, we have obtained an average accuracy, sensitivity and specificity of 100% in the detection of NAFLD using FS classifier. Also, we have devised an integrated index named as Fatty Liver Disease Index (FLDI) by fusing two significant LSDA components to distinguish normal and FLD class with single number