METHODS: We prospectively followed 100 patients (50:50 cuffed and non-cuffed PICC) and compared CRBSI rate between these groups. Daily review and similar catheter care were performed until a PICC-related complication, completion of therapy, death or defined end-of-study date necessitate removal. CRBSI was confirmed in each case by demonstrating concordance between isolates colonizing the PICC at the time of infection and from peripheral blood cultures.

RESULTS: A total of 50 cuffed PICC were placed for 1864 catheter-days. Of these, 12 patients (24%) developed infection, for which 5 patients (10%) had a CRBSI for a rate of 2.7 per 1000 catheter-days. Another 50 tunnelled non-cuffed PICCs were placed for 2057 catheter-days. Of these, 7 patients (14%) developed infection, for which 3 patients (6%) had a CRBSI. for a rate of 1.5 per 1000 catheter-days. The mean time to development of infection is 24 days in cuffed and 19 days in non-cuffed groups. The mean duration of utilization was significantly longer in non-cuffed than in cuffed group (43 days in non-cuffed vs 37 days in cuffed group, p = 0.008).

OBJECTIVE: This study aims to review the typical and relatively atypical CXR manifestations of COVID-19 pneumonia in a tertiary care hospital.

METHODS: The CXRs of 136 COVID-19 patients confirmed through real-time RT-PCR from March to May 2020 were reviewed. A literature search was performed using PubMed.

RESULTS: A total of 54 patients had abnormal CXR whilst the others were normal. Typical CXR findings included pulmonary consolidation or ground-glass opacities in a multifocal, bilateral peripheral, or lower zone distribution, whereas atypical CXR features comprised cavitation and pleural effusion.

METHODS: A Total of 51 patients with 105 carotid artery plaques were screened using 3D and 2D US probes attached to the same US scanner. Two independent observers characterized the plaques based on the morphological features namely echotexture, echogenicity and surface characteristics. The scores assigned to each morphological feature were used to determine intra- and inter-observer performance. The level of agreement was measured using Kappa coefficient.

RESULTS: The first observer with 2D US showed fair (k=0.4-0.59) and very strong (k>0.8) with 3D US intra-observer agreements using three morphological features. The second observer indicated moderate strong (k=0.6-0.79) with 2D US and very strong with 3D US (k>0.8) intra-observer performances. Moderate strong (k=0.6-0.79) and very strong (k>0.8) inter-observer agreements were reported with 2D US and 3D US respectively. The results with 2D and 3D US were correlated 62% using only echotexture and 56% using surface morphology coupled with echogenicity. 3D US gave a lower score than 2D 71% of the time (p=0.005) in disagreement cases.

PURPOSE: To evaluate the accuracy, safety, and diagnostic outcome of fluoroscopic guided and CT transpedicular biopsy techniques.

STUDY DESIGN: Prospective randomized trial.

PATIENT SAMPLE: Sixty consecutive patients with clinical symptoms and radiological features suggestive of spinal infection or malignancy were recruited and randomized into fluoroscopic or CT guided spinal biopsy groups. Both groups were similar in terms of patient demographics, distribution of spinal infections and malignancy cases, and the level of biopsies.

OUTCOME MEASURES: The primary outcome measure was diagnostic accuracy of both methods, determined based on true positive, true negative, false positive, and false negative biopsy findings. Secondary outcome measures included radiation exposure to patients and doctors, complications, and postbiopsy pain score.

METHODS: A transpedicular approach was performed with an 8G core biopsy needle. Specimens were sent for histopathological and microbiological examinations. Diagnosis was made based on biopsy results, clinical criteria and monitoring of disease progression during a 6-month follow up duration. Clinical criteria included presence of risk factors, level of inflammatory markers and magnetic resonance imaging findings. Radiation exposure to patients and doctors was measured with dosimeters.

RESULTS: There was no significant difference between the diagnostic accuracy of fluoroscopic and CT guided spinal biopsy (p=0.67) or between the diagnostic accuracy of spinal infection and spinal tumor in both groups (p=0.402 for fluoroscopy group and p=0.223 for CT group). Radiation exposure to patients was approximately 26 times higher in the CT group. Radiation exposure to doctors in the CT group was approximately 2 times higher compared to the fluoroscopic group if a lead shield was not used. Lead shields significantly reduced radiation exposure to doctors anywhere from 2 to 8 times. No complications were observed for either group and the differences in postbiopsy pain scores were not significant.

MATERIALS AND METHODS: Contrast enhanced computed tomography (CT) images of 194 multi-racial NSCLC patients (79 EGFR mutants and 115 wildtypes) were collected from three different countries using 5 manufacturers' scanners with a variety of scanning parameters. Ninety-nine cases obtained from the University of Malaya Medical Centre (UMMC) in Malaysia were used for training and validation procedures. Forty-one cases collected from the Kyushu University Hospital (KUH) in Japan and fifty-four cases obtained from The Cancer Imaging Archive (TCIA) in America were used for a test procedure. Radiomic features were obtained from BN maps, which represent topologically invariant heterogeneous characteristics of lung cancer on CT images, by applying histogram- and texture-based feature computations. A BN-based signature was determined using support vector machine (SVM) models with the best combination of features that maximized a robustness index (RI) which defined a higher total area under receiver operating characteristics curves (AUCs) and lower difference of AUCs between the training and the validation. The SVM model was built using the signature and optimized in a five-fold cross validation. The BN-based model was compared to conventional original image (OI)- and wavelet-decomposition (WD)-based models with respect to the RI between the validation and the test.

RESULTS: The BN-based model showed a higher RI of 1.51 compared with the models based on the OI (RI: 1.33) and the WD (RI: 1.29).

METHODS: The proposed method uses a 2D contourlet transform and a set of texture features that are efficiently extracted from the transformed image. Then, the combination of a kernel discriminant analysis (KDA)-based feature reduction technique and analysis of variance (ANOVA)-based feature ranking technique was used, and the images were then classified into various stages of liver fibrosis.

RESULTS: Our 2D contourlet transform and texture feature analysis approach achieved a 91.46% accuracy using only four features input to the probabilistic neural network classifier, to classify the five stages of liver fibrosis. It also achieved a 92.16% sensitivity and 88.92% specificity for the same model. The evaluation was done on a database of 762 ultrasound images belonging to five different stages of liver fibrosis.

METHODS: In total, 154 patients (wild-type EGFR, 72 patients; Del19 mutation, 45 patients; and L858R mutation, 37 patients) were retrospectively enrolled and randomly divided into 92 training and 62 test cases. Two support vector machine (SVM) models to distinguish between wild-type and mutant EGFR (mutation [M] classification) as well as between the Del19 and L858R subtypes (subtype [S] classification) were trained using 3DBN features. These features were computed from 3DBN maps by using histogram and texture analyses. The 3DBN maps were generated using computed tomography (CT) images based on the Čech complex constructed on sets of points in the images. These points were defined by coordinates of voxels with CT values higher than several threshold values. The M classification model was built using image features and demographic parameters of sex and smoking status. The SVM models were evaluated by determining their classification accuracies. The feasibility of the 3DBN model was compared with those of conventional radiomic models based on pseudo-3D BN (p3DBN), two-dimensional BN (2DBN), and CT and wavelet-decomposition (WD) images. The validation of the model was repeated with 100 times random sampling.

RESULTS: The mean test accuracies for M classification with 3DBN, p3DBN, 2DBN, CT, and WD images were 0.810, 0.733, 0.838, 0.782, and 0.799, respectively. The mean test accuracies for S classification with 3DBN, p3DBN, 2DBN, CT, and WD images were 0.773, 0.694, 0.657, 0.581, and 0.696, respectively.