The diagnostic accuracy of high b-value diffusion- and T2-weighted imaging for the detection of prostate cancer: a meta-analysis

Purpose: This study aims to investigate the role of diffusion-weighted imaging (DWI) and T2-weighted imaging (T2WI) in combination for the detection of prostate cancer, specifically assessing the role of high b-values (> 1000 s/mm2), with a systematic review and meta-analysis of the existing published data.  Methods: The electronic databases MEDLINE, EMBASE, and OpenSIGLE were searched between inception and September 1, 2017. Eligible studies were those that reported the sensitivity and specificity of DWI and T2WI for the diagnosis of prostate cancer by visual assessment using a histopathologic reference standard. The QUADAS-2 critical appraisal tool was used to assess the quality of included studies. A meta-analysis with pooling of sensitivity, specificity, likelihood, and diagnostic odds ratios was undertaken, and a summary receiver-operating characteristics (sROC) curve was constructed. Predetermined subgroup analysis was also performed.  Results: Thirty-three studies were included in the final analysis, evaluating 2949 patients. The pooled sensitivity and specificity were 0.69 (95% CI 0.68–0.69) and 0.84 (95% CI 0.83–0.85), respectively, and the sROC AUC was 0.84 (95% CI 0.81–0.87). Subgroup analysis showed significantly better sensitivity with high b-values (> 1000 s/mm2). There was high statistical heterogeneity between studies.  Conclusion: The diagnostic accuracy of combined DWI and T2WI is good with high b-values (> 1000 s/mm2) seeming to improve overall sensitivity while maintaining specificity. However, further large-scale studies specifically looking at b-value choice are required before a categorical recommendation can be made

Parameter Estimation Error Dependency on the Acquisition Protocol in Diffusion Kurtosis Imaging

Mono-exponential kurtosis model is routinely fitted on diffusion weighted, magnetic resonance imaging data to describe non-Gaussian diffusion. Here, the purpose was to optimize acquisitions for this model to minimize the errors in estimating diffusion coefficient and kurtosis. Similar to a previous study, covariance matrix calculations were used, and coefficients of variation in estimating each parameter of this model were calculated. The acquisition parameter, b values, varied in discrete grids to find the optimum ones that minimize the coefficient of variation in estimating the two non-Gaussian parameters. Also, the effect of variation of the target values on the optimized values was investigated. Additionally, the results were benchmarked with Monte Carlo noise simulations. Simple correlations were found between the optimized b values and target values of diffusion and kurtosis. For small target values of the two parameters, there is higher chance of having significant errors; this is caused by maximum b value limits imposed by the scanner than the mathematical bounds. The results here, cover a wide range of parameters D and K so that they could be used in many directionally averaged diffusion weighted cases such as head and neck, prostate, etc

MR imaging features of benign retroperitoneal extra-adrenal paragangliomas

The goal of this study was to retrospectively review the magnetic resonance imaging (MRI) features of retroperitoneal extra-adrenal paragangliomas and to evaluate the diagnostic capabilities of MRI. Twenty-four patients with confirmed benign retroperitoneal extra-adrenal paragangliomas who underwent preoperative MRI and surgical resection were enrolled. The patients’ clinical characteristics and MRI features were reviewed by two radiologists. There were no significant differences in the qualitative and quantitative MRI features were determined by the reviewers. High signal intensity in T2- weighted imaging (T2WI) and diffusion-weighted imaging (DWI) was observed in all tumors. In contrast T1-weighted imaging (T1WI) in the arterial phase, 83.33% of the tumors were clearly enhanced. In 87.5% of cases, a persistent enhancement pattern was observed in the venous and delayed phases, and 12.5% of tumors showed a “washout” pattern. The tumor capsule, intratumoral septum and degenerations were visualized in the tumors and may be helpful in the qualitative diagnosis of extraadrenal paragangliomas in MRI. MRI was useful in locating the position, determining the tumor ranges and visualizing the relationship between the tumors and adjacent structures. The presence of typical clinical symptoms and positivity of biochemical tests are also important factors in making an accurate preoperative diagnosis

Multiparametric MRI for the detection of local recurrence of prostate cancer in the setting of biochemical recurrence after low dose rate brachytherapy

PURPOSE Prostate multiparametric magnetic resonance imaging (mpMRI) has utility in detecting post-ra-diotherapy local recurrence. We conducted a multireader study to evaluate the diagnostic performance of mpMRI for local recurrence after low dose rate (LDR) brachytherapy. METHODS A total of 19 patients with biochemical recurrence after LDR brachytherapy underwent 3T en-dorectal coil mpMRI with T2-weighted imaging, dynamic contrast-enhanced imaging (DCE) and diffusion-weighted imaging (DWI) with pathologic confirmation. Prospective reads by an experienced prostate radiologist were compared with reads from 4 radiologists of varying experience. Readers identified suspicious lesions and rated each MRI detection parameter. MRI-detected lesions were considered true-positive with ipsilateral pathologic confirmation. Inferences for sensitivity, specificity, positive predictive value (PPV), kappa, and index of specific agreement were made with the use of bootstrap resampling. RESULTS Pathologically confirmed recurrence was found in 15 of 19 patients. True positive recurrences identified by mpMRI were frequently located in the transition zone (46.7%) and seminal vesicles (30%). On patient-based analysis, average sensitivity of mpMRI was 88% (standard error [SE], 3.5%). For highly suspicious lesions, specificity of mpMRI was 75% (SE, 16.5%). On lesion-based analysis, the average PPV was 62% (SE, 6.7%) for all lesions and 78.7% (SE, 10.3%) for highly suspicious lesions. The average PPV for lesions invading the seminal vesicles was 88.8% (n=13). The average PPV was 66.6% (SE, 5.8%) for lesions identified with T2-weighted imaging, 64.9% (SE, 7.3%) for DCE, and 70% (SE, 7.3%) for DWI. CONCLUSION This series provides evidence that mpMRI after LDR brachytherapy is feasible with a high patient-based cancer detection rate. Radiologists of varying experience demonstrated moderate agreement in detecting recurrence.This research was supported in part by the Intramural Research Program of the National Institutes of Health, National Cancer Institute, Center for Cancer Research (ZIA BC 011552). This research was also made possible in part through the NIH Medical Research Scholars Program, a public-private partnership supported jointly by the NIH and generous contributions to the Foundation for the NIH from the Doris Duke Charitable Foundation, the Howard Hughes Medical Institute, the American Association for Dental Research, the Colgate-Palmolive Company, and other private donors

Multiple publications: The main reason for the retraction of papers in computer science

This paper intends to review the reasons for the retraction over the last decade. The paper particularly aims at reviewing these reasons with reference to computer science field to assist authors in comprehending the style of writing. To do that, a total of thirty-six retracted papers found on the Web of Science within Jan 2007 through July 2017 are explored. Given the retraction notices which are based on ten common reasons, this paper classifies the two main categories, namely random and nonrandom retraction. Retraction due to the duplication of publications scored the highest proportion of all other reasons reviewed

Texture analysis-and support vector machine-assisted diffusional kurtosis imaging may allow in vivo gliomas grading and IDH-mutation status prediction:a preliminary study

We sought to investigate, whether texture analysis of diffusional kurtosis imaging (DKI) enhanced by support vector machine (SVM) analysis may provide biomarkers for gliomas staging and detection of the IDH mutation. First-order statistics and texture feature extraction were performed in 37 patients on both conventional (FLAIR) and mean diffusional kurtosis (MDK) images and recursive feature elimination (RFE) methodology based on SVM was employed to select the most discriminative diagnostic biomarkers. The first-order statistics demonstrated significantly lower MDK values in the IDH-mutant tumors. This resulted in 81.1% accuracy (sensitivity = 0.96, specificity = 0.45, AUC 0.59) for IDH mutation diagnosis. There were non-significant differences in average MDK and skewness among the different tumour grades. When texture analysis and SVM were utilized, the grading accuracy achieved by DKI biomarkers was 78.1% (sensitivity 0.77, specificity 0.79, AUC 0.79); the prediction accuracy for IDH mutation reached 83.8% (sensitivity 0.96, specificity 0.55, AUC 0.87). For the IDH mutation task, DKI outperformed significantly the FLAIR imaging. When using selected biomarkers after RFE, the prediction accuracy achieved 83.8% (sensitivity 0.92, specificity 0.64, AUC 0.88). These findings demonstrate the superiority of DKI enhanced by texture analysis and SVM, compared to conventional imaging, for gliomas staging and prediction of IDH mutational status

Siamese neural networks for continuous disease severity evaluation and change detection in medical imaging

Using medical images to evaluate disease severity and change over time is a routine and important task in clinical decision making. Grading systems are often used, but are unreliable as domain experts disagree on disease severity category thresholds. These discrete categories also do not reflect the underlying continuous spectrum of disease severity. To address these issues, we developed a convolutional Siamese neural network approach to evaluate disease severity at single time points and change between longitudinal patient visits on a continuous spectrum. We demonstrate this in two medical imaging domains: retinopathy of prematurity (ROP) in retinal photographs and osteoarthritis in knee radiographs. Our patient cohorts consist of 4861 images from 870 patients in the Imaging and Informatics in Retinopathy of Prematurity (i-ROP) cohort study and 10,012 images from 3021 patients in the Multicenter Osteoarthritis Study (MOST), both of which feature longitudinal imaging data. Multiple expert clinician raters ranked 100 retinal images and 100 knee radiographs from excluded test sets for severity of ROP and osteoarthritis, respectively. The Siamese neural network output for each image in comparison to a pool of normal reference images correlates with disease severity rank (ρ = 0.87 for ROP and ρ = 0.89 for osteoarthritis), both within and between the clinical grading categories. Thus, this output can represent the continuous spectrum of disease severity at any single time point. The difference in these outputs can be used to show change over time. Alternatively, paired images from the same patient at two time points can be directly compared using the Siamese neural network, resulting in an additional continuous measure of change between images. Importantly, our approach does not require manual localization of the pathology of interest and requires only a binary label for training (same versus different). The location of disease and site of change detected by the algorithm can be visualized using an occlusion sensitivity map-based approach. For a longitudinal binary change detection task, our Siamese neural networks achieve test set receiving operator characteristic area under the curves (AUCs) of up to 0.90 in evaluating ROP or knee osteoarthritis change, depending on the change detection strategy. The overall performance on this binary task is similar compared to a conventional convolutional deep-neural network trained for multi-class classification. Our results demonstrate that convolutional Siamese neural networks can be a powerful tool for evaluating the continuous spectrum of disease severity and change in medical imaging

Implementation of Dual-Source RF Excitation in 3 T MR-Scanners Allows for Nearly Identical ADC Values Compared to 1.5 T MR Scanners in the Abdomen

Background: To retrospectively and prospectively compare abdominal apparent diffusion coefficient (ADC) values obtained within in a 1.5 T system and 3 T systems with and without dual-source parallel RF excitation techniques. Methodology/Principal Findings: After IRB approval, diffusion-weighted (DW) images of the abdomen were obtained on three different MR systems (1.5 T, a first generation 3 T, and a second generation 3 T which incorporates dual-source parallel RF excitation) on 150 patients retrospectively and 19 volunteers (57 examinations total) prospectively. Seven regions of interest (ROI) were throughout the abdomen were selected to measure the ADC. Statistical analysis included independent two-sided t-tests, Mann-Whitney U tests and correlation analysis. In the DW images of the abdomen, mean ADC values were nearly identical with nonsignificant differences when comparing the 1.5 T and second generation 3 T systems in all seven anatomical regions in the patient population and six of the seven in the volunteer population (p.0.05 in all distributions). The strength of correlation measured in the volunteer population between the two scanners in the kidneys ranged from r = 0.64–0.88 and in the remaining regions (besides the spleen), r.0.85. In the patient population the first generation 3 T scanner had different mean ADC values with significant differences (p,0.05) compared to the other two scanners in each of the seven distributions. In the volunteer population, the kidneys shared similar ADC mean values in comparison to the other two scanners with nonsignificant differences