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

Adoption of Enterprise Application Software and Firm Performance

Due to the rapidly changing business and IT environments, firm-level adoption of IT shifted from in-house development to purchasing EA software. This paper analyzes the effects of EA (Enterprise Application) software – ERP, CRM, SCM, Groupware, KM, EAI – on SMEs’ productivity. The distinct feature of this paper is that I use a formal econometric approach with combined data of SMEs’ accounting and IT usage aspects, while case studies have been mostly used in the previous works. The empirical results show that Groupware and SCM significantly raise the SMEs’ productivity, and the manufacturing sector has stronger effects than the service sector. From these results, the following implications are derived. First, the adoption rate and the real benefits of EA software are not closely related domestically. Second, in SMEs, EA software facilitating the inter-firm relationship is more effective than EA software focusing on the internal efficiency. Third, easy-to-understand, and relatively long-experienced enterprise applications are more effective than hard-to-understand and brand-new applications. Finally, the government IT policy on SMEs should focus on the process coordination and standardization of the manufacturing sector with upstream and downstream firms. Copyright Springer 2006SMEs, Enterprise Application Software, ERP, CRM, productivity, D21, D24,

Phantom-based quality assurance for multicenter quantitative MRI in locally advanced cervical cancer

Background and purpose: A wide variation of MRI systems is a challenge in multicenter imaging biomarker studies as it adds variation in quantitative MRI values. The aim of this study was to design and test a quality assurance (QA) framework based on phantom measurements, for the quantitative MRI protocols of a multicenter imaging biomarker trial of locally advanced cervical cancer. Materials and methods: Fifteen institutes participated (five 1.5 T and ten 3 T scanners). Each institute optimized protocols for T2, diffusion-weighted imaging, T1, and dynamic contrast-enhanced (DCE–)MRI according to system possibilities, institutional preferences and study-specific constraints. Calibration phantoms with known values were used for validation. Benchmark protocols, similar on all systems, were used to investigate whether differences resulted from variations in institutional protocols or from system variations. Bias, repeatability (%RC), and reproducibility (%RDC) were determined. Ratios were used for T2 and T1 values. Results: The institutional protocols showed a range in bias of 0.88–0.98 for T2 (median %RC = 1%; %RDC = 12%), −0.007 to 0.029 × 10−3 mm2/s for the apparent diffusion coefficient (median %RC = 3%; %RDC = 18%), and 0.39–1.29 for T1 (median %RC = 1%; %RDC = 33%). For DCE a nonlinear vendor-specific relation was observed between measured and true concentrations with magnitude data, whereas the relation was linear when phase data was used. Conclusion: We designed a QA framework for quantitative MRI protocols and demonstrated for a multicenter trial for cervical cancer that measurement of consistent T2 and apparent diffusion coefficient values is feasible despite protocol differences. For DCE–MRI and T1 mapping with the variable flip angle method, this was more challenging