Sentence examples for classification mr from inspiring English sources

The correlation between preoperative MRI staging using 1988 FIGO classification and final histology was moderate (Kappa = 0.24) Kappa test = 0.45 (moderate) Using 2009 FIGO classification, MR imaging detected an endometrial cancer of stage IA, IB, II, or IIIA disease in 52 cases (63.5%), 27 cases (32.9%), 2 cases (2.4%), and 1 case (1.2%), respectively.

Kappa test = 0.24 (moderate) Using 1988 FIGO classification, MR imaging detected an endometrial cancer of stage IA, IB, IC, IIA, IIB or IIIA disease in 14 cases (17.3%), 35 cases (43.2%), 27 cases (33.3%), 3 cases (3.7%), 2 cases (2.5%) and 1 case (1.1%), respectively.

The different classifications, Mr Shayler pointed out, referred not to the protection of "national security" but to "the interests of the nation".

Table 11 Classification of MR for irrigation (Szaboles and Darab 1964) MR Suitability Sample Numbers Per cent < 50 Suitable – – > 50 Unsuitable 1 30 1 30

"It appears as if when a defendant appeals the classification," Mr. Sutter said, "the convicted defendant goes into limbo until the appeal is resolved.

"This is the cornerstone of the entire apparatus of classification, and if you cast doubt on this, you call into question many other issues of classification," Mr. Aftergood said Wednesday in explanation of the government's opposition to his efforts.

Mr. Stickles is from Glen Rock, in the northeast part of the state, slightly out of reach of the ring of defiant hamlets of Mr. Leo's classification; Mr. Stickles's personal catharsis is far from Mr. Leo's political defiance.

It focuses on integrating and improving an existing algorithm by proposing a coding framework based on MapReduce and the decision-tree classification method MR-DIDC of the SPRINT algorithm, which takes advantage of the outstanding features of MapReduce to make the approach more suitable for data-intensive environments.

To assess whether the BI-RADS classification in MR-Mammography (MRM) can distinguish between benign and malignant lesions.

We computed the proportion of classifications in the MR that received concordant classifications by TMA and computed Kappa statistics for each of the four ER/PR subtypes (similar to the analysis in the GEM dataset).

By comparing this classification with the MR data, it is therefore possible to determine the most likely tissue class corresponding to each UTE echo pair.