Background Targeted localized biopsies and treatments for diffuse gliomas rely on accurate identification of tissue subregions, for which current MRI techniques lack specificity. Purpose To explore the complementary and competitive roles of a variety of conventional and quantitative MRI methods for distinguishing subregions of brain gliomas. Study Type Prospective. Population Fifty-one tissue specimens were collected using image-guided localized biopsy surgery from 10 patients with newly diagnosed gliomas. Field Strength/Sequence Conventional and quantitative MR images consisting of pre- and postcontrast T1w, T2w, T2-FLAIR, T2-relaxometry, DWI, DTI, IVIM, and DSC-MRI were acquired preoperatively at 3T. Assessment Biopsy specimens were histopathologically attributed to glioma tissue subregion categories of active tumor (AT), infiltrative edema (IE), and normal tissue (NT) subregions. For each tissue sample, a feature vector comprising 15 MRI-based parameters was derived from preoperative images and assessed by a machine learning algorithm to determine the best multiparametric feature combination for characterizing the tissue subregions. Statistical Tests For discrimination of AT, IE, and NT subregions, a one-way analysis of variance (ANOVA) test and for pairwise tissue subregion differentiation, Tukey honest significant difference, and Games-Howell tests were applied (P < 0.05). Cross-validated feature selection and classification methods were implemented for identification of accurate multiparametric MRI parameter combination. Results After exclusion of 17 tissue specimens, 34 samples (AT = 6, IE = 20, and NT = 8) were considered for analysis. Highest accuracies and statistically significant differences for discrimination of IE from NT and AT from NT were observed for diffusion-based parameters (AUCs >90%), and the perfusion-derived parameter as the most accurate feature in distinguishing IE from AT. A combination of “CBV, MD, T2_ISO, FLAIR” parameters showed high diagnostic performance for identification of the three subregions (AUC ~90%). Data Conclusion Integration of a few quantitative along with conventional MRI parameters may provide a potential multiparametric imaging biomarker for predicting the histopathologically proven glioma tissue subregions. Level of Evidence 2 Technical Efficacy Stage 3

2 J. Magn. Reson. Imaging 2017;45:418-427.

Purpose To develop a one‐step quantification approach that accounts for joint preprocessing and quantification of whole‐range kinetics (early and late‐phase washout) of dynamic contrast‐enhanced (DCE) MRI of indeterminate adnexal masses. Methods Preoperative DCE‐MRI of 43 (24 benign, 19 malignant) sonographically indeterminate adnexal masses were analyzed prospectively. A five‐parameter sigmoid function was implemented to model the enhancement curves calculated within regions of interest. Diagnostic performance of five‐parameter sigmoid model parameters (P1 through P5) was compared with pharmacokinetic (PK) modeling, semiquantitative analysis, and three‐parameter sigmoid. Statistical analysis was performed using two‐tailed student's t‐test. Results The results revealed that P2, representing the enhancement amplitude, is significantly higher, and P5, indicating the terminal phase, is generally negative in malignant lesions (P < 0.001). P2 (sensitivity = 79%, specificity = 87.5%, accuracy = 84%, area under the receiver operating characteristic curve = 91%) outperforms classification performances of PK and semiquantitative parameters. A combination of P2 and P5 shows comparable performance (sensitivity = 79%, specificity = 87.5%, accuracy = 84%, area under the receiver operating characteristic curve = 92%) to that of the combination of PK parameters, whereas the five‐parameter sigmoid function maintains fewer assumptions than PK. Conclusions The presented one‐step quantification approach is helpful for accurate discrimination of benign from malignant indeterminate adnexal masses. Accordingly, P2 has considerably high diagnostic performance and terminal slope (P5), as a previously overlooked feature, contributes more than widely accepted early‐enhancement kinetic features. Magn Reson Med 79:1165–1171, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1061-1071.

No abstract