Radiomic Analysis of Multi-parametric MR Images (MRI) for Classification of Parotid Tumors.

Background: Characterization of parotid tumors before surgery using multi-parametric magnetic resonance imaging (MRI) scans can support clinical decision making about the best-suited therapeutic strategy for each patient. Objective: This study aims to differentiate benign from malignant parotid tumors through radiomics analysis of multi-parametric MR images, incorporating T2-w images with ADC-map and parametric maps generated from Dynamic Contrast Enhanced MRI (DCE-MRI). Material and Methods: MRI scans of 31 patients with histopathologically-confirmed parotid gland tumors (23 benign, 8 malignant) were included in this retrospective study. For DCE-MRI, semi-quantitative analysis, Tofts pharmacokinetic (PK) modeling, and five-parameter sigmoid modeling were performed and parametric maps were generated. For each patient, borders of the tumors were delineated on whole tumor slices of T2-w image, ADC-map, and the late-enhancement dynamic series of DCE-MRI, creating regions-of-interest (ROIs). Radiomic analysis was performed for the specified ROIs. Results: Among the DCE-MRI-derived parametric maps, wash-in rate (WIR) and PK-derived Ktrans parameters surpassed the accuracy of other parameters based on support vector machine (SVM) classifier. Radiomics analysis of ADC-map outperformed the T2-w and DCE-MRI techniques using the simpler classifier, suggestive of its inherently high sensitivity and specificity. Radiomics analysis of the combination of T2-w image, ADC-map, and DCE-MRI parametric maps resulted in accuracy of 100% with both classifiers with fewer numbers of selected texture features than individual images. Conclusion: In conclusion, radiomics analysis is a reliable quantitative approach for discrimination of parotid tumors and can be employed as a computer-aided approach for pre-operative diagnosis and treatment planning of the patients.

Effect of Post-mortem Interval and Perfusion on the Biophysical Properties of ex vivo Liver Tissue Investigated Longitudinally by MRE and DWI.

Structural changes of soft tissues on the cellular level can be characterized by histopathology, but not longitudinally in the same tissue. Alterations of cellular structures and tissue matrix are associated with changes in biophysical properties which can be monitored longitudinally by quantitative diffusion-weighted imaging (DWI) and magnetic resonance elastography (MRE). In this work, DWI and MRE examinations were performed in a 0.5-Tesla compact scanner to investigate longitudinal changes in water diffusivity, stiffness and viscosity of ex-vivo rat livers for up to 20 h post-mortem (pm). The effect of blood on biophysical parameters was examined in 13 non-perfused livers (containing blood, NPLs) and 14 perfused livers (blood washed out, PLs). Changes in cell shape, cell packing and cell wall integrity were characterized histologically. In all acquisitions, NPLs presented with higher shear-wave speed (c), higher shear-wave penetration rate (a) and smaller apparent-diffusion-coefficients (ADCs) than PL. Time-resolved analysis revealed three distinct phases: (i) an initial phase (up to 2 h pm) with markedly increased c and a and reduced ADCs; (ii) an extended phase with relatively stable values; and (iii) a degradation phase characterized by significant increases in a (10 h pm in NPLs and PLs) and ADCs (10 h pm in NPLs, 13 h pm in PLs). Histology revealed changes in cell shape and packing along with decreased cell wall integrity, indicating tissue degradation in NPLs and PLs 10 h pm. Taken together, our results demonstrate that the biophysical properties of fresh liver tissue rapidly change within 2 h pm, which seems to be an effect of both cytotoxic edema and vascular blood content. Several hours later, disruption of cell walls resulted in higher water diffusivity and wave penetration. These results reveal the individual contributions of vascular components and cellular integrity to liver elastography and provide a biophysical, imaging-based fingerprint of liver tissue degradation.

Prognostic imaging variables for recurrent laryngeal and hypopharyngeal carcinoma treated with primary chemoradiotherapy: A systematic review and meta-analysis.

BACKGROUND: In this systematic review, we aim to identify prognostic imaging variables of recurrent laryngeal or hypopharyngeal carcinoma after chemoradiotherapy. METHODS: A systematic search was performed in PubMed and EMBASE (1990-2020). The crude data and effect estimates were extracted for each imaging variable. The level of evidence of each variable was assessed and pooled risk ratios (RRs) were calculated. RESULTS: Twenty-two articles were included in this review, 17 on computed tomography (CT) and 5 on magnetic resonance imaging (MRI) variables. We found strong evidence for the prognostic value of tumor volume at various cut-off points (pooled RRs ranging from 2.09 to 3.03). Anterior commissure involvement (pooled RR 2.19), posterior commissure involvement (pooled RR 2.44), subglottic extension (pooled RR 2.25), and arytenoid cartilage extension (pooled RR 2.10) were also strong prognostic factors. CONCLUSION: Pretreatment tumor volume and involvement of several subsites are prognostic factors for recurrent laryngeal or hypopharyngeal carcinoma after chemoradiotherapy.

Semiquantitative dynamic contrast-enhanced MRI for accurate classification of complex adnexal masses.

PURPOSE: To identify the best dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) descriptive parameters in predicting malignancy of complex ovarian masses, and develop an optimal decision tree for accurate classification of benign and malignant complex ovarian masses. MATERIALS AND METHODS: Preoperative DCE-MR images of 55 sonographically indeterminate ovarian masses (27 benign and 28 malignant) were analyzed prospectively. Four descriptive parameters of the dynamic curve, namely, time-to-peak (TTP), wash-in-rate (WIR), relative signal intensity (SIrel ), and the initial area under the curve (IAUC60 ) were calculated on the normalized curves of specified regions-of-interest (ROIs). A two-tailed Student's t-test and two automated classifiers, linear discriminant analysis (LDA) and support vector machines (SVMs), were used to compare the performance of the mentioned parameters individually and in combination with each other. RESULTS: TTP (P = 6.15E-8) and WIR (P = 5.65E-5) parameters induced the highest sensitivity (89% for LDA, and 97% for SVM) and specificity (93% for LDA, and 100% for SVM), respectively. Regarding the high sensitivity of TTP and high specificity of WIR and through their combination, an accurate and simple decision-tree classifier was designed using the line equation obtained by LDA classification model. The proposed classifier achieved an accuracy of 89% and area under the ROC curve of 93%. CONCLUSION: In this study an accurate decision-tree classifier based on a combination of TTP and WIR parameters was proposed, which provides a clinically flexible framework to aid radiologists/clinicians to reach a conclusive preoperative diagnosis and patient-specific therapy plan for distinguishing malignant from benign complex ovarian masses. LEVEL OF EVIDENCE: 2 J. Magn. Reson. Imaging 2017;45:418-427.