Publications by authors named "Mahnaz Nabil"

5 Publications

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Liquid Calibration Phantoms in Ultra-Low-Dose QCT for the Assessment of Bone Mineral Density.

J Clin Densitom 2020 Jan - Mar;23(1):108-116. Epub 2019 Feb 22.

Medical Physics Department, School of Medicine, Iran University of Medical Sciences, Tehran, Iran. Electronic address:

Introduction: Cortical bone is affected by metabolic diseases. Some studies have shown that lower cortical bone mineral density (BMD) is related to increases in fracture risk which could be diagnosed by quantitative computed tomography (QCT). Nowadays, hybrid iterative reconstruction-based (HIR) computed tomography (CT) could be helpful to quantify the peripheral bone tissue. A key focus of this paper is to evaluate liquid calibration phantoms for BMD quantification in the tibia and under hybrid iterative reconstruction-based-CT with the different hydrogen dipotassium phosphate (KHPO) concentrations phantoms.

Methodology: Four ranges of concentrations of KHPO were made and tested with 2 exposure settings. Accuracy of the phantoms with ash gravimetry and intermediate KHPO concentration as hypothetical patients were evaluated. The correlations and mean differences between measured equivalent QCT BMD and ash density as a gold standard were calculated. Relative percentage error (RPE) in CT numbers of each concentration over a 6-mo period was reported.

Results: The correlation values (R was close to 1.0), suggested that the precision of QCT-BMD measurements using standard and ultra-low dose settings were similar for all phantoms. The mean differences between QCT-BMD and the ash density for low concentrations (about 93 mg/cm) were lower than high concentration phantoms with 135 and 234 mg/cm biases. In regard to accuracy test for hypothetical patient, RPE was up to 16.1% for the low concentration (LC) phantom for the case of high mineral content. However, the lowest RPE (0.4 to 1.8%) was obtained for the high concentration (HC) phantom, particularly for the high mineral content case. In addition, over 6 months, the KHPO concentrations increased 25% for 50 mg/cm solution and 0.7 % for 1300 mg/cm solution in phantoms.

Conclusion: The excellent linear correlations between the QCT equivalent density and the ash density gold standard indicate that QCT can be used with submilisivert radiation dose. We conclude that using liquid calibration phantoms with a range of mineral content similar to that being measured will minimize bias. Finally, we suggest performing BMD measurements with ultra-low dose scan concurrent with iterative-based reconstruction to reduce radiation exposure.
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February 2019

Characterization of active and infiltrative tumorous subregions from normal tissue in brain gliomas using multiparametric MRI.

J Magn Reson Imaging 2018 10 7;48(4):938-950. Epub 2018 Feb 7.

Quantitative MR Imaging and Spectroscopy Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran.

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 T w, T w, T -FLAIR, T -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, T _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 J. Magn. Reson. Imaging 2018;48:938-950.
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October 2018

ADC-derived spatial features can accurately classify adnexal lesions.

J Magn Reson Imaging 2018 04 13;47(4):1061-1071. Epub 2017 Sep 13.

Quantitative MR Imaging and Spectroscopy Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Iran.

Background: The role of quantitative apparent diffusion coefficient (ADC) maps in differentiating adnexal masses is unresolved.

Purpose/hypothesis: To propose an objective diagnostic method devised based on spatial features for predicting benignity/malignancy of adnexal masses in ADC maps.

Study Type: Prospective.

Population: In all, 70 women with sonographically indeterminate and histopathologically confirmed adnexal masses (38 benign, 3 borderline, and 29 malignant) were considered for this study.

Field Strength/sequence: Conventional and diffusion-weighted magnetic resonance (MR) images (b-values = 50, 400, 1000 s/mm ) were acquired on a 3T scanner.

Assessment: For each patient, two radiologists in consensus manually delineated lesion borders in whole ADC map volumes, which were consequently analyzed using spatial models (first-order histogram [FOH], gray-level co-occurrence matrix [GLCM], run-length matrix [RLM], and Gabor filters). Two independent radiologists were asked to identify the attributed (benign/malignant) classes of adnexal masses based on morphological features on conventional MRI.

Statistical Tests: Leave-one-out cross-validated feature selection followed by cross-validated classification were applied to the feature space to choose the spatial models that best discriminate benign from malignant adnexal lesions. Two schemes of feature selection/classification were evaluated: 1) including all benign and malignant masses, and 2) scheme 1 after excluding endometrioma, hemorrhagic cysts, and teratoma (14 benign, 29 malignant masses). The constructed feature subspaces for benign/malignant lesion differentiation were tested for classification of benign/borderline/malignant and also borderline/malignant adnexal lesions.

Results: The selected feature subspace consisting of RLM features differentiated benign from malignant adnexal masses with a classification accuracy of ∼92%. The same model discriminated benign, borderline, and malignant lesions with 87% and borderline from malignant with 100% accuracy. Qualitative assessment of the radiologists based on conventional MRI features reached an accuracy of 80%.

Data Conclusion: The spatial quantification methodology proposed in this study, which works based on cellular distributions within ADC maps of adnexal masses, may provide a helpful computer-aided strategy for objective characterization of adnexal masses.

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

A one-step biomarker quantification methodology for DCE-MRI of adnexal masses: Capturing kinetic pattern from early to late enhancement.

Magn Reson Med 2018 02 7;79(2):1165-1171. Epub 2017 May 7.

Quantitative MR Imaging and Spectroscopy Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran.

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 (P through P ) 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 P , representing the enhancement amplitude, is significantly higher, and P , indicating the terminal phase, is generally negative in malignant lesions (P < 0.001). P (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 P and P 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, P has considerably high diagnostic performance and terminal slope (P ), 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.
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February 2018

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

J Magn Reson Imaging 2017 02 1;45(2):418-427. Epub 2016 Jul 1.

Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran.

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 (SI ), and the initial area under the curve (IAUC ) 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.
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February 2017