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1.
Br J Neurosurg ; 37(5): 1031-1039, 2023 Oct.
Article in English | MEDLINE | ID: mdl-33263433

ABSTRACT

OBJECTIVE: To determine the border of glial tumors by diffusion weighted imaging (DWI), apparent diffusion co-efficient (ADC), magnetic resonance spectroscopy (MRS) and perfusion brain MRI. PATIENTS AND METHODS: Ten patients with brain gliomas were enrolled [mean age: 35.3 ± 13.2, range: 20-62]. Conventional MRI was performed for all patients. Besides, tumor mapping based on Choline (Cho)/Creatine (Cr) color map in MRS, perfusion and diffusion color maps, were gathered. Different tumoral and peritumoral regions [normal tissue, reactive edema, infiltrative edema, and tumor core] were defined. MRI criteria were evaluated in areas targeted for biopsy and histopathologic evaluation was determined. RESULTS: Tumor cell positive samples [one necrosis, 26 infiltrative and nine tumor cores] composed 36 (75%) of the 48 samples. Seven (19.4%) of the positive samples were interpreted as not tumor on MRI. Five were identified as reactive edema and two as normal tissue] [kappa: .67, p-value < .001]. Mean of ADC, median of N-acetylaspartate (NAA) and NAA/Cho were statistically different between positive and negative samples (p = .02 and p < .001, respectively). Mean ADC and median Cho/NAA were statistically different in missed tumor containing tissue presented as reactive edema compared to normal and correctly diagnosed reactive edema samples together (p-values < .05). CONCLUSIONS: Multimodal MRI could define infiltrated borders of brain gliomas.


Subject(s)
Brain Neoplasms , Glioma , Humans , Young Adult , Adult , Middle Aged , Brain Neoplasms/complications , Brain Neoplasms/diagnostic imaging , Magnetic Resonance Imaging/methods , Glioma/complications , Glioma/diagnostic imaging , Diffusion Magnetic Resonance Imaging/methods , Edema/diagnostic imaging , Edema/pathology , Brain/diagnostic imaging , Brain/pathology
2.
Asian Pac J Cancer Prev ; 20(6): 1603-1611, 2019 06 01.
Article in English | MEDLINE | ID: mdl-31244278

ABSTRACT

Objective: The present study aimed to compare the qualitative (time intensity curve analysis), the semi-quantitative and the quantitative multiphase 3T dynamic contrast-enhanced (DCE) MRI parameters as predictors of malignancy in adnexal masses. Materials and Methods: In this prospective study, women with an adnexal mass who were scheduled for surgical resection or were followed for more than one year period to confirm the benignity of their lesions, underwent multiphase 3T DCE-MRI. The qualitative (time intensity curve), semi-quantitative (SImax, SIrel, WIR) and quantitative (Ktrans, Kep, Vb) analyses were performed on DCE-MRI sequences and their predictive values were compared. Results: A total of 17 benign and 14 malignant lesions were included. According to the qualitative analysis, none of the lesions with Type I time intensity curves (TIC) were malignant and none of the masses with Type III TICs were benign. The accuracy of the quantitative parameters in detection of malignancy was found to be higher than that of semi-quantitative variables, particularly when calculated for a small ROI within the high signal area of the mass (sROI) rather than the largest ROI including the whole mass (lROI), and when inter-MRI variations were omitted using ratios. The Kep(tumor)/Kep(myometrium) ratio measured from sROI was the best parameter for differentiating a malignant lesion with a sensitivity of 100% and a specificity of 92.3%. Conclusion: We concluded that a Type I TIC confirms a benign lesion, and a type III TIC confirms the malignancy and further evaluation is not recommended for these lesions. So complementary quantitative analysis is only recommended for adnexal masses with type II TICs.


Subject(s)
Adnexal Diseases/classification , Adnexal Diseases/diagnosis , Contrast Media/metabolism , Image Interpretation, Computer-Assisted/methods , Magnetic Resonance Imaging/methods , Adnexal Diseases/metabolism , Diagnosis, Differential , Female , Follow-Up Studies , Humans , Prognosis , Prospective Studies , ROC Curve
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