scholarly journals Differential Diagnosis of Uterine Leiomyoma and Uterine Sarcoma Using Magnetic Resonance Images: A Literature Review

Healthcare ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 158 ◽  
Author(s):  
Ayako Suzuki ◽  
Masato Aoki ◽  
Chiho Miyagawa ◽  
Kosuke Murakami ◽  
Hisamitsu Takaya ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Diffusion Weighted Imaging ◽  
Uterine Leiomyoma ◽  
Essential Role ◽  
Uterine Sarcoma ◽  
Magnetic Resonance Images ◽  
Mr Images ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced ◽  
Contrast Enhanced Mri

MRI plays an essential role in patients before treatment for uterine mesenchymal malignancies. Although MRI includes methods such as diffusion-weighted imaging and dynamic contrast-enhanced MRI, the differentiation between uterine myoma and sarcoma always becomes problematic. The present paper discusses important findings to ensure that sarcomas are not overlooked in magnetic resonance (MR) images, and we describe the update in the differentiation between uterine leiomyoma and sarcoma with recent reports.

scholarly journals Detection and Diagnosis of Breast Cancer Using Artificial Intelligence Based Assessment of Maximum Intensity Projection Dynamic Contrast-Enhanced Magnetic Resonance Images

Diagnostics ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 330
Author(s):  
Mio Adachi ◽  
Tomoyuki Fujioka ◽  
Mio Mori ◽  
Kazunori Kubota ◽  
Yuka Kikuchi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Magnetic Resonance ◽  
Diagnostic Performance ◽  
Maximum Intensity Projection ◽  
Breast Mri ◽  
Magnetic Resonance Images ◽  
Maximum Intensity ◽  
Validation Data ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced

We aimed to evaluate an artificial intelligence (AI) system that can detect and diagnose lesions of maximum intensity projection (MIP) in dynamic contrast-enhanced (DCE) breast magnetic resonance imaging (MRI). We retrospectively gathered MIPs of DCE breast MRI for training and validation data from 30 and 7 normal individuals, 49 and 20 benign cases, and 135 and 45 malignant cases, respectively. Breast lesions were indicated with a bounding box and labeled as benign or malignant by a radiologist, while the AI system was trained to detect and calculate possibilities of malignancy using RetinaNet. The AI system was analyzed using test sets of 13 normal, 20 benign, and 52 malignant cases. Four human readers also scored these test data with and without the assistance of the AI system for the possibility of a malignancy in each breast. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were 0.926, 0.828, and 0.925 for the AI system; 0.847, 0.841, and 0.884 for human readers without AI; and 0.889, 0.823, and 0.899 for human readers with AI using a cutoff value of 2%, respectively. The AI system showed better diagnostic performance compared to the human readers (p = 0.002), and because of the increased performance of human readers with the assistance of the AI system, the AUC of human readers was significantly higher with than without the AI system (p = 0.039). Our AI system showed a high performance ability in detecting and diagnosing lesions in MIPs of DCE breast MRI and increased the diagnostic performance of human readers.

Multiparametric Approach with Dynamic Contrast-Enhanced Mri, Diffusion-Weighted Imaging and Mrs of The Breast in Assessment of Suspicious Breast Lesions

2020 ◽  
Vol 106 (1_suppl) ◽  
pp. 16-16
Author(s):  
SM Abd Elsalam ◽  
AH Said ◽  
MH Sarah
Keyword(s):  
Magnetic Resonance ◽  
Diffusion Weighted Imaging ◽  
Kinetic Curve ◽  
Resonance Spectroscopy ◽  
Breast Lesions ◽  
Adc Value ◽  
Dynamic Contrast Enhanced ◽  
Cancer Breast ◽  
Diffusion Weighted ◽  
Contrast Enhanced

Introduction: Breast cancer is the most invasive malignant tumour in females worldwide and is the second leading cause of cancer death in females after lung cancer. In Egypt, cancer breast is the first common site of tumours among females (32%) and the second common tumour site in both sexes. The aim of this study was to evaluate and compare the diagnostic performance of quantitative parameters derived from diffusion-weighted imaging (DWI), dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and magnetic resonance spectroscopy (MRS) with ultrasound +/- conventional mammography in differentiating suspicious breast masses (BI-RADS III and IV) and to suggest the most accurate imaging combination for early diagnosis and treatment of cancer breast. Materials and Methods: The study included 40 patients with suspicious breast lesions detected by clinical examination, ultrasound+/- mammography . Dynamic MRI study was performed using 1.5T MRI. Lesions were analyzed regarding their morphology, kinetic curve pattern, ADC value and tCho peak measurement. The results of each MRI parameter were correlated to histo-pathology. Results: In this study sensitivity of sono-mammography was 70% and its specificity was 66.6%. According to using MRI sensitivity was 90% and specificity was 80% by using morphological and curve patterns. As regarding MRS sensitivity was 50% and specificity was 86.7%. Regarding sensitivity of ADC was 90%while its specificity was 93%. The cut off ADC value was 0.95 x 10−3 mm2/s. By using MRI with additive modalities (ADC and MRS) sensitivity was 100% and specificity was 93%. Conclusion: In comparison with MRI, sonomammography alone lack both sensitivity and specificity in detection of breast lesions in suspicious cases. MRI with additive modalities (ADC and MRS) is the best imaging modality in detection of malignant cases and exclusion of benign cases.

Diagnostic performance of diffusion-weighted imaging combined with dynamic contrast-enhanced magnetic resonance imaging for prostate cancer: a systematic review and meta-analysis

2020 ◽  
pp. 028418512095626
Author(s):  
Lu Yang ◽  
Yuchuan Tan ◽  
Hanli Dan ◽  
Lin Hu ◽  
Jiuquan Zhang
Keyword(s):  
Magnetic Resonance Imaging ◽  
Prostate Cancer ◽  
Magnetic Resonance ◽  
Likelihood Ratio ◽  
Diffusion Weighted Imaging ◽  
Diagnostic Performance ◽  
Resonance Imaging ◽  
Dce Mri ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced

Background The diagnostic performance of diffusion-weighted imaging (DWI) combined with dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) for the detection of prostate cancer (PCa) has not been studied systematically to date. Purpose To investigate the value of DWI combined with DCE-MRI quantitative analysis in the diagnosis of PCa. Material and Methods A systematic search was conducted through PubMed, MEDLINE, the Cochrane Library, and EMBASE databases without any restriction to language up to 10 December 2019. Studies that used a combination of DWI and DCE-MRI for diagnosing PCa were included. Results Nine studies with 778 participants were included. The combination of DWI and DCE-MRI provide accurate performance in diagnosing PCa with pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratios of 0.79 (95% confidence interval [CI] = 0.76–0.81), 0.85 (95% CI = 0.83–0.86), 6.58 (95% CI = 3.93–11.00), 0.24 (95% CI = 0.17–0.34), and 36.43 (95% CI = 14.41–92.12), respectively. The pooled area under the summary receiver operating characteristic curve was 0.9268. Moreover, 1.5-T MR scanners demonstrated a slightly better performance than 3.0-T scanners. Conclusion Combined DCE-MRI and DWI could demonstrate a highly accurate area under the curve, sensitivity, and specificity for detecting PCa. More studies with large sample sizes are warranted to confirm these results.

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