Enhancement parameters on dynamic contrast enhanced breast MRI: do they correlate with prognostic factors and subtypes of breast cancers?

2015 ◽  
Vol 33 (1) ◽  
pp. 72-80 ◽  
Author(s):  
Ji Youn Kim ◽  
Sung Hun Kim ◽  
Yun Ju Kim ◽  
Bong Joo Kang ◽  
Yeong Yi An ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Breast Mri ◽  
Breast Cancers ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced

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.

scholarly journals Dynamic Contrast-Enhanced Imaging as a Prognostic Tool in Early Diagnosis of Prostate Cancer: Correlation with PSA and Clinical Stage

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Xingchen Wu ◽  
Petri Reinikainen ◽  
Mika Kapanen ◽  
Tuula Vierikko ◽  
Pertti Ryymin ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Prognostic Factors ◽  
Early Diagnosis ◽  
Early Stage ◽  
Clinical Stage ◽  
Specific Antigen ◽  
Transfer Constant ◽  
Dce Mri ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced

Background and Purpose. Although several methods have been developed to predict the outcome of patients with prostate cancer, early diagnosis of individual patient remains challenging. The aim of the present study was to correlate tumor perfusion parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and clinical prognostic factors and further to explore the diagnostic value of DCE-MRI parameters in early stage prostate cancer. Patients and Methods. Sixty-two newly diagnosed patients with histologically proven prostate adenocarcinoma were enrolled in our prospective study. Transrectal ultrasound-guided biopsy (12 cores, 6 on each lobe) was performed in each patient. Pathology was reviewed and graded according to the Gleason system. DCE-MRI was performed and analyzed using a two-compartmental model; quantitative parameters including volume transfer constant (Ktrans), reflux constant (Kep), and initial area under curve (iAUC) were calculated from the tumors and correlated with prostate-specific antigen (PSA), Gleason score, and clinical stage. Results. Ktrans (0.11 ± 0.02 min−1 versus 0.16 ± 0.06 min−1; p<0.05), Kep (0.38 ± 0.08 min−1 versus 0.60 ± 0.23 min−1; p<0.01), and iAUC (14.33 ± 2.66 mmoL/L/min versus 17.40 ± 5.97 mmoL/L/min; p<0.05) were all lower in the clinical stage T1c tumors (tumor number, n=11) than that of tumors in clinical stage T2 (n=58). Serum PSA correlated with both tumor Ktrans (r=0.304, p<0.05) and iAUC (r=0.258, p<0.05). Conclusions. Our study has confirmed that DCE-MRI is a promising biomarker that reflects the microcirculation of prostate cancer. DCE-MRI in combination with clinical prognostic factors may provide an effective new tool for the basis of early diagnosis and treatment decisions.

Variable spatiotemporal resolution three-dimensional dixon sequence for rapid dynamic contrast-enhanced breast MRI

2014 ◽  
Vol 40 (6) ◽  
pp. spcone-spcone
Author(s):  
Manojkumar Saranathan ◽  
Dan W. Rettmann ◽  
Brian A. Hargreaves ◽  
Jafi A. Lipson ◽  
Bruce L. Daniel
Keyword(s):  
Three Dimensional ◽  
Breast Mri ◽  
Spatiotemporal Resolution ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced

Characterization of Metastatic Sternal Lesions on Dynamic Contrast-Enhanced Breast MRI in Women with Invasive Breast Cancer

2019 ◽  
Vol 26 (10) ◽  
pp. 1358-1362
Author(s):  
Amie Y. Lee ◽  
Ryan Navarro ◽  
Lindsay P. Busby ◽  
Heather I. Greenwood ◽  
Matthew D. Bucknor ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Invasive Breast Cancer ◽  
Breast Mri ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced

Association between breast cancer’s prognostic factors and 3D textural features of non-contrast-enhanced T1-weighted breast MRI

2021 ◽  
Author(s):  
Anni Lepola ◽  
Otso Arponen ◽  
Hidemi Okuma ◽  
Kirsi Holli-Helenius ◽  
Heikki Junkkari ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Texture Analysis ◽  
Proportional Hazards Model ◽  
Histological Grade ◽  
Prognostic Index ◽  
Disease Free Survival ◽  
Breast Mri ◽  
Cox Proportional Hazards Model ◽  
Tumour Grade ◽  
Contrast Enhanced

Objectives: The aim of this exploratory study was to evaluate whether three-dimensional texture analysis (3D-TA) features of non-contrast-enhanced T1-weighted MRI associate with traditional prognostic factors and disease-free survival (DFS) of breast cancer. Methods: 3D-T1-weighted images from 78 patients with 81 malignant histopathologically verified breast lesions were retrospectively analysed using standard-size volumes of interest. Grey-level co-occurrence matrix (GLCM) based features were selected for statistical analysis. In statistics the Mann–Whitney U and the Kruskal–Wallis tests, the Cox proportional hazards model and the Kaplan-Meier method were used. Results: Tumours with higher histological grade were significantly associated with higher contrast (1voxel: p = 0.033, two voxels: p = 0.036). All the entropy parameters showed significant correlation with tumour grade (p = 0.015–0.050) but there were no statistically significant associations between other TA parameters and tumour grade. The Nottingham Prognostic Index (NPI) was correlated with contrast and sum entropy parameters. A higher sum variance TA parameter was a significant predictor of shorter DFS. Conclusion: Texture parameters, assessed by 3D-TA from non-enhanced T1-weighted images, indicate tumour heterogeneity but have limited independent prognostic value. However, they are associated with tumour grade, NPI, and DFS. These parameters could be used as an adjunct to contrast-enhanced TA parameters. Advances in knowledge: 3D texture analysis of non-contrast enhanced T1-weighted breast MRI associates with tumour grade, NPI, and DFS. The use of non-contrast 3D TA parameters in adjunct with contrast-enhanced 3D TA parameters warrants further research.

Dynamic contrast-enhanced and ultra-high-resolution breast MRI at 7.0 Tesla

2013 ◽  
Vol 23 (11) ◽  
pp. 2961-2968 ◽  
Author(s):  
Bertine L. Stehouwer ◽  
Dennis W. J. Klomp ◽  
Maurice A. A. J. van den Bosch ◽  
Mies A. Korteweg ◽  
Kenneth G. A. Gilhuijs ◽  
...  
Keyword(s):  
High Resolution ◽  
Breast Mri ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced

scholarly journals Novel High Spatiotemporal Resolution Versus Standard-of-Care Dynamic Contrast-Enhanced Breast MRI

2017 ◽  
Vol 52 (4) ◽  
pp. 198-205 ◽  
Author(s):  
Courtney K. Morrison ◽  
Leah C. Henze Bancroft ◽  
Wendy B. DeMartini ◽  
James H. Holmes ◽  
Kang Wang ◽  
...  
Keyword(s):  
Breast Mri ◽  
Standard Of Care ◽  
Spatiotemporal Resolution ◽  
Dynamic Contrast Enhanced ◽  
High Spatiotemporal Resolution ◽  
Contrast Enhanced
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