scholarly journals Application of MRI Image Based on Computer Semiautomatic Segmentation Algorithm in the Classification Prediction of Breast Cancer Histology

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Aizhu Sheng ◽  
Aijing Li ◽  
Jianbi Xia ◽  
Yizhai Ye
Keyword(s):  
Breast Cancer ◽  
Area Under Curve ◽  
Histological Grade ◽  
Segmentation Algorithm ◽  
Dce Mri ◽  
Gland Area ◽  
Semiautomatic Segmentation ◽  
Auc Value ◽  
Mri Image ◽  
Tumor Area

Objective. The study aimed to investigate the predictive classification accuracy of computer semiautomatic segmentation algorithm for the histological grade of breast tumors through the magnetic resonance imaging (MRI) examination. Methods. Five dynamic contrast-enhanced (DCE) MRI regions of interest (ROIs) were captured using computer semiautomatic segmentation method, referring to the entire tumor area, tumor border area, proximal gland area, middle gland area, and distal gland area. According to the mutual information maximum protocol, the corresponding five ROIs were extracted from diffusion weighted imaging (DWI) combined with DCE-MRI images. To use the features in the nonoverlapping area of DWI image and DCE-MRI image as elements, a single-variable logistic regression model was established corresponding to element characteristics. After multiple training, the model was evaluated using the receiver operating characteristic (ROC) curve and area under curve (AUC). Results. This DCE-MRI combined with DWI was superior to DCE-MRI and DW in the prediction of tumor area features. To use DCE-MRI or DWI alone was less effective than DCE-MRI combined with DWI. The DWI combined DCE-MRI demonstrated good regional segmentation effects in the tumour area, with luminal A value being 0.767 and the area under curve (AUC) value being 0.758. After optimization, the AUC value of the tumor area was 0.929, indicating that classification effects can be enhanced by combining the two imaging methods, which complemented each other. Conclusions. The DWI combined DCE-MRI imaging has improved the early diagnosis effects of breast cancer by predicting the occurrence of breast cancer through the labeling of biomarkers.

scholarly journals Semi-automatic segmentation from intrinsically-registered 18F-FDG–PET/MRI for treatment response assessment in a breast cancer cohort: comparison to manual DCE–MRI

2019 ◽  
Vol 33 (2) ◽  
pp. 317-328
Author(s):  
Maren Marie Sjaastad Andreassen ◽  
Pål Erik Goa ◽  
Torill Eidhammer Sjøbakk ◽  
Roja Hedayati ◽  
Hans Petter Eikesdal ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Treatment Response ◽  
Neoadjuvant Treatment ◽  
Gaussian Mixture ◽  
Tumor Segmentation ◽  
Dice Similarity Coefficient ◽  
Adc Value ◽  
Dce Mri ◽  
18F Fdg ◽  
Tumor Area

Abstract Objectives To investigate the reliability of simultaneous positron emission tomography and magnetic resonance imaging (PET/MRI)-derived biomarkers using semi-automated Gaussian mixture model (GMM) segmentation on PET images, against conventional manual tumor segmentation on dynamic contrast-enhanced (DCE) images. Materials and methods Twenty-four breast cancer patients underwent PET/MRI (following 18F-fluorodeoxyglucose (18F-FDG) injection) at baseline and during neoadjuvant treatment, yielding 53 data sets (24 untreated, 29 treated). Two-dimensional tumor segmentation was performed manually on DCE–MRI images (manual DCE) and using GMM with corresponding PET images (GMM–PET). Tumor area and mean apparent diffusion coefficient (ADC) derived from both segmentation methods were compared, and spatial overlap between the segmentations was assessed with Dice similarity coefficient and center-of-gravity displacement. Results No significant differences were observed between mean ADC and tumor area derived from manual DCE segmentation and GMM–PET. There were strong positive correlations for tumor area and ADC derived from manual DCE and GMM–PET for untreated and treated lesions. The mean Dice score for GMM–PET was 0.770 and 0.649 for untreated and treated lesions, respectively. Discussion Using PET/MRI, tumor area and mean ADC value estimated with a GMM–PET can replicate manual DCE tumor definition from MRI for monitoring neoadjuvant treatment response in breast cancer.

scholarly journals Expression of PCNA, Ki-67 and COX-2 in breast cancer based on DCE-MRI image information

2020 ◽  
Vol 13 (12) ◽  
pp. 2032-2037 ◽  
Author(s):  
Xiaoming Qiu ◽  
Hong Wang ◽  
Zhen Wang ◽  
Yufei Fu ◽  
Jianjun Yin
Keyword(s):  
Breast Cancer ◽  
Ki 67 ◽  
Image Information ◽  
Dce Mri ◽  
Cox 2 ◽  
Mri Image

scholarly journals Joint Prediction of Breast Cancer Histological Grade and Ki-67 Expression Level Based on DCE-MRI and DWI Radiomics

2020 ◽  
Vol 24 (6) ◽  
pp. 1632-1642 ◽  
Author(s):  
Ming Fan ◽  
Wei Yuan ◽  
Wenrui Zhao ◽  
Maosheng Xu ◽  
Shiwei Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Histological Grade ◽  
Expression Level ◽  
Ki 67 ◽  
Dce Mri ◽  
Joint Prediction

scholarly journals Tumor collagen framework from bright-field histology images predicts overall survival of breast carcinoma patients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mindaugas Morkunas ◽  
Dovile Zilenaite ◽  
Aida Laurinaviciene ◽  
Povilas Treigys ◽  
Arvydas Laurinavicius
Keyword(s):  
Breast Cancer ◽  
Breast Carcinoma ◽  
Cancer Patient ◽  
Breast Cancer Patient ◽  
Cox Regression ◽  
Regional Lymph Node ◽  
Histological Grade ◽  
Biological Information ◽  
Lymph Node Status ◽  
Bright Field

AbstractWithin the tumor microenvironment, specifically aligned collagen has been shown to stimulate tumor progression by directing the migration of metastatic cells along its structural framework. Tumor-associated collagen signatures (TACS) have been linked to breast cancer patient outcome. Robust and affordable methods for assessing biological information contained in collagen architecture need to be developed. We have developed a novel artificial neural network (ANN) based approach for tumor collagen segmentation from bright-field histology images and have tested it on a set of tissue microarray sections from early hormone receptor-positive invasive ductal breast carcinoma stained with Sirius Red (1 core per patient, n = 92). We designed and trained ANNs on sets of differently annotated image patches to segment collagen fibers and extracted 37 features of collagen fiber morphometry, density, orientation, texture, and fractal characteristics in the entire cohort. Independent instances of ANN models trained on highly differing annotations produced reasonably concordant collagen segmentation masks and allowed reliable prognostic Cox regression models (with likelihood ratios 14.11–22.99, at p-value < 0.05) superior to conventional clinical parameters (size of the primary tumor (T), regional lymph node status (N), histological grade (G), and patient age). Additionally, we noted statistically significant differences of collagen features between tumor grade groups, and the factor analysis revealed features resembling the TACS concept. Our proposed method offers collagen framework segmentation from bright-field histology images and provides novel image-based features for better breast cancer patient prognostication.

scholarly journals Age-Related Biology of Early-Stage Operable Breast Cancer and Its Impact on Clinical Outcome

Cancers ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1417
Author(s):  
Binafsha M. Syed ◽  
Andrew R. Green ◽  
Emad A. Rakha ◽  
David A.L. Morgan ◽  
Ian O. Ellis ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Early Stage ◽  
Histological Grade ◽  
Survival Rates ◽  
Growth Factor Receptor ◽  
Progesterone Receptors ◽  
Biological Characteristics ◽  
Cancer Specific Survival ◽  
Age Related ◽  
Significant Difference

As age advances, breast cancer (BC) tends to change its biological characteristics. This study aimed to explore the natural progression of such changes. The study included 2383 women with clinically T0-2N0-1M0 BC, managed by primary surgery and optimal adjuvant therapy in a dedicated BC facility. Tissue micro-arrays were constructed from their surgical specimens and indirect immunohistochemistry was used for analysis of a large panel (n = 16) of relevant biomarkers. There were significant changes in the pattern of expression of biomarkers related to luminal (oestrogen receptor (ER), progesterone receptors (PgR), human epidermal growth factor receptor (HER-2), E-cadherin, MUC1, bcl2 CK7/8, CK18 and bcl2) and basal (CK5/6, CK14, p53 and Ki67) phenotypes, lymph node stage, histological grade and pathological size when decade-wise comparison was made (p < 0.05). The ages of 40 years and 70 years appeared to be the milestones marking a change of the pattern. There were significantly higher metastasis free and breast cancer specific survival rates among older women with ER positive tumours while there was no significant difference in the ER negative group according to age. Biological characteristics of BC show a pattern of change with advancing age, where 40 years and 70 years appear as important milestones. The pattern suggests <40 years as the phase with aggressive phenotypes, >70 years as the less aggressive phase and 40–70 years being the transitional phase.

scholarly journals Diffusion tensor imaging on 3-T MRI breast: diagnostic performance in comparison to diffusion-weighted imaging

2021 ◽  
Vol 52 (1) ◽  
Author(s):  
Dalia Abdelhady ◽  
Amany Abdelbary ◽  
Ahmed H. Afifi ◽  
Alaa-eldin Abdelhamid ◽  
Hebatallah H. M. Hassan
Keyword(s):  
Breast Cancer ◽  
Diffusion Tensor Imaging ◽  
Sensitivity And Specificity ◽  
Diffusion Weighted Imaging ◽  
Diffusion Tensor ◽  
Breast Mri ◽  
Breast Lesions ◽  
Dce Mri ◽  
Additional Tool ◽  
Diffusion Weighted

Abstract Background Breast cancer is the most prevalent cancer among females. Dynamic contrast-enhanced MRI (DCE-MRI) breast is highly sensitive (90%) in the detection of breast cancer. Despite its high sensitivity in detecting breast cancer, its specificity (72%) is moderate. Owing to 3-T breast MRI which has the advantage of a higher signal to noise ratio and shorter scanning time rather than the 1.5-T MRI, the adding of new techniques as diffusion tensor imaging (DTI) to breast MRI became more feasible. Diffusion-weighted imaging (DWI) which tracks the diffusion of the tissue water molecule as well as providing data about the integrity of the cell membrane has been used as a valuable additional tool of DCE-MRI to increase its specificity. Based on DWI, more details about the microstructure could be detected using diffusion tensor imaging. The DTI applies diffusion in many directions so apparent diffusion coefficient (ADC) will vary according to the measured direction raising its sensitivity to microstructure elements and cellular density. This study aimed to investigate the diagnostic accuracy of DTI in the assessment of breast lesions in comparison to DWI. Results By analyzing the data of the 50 cases (31 malignant cases and 19 benign cases), the sensitivity and specificity of DWI in differentiation between benign and malignant lesions were about 90% and 63% respectively with PPV 90% and NPV 62%, while the DTI showed lower sensitivity and specificity about 81% and 51.7%, respectively, with PPV 78.9% and NPV 54.8% (P-value ≤ 0.05). Conclusion While the DWI is still the most established diffusion parameter, DTI may be helpful in the further characterization of tumor microstructure and differentiation between benign and malignant breast lesions.

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