scholarly journals Evaluation of the Relationship Between 3.0 Tesla Diffusion Tensor MR Imaging Parameters and Breast Cancer Subtypes

2019 ◽  
Author(s):  
Safiye Tokgöz Özal ◽  
Ayşegül Akdoğan Gemici ◽  
Ercan İnci ◽  
Senem Karabulut
Keyword(s):  
Breast Cancer ◽  
Mr Imaging ◽  
Diffusion Tensor ◽  
Breast Cancer Subtypes ◽  
3.0 Tesla ◽  
Cancer Subtypes ◽  
Imaging Parameters ◽  
The Relationship ◽  
Diffusion Tensor Mr Imaging

scholarly journals Role of DCE-MR imaging of the breast in predicting breast cancer subtypes: where are we going?

2017 ◽  
Vol 28 ◽  
pp. vi33
Author(s):  
L. Bastianelli ◽  
M. Pistelli ◽  
G.M. Giuseppetti ◽  
M. De Lisa ◽  
M. Macchini ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Mr Imaging ◽  
Breast Cancer Subtypes ◽  
Cancer Subtypes

Can 3.0 Tesla diffusion tensor Imaging parameters be prognostic indicators in breast cancer?

2018 ◽  
Vol 51 ◽  
pp. 240-247 ◽  
Author(s):  
Safiye Tokgoz Ozal ◽  
Ercan Inci ◽  
Aysegul Akdogan Gemici ◽  
Hurriyet Turgut ◽  
Murat Cikot ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Diffusion Tensor Imaging ◽  
Diffusion Tensor ◽  
Prognostic Indicators ◽  
3.0 Tesla ◽  
Imaging Parameters

scholarly journals Computer-Aided Diagnosis Evaluation of the Correlation Between Magnetic Resonance Imaging With Molecular Subtypes in Breast Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Wei Meng ◽  
Yunfeng Sun ◽  
Haibin Qian ◽  
Xiaodan Chen ◽  
Qiujie Yu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Mr Imaging ◽  
Clinical Features ◽  
Molecular Subtypes ◽  
Molecular Subtype ◽  
Texture Features ◽  
Breast Cancer Subtypes ◽  
Cancer Subtypes ◽  
Computer Aided ◽  
Aided Diagnosis

BackgroundThere is a demand for additional alternative methods that can allow the differentiation of the breast tumor into molecular subtypes precisely and conveniently.PurposeThe present study aimed to determine suitable optimal classifiers and investigate the general applicability of computer-aided diagnosis (CAD) to associate between the breast cancer molecular subtype and the extracted MR imaging features.MethodsWe analyzed a total of 264 patients (mean age: 47.9 ± 9.7 years; range: 19–81 years) with 264 masses (mean size: 28.6 ± 15.86 mm; range: 5–91 mm) using a Unet model and Gradient Tree Boosting for segmentation and classification.ResultsThe tumors were segmented clearly by the Unet model automatically. All the extracted features which including the shape features,the texture features of the tumors and the clinical features were input into the classifiers for classification, and the results showed that the GTB classifier is superior to other classifiers, which achieved F1-Score 0.72, AUC 0.81 and score 0.71. Analyzed the different features combinations, we founded that the texture features associated with the clinical features are the optimal features to different the breast cancer subtypes.ConclusionCAD is feasible to differentiate the breast cancer subtypes, automatical segmentation were feasible by Unet model and the extracted texture features from breast MR imaging with the clinical features can be used to help differentiating the molecular subtype. Moreover, in the clinical features, BPE and age characteristics have the best potential for subtype.

scholarly journals The Relationship of Breast Cancer Subtypes with the Event of Metastasis in Dr. M. Djamil Hospital Padang

2021 ◽  
Vol 5 (4) ◽  
pp. 1199-1205
Author(s):  
Ahmad Fakhrozi Helmi ◽  
Daan Khambri ◽  
Rony Rustam
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Odds Ratio ◽  
Breast Cancer Subtypes ◽  
Control Group ◽  
Breast Cancer Patients ◽  
Luminal A ◽  
Cancer Subtypes ◽  
Luminal B ◽  
The Relationship

Background: One of the high mortality rates from breast cancer is related to the incidence of metastases. It is known that >90% of deaths in breast cancer are related to the incidence of metastases and the complications that follow. Breast cancer is divided into several subtypes based on the expression of receptor genes in breast cancer tissue, namely Luminal A, Luminal B, HER 2 and Triple Negative Breast Cancer (TNBC). This study aims to determine the relationship between breast cancer subtypes and the incidence of metastases in Dr. M. Djamil Padang. Methods: This study used a retrospective case-control study to breast cancer patients with metastatic at Dr M Djamil Hospital, Padang from 2016-2021. The research subjects were 260 breast cancer patients who met the inclusion criteria. The study subjects were divided into 130 patients as the case group with metastases and 130 patients as the control group with no metastases. To determine the relationship between breast cancer subtypes and the incidence of metastases, the chi-square test was used. If the p value <0.05, it can be concluded that it is significant. Furthermore, analysis is continued to obtain an odds ratio (OR) in identifying risk opportunities with Cochran's and Mantle-Haenszel statistics common odds ratio estimate. The data were analysed using the Statistical Package for Social Sciences (SPSS) program. Result: Characteristics of the subjects in this study can be seen that there is a relationship between hormonal contraception, T and N status with the incidence of metastasis (p <0.05). Patients with metastases were more common with breast cancer subtypes luminal B (61.5%), HER2+ (21.5%), TNBC (14.6%) and luminal A (2.3%). The most common locations for breast cancer metastases were lung (48.5%), bone (26.2%), liver (19.2%), brain (5.4%) and other places (0.8%). There was a relationship between breast cancer subtypes and the incidence of metastasis (p<0.038). The highest risk of metastases was in patients with TNBC subtype with OR = 7.74 (95% CI 1.72-34.79). There was no relationship between breast cancer subtypes with metastatic location (p>0.05) and breast cancer subtypes TNBC had a risk (OR) of 9.60 (95% CI 1.96-47.14) times increasing the risk of metastases in brain. Conclusion: It can be concluded that there was a relationship between breast cancer subtypes and the incidence of metastasis

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