Prediction of histological grade in breast cancer by combining DCE-MRI and DWI features

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.

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

Scientific Reports ◽

10.1038/s41598-021-94862-6 ◽

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.

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

Cancers ◽

10.3390/cancers13061417 ◽

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.

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

Egyptian Journal of Radiology and Nuclear Medicine ◽

10.1186/s43055-021-00473-6 ◽

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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Diagnosing breast cancer tumors using stacked ensemble model

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-219176 ◽

2021 ◽

pp. 1-9

Author(s):

Ahmet Haşim Yurttakal ◽

Hasan Erbay ◽

Türkan İkizceli ◽

Seyhan Karaçavuş ◽

Cenker Biçer

Keyword(s):

Breast Cancer ◽

Deep Learning ◽

Medical Imaging ◽

Early Stage ◽

False Negative ◽

Gradient Boosting ◽

Physical Sign ◽

Ensemble Model ◽

Learning Methods ◽

Dce Mri

Breast cancer is the most common cancer that progresses from cells in the breast tissue among women. Early-stage detection could reduce death rates significantly, and the detection-stage determines the treatment process. Mammography is utilized to discover breast cancer at an early stage prior to any physical sign. However, mammography might return false-negative, in which case, if it is suspected that lesions might have cancer of chance greater than two percent, a biopsy is recommended. About 30 percent of biopsies result in malignancy that means the rate of unnecessary biopsies is high. So to reduce unnecessary biopsies, recently, due to its excellent capability in soft tissue imaging, Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has been utilized to detect breast cancer. Nowadays, DCE-MRI is a highly recommended method not only to identify breast cancer but also to monitor its development, and to interpret tumorous regions. However, in addition to being a time-consuming process, the accuracy depends on radiologists’ experience. Radiomic data, on the other hand, are used in medical imaging and have the potential to extract disease characteristics that can not be seen by the naked eye. Radiomics are hard-coded features and provide crucial information about the disease where it is imaged. Conversely, deep learning methods like convolutional neural networks(CNNs) learn features automatically from the dataset. Especially in medical imaging, CNNs’ performance is better than compared to hard-coded features-based methods. However, combining the power of these two types of features increases accuracy significantly, which is especially critical in medicine. Herein, a stacked ensemble of gradient boosting and deep learning models were developed to classify breast tumors using DCE-MRI images. The model makes use of radiomics acquired from pixel information in breast DCE-MRI images. Prior to train the model, radiomics had been applied to the factor analysis to refine the feature set and eliminate unuseful features. The performance metrics, as well as the comparisons to some well-known machine learning methods, state the ensemble model outperforms its counterparts. The ensembled model’s accuracy is 94.87% and its AUC value is 0.9728. The recall and precision are 1.0 and 0.9130, respectively, whereas F1-score is 0.9545.

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Cancer Grade Model: a multi-gene machine learning-based risk classification for improving prognosis in breast cancer

British Journal of Cancer ◽

10.1038/s41416-021-01455-1 ◽

2021 ◽

Author(s):

E. Amiri Souri ◽

A. Chenoweth ◽

A. Cheung ◽

S. N. Karagiannis ◽

S. Tsoka

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Clinical Decision Making ◽

Histological Grade ◽

Tumour Size ◽

Clinical Decision ◽

Gene Signature ◽

Risk Classification ◽

Breast Cancers ◽

Grade 3

Abstract Background Prognostic stratification of breast cancers remains a challenge to improve clinical decision making. We employ machine learning on breast cancer transcriptomics from multiple studies to link the expression of specific genes to histological grade and classify tumours into a more or less aggressive prognostic type. Materials and methods Microarray data of 5031 untreated breast tumours spanning 33 published datasets and corresponding clinical data were integrated. A machine learning model based on gradient boosted trees was trained on histological grade-1 and grade-3 samples. The resulting predictive model (Cancer Grade Model, CGM) was applied on samples of grade-2 and unknown-grade (3029) for prognostic risk classification. Results A 70-gene signature for assessing clinical risk was identified and was shown to be 90% accurate when tested on known histological-grade samples. The predictive framework was validated through survival analysis and showed robust prognostic performance. CGM was cross-referenced with existing genomic tests and demonstrated the competitive predictive power of tumour risk. Conclusions CGM is able to classify tumours into better-defined prognostic categories without employing information on tumour size, stage, or subgroups. The model offers means to improve prognosis and support the clinical decision and precision treatments, thereby potentially contributing to preventing underdiagnosis of high-risk tumours and minimising over-treatment of low-risk disease.

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SLC6A8-mediated intracellular creatine accumulation enhances hypoxic breast cancer cell survival via ameliorating oxidative stress

Journal of Experimental & Clinical Cancer Research ◽

10.1186/s13046-021-01933-7 ◽

2021 ◽

Vol 40 (1) ◽

Author(s):

Qiao Li ◽

Manran Liu ◽

Yan Sun ◽

Ting Jin ◽

Pengpeng Zhu ◽

...

Keyword(s):

Breast Cancer ◽

Overall Survival ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Histological Grade ◽

Metabolic Adaptation ◽

Mitochondrial Activity ◽

Cell Viability Assay ◽

Ki 67 ◽

The Impact

Abstract Background Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer, with poor prognosis and limited treatment options. Hypoxia is a key hallmark of TNBC. Metabolic adaptation promotes progression of TNBC cells that are located within the hypoxic tumor regions. However, it is not well understood regarding the precise molecular mechanisms underlying the regulation of metabolic adaptions by hypoxia. Methods RNA sequencing was performed to analyze the gene expression profiles in MDA-MB-231 cell line (20% O2 and 1% O2). Expressions of Slc6a8, which encodes the creatine transporter protein, were detected in breast cancer cells and tissues by quantitative real-time PCR. Immunohistochemistry was performed to detect SLC6A8 protein abundances in tumor tissues. Clinicopathologic correlation and overall survival were evaluated by chi-square test and Kaplan-Meier analysis, respectively. Cell viability assay and flow cytometry analysis with Annexin V/PI double staining were performed to investigate the impact of SLC6A8-mediated uptake of creatine on viability of hypoxic TNBC cells. TNBC orthotopic mouse model was used to evaluate the effects of creatine in vivo. Results SLC6A8 was aberrantly upregulated in TNBC cells in hypoxia. SLC6A8 was drastically overexpressed in TNBC tissues and its level was tightly associated with advanced TNM stage, higher histological grade and worse overall survival of TNBC patients. We found that SLC6A8 was transcriptionally upregulated by p65/NF-κB and mediated accumulation of intracellular creatine in hypoxia. SLC6A8-mediated accumulation of creatine promoted survival and suppressed apoptosis via maintaining redox homeostasis in hypoxic TNBC cells. Furthermore, creatine was required to facilitate tumor growth in xenograft mouse models. Mechanistically, intracellular creatine bolstered cell antioxidant defense by reducing mitochondrial activity and oxygen consumption rates to reduce accumulation of intracellular reactive oxygen species, ultimately activating AKT-ERK signaling, the activation of which protected the viability of hypoxic TNBC cells via mediating the upregulation of Ki-67 and Bcl-2, and the downregulation of Bax and cleaved Caspase-3. Conclusions Our study indicates that SLC6A8-mediated creatine accumulation plays an important role in promoting TNBC progression, and may provide a potential therapeutic strategy option for treatment of SLC6A8 high expressed TNBC.

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