Breast Cancer Lesion Detection From Cranial-Caudal View of Mammogram Images Using Statistical and Texture Features Extraction

Kavya, N; Sriraam, N.; Usha, N.; Hiremath, Bharathi; Suresh, Anusha; Sharath, D.; Venkatraman, B.; Menaka, M.

doi:10.4018/ijbce.2020010102

Cited by 8 publications

(6 citation statements)

References 15 publications

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“…Previous studies have mainly extracted texture features from the whole tumor area in MRI images. However, the texture features derived from subregions within the breast tumor may provide valuable information to aid in clinical diagnosis and help patients develop personal treatment plans (22)(23)(24)(25). Fan et al (26) have shown that the texture features extracted from intratumoral subregions of DCE-MRI can be used to predict Ki-67 status in estrogen receptor (ER)-positive breast cancer.…”

Section: Introductionmentioning

confidence: 99%

Texture Analysis of DCE-MRI Intratumoral Subregions to Identify Benign and Malignant Breast Tumors

2021

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PurposeTo evaluate the potential of the texture features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) intratumoral subregions to distinguish benign from malignant breast tumors.Materials and MethodsA total of 299 patients with pathologically verified breast tumors who underwent breast DCE-MRI examination were enrolled in this study, including 124 benign cases and 175 malignant cases. The whole tumor area was semi-automatically segmented on the basis of subtraction images of DCE-MRI in Matlab 2018b. According to the time to peak of the contrast agent, the whole tumor area was partitioned into three subregions: early, moderate, and late. A total of 467 texture features were extracted from the whole tumor area and the three subregions, respectively. Patients were divided into training (n = 209) and validation (n = 90) cohorts by different MRI scanners. The least absolute shrinkage and selection operator (LASSO) method was used to select the optimal feature subset in the training cohort. The Kolmogorov-Smirnov test was first performed on texture features selected by LASSO to test whether the samples followed a normal distribution. Two machine learning methods, decision tree (DT) and support vector machine (SVM), were used to establish classification models with a 10-fold cross-validation method. The performance of the classification models was evaluated with receiver operating characteristic (ROC) curves.ResultsIn the training cohort, the areas under the ROC curve (AUCs) for the DT_Whole model and SVM_Whole model were 0.744 and 0.806, respectively. In contrast, the AUCs of the DT_Early model (P = 0.004), DT_Late model (P = 0.015), SVM_Early model (P = 0.002), and SVM_Late model (P = 0.002) were significantly higher: 0.863 (95% CI, 0.808–0.906), 0.860 (95% CI, 0.806–0.904), 0.934 (95% CI, 0.891–0.963), and 0.921 (95% CI, 0.876–0.954), respectively. The SVM_Early model and SVM_Late model achieved better performance than the DT_Early model and DT_Late model (P = 0.003, 0.034, 0.008, and 0.026, respectively). In the validation cohort, the AUCs for the DT_Whole model and SVM_Whole model were 0.670 and 0.708, respectively. In comparison, the AUCs of the DT_Early model (P = 0.006), DT_Late model (P = 0.043), SVM_Early model (P = 0.001), and SVM_Late model (P = 0.007) were significantly higher: 0.839 (95% CI, 0.747–0.908), 0.784 (95% CI, 0.601–0.798), 0.890 (95% CI, 0.806–0.946), and 0.865 (95% CI, 0.777–0.928), respectively.ConclusionThe texture features from intratumoral subregions of breast DCE-MRI showed potential in identifying benign and malignant breast tumors.

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Section: Introductionmentioning

confidence: 99%

Texture Analysis of DCE-MRI Intratumoral Subregions to Identify Benign and Malignant Breast Tumors

2021

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“…e second stage is a simple classification of the subimages extracted in the previous step [19]. is method has been successfully applied to the detection of microbleeds in the brain with a sensitivity of 93% [20].…”

Section: Introductionmentioning

confidence: 99%

Microscopic Tumour Classification by Digital Mammography

Yang

Shi

et al. 2021

Journal of Healthcare Engineering

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In this paper, we investigate the classification of microscopic tumours using full digital mammography images. Firstly, to address the shortcomings of traditional image segmentation methods, two different deep learning methods are designed to achieve the segmentation of uterine fibroids. The deep lab model is used to optimize the lesion edge detailed information by using the void convolution algorithm and fully connected CRF, and the two semantic segmentation networks are compared to obtain the best results. The Mask RCNN case segmentation model is used to effectively extract features through the ResNet structure, combined with the RPN network to achieve effective use and fusion of features, and continuously optimize the network training to achieve a fine segmentation of the lesion area, and demonstrate the accuracy and feasibility of the two models in medical image segmentation. Histopathology was used to obtain ER, PR, HER scores, and Ki-67 percentage values for all patients. The Kaplan-Meier method was used for survival estimation, the Log-rank test was used for single-factor analysis, and Cox proportional risk regression was used for multifactor analysis. The prognostic value of each factor was calculated, as well as the factors affecting progression-free survival. This study was done to compare the imaging characteristics and diagnostic value of mammography and colour Doppler ultrasonography in nonspecific mastitis, improve the understanding of the imaging characteristics of nonspecific mastitis in these two examinations, improve the accuracy of the diagnosis of this type of disease, improve the ability of distinguishing it from breast cancer, and reduce the rate of misdiagnosis.

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“…Breast cancer is one of the aggressive and therapy-resistant malignancies. 20 One main reason for the aggressive feature of breast cancer may be related to the heterogeneous population of breast cancer. 21 Hence, several endeavours are collected to examine the effect of various chemotherapeutic compounds alone and/or in combination form to decrease the side effects and improve the therapeutic efficacy rate.…”

Section: Discussionmentioning

confidence: 99%

Sulindac and vitamin D3 synergically inhibit proliferation of MCF‐7 breast cancer cell through AMPK/Akt/β‐catenin axis in vitro

Poursoltani

Nejati

Pazhang

et al. 2021

Cell Biochemistry & Function

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Breast cancer is associated with a high rate of recurrence, resistance therapy and mortality worldwide. We aimed at investigating the inhibitory effects of Sulindac and vitamin D3 (VD) on MCF-7 human breast cancer cells. MCF-7 cells were cultured with different concentrations of Sulindac and VD over a period of 24, 48 and 72 hours for cell viability and IC50 experiments. Hochst staining was used to evaluate apoptosis, whereas quantitative PCR (qPCR) was performed to measure mRNA levels of BCL-2 and BAX genes. Immunofluorescence staining was used to monitor intracellular β-catenin expression. The protein levels of AKT, AMPK and P65 were measured by western blotting. The result showed that cell viability decreased in treated cells dose/time dependently (P < .05). Hochst staining showed an increase in fragmented nuclei in treated cells. The expression of BCL-2 and BAX genes decreased and increased in treated cells, respectively (P < .05). Immunofluorescence staining indicated that the expression of β-catenin significantly reduced in treated cells. The AKT-1/p-Akt-1 and AMPK/p-AMPK ratio increased in treated cells (P < .05), but the P65/p-P65 ratio did not change significantly (P > .05). Our results indicated that the combination of Sulindac and VD has a growth-inhibiting effect on MCF-7 cells through AMPK/Akt/β-catenin axis.

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Breast Cancer Lesion Detection From Cranial-Caudal View of Mammogram Images Using Statistical and Texture Features Extraction

Cited by 8 publications

References 15 publications

Texture Analysis of DCE-MRI Intratumoral Subregions to Identify Benign and Malignant Breast Tumors

Texture Analysis of DCE-MRI Intratumoral Subregions to Identify Benign and Malignant Breast Tumors

Microscopic Tumour Classification by Digital Mammography

Sulindac and vitamin D3 synergically inhibit proliferation of MCF‐7 breast cancer cell through AMPK/Akt/β‐catenin axis in vitro

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