Ultrasound Image Texture Feature Learning-Based Breast Cancer Benign and Malignant Classification

The use of ultrasound images to acquire breast cancer diagnosis information without invasion can reduce the physical and psychological pain of breast cancer patients and is of great significance for the diagnosis and treatment of breast cancer. There are some differences in the texture of breast cancer between benign and malignant cases. Therefore, this paper proposes an adaptive learning method based on ultrasonic image texture features to identify breast cancer. Specifically, firstly, we used dictionary learning and sparse representation to learn the ultrasonic image texture dictionary of benign and malignant cases, respectively, and then used the combination of the two dictionaries to represent the test image to obtain the texture distribution characteristics of the test image under the two dictionary representations, which called the sparse representation coefficient. Finally, these above features were filtered by sparse representation and sent to sparse representation classifier to establish benign and malignant classification model. 128 cases were randomly divided into training and testing sets according to 2: 1 for training and testing. The proposed method has achieved state-of-the-art results, with an accuracy of 0.9070 and the area under the receiver operating characteristic curve of 0.9459. The results demonstrate that the proposed method has the potential to be used in the clinical diagnosis of benign and malignant breast cancer.

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Radiomics Analysis Based on Automatic Image Segmentation of DCE-MRI for Predicting Triple-Negative and Nontriple-Negative Breast Cancer

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/2140465 ◽

2021 ◽

Vol 2021 ◽

pp. 1-7

Author(s):

Mingming Ma ◽

Liangyu Gan ◽

Yuan Jiang ◽

Naishan Qin ◽

Changxin Li ◽

...

Keyword(s):

Breast Cancer ◽

Image Segmentation ◽

Cross Validation ◽

Triple Negative ◽

Characteristic Curve ◽

Three Dimensional ◽

Training Dataset ◽

Breast Cancer Patients ◽

Dce Mri ◽

Model Based

Purpose. To investigate whether quantitative radiomics features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) could be used to differentiate triple-negative breast cancer (TNBC) and nontriple-negative breast cancer (non-TNBC). Materials and Methods. This retrospective study included DCE-MRI images of 81 breast cancer patients (44 TNBC and 37 non-TNBC) from August 2018 to October 2019. The MR scans were achieved at a 1.5 T MR scanner. For each patient, the largest tumor mass was selected to analyze. Three-dimensional (3D) images of the regions of interest (ROIs) were automatically segmented on the third DCE phase by a deep learning segmentation model; then, the ROIs were checked and revised by 2 radiologists. DCE-MRI radiomics features were extracted from the 3D tumor volume. The patients were randomly divided into training ( N = 57 ) and test ( N = 24 ) cohorts. The machine learning classifier was built in the training dataset, and 5-fold cross-validation was performed on the training cohort to train and validate. The data of the test cohort were used to investigate the predictive power of the radiomics model in predicting TNBC and non-TNBC. The performance of the model was evaluated by the area under receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Results. The radiomics model based on 15 features got the best performance. The AUC achieved 0.741 for the cross-validation, and 0.867 for the independent testing cohort. Conclusion. The radiomics model based on automatic image segmentation of DCE-MRI can be used to distinguish TNBC and non-TNBC.

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Prevalence and Potential Factors Related to Irreversible Chemotherapy-Induced Amenorrhea in Premenopausal Breast Cancer Patients

Asian Pacific Journal of Cancer Care ◽

10.31557/apjcc.2020.5.3.167-172 ◽

2020 ◽

Vol 5 (3) ◽

pp. 167-172

Author(s):

Prapaporn Suprasert ◽

Pannarat Khunthong ◽

Areewan Somwangprasert

Keyword(s):

Breast Cancer ◽

Characteristic Curve ◽

Univariate Analysis ◽

Premenopausal Women ◽

Independent Factor ◽

Breast Cancer Patients ◽

Premenopausal Breast Cancer ◽

Estrogen Receptor Positive ◽

Potential Factors ◽

Clinicopathological Variables

Objective: To evaluate the prevalence and potential factors related to irreversible chemotherapy-induced amenorrhea (CIA) in premenopausal women with breast cancer. Methods: First diagnosis breast cancer women in Stages I-III who had menstruation within three months before receiving chemotherapy and completed a course of treatment were interviewed about the menstrual cycle after a complete course of chemotherapy and the subsequent menstrual status. Clinical data were retrospectively reviewed. Age at starting chemotherapy was calculated for an optimal cut-off point by using the receiver operating characteristic curve to predict irreversible CIA. The clinicopathological variables were compared using univariate and multivariate analysis to identify the independent factors related to irreversible CIA. Results: One hundred and fifty-four premenopausal breast cancer women who met the inclusion criteria were interviewed. They were treated with chemotherapy between October 1999 and September 2018. The median age at the start of treatment was 43.5 years. One hundred forty-two patients (92.2%) developed CIA and 37 cases subsequently resumed menstruation (RM). Thus, the prevalence of irreversible CIA was 68.2%. The group > 45 years of age, estrogen receptor-positive, progesterone receptor-positive and maintenance treatment with tamoxifen significantly developed irreversible CIA in univariate analysis. However, only the > 45-year-old group was an independent factor for the CIA with an adjusted odds ratio of 23.04. Conclusion: Nearly 70% of premenopausal breast cancer women developed irreversible CIA and the independent factor for this event was being older than 45-years-old when receiving chemotherapy.

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Classification of Breast Cancer Patients Using Neural Network Technique

Journal of Soft Computing and Data Mining ◽

10.30880/jscdm.2021.02.01.002 ◽

2021 ◽

Vol 2 (1) ◽

Author(s):

Putri Marhida Badarudin ◽

◽

Rozaida Ghazali ◽

Abdullah Alahdal ◽

N.A.M. Alduais ◽

...

Keyword(s):

Breast Cancer ◽

Neural Network ◽

Classification Model ◽

Breast Cancer Patients ◽

Ann Model ◽

Average Accuracy ◽

Artificial Neural Network Ann ◽

The One ◽

Detect Breast Cancer

This work develops an Artificial Neural Network (ANN) model for performing Breast Cancer (BC) classification tasks. The design of the model considers studying different ANN architectures from the literature and chooses the one with the best performance. This ANN model aims to classify BC cases more systematically and more quickly. It provides facilities in the field of medicine to detect breast cancer among women. The ANN classification model is able to achieve an average accuracy of 98.88 % with an average run time of 0.182 seconds. Using this model, the classification of BC can be carried out much more faster than manual diagnosis and with good enough accuracy.

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Multiparametric ultrasound examination for response assessment in breast cancer patients undergoing neoadjuvant therapy

Scientific Reports ◽

10.1038/s41598-021-82141-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

K. Dobruch-Sobczak ◽

H. Piotrzkowska-Wróblewska ◽

Z. Klimonda ◽

P. Karwat ◽

K. Roszkowska-Purska ◽

...

Keyword(s):

Breast Cancer ◽

Cancer Patients ◽

Characteristic Curve ◽

Poor Response ◽

Maximum Diameter ◽

Breast Cancer Patients ◽

Backscatter Coefficient ◽

Linear Discriminant ◽

The Third ◽

Multiparametric Ultrasound

AbstractTo investigate the performance of multiparametric ultrasound for the evaluation of treatment response in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). The IRB approved this prospective study. Breast cancer patients who were scheduled to undergo NAC were invited to participate in this study. Changes in tumour echogenicity, stiffness, maximum diameter, vascularity and integrated backscatter coefficient (IBC) were assessed prior to treatment and 7 days after four consecutive NAC cycles. Residual malignant cell (RMC) measurement at surgery was considered as standard of reference. RMC < 30% was considered a good response and > 70% a poor response. The correlation coefficients of these parameters were compared with RMC from post-operative histology. Linear Discriminant Analysis (LDA), cross-validation and Receiver Operating Characteristic curve (ROC) analysis were performed. Thirty patients (mean age 56.4 year) with 42 lesions were included. There was a significant correlation between RMC and echogenicity and tumour diameter after the 3rd course of NAC and average stiffness after the 2nd course. The correlation coefficient for IBC and echogenicity calculated after the first four doses of NAC were 0.27, 0.35, 0.41 and 0.30, respectively. Multivariate analysis of the echogenicity and stiffness after the third NAC revealed a sensitivity of 82%, specificity of 90%, PPV = 75%, NPV = 93%, accuracy = 88% and AUC of 0.88 for non-responding tumours (RMC > 70%). High tumour stiffness and persistent hypoechogenicity after the third NAC course allowed to accurately predict a group of non-responding tumours. A correlation between echogenicity and IBC was demonstrated as well.

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A nomogram for predicting probability of low risk of MammaPrint results in women with clinically high-risk breast cancer

Scientific Reports ◽

10.1038/s41598-021-02992-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Young Joo Lee ◽

Young Sol Hwang ◽

Junetae Kim ◽

Sei-Hyun Ahn ◽

Byung Ho Son ◽

...

Keyword(s):

Breast Cancer ◽

Predictive Value ◽

Characteristic Curve ◽

Low Risk ◽

Nuclear Grade ◽

Clinicopathological Factors ◽

Breast Cancer Patients ◽

Ki 67 ◽

Her2 Breast Cancer ◽

Genomic Risk

AbstractWe aimed to develop a prediction MammaPrint (MMP) genomic risk assessment nomogram model for hormone-receptor positive (HR+) and human epidermal growth factor receptor-2 negative (HER2–) breast cancer and minimal axillary burden (N0-1) tumors using clinicopathological factors of patients who underwent an MMP test for decision making regarding adjuvant chemotherapy. A total of 409 T1-3 N0-1 M0 HR + and HER2– breast cancer patients whose MMP genomic risk results and clinicopathological factors were available from 2017 to 2020 were analyzed. With randomly selected 306 patients, we developed a nomogram for predicting a low-risk subgroup of MMP results and externally validated with remaining patients (n = 103). Multivariate analysis revealed that the age at diagnosis, progesterone receptor (PR) score, nuclear grade, and Ki-67 were significantly associated with MMP risk results. We developed an MMP low-risk predictive nomogram. With a cut off value at 5% and 95% probability of low-risk MMP, the nomogram accurately predicted the results with 100% positive predictive value (PPV) and negative predictive value respectively. When applied to cut-off value at 35%, the specificity and PPV was 95% and 86% respectively. The area under the receiver operating characteristic curve was 0.82 (95% confidence interval [CI] 0.77 to 0.87). When applied to the validation group, the nomogram was accurate with an area under the curve of 0.77 (95% CI 0.68 to 0.86). Our nomogram, which incorporates four traditional prognostic factors, i.e., age, PR, nuclear grade, and Ki-67, could predict the probability of obtaining a low MMP risk in a cohort of high clinical risk patients. This nomogram can aid the prompt selection of patients who does not need additional MMP testing.

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Artificial Intelligence System Reduces False-Positive Findings in the Interpretation of Breast Ultrasound Exams

10.1101/2021.04.28.21256203 ◽

2021 ◽

Author(s):

Yiqiu Shen ◽

Farah E. Shamout ◽

Jamie R. Oliver ◽

Jan Witowski ◽

Kawshik Kannan ◽

...

Keyword(s):

Breast Cancer ◽

Artificial Intelligence ◽

False Positive ◽

Imaging Modality ◽

Characteristic Curve ◽

Breast Ultrasound ◽

Ultrasound Images ◽

Intelligence System ◽

External Test

AbstractUltrasound is an important imaging modality for the detection and characterization of breast cancer. Though consistently shown to detect mammographically occult cancers, especially in women with dense breasts, breast ultrasound has been noted to have high false-positive rates. In this work, we present an artificial intelligence (AI) system that achieves radiologist-level accuracy in identifying breast cancer in ultrasound images. To develop and validate this system, we curated a dataset consisting of 288,767 ultrasound exams from 143,203 patients examined at NYU Langone Health, between 2012 and 2019. On a test set consisting of 44,755 exams, the AI system achieved an area under the receiver operating characteristic curve (AUROC) of 0.976. In a reader study, the AI system achieved a higher AUROC than the average of ten board-certified breast radiologists (AUROC: 0.962 AI, 0.924±0.02 radiologists). With the help of the AI, radiologists decreased their false positive rates by 37.4% and reduced the number of requested biopsies by 27.8%, while maintaining the same level of sensitivity. To confirm its generalizability, we evaluated our system on an independent external test dataset where it achieved an AUROC of 0.911. This highlights the potential of AI in improving the accuracy, consistency, and efficiency of breast ultrasound diagnosis worldwide.

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Identification of an immune-related long noncoding RNA signature that predicts prognosis in breast cancer patients

Biomarkers in Medicine ◽

10.2217/bmm-2020-0268 ◽

2021 ◽

Vol 15 (3) ◽

pp. 167-180

Author(s):

Na Li ◽

Zubin Li ◽

Xin Li ◽

Bingjie Chen ◽

Huibo Sun ◽

...

Keyword(s):

Breast Cancer ◽

Cancer Patients ◽

Long Noncoding Rna ◽

Noncoding Rna ◽

Cox Regression ◽

Expression Profiles ◽

Characteristic Curve ◽

The Cancer Genome Atlas ◽

Breast Cancer Patients ◽

Cox Regression Analysis

Aim: The purpose of this study was to identify an immune-related long noncoding RNA (lncRNA) signature that predicts the prognosis of breast cancer. Materials & methods: The expression profiles of breast cancer were downloaded from The Cancer Genome Atlas. Cox regression analysis was used to identify an immune-related lncRNA signature. Results: The five immune-related lncRNAs could be used to construct a breast cancer survival prognosis model. The receiver operating characteristic curve evaluation found that the accuracy of the model for predicting the 1-, 3- and 5-year prognosis of breast cancer was 0.688, 0.708 and 0.686. Conclusion: This signature may have an important clinical significance for improving predictive results and guiding the treatment of breast cancer patients.

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Classification of Breast Cancer Patients Using Neural Network Technique

Journal of Soft Computing and Data Mining ◽

10.30880/ijie.2021.02.01.002 ◽

2021 ◽

Vol 2 (1) ◽

Author(s):

Putri Marhida Badarudin ◽

◽

Rozaida Ghazali ◽

Abdullah Alahdal ◽

N.A.M. Alduais ◽

...

Keyword(s):

Breast Cancer ◽

Neural Network ◽

Classification Model ◽

Breast Cancer Patients ◽

Ann Model ◽

Average Accuracy ◽

Artificial Neural Network Ann ◽

The One ◽

Detect Breast Cancer

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A gene signature predicts response to neoadjuvant chemotherapy in triple-negative breast cancer patients

Bioscience Reports ◽

10.1042/bsr20190414 ◽

2019 ◽

Vol 39 (5) ◽

Cited By ~ 2

Author(s):

Tianzhi Zheng ◽

Zhiyuan Pang ◽

Zhao Zhao

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Neoadjuvant Chemotherapy ◽

Triple Negative Breast Cancer ◽

Triple Negative ◽

Characteristic Curve ◽

Complete Response ◽

Gene Signature ◽

Differentially Expressed ◽

Breast Cancer Patients

Abstract Triple-negative breast cancer (TNBC) accounts for approximately 15% of all breast cancer cases. TNBC is highly aggressive and associated with poor prognosis. The present study aimed to compare gene expression between TNBC patients with pathological complete response (pCR) and those with not complete response (nCR) to neoadjuvant chemotherapy. Microarray data of 16 TNBC patients received neoadjuvant chemotherapy were identified from the Gene Expression Omnibus database and 10 patients of them had pCR. We found that 250 coding genes and 155 long noncoding RNAs (lncRNAs) were statistically differentially expressed between patients with pCR and nCR. Receiver operator characteristic curve and area under the curve (AUC) were calculated to assess predictive value of differentially expressed genes. A gene signature of three coding genes and two lncRNA was developed: 2.318*TCF3 + 7.349*CREB1 + 0.891*CEP44 + 0.091*NR_023392.1 + 1.424*NR_048561.1 − 106.682. The gene signature was further validated and had an AUC = 0.829. In summary, we profiled gene expression in pCR patients and developed a gene signature, which was effective to predict pCR among TNBC patients received neoadjuvant chemotherapy.

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Expression of Malic Enzyme 3 in Breast Cancer and Precancerous Lesions: a Promising Novel Biomarker for Carcinogenesis and Prognosis

10.21203/rs.3.rs-144916/v1 ◽

2021 ◽

Author(s):

Duo You ◽

Danfeng Du ◽

Xinmin Li ◽

Xun Hu

Keyword(s):

Breast Cancer ◽

Cancer Progression ◽

Precancerous Lesions ◽

Characteristic Curve ◽

Tnm Staging ◽

Normal Breast ◽

Breast Cancer Patients ◽

Usual Ductal Hyperplasia ◽

Ductal Hyperplasia ◽

Positive Immunostaining

Abstract Purpose: While malic enzymes 1 (ME1) was correlated with breast cancer progression and prognosis, the association of ME3 (a homologue of ME1) with breast cancer is not known. The aim of this study is to explore the potential of ME3 as a biomarker in breast cancer carcinogenesis and prognosis.Methods: A total of 107 patients confirmed with breast cancer were enrolled. The ME3 expression was evaluated by IHC and correlated with clinicopathological indicators.Results: The ME3 positive immunostaining rate was higher in normal breast tissues and decreased stepwise from normal (97.60%) to usual ductal hyperplasia (91.1%), atypical ductal hyperplasia (64.2%), carcinoma in situ (62.5%) and invasive carcinoma (45.5%). Similarly, the decreasing tendency was observed for ME3 positive immunostaining rate from Tis (75.0%) through T1 (62.5%) and T2 (37.5%) to T3 (33.3%) and from stag 0 (75.0%) through I (72.0%), II (44.4%) to III (24.1%). ME3 expression was related with negative lymph node metastasis. Patients with positive expression of ME3 had better outcome. By incorporating ME3 into tumor TNM staging, the area under receiver operating characteristic curve for the 5-year survival was increased from 84.0% to 87.5%. Conclusions: ME3 may be a promising biomarker for better prognosis for breast cancer patients.

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