scholarly journals Empirical Processing of Breast Cancer Prediction Strategies using DEFS Algorithm

2020 ◽  
Vol 8 (5) ◽  
pp. 3081-3087
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Differential Evolution ◽  
Ensemble Learning ◽  
Prediction Accuracy ◽  
Selection Process ◽  
False Negative ◽  
Pollution Level ◽  
Survival Ratio ◽  
True Negative

Now-a-days an important threat to women over global manner is Breast-Cancer, which is the major disease cause drastic affection to female especially. Identification of Breast Cancer over earlier stages is must to save one's life and the significant affection range of Breast-Cancer is drastically improved day by day due to the improper food-habits, pollution-level and improper-life style as well as genetic-issues also. The main cause of this disease is the arising of breast-ample over the ‘breast-area, which develops the cancer to women in several cases. If the detection or prediction of such masses over earlier stage will helps to women to get more survival ratio as well as this leads a proportion to researchers to make an systematic process to detect such diseases on initial stages by using intelligent prediction methodologies with high accuracy rates. In this paper, the proposed system handles several stages of processing to make sure the prediction accuracy, such steps are as follows: Data acquisition, Feature vector formation by normalization, Feature Selection by using Differential Evolution based selection methodology, Classification using Subspace Ensemble Learning and different Performance Measures. By using these strategies the entire work assures the proposed system is perfect to predict or identify the breast cancer benign/malignant stages more accurately compare to the classical Margin-Based Feature-Selection process. Compared to the classical biopsy methodology, a systematic diagnosis attains more impact due to its prediction accuracy. This proposed system is powered by a powerful approach called Differential-Evolution Feature’-Selection (“DEFS”) with the association of Subspace Ensemble Learning Classification principle, which provides highest accuracy and prediction rates compare to the classical methodologies. This proposed paper assures effective and robust mining strategies in Breast Cancer identification/prediction as well as efficient decision-making norms. The proposed outcome proves the good accuracy and resulting levels by means of Precision-Recall, Sensitivity and Specificity, True Positive/True Negative, False Positive/False Negative, Accuracy and Time Consumption.

scholarly journals A multicenter, hospital-based and non-inferiority study for diagnostic efficacy of automated whole breast ultrasound for breast cancer in China

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yujing Xin ◽  
Xinyuan Zhang ◽  
Yi Yang ◽  
Yi Chen ◽  
Yanan Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
False Negative ◽  
Final Analysis ◽  
Age Group ◽  
Surgical Biopsy ◽  
True Negative ◽  
Diagnostic Efficacy ◽  
Negative Results ◽  
Informed Consent Form ◽  
Magnetic Resonance Imaging Mri

AbstractThis study is the first multi-center non-inferiority study that aims to critically evaluate the effectiveness of HHUS/ABUS in China breast cancer detection. This was a multicenter hospital-based study. Five hospitals participated in this study. Women (30–69 years old) with defined criteria were invited for breast examination by HHUS, ABUS or/and mammography. For BI-RADS category 3, an additional magnetic resonance imaging (MRI) test was provided to distinguish the true negative results from false negative results. For women classified as BI-RADS category 4 or 5, either core aspiration biopsy or surgical biopsy was done to confirm the diagnosis. Between February 2016 and March 2017, 2844 women signed the informed consent form, and 1947 of them involved in final analysis (680 were 30 to 39 years old, 1267 were 40 to 69 years old).For all participants, ABUS sensitivity (91.81%) compared with HHUS sensitivity (94.70%) with non-inferior Z tests, P = 0.015. In the 40–69 age group, non-inferior Z tests showed that ABUS sensitivity (93.01%) was non-inferior to MG sensitivity (86.02%) with P < 0.001 and HHUS sensitivity (95.44%) was non-inferior to MG sensitivity (86.02%) with P < 0.001. Sensitivity of ABUS and HHUS are all superior to that of MG with P < 0.001 by superior test.For all participants, ABUS specificity (92.89%) was non-inferior to HHUS specificity (89.36%) with P < 0.001. Superiority test show that specificity of ABUS was superior to that of HHUS with P < 0.001. In the 40–69 age group, ABUS specificity (92.86%) was non-inferior to MG specificity (91.68%) with P < 0.001 and HHUS specificity (89.55%) was non-inferior to MG specificity (91.68%) with P < 0.001. ABUS is not superior to MG with P = 0.114 by superior test. The sensitivity of ABUS/HHUS is superior to that of MG. The specificity of ABUS/HHUS is non-inferior to that of MG. In China, for an experienced US radiologist, both HHUS and ABUS have better diagnostic efficacy than MG in symptomatic individuals.

scholarly journals Integrating ensemble systems biology feature selection and bimodal deep neural network for breast cancer prognosis prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Li-Hsin Cheng ◽  
Te-Cheng Hsu ◽  
Che Lin
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Feature Selection ◽  
Systems Biology ◽  
Ensemble Learning ◽  
Microarray Data ◽  
Deep Neural Network ◽  
Prediction Models ◽  
Biological Knowledge ◽  
Prognosis Prediction

AbstractBreast cancer is a heterogeneous disease. To guide proper treatment decisions for each patient, robust prognostic biomarkers, which allow reliable prognosis prediction, are necessary. Gene feature selection based on microarray data is an approach to discover potential biomarkers systematically. However, standard pure-statistical feature selection approaches often fail to incorporate prior biological knowledge and select genes that lack biological insights. Besides, due to the high dimensionality and low sample size properties of microarray data, selecting robust gene features is an intrinsically challenging problem. We hence combined systems biology feature selection with ensemble learning in this study, aiming to select genes with biological insights and robust prognostic predictive power. Moreover, to capture breast cancer's complex molecular processes, we adopted a multi-gene approach to predict the prognosis status using deep learning classifiers. We found that all ensemble approaches could improve feature selection robustness, wherein the hybrid ensemble approach led to the most robust result. Among all prognosis prediction models, the bimodal deep neural network (DNN) achieved the highest test performance, further verified by survival analysis. In summary, this study demonstrated the potential of combining ensemble learning and bimodal DNN in guiding precision medicine.

scholarly journals Is there a role of frozen section of sentinel lymph node in patients with early breast cancer for the management of axilla in India: a prospective study

2019 ◽  
Vol 6 (6) ◽  
pp. 2126
Author(s):  
Anshika Arora ◽  
Neena Chauhan ◽  
Sunil Saini ◽  
Nishish Vishwakarma ◽  
Tanvi Luthra
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Sentinel Lymph Node ◽  
Early Breast Cancer ◽  
False Positive ◽  
Frozen Section ◽  
False Negative ◽  
Lymph Node Biopsy ◽  
True Negative ◽  
Node Negative

Background: Evaluation of axilla using sentinel lymph node biopsy (SLNB) is the standard of care in node negative early breast cancer. Intra operative assessment of SLNB with frozen section (FS) often guides the surgeon regarding decision for level of axillary dissection. The aim of this study was to evaluate accuracy of FS of SLNB in these patients with histopathology examination (HPE) as the gold standard.Methods: This study was performed between July 2017 and November 2018. After gross evaluation of SLNB, nodes were cut in half and frozen; the other half was preserved for HPE. For FS, nodes were sectioned to 4 mm width and examined.Results: A total of 61 patients underwent SLNB, 55 patients undergoing intra-operative FS. The mean age was 53 years (range 30-84, ± 15.09 SD), primary tumor was clinically T1 in 23.6%, T2 in 76.4% patients. A median of four sentinel nodes were identified, mean size 13.84 mm. On FS SLNB was positive for metastasis in 14 (25.5%), on HPE in 16 (29.1%) patients. There were 13 true positive, 38 true negative, 3 false negative and 1 false positive result for FS. The sensitivity, specificity, positive and negative predictive value, false negative and false positive rates were 81.25%, 97.44%, 92.86%, 92.73%, 18.75% and 2.56% respectively in this study. The overall accuracy of FS of SLNB in early carcinoma breast was found to be 92.73%.Conclusions: An intra-operative FS of the SLN in node negative early breast cancer is a highly sensitive tool in axilla management.

The sensitivity and specificity of screening mammography in primary care setting in Saudi Arabia.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. e12551-e12551 ◽  
Author(s):  
Abdullah Alkhenizan ◽  
Aneela Hussain ◽  
Adher Alsayed
Keyword(s):  
Breast Cancer ◽  
Primary Care ◽  
Saudi Arabia ◽  
Predictive Value ◽  
Breast Imaging ◽  
Screening Program ◽  
False Negative ◽  
Screening Mammography ◽  
True Negative ◽  
Catchment Population

e12551 Background: Breast cancer is the leading cancer diagnosed in women in Saudi Arabia, accounting for 25% of all cancers diagnosed in women. The mammogram screening program at King Faisal Specialist Hospital and Research Center (KFSHRC) is the only structured screening program in the country. KFSHRC provides primary care services for a catchment population of 30,000 patients. This program covers all women above the age of 40 within this catchment population. Methods: A retrospective review of electronic and paper records were reviewed for mammograms done between January 2002- January 2012. Summary statistics were used to describe patient and examination characteristics. Results from mammograms were reported using the Breast Imaging Reporting and Data System (BI-RADS) of the American College of Radiology. The stage of diagnosis was reported using the American Joint Committee on Cancer (AJCC) system using stages one through four. ACR BIRADS classification and cancer status definitions mammograms were linked with cancer outcomes to identify true-positive, true-negative, false-positive, and false-negative examinations. On the basis of these classifications, sensitivity, specificity, positive predictive value, and negative predictive value were estimated. All mammograms and tissue biopsies were read by board certified specialists. Results: During the first round of screening 1694 mammograms were analyzed, and 12 cases of cancer were diagnosed. Cancer detection rate (per 1000 examination) was 7.1. Biopsy rate was 3.7 per 100 mammograms. Follow up ultrasounds rate was 2.7 per 100 mammograms. Sensitivity of mammogram screening was 80%, and specificity was 76%. Conclusions: The yield of a structured mammogram screening program in Saudi Arabia is high. There is a need to implement a national program for breast cancer screening in Arabian world in general and within Saudi Arabia in particular.

scholarly journals Performance of systematic and non-systematic (‘opportunistic’) screening mammography: a comparative study from Denmark

2008 ◽  
Vol 15 (1) ◽  
pp. 23-26 ◽  
Author(s):  
Kristine Bihrmann ◽  
Allan Jensen ◽  
Anne Helene Olsen ◽  
Sisse Njor ◽  
Walter Schwartz ◽  
...  
Keyword(s):  
Breast Cancer ◽  
False Negative ◽  
Population Based ◽  
Screening Mammography ◽  
Opportunistic Screening ◽  
True Negative ◽  
Cancer Data ◽  
European Guidelines ◽  
Adjusted Sensitivity

Objectives Evaluation and comparison of the performance of organized and opportunistic screening mammography. Methods Women attending screening mammography in Denmark in 2000. The study included 37,072 women attending organized screening. Among these, 320 women were diagnosed with breast cancer during follow-up. Opportunistic screening was attended by 2855 women with 26 women being diagnosed with breast cancer. Data on women attending screening were linked with information on cancer status. Each woman was followed with respect to diagnosis of breast cancer (invasive as well as in situ) for a period of two years. Screening outcome and cancer status during follow-up were combined to assess whether the result of the examination was true-positive, true-negative, false-positive or false-negative. Based on this classification, age-adjusted sensitivity and specificity of organized and opportunistic screening were calculated. Results Defining BI-RADS™ 4-5 as a positive screening outcome, the overall sensitivity of opportunistic screening was 33.6% and the specificity was 99.1%. Using BI-RADS™ 3-5 as positive, the sensitivity was 37.4% and the specificity was 97.9%. Organized screening (which was not categorized according to BI-RADS™) had an overall sensitivity of 67.2% and a specificity of 98.4%. Conclusion Our study showed a considerably higher sensitivity in organized screening than in opportunistic screening, while the specificity was fairly similar in the two settings. The findings support implementation of population-based breast screening programmes, as recommended in the ‘European guidelines for quality assurance in breast cancer screening and diagnosis’.

scholarly journals An Improved Power System Transient Stability Prediction Model Based on mRMR Feature Selection and WTA Ensemble Learning

2020 ◽  
Vol 10 (7) ◽  
pp. 2255
Author(s):  
Jun Liu ◽  
Huiwen Sun ◽  
Yitong Li ◽  
Wanliang Fang ◽  
Shuanbao Niu
Keyword(s):  
Feature Selection ◽  
Power Systems ◽  
Power System ◽  
Prediction Model ◽  
Ensemble Learning ◽  
Prediction Accuracy ◽  
Transient Stability ◽  
Stability Assessment ◽  
Stability Prediction ◽  
Electromagnetic Power

Fast online transient stability assessment (TSA) is very important to maintain the stable operation of power systems. However, the existing transient stability assessment methods suffer the drawbacks of unsatisfactory prediction accuracy, difficult applicability, or a heavy computational burden. In light of this, an improved high accuracy power system transient stability prediction model is proposed, based on min-redundancy and max-relevance (mRMR) feature selection and winner take all (WTA) ensemble learning. Firstly, the contributions of four different series of raw sampled data from all of the three-time stages, namely the pre-fault, during-fault and post-fault, to transient stability are compared. The new feature of generator electromagnetic power is introduced and compared with three conventional types of input features, through a support vector machine (SVM) classifier. Furthermore, the two types of most contributive input features are obtained by the mRMR feature selection method. Finally, the prediction results of the electromagnetic power of generators and the voltage amplitude of buses are combined using the WTA ensemble learning method, and an improved transient stability prediction model with higher accuracy for unstable samples is obtained, whose overall prediction accuracy would not decrease either. The real-time data collected by wide area monitoring systems (WAMS) can be fed into this model for fast online transient stability prediction; the results can also provide a basis for the future emergency control decision-making of power systems.

scholarly journals Classifying Breast Cancer Subtypes Using Deep Neural Networks Based on Multi-Omics Data

Genes ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 888
Author(s):  
Yuqi Lin ◽  
Wen Zhang ◽  
Huanshen Cao ◽  
Gaoyang Li ◽  
Wei Du
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Feature Selection ◽  
Deep Neural Networks ◽  
Selection Process ◽  
Breast Cancer Subtype ◽  
The Cancer Genome Atlas ◽  
Breast Cancer Subtypes ◽  
Omics Data ◽  
Cancer Subtypes

With the high prevalence of breast cancer, it is urgent to find out the intrinsic difference between various subtypes, so as to infer the underlying mechanisms. Given the available multi-omics data, their proper integration can improve the accuracy of breast cancer subtype recognition. In this study, DeepMO, a model using deep neural networks based on multi-omics data, was employed for classifying breast cancer subtypes. Three types of omics data including mRNA data, DNA methylation data, and copy number variation (CNV) data were collected from The Cancer Genome Atlas (TCGA). After data preprocessing and feature selection, each type of omics data was input into the deep neural network, which consists of an encoding subnetwork and a classification subnetwork. The results of DeepMO based on multi-omics on binary classification are better than other methods in terms of accuracy and area under the curve (AUC). Moreover, compared with other methods using single omics data and multi-omics data, DeepMO also had a higher prediction accuracy on multi-classification. We also validated the effect of feature selection on DeepMO. Finally, we analyzed the enrichment gene ontology (GO) terms and biological pathways of these significant genes, which were discovered during the feature selection process. We believe that the proposed model is useful for multi-omics data analysis.

BioDog, biomarker detection for improving identification power of breast cancer histologic grade in methylomics

Epigenomics ◽  
2019 ◽  
Vol 11 (15) ◽  
pp. 1717-1732 ◽  
Author(s):  
Yexian Zhang ◽  
Chaorong Chen ◽  
Meiyu Duan ◽  
Shuai Liu ◽  
Lan Huang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Prediction Accuracy ◽  
Somatic Mutations ◽  
Bias Reduction ◽  
Histologic Grade ◽  
Biomarker Detection ◽  
Reduction Strategy ◽  
Incremental Feature Selection ◽  
Cancer Types

Aim: Breast cancer histologic grade (HG) is a well-established prognostic factor. This study aimed to select methylomic biomarkers to predict breast cancer HGs. Materials & methods: The proposed algorithm BioDog firstly used correlation bias reduction strategy to eliminate redundant features. Then incremental feature selection was applied to find the features with a high HG prediction accuracy. The sequential backward feature elimination strategy was employed to further refine the biomarkers. A comparison with existing algorithms were conducted. The HG-specific somatic mutations were investigated. Results & conclusions: BioDog achieved accuracy 0.9973 using 92 methylomic biomarkers for predicting breast cancer HGs. Many of these biomarkers were within the genes and lncRNAs associated with the HG development in breast cancer or other cancer types.

scholarly journals Efektivitas Penggunaan Seleksi Ciri CFS pada Klasifikasi Ciri Bentuk Nodul Kanker Payudara dengan Citra Ultrasonografi

2021 ◽  
Vol 3 (1) ◽  
pp. 11-18
Author(s):  
Hesti Khuzaimah Nurul Yusufiyah ◽  
Juan Pandu Gya Nur Rochman
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Sensitivity And Specificity ◽  
Selection Process ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Shape Features ◽  
Characteristic Shape ◽  
Nodule Shape ◽  
Shape Characteristics

The implementation of nodule shape characteristics is one of the parameters used in determining breast cancer malignancy. Mathematical calculations are used as a second decision to strengthen radiologists in determining breast cancer malignancy using ultrasound images (USG). The method used in this research is to filter ultrasound images that contain speckle noise, then continue the segmentation process, extracting shape features, selecting shape features, and classifying them. The feature selection process using Correlated based Feature Selection (CFS) is used to select the dominant shape features in the image. The classification results obtained show that the results of feature selection using CFS can improve the results of image accuracy, sensitivity and specificity, so as to be able to better distinguish the characteristic shape of the cancer nodule.

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