scholarly journals Cloud-Based Breast Cancer Prediction Empowered with Soft Computing Approaches

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Farrukh Khan ◽  
Muhammad Adnan Khan ◽  
Sagheer Abbas ◽  
Atifa Athar ◽  
Shahan Yamin Siddiqui ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Expert System ◽  
Soft Computing ◽  
Early Stage ◽  
Health Sector ◽  
Cancer Prediction ◽  
Detection Model ◽  
Initial Stage ◽  
Precision Rate ◽  
Better Than

The developing countries are still starving for the betterment of health sector. The disease commonly found among the women is breast cancer, and past researches have proven results that if the cancer is detected at a very early stage, the chances to overcome the disease are higher than the disease treated or detected at a later stage. This article proposed cloud-based intelligent BCP-T1F-SVM with 2 variations/models like BCP-T1F and BCP-SVM. The proposed BCP-T1F-SVM system has employed two main soft computing algorithms. The proposed BCP-T1F-SVM expert system specifically defines the stage and the type of cancer a person is suffering from. Expert system will elaborate the grievous stages of the cancer, to which extent a patient has suffered. The proposed BCP-SVM gives the higher precision of the proposed breast cancer detection model. In the limelight of breast cancer, the proposed BCP-T1F-SVM expert system gives out the higher precision rate. The proposed BCP-T1F expert system is being employed in the diagnosis of breast cancer at an initial stage. Taking different stages of cancer into account, breast cancer is being dealt by BCP-T1F expert system. The calculations and the evaluation done in this research have revealed that BCP-SVM is better than BCP-T1F. The BCP-T1F concludes out the 96.56 percentage accuracy, whereas the BCP-SVM gives accuracy of 97.06 percentage. The above unleashed research is wrapped up with the conclusion that BCP-SVM is better than the BCP-T1F. The opinions have been recommended by the medical expertise of Sheikh Zayed Hospital Lahore, Pakistan, and Cavan General Hospital, Lisdaran, Cavan, Ireland.

scholarly journals The Role of Radiotherapy Fractionation And Volume In Patients With Early Breast Cancer After Conserving Surgery: A Systematic Review And Network Meta-Analysis 

2021 ◽  
Author(s):  
Xian Chen ◽  
Tong-Xin Yang ◽  
Yao-Xiong Xia ◽  
Qi Shen ◽  
Yu Hou ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Early Breast Cancer ◽  
Fixed Effects ◽  
Early Stage ◽  
Meta Analysis ◽  
Indirect Comparison ◽  
Whole Breast Irradiation ◽  
Breast Irradiation ◽  
First Choice ◽  
Better Than

Abstract Background：Hypofractionated whole breast irradiation (HF-WBI) can achieve the same treatment effect as conventional fractionated whole breast irradiation (CF-WBI) within limits , without increasing adverse reactions. Because of its characteristics of reducing the number of radiation therapy (RT) during the COVID-19 Pandemic, it is recommended as the first choice of treatment for patients with early breast cancer after breast conserving surgery. However, the choice of RT is still under exploration. Here, we conducted a network meta-analysis to evaluate the problem comprehensively using data from new randomized trials. Methods: We analyzed data from eligible studies for published events for ipsilateral breast tumor recurrence (IBTR), distant metastasis, total deaths, and non-breast cancer-related deaths. Statistical analysis was performed using a fixed-effects or random-effects model in cases of low and high heterogeneity, respectively. Network meta-analysis was conducted using a node-splitting model for two-category data among three RTs based on a Bayesian approach.Results: 16 studies with 23,418 patients were included. For IBTR, pairwise comparison showed that CF-WBI was significantly better than PBI, and HF-WBI was similar to CF-WBI. HF-WBI was superior to PBI, but the difference was not significant. However, indirect comparison of three RTs by network meta-analysis showed that HF-WBI was significantly better than PBI (OR=0.67, CI95%: 0.46–0.95). Paired and network meta-analyses found no significant differences in other endpoints among three radiotherapies. Conclusion: This meta-analysis demonstrated PBI was associated with increased IBTR compared with HF-WBI or CF-WBI in early-stage breast cancer patients.

scholarly journals Breast Cancer Monitoring and Tracking System using Soft Computing and Expert System for Healthcare Support

2014 ◽  
Vol 16 (3) ◽  
pp. 36-46
Author(s):  
Atiya Masood Rana ◽  
◽  
Somaya Abdullah Al Gamdi ◽  
Roa'a Abdulraheem bukhari ◽  
Maryam Tami Al-Osaimi
Keyword(s):  
Breast Cancer ◽  
Expert System ◽  
Soft Computing ◽  
Tracking System

scholarly journals Using parallel pre-trained types of DCNN model to predict breast cancer with color normalization

2022 ◽  
Vol 15 (1) ◽  
Author(s):  
William Al Noumah ◽  
Assef Jafar ◽  
Kadan Al Joumaa
Keyword(s):  
Breast Cancer ◽  
Expert System ◽  
Early Diagnosis ◽  
Suggested Model ◽  
Proposed Model ◽  
Joint Experience ◽  
Better Than

Abstract Objective Breast cancer is the most common among women, and it causes many deaths every year. Early diagnosis increases the chance of cure through treatment. The traditional manual diagnosis requires effort and time from pathological experts, as it needs a joint experience of a number of pathologists. Diagnostic mistakes can lead to catastrophic results and endanger the lives of patients. The presence of an expert system that is able to specify whether the examined tissue is healthy or not, thus improves the quality of diagnosis and saves the time of experts. In this paper, a model capable of classifying breast cancer anatomy by making use of a pre-trained DCNN has been proposed. To build this model, first of all the image should be color stained by using Vahadane algorithm, then the model which combines three pre-trained DCNN (Xception, NASNet and Inceptoin_Resnet_V2) should be built in parallel, then the three branches should be aggregated to take advantage of each other. The suggested model was tested under different values of threshold ratios and also compared with other models. Results The proposed model on the BreaKHis dataset achieved 98% accuracy, which is better than the accuracy of other models used in this field.

Metastatic presentations of previously treated early stage breast cancer patients.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e23065-e23065
Author(s):  
Najla Itani ◽  
Sneha Deepak Phadke ◽  
Nicole Grogan ◽  
Sarah L Mott
Keyword(s):  
Breast Cancer ◽  
Metastatic Disease ◽  
Cox Regression ◽  
Early Stage ◽  
Multivariable Analysis ◽  
Early Stage Breast Cancer ◽  
Comprehensive Cancer Center ◽  
Receptor Subtype ◽  
Receptor Status ◽  
Initial Stage

e23065 Background: Breast cancer (BC) patients on surveillance often suffer from a fear of recurrence. Given that routine surveillance imaging is not recommended, the ability to recognize metastatic disease early on requires a knowledge of recurrence patterns. The aim of this study was to analyze the most common presentations of metastatic disease. Methods: A retrospective review was conducted of all adult patients at the Holden Comprehensive Cancer Center that were initially diagnosed with early stage BC and later developed metastatic disease, from 2000-2017. Chi-squared tests, and logistic and Cox regression models were used. Results: Of the 2,033 patient charts reviewed, 372 were deemed adequate for analysis. While most metastatic diagnoses were made as a result of reported symptoms (77.6%), 3.2% were made with clinical exam findings and 7.8% incidentally on imaging. Among those with symptoms, musculoskeletal pain was the most common (33.7%) and more frequently noted at scheduled (48.9%) compared to unscheduled visits (26%, p < 0.01). Receptor status was associated with nervous system (NS) symptoms at metastatic diagnosis (p = 0.01), with higher odds of NS symptoms in triple negative (TN) (OR = 3.0) and HER2+ cases compared to ER/PR+ HER2- cases (OR for ER/PR+ HER2+ = 3.5, OR for ER/PR- HER2+ = 3.8). Bone pain was not associated with any specific receptor subtype (p = 0.12). Initial stage and receptor status were associated with time to recurrence (TTR) (both p < 0.01), with stage III disease (versus stage I, HR = 1.60) and TNBC (versus [ER/PR+ HER2-, HR = 2.52) having the shortest TTR. On multivariable analysis, initial stage (p = 0.03), receptor status (p < 0.01), age (p < 0.01), and TTR (p < 0.01) were significantly associated with 10-year survival after metastatic diagnosis whereas the presence of symptoms at metastatic diagnosis was not (p = 0.27). Conclusions: Providers of patients on surveillance for a history of early stage breast cancer should modify their threshold of suspicion for recurrence depending on the characteristics of the initial diagnosis and symptoms subsequently reported. Although most metastatic recurrences were diagnosed by symptoms, patients did not necessarily have a shorter survival as a result of presenting with symptoms.

scholarly journals Classification of Breast Cancer using Deep Learning Architecture

2019 ◽  
Vol 8 (4) ◽  
pp. 7451-7454
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Early Stage ◽  
Minimal Amount ◽  
Human Beings ◽  
Initial Stage ◽  
Benchmark Datasets ◽  
Fully Connected ◽  
Deep Learning Model

Human beings are affected by several diseases nowadays. All those diseases are healable with minimal amount of treatment when they are identified at its early stage. Several patients were not serious enough in diagnosing the disease initially, which makes the disease incurable for the patient lifelong. Hence in recent days number of death rates are getting increased. Cancer is the most dangerous diseases. Among several types of cancers women are mostly affected by breast cancer. In most of the developing and under developing countries breast cancer is the most prominent reason for women mortality. It is also curable when it is identified at its starting stage. During the later stages the cancer cells will be disseminated all over body hence it is difficult to remove it completely. Hence it has to be identified at its initial stage in order to give best treatment for the patient at right time. In this paper , Convolution Neural Network (CNN) a deep learning model is proposed for the investigation of breast cancer images for finding whether the person is affected by cancer or not. In the proposed work , features from images are extracted using convolution layers and then it is passed to the fully connected layer where it classifying the images as either malignant or benign. Experiments using standard benchmark datasets for the proposed CNN Model and standard Visual Geometry Group Network (VGGNet) has been conducted to measure its performances. From the results ,it is clear that CNN outperformed with the accuracy of 86.32% when compared to VGGNet which provides only 50% accuracy for the identification of breast cancer.

scholarly journals Evaluation of Ensemble Machines in Breast Cancer Prediction

2020 ◽  
Author(s):  
LeenaNesamani S ◽  
NirmalaSugirthaRajini S
Keyword(s):  
Breast Cancer ◽  
Ensemble Methods ◽  
Machine Learning Techniques ◽  
Prediction System ◽  
Weighted Voting ◽  
Cancer Prediction ◽  
Learning Techniques ◽  
Deadly Disease ◽  
The Individual ◽  
Better Than

Breast cancer is one of the most deadly diseases encountered among women for which the cause is not clearly defined yet. Early diagnosis may help the physicians in the treatment of this deadly disease which could turn out fatal otherwise. Machine Learning techniques are employed in the process of detecting breast cancer with greater accuracy. Individual classifiers employed in this process, predicted the disease with less accuracy when compared with ensemble models. Ensemble methods employ a group of classifiers to individually classify the data. It then combines the result of the individual classifiers using weighted voting of their predictions. Ensemble machines perform better than individual models and show improved levels in the accuracy of the prediction system. This paper examines and evaluates different ensemble machines that are used in the prediction of breast cancer and tries to identify the combinations that prove to be better than the existing ones.

Network meta-analysis of adjuvant chemotherapy in early breast cancer.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e12071-e12071
Author(s):  
Preethi John ◽  
Raveendhara R Bannuru ◽  
Joshua T. Cohen ◽  
Rachel J. Buchsbaum ◽  
John Kalil Erban
Keyword(s):  
Breast Cancer ◽  
Randomized Clinical Trials ◽  
Early Stage ◽  
Meta Analysis ◽  
Progression Free Survival ◽  
Survival Outcomes ◽  
Free Survival ◽  
Dose Dense ◽  
Optimal Regimen ◽  
Better Than

e12071 Background: The NCCN recommends several adjuvant regimens for early stage breast cancer (ESBC) that have not been directly compared in randomized clinical trials (RCTs) making the optimal regimen unclear. Regimens of interest include dose dense doxorubicin/cyclophosphamide followed by paclitaxel (DDAC-T), doxorubicin/cyclophosphamide followed by weekly paclitaxel (ACwkT), docetaxel/doxorubicin/cyclophosphamide (TAC), and docetaxel/cyclophosphamide (TC) x 4 cycles. This is the first network meta-analysis (NMA) to compare the effectiveness of these regimens. Methods: A systematic literature review was performed to identify RCTs that included the above regimens. To complete the network, doxorubicin/cyclophosphamide (AC), doxorubicin/cyclophosphamide followed by paclitaxel (AC-T) every 3 wks, and doxorubicin/cyclophosphamide followed by docetaxel (AC-D) every 3 wks were included. Primary outcomes were progression free survival (PFS) and overall survival (OS) estimated as odds ratios (OR). OR > 1 indicates better survival. Bayesian random effects model with non-informative priors was used. Results: 5 RCTs involving 12,579 females with mainly node positive, Her2- ESBC were analyzed. Although there were no statistically significant differences in PFS or OS among these regimens, AC-D, ACwkT, DDAC-T, TAC, and TC demonstrated better survival outcomes compared to AC and AC-T (not shown). Survival outcomes among DDAC-T, ACwkT, TAC, and TC were comparable. DDAC-T survival outcomes were marginally better than the other regimens. Conclusions: DDAC-T, ACwkT, TC, and TAC were similar in efficacy. Final results with at least one additional RCT will be presented. Although NMA is not a substitute for direct comparison RCTs, it allows indirect comparisons to aid in decision making. Future results from ongoing RCTs will refine estimates of anthracycline vs non anthracycline efficacy and toxicity. [Table: see text]

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