scholarly journals Machine learning based tissue analysis reveals Brachyury has a diagnosis value in breast cancer

2021 ◽  
Author(s):  
Kaichun Li ◽  
Qiaoyun Wang ◽  
Yanyan Lu ◽  
Xiaorong Pan ◽  
Long Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Decision Tree ◽  
Cancer Patients ◽  
Decision Tree Model ◽  
Superior Performance ◽  
Breast Cancer Patients ◽  
Learning Models ◽  
Tree Model ◽  
Machine Learning Models

Background The aim of this study was to confirm the role of Brachyury in breast cancer and to verify whether four types of machine learning models can use Brachyury expression to predict the survival of patients.</p>  Methods We conducted a retrospective review of the medical records to obtain patient information, and made the patient's paraffin tissue into tissue chips for staining analysis. We selected  303 patients for research and implemented four machine learning algorithms, including multivariate logistic regression model, decision tree, artificial neural network and random forest, and compared the results of these models with each other. Area under the receiver operating characteristic (ROC) curve (AUC) was used to compare the results.</p>  Results The chi-square test results of relevant data suggested that the expression of Brachyury protein in cancer tissues was significantly higher than that in paracancerous tissues (p=0.0335); breast cancer patients with high Brachyury expression had a worse overall survival (OS) compared with patients with low Brachyury expression. We also found that Brachyury expression was associated with ER expression (p=0.0489). Subsequently, we used four machine learning models to verify the relationship between Brachyury expression and the survival of breast cancer patients. The results showed that the decision tree model had the best performance (AUC=0.781).</p>  Conclusions Brachyury is highly expressed in breast cancer and indicates that patients had a poor prognosis. Compared with conventional statistical methods, decision tree model shows superior performance in predicting the survival status of breast cancer patients.

Machine Learning based Tissue Analysis Reveals Brachyury has a Diagnosis Value in Breast Cancer

2020 ◽  
Author(s):  
Kaichun Li ◽  
Qiaoyun Wang ◽  
Yanyan Lu ◽  
Xiaorong Pan ◽  
Long Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Decision Tree ◽  
Cancer Patients ◽  
Decision Tree Model ◽  
Superior Performance ◽  
Breast Cancer Patients ◽  
Learning Models ◽  
Tree Model ◽  
Machine Learning Models

Abstract Background The aim of this study was to confirm the role of Brachyury in breast cells and to establish and verify whether four types of machine learning models can use Brachyury expression to predict the survival of patients.Methods We conducted a retrospective review of the medical records to obtain patient information, and made the patient's paraffin tissue into tissue chips for staining analysis. We selected a total of 303 patients for research and implemented four machine learning prediction algorithms, including multivariate logistic regression model, decision tree, artificial neural network and random forest, and compared the results of these models with each other. Area under the receiver operating characteristic (ROC) curve (AUC) was used to compare the results.Results The chi-square test results of relevant data suggested that the expression of Brachyury protein in cancer tissues was significantly higher than that in paracancerous tissues (p=0.0335); breast cancer patients with high Brachyury expression had a worse overall survival (OS) compared with patients with low Brachyury expression. We also found that Brachyury expression was associated with ER expression (p=0.0489). Subsequently, we used four machine learning models to verify the relationship between Brachyury expression and the survival of breast cancer patients. The results showed that the decision tree model had the best performance (AUC=0.781).Conclusions Brachyury is highly expressed in breast cancer and indicates that the patient had a poor chance of survival. Compared with conventional statistical methods, decision tree model shows superior performance in predicting the survival status of breast cancer patients. This indicates that machine learning can thus be applied in a wide range of clinical studies.

scholarly journals Machine Learning Models That Integrate Tumor Texture and Perfusion Characteristics Using Low-Dose Breast Computed Tomography Are Promising for Predicting Histological Biomarkers and Treatment Failure in Breast Cancer Patients

Cancers ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 6013
Author(s):  
Hyun-Soo Park ◽  
Kwang-sig Lee ◽  
Bo-Kyoung Seo ◽  
Eun-Sil Kim ◽  
Kyu-Ran Cho ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Random Forest ◽  
Cancer Patients ◽  
Low Dose ◽  
Random Forest Model ◽  
Breast Cancer Patients ◽  
Learning Models ◽  
Forest Model ◽  
Machine Learning Models

This prospective study enrolled 147 women with invasive breast cancer who underwent low-dose breast CT (80 kVp, 25 mAs, 1.01–1.38 mSv) before treatment. From each tumor, we extracted eight perfusion parameters using the maximum slope algorithm and 36 texture parameters using the filtered histogram technique. Relationships between CT parameters and histological factors were analyzed using five machine learning algorithms. Performance was compared using the area under the receiver-operating characteristic curve (AUC) with the DeLong test. The AUCs of the machine learning models increased when using both features instead of the perfusion or texture features alone. The random forest model that integrated texture and perfusion features was the best model for prediction (AUC = 0.76). In the integrated random forest model, the AUCs for predicting human epidermal growth factor receptor 2 positivity, estrogen receptor positivity, progesterone receptor positivity, ki67 positivity, high tumor grade, and molecular subtype were 0.86, 0.76, 0.69, 0.65, 0.75, and 0.79, respectively. Entropy of pre- and postcontrast images and perfusion, time to peak, and peak enhancement intensity of hot spots are the five most important CT parameters for prediction. In conclusion, machine learning using texture and perfusion characteristics of breast cancer with low-dose CT has potential value for predicting prognostic factors and risk stratification in breast cancer patients.

scholarly journals Explainable Artificial Intelligence (XAI) to Enhance Trust Management in Intrusion Detection Systems Using Decision Tree Model

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Basim Mahbooba ◽  
Mohan Timilsina ◽  
Radhya Sahal ◽  
Martin Serrano
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Decision Tree ◽  
Trust Management ◽  
Decision Tree Model ◽  
Learning Models ◽  
Tree Model ◽  
Explainable Artificial Intelligence ◽  
Machine Learning Models

Despite the growing popularity of machine learning models in the cyber-security applications (e.g., an intrusion detection system (IDS)), most of these models are perceived as a black-box. The eXplainable Artificial Intelligence (XAI) has become increasingly important to interpret the machine learning models to enhance trust management by allowing human experts to understand the underlying data evidence and causal reasoning. According to IDS, the critical role of trust management is to understand the impact of the malicious data to detect any intrusion in the system. The previous studies focused more on the accuracy of the various classification algorithms for trust in IDS. They do not often provide insights into their behavior and reasoning provided by the sophisticated algorithm. Therefore, in this paper, we have addressed XAI concept to enhance trust management by exploring the decision tree model in the area of IDS. We use simple decision tree algorithms that can be easily read and even resemble a human approach to decision-making by splitting the choice into many small subchoices for IDS. We experimented with this approach by extracting rules in a widely used KDD benchmark dataset. We also compared the accuracy of the decision tree approach with the other state-of-the-art algorithms.

scholarly journals Value of the Application of CE-MRI Radiomics and Machine Learning in Preoperative Prediction of Sentinel Lymph Node Metastasis in Breast Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Yadi Zhu ◽  
Ling Yang ◽  
Hailin Shen
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Lymph Node ◽  
Sentinel Lymph Node ◽  
Breast Cancer Patients ◽  
Learning Models ◽  
Preoperative Prediction ◽  
Machine Learning Model ◽  
Validation Set ◽  
Machine Learning Models

PurposeTo explore the value of machine learning model based on CE-MRI radiomic features in preoperative prediction of sentinel lymph node (SLN) metastasis of breast cancer.MethodsThe clinical, pathological and MRI data of 177 patients with pathologically confirmed breast cancer (81 with SLN positive and 96 with SLN negative) and underwent conventional DCE-MRI before surgery in the First Affiliated Hospital of Soochow University from January 2015 to May 2021 were analyzed retrospectively. The samples were randomly divided into the training set (n=123) and validation set (n= 54) according to the ratio of 7:3. The radiomic features were derived from DCE-MRI phase 2 images, and 1,316 original eigenvectors are normalized by maximum and minimum normalization. The optimal feature filter and selection operator (LASSO) algorithm were used to obtain the optimal features. Five machine learning models of Support Vector Machine, Random Forest, Logistic Regression, Gradient Boosting Decision Tree, and Decision Tree were constructed based on the selected features. Radiomics signature and independent risk factors were incorporated to build a combined model. The receiver operating characteristic curve and area under the curve were used to evaluate the performance of the above models, and the accuracy, sensitivity, and specificity were calculated.ResultsThere is no significant difference between all clinical and histopathological variables in breast cancer patients with and without SLN metastasis (P >0.05), except tumor size and BI-RADS classification (P< 0.01). Thirteen features were obtained as optimal features for machine learning model construction. In the validation set, the AUC (0.86) of SVM was the highest among the five machine learning models. Meanwhile, the combined model showed better performance in sentinel lymph node metastasis (SLNM) prediction and achieved a higher AUC (0.88) in the validation set.ConclusionsWe revealed the clinical value of machine learning models established based on CE-MRI radiomic features, providing a highly accurate, non-invasive, and convenient method for preoperative prediction of SLNM in breast cancer patients.

scholarly journals Detection of Breast Cancer Using Machine Learning Algorithms

2021 ◽  
pp. 223-227
Author(s):  
Vijaylaxmi Kochari
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Linear Regression ◽  
Decision Tree ◽  
Machine Learning Algorithms ◽  
Training Dataset ◽  
Proper Treatment ◽  
Learning Models ◽  
Initial Stage ◽  
Machine Learning Models

Breast cancer represents one of the dangerous diseases that causes a high number of deaths every year. The dataset containing the features present in the CSV format is used to identify whether the digitalized image is benign or malignant. The machine learning models such as Linear Regression, Decision Tree, Radom Forest are trained with the training dataset and used to classify. The accuracy of these classifiers is compared to get the best model. This will help the doctors to give proper treatment at the initial stage and save their lives.

scholarly journals A machine learning approach to predict healthcare cost of breast cancer patients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pratyusha Rakshit ◽  
Onintze Zaballa ◽  
Aritz Pérez ◽  
Elisa Gómez-Inhiesto ◽  
Maria T. Acaiturri-Ayesta ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cancer Patients ◽  
Healthcare Cost ◽  
Percentage Error ◽  
Learning Approach ◽  
Early Prediction ◽  
Breast Cancer Patients ◽  
Machine Learning Approach ◽  
Clinical Records

AbstractThis paper presents a novel machine learning approach to perform an early prediction of the healthcare cost of breast cancer patients. The learning phase of our prediction method considers the following two steps: (1) in the first step, the patients are clustered taking into account the sequences of actions undergoing similar clinical activities and ensuring similar healthcare costs, and (2) a Markov chain is then learned for each group to describe the action-sequences of the patients in the cluster. A two step procedure is undertaken in the prediction phase: (1) first, the healthcare cost of a new patient’s treatment is estimated based on the average healthcare cost of its k-nearest neighbors in each group, and (2) finally, an aggregate measure of the healthcare cost estimated by each group is used as the final predicted cost. Experiments undertaken reveal a mean absolute percentage error as small as 6%, even when half of the clinical records of a patient is available, substantiating the early prediction capability of the proposed method. Comparative analysis substantiates the superiority of the proposed algorithm over the state-of-the-art techniques.

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