scholarly journals Application of radiomics features selection and classification algorithms for medical imaging decision: MRI radiomics breast cancer cases study

2021 ◽  
pp. 100801
Author(s):  
Rihab Laajili ◽  
Mourad Said ◽  
Moncef Tagina
Keyword(s):  
Breast Cancer ◽  
Medical Imaging ◽  
Classification Algorithms ◽  
Features Selection ◽  
Cases Study

Diagnosing breast cancer tumors using stacked ensemble model

2021 ◽  
pp. 1-9
Author(s):  
Ahmet Haşim Yurttakal ◽  
Hasan Erbay ◽  
Türkan İkizceli ◽  
Seyhan Karaçavuş ◽  
Cenker Biçer
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Medical Imaging ◽  
Early Stage ◽  
False Negative ◽  
Gradient Boosting ◽  
Physical Sign ◽  
Ensemble Model ◽  
Learning Methods ◽  
Dce Mri

Breast cancer is the most common cancer that progresses from cells in the breast tissue among women. Early-stage detection could reduce death rates significantly, and the detection-stage determines the treatment process. Mammography is utilized to discover breast cancer at an early stage prior to any physical sign. However, mammography might return false-negative, in which case, if it is suspected that lesions might have cancer of chance greater than two percent, a biopsy is recommended. About 30 percent of biopsies result in malignancy that means the rate of unnecessary biopsies is high. So to reduce unnecessary biopsies, recently, due to its excellent capability in soft tissue imaging, Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has been utilized to detect breast cancer. Nowadays, DCE-MRI is a highly recommended method not only to identify breast cancer but also to monitor its development, and to interpret tumorous regions. However, in addition to being a time-consuming process, the accuracy depends on radiologists’ experience. Radiomic data, on the other hand, are used in medical imaging and have the potential to extract disease characteristics that can not be seen by the naked eye. Radiomics are hard-coded features and provide crucial information about the disease where it is imaged. Conversely, deep learning methods like convolutional neural networks(CNNs) learn features automatically from the dataset. Especially in medical imaging, CNNs’ performance is better than compared to hard-coded features-based methods. However, combining the power of these two types of features increases accuracy significantly, which is especially critical in medicine. Herein, a stacked ensemble of gradient boosting and deep learning models were developed to classify breast tumors using DCE-MRI images. The model makes use of radiomics acquired from pixel information in breast DCE-MRI images. Prior to train the model, radiomics had been applied to the factor analysis to refine the feature set and eliminate unuseful features. The performance metrics, as well as the comparisons to some well-known machine learning methods, state the ensemble model outperforms its counterparts. The ensembled model’s accuracy is 94.87% and its AUC value is 0.9728. The recall and precision are 1.0 and 0.9130, respectively, whereas F1-score is 0.9545.

scholarly journals Feasibility of circulating tumor cells (CTCs) combined with medical imaging examinations to screen for cancer and improve diagnosis in non-metastatic invasive breast cancer patients: a cohort study

2020 ◽  
Author(s):  
Xuan Shao ◽  
xiao yan jin ◽  
zhi gang chen ◽  
zhi gang zhang ◽  
ke wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Medical Imaging ◽  
Tumor Cells ◽  
Circulating Tumor Cells ◽  
Breast Imaging ◽  
Chinese Women ◽  
Clinicopathological Characteristics ◽  
Breast Cancer Patients ◽  
Detection Rates ◽  
Gamma Imaging

Abstract Background: Previous study has reported that circulating tumor cells (CTCs) could be served as a diagnostic biomarker in breast cancer (BC) screening. However, the differential efficacy of routine examination including ultrasound (US), mammogram (MG), magnetic resonance imaging (MR), and breast-specific gamma imaging (BSGI) and CTCs is unknown. This study aimed to compare CTCs with common used BC screening imaging modalities and to evaluate whether their combination would enhance the diagnostic potency in non-metastatic BC patients.Methods: 102 treatment-naive non-metastatic BC patients, 177 patients with breast benign diseases (BBD) and 64 healthy females, who had CTC detection and at least one of the following medical imaging examinations, US, MG or MR between December 2017 and November 2018, were enrolled in this study.Correlations of CTC enumeration with patients’ clinicopathological characteristics and medical imaging examinations were evaluated. Results: CTC detection rates (average CTC counts) in stage I-III BC patients were 92.9% (2.1), 87.2% (2.4) and 100% (4.2), respectively. CTCs counts were positively associated with cancer stage (p = 0.0084) and tumor size (p = 0.0301). CTC counts were more correlated with US than MR or MG. CTC counts were not associated with molecular subtypes of BC nor breast-specific gamma imaging (BSGI) results, indicating that CTC enumeration cannot be used to predict molecular signatures of BC. CTCs and medical imaging examinations would have the best diagnostic performance for BC when CTC cut-off was set to 2 and imaging Breast Imaging-Reporting and Data System (BI-RADS) was set to 4b. Combination of CTC with US, MG or MR increased the sensitivity for BC diagnosis, especially for MG. Sensitivity of MG increased from 0.694 to 0.917, even more than in conjugation with US (0.901). Conclusion: CTCs counts can be used as a diagnostic aid in BC screening and early diagnosis. CTCs counts were more relevant to US than MR or MG. Conjugation of CTCs counts would improve the diagnostic potency of medical imaging examinations for diagnosing BC, especially for MG in Chinese women.

scholarly journals Mammography-based radiomics nomogram: a potential biomarker to predict axillary lymph node metastasis in breast cancer

2020 ◽  
Vol 93 (1111) ◽  
pp. 20191019 ◽  
Author(s):  
Hongna Tan ◽  
Yaping Wu ◽  
Fengchang Bao ◽  
Jing Zhou ◽  
Jianzhong Wan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
Lymph Node ◽  
Axillary Lymph Node ◽  
Multivariate Logistic Regression Analysis ◽  
Support Vector ◽  
Axillary Lymph ◽  
Features Selection ◽  
Potential Biomarker ◽  
Radiomics Signature

Objective: To establish a radiomics nomogram by integrating clinical risk factors and radiomics features extracted from digital mammography (MG) images for pre-operative prediction of axillary lymph node (ALN) metastasis in breast cancer. Methods: 216 patients with breast cancer lesions confirmed by surgical excision pathology were divided into the primary cohort (n = 144) and validation cohort (n = 72). Radiomics features were extracted from craniocaudal (CC) view of mammograms, and radiomics features selection were performed using the methods of ANOVA F-value and least absolute shrinkage and selection operator; then a radiomics signature was constructed with the method of support vector machine. Multivariate logistic regression analysis was used to establish a radiomics nomogram based on the combination of radiomics signature and clinical factors. The C-index and calibration curves were derived based on the regression analysis both in the primary and validation cohorts. Results: 95 of 216 patients were confirmed with ALN metastasis by pathology, and 52 cases were diagnosed as ALN metastasis based on MG-reported criteria. The sensitivity, specificity, accuracy and AUC (area under the receiver operating characteristic curve of MG-reported criteria were 42.7%, 90.8%, 24.1% and 0.666 (95% confidence interval: 0.591–0.741]. The radiomics nomogram, comprising progesterone receptor status, molecular subtype and radiomics signature, showed good calibration and better favorite performance for the metastatic ALN detection (AUC 0.883 and 0.863 in the primary and validation cohorts) than each independent clinical features (AUC 0.707 and 0.657 in the primary and validation cohorts) and radiomics signature (AUC 0.876 and 0.862 in the primary and validation cohorts). Conclusion: The MG-based radiomics nomogram could be used as a non-invasive and reliable tool in predicting ALN metastasis and may facilitate to assist clinicians for pre-operative decision-making. Advances in knowledge: ALN status remains among the most important breast cancer prognostic factors and is essential for making treatment decisions. However, the value of detecting metastatic ALN by MG is very limited. The studies on pre-operative ALN metastasis prediction using the method of MG-based radiomics in breast cancer are very few. Therefore, we studied whether MG-based radiomics nomogram could be used as a predictive biomarker for the detection of metastatic ALN.

scholarly journals Preprocessing Breast Cancer Data to Improve the Data Quality, Diagnosis Procedure, and Medical Care Services

2020 ◽  
Vol 19 ◽  
pp. 117693512091795
Author(s):  
Zeinab Sajjadnia ◽  
Raof Khayami ◽  
Mohammad Reza Moosavi
Keyword(s):  
Breast Cancer ◽  
Cancer Recurrence ◽  
Breast Cancer Recurrence ◽  
Classification Algorithms ◽  
Data Preparation ◽  
Recurrence Prediction ◽  
Null Values ◽  
Error Removal ◽  
The Impact ◽  
F Measure

In recent years, due to an increase in the incidence of different cancers, various data sources are available in this field. Consequently, many researchers have become interested in the discovery of useful knowledge from available data to assist faster decision-making by doctors and reduce the negative consequences of such diseases. Data mining includes a set of useful techniques in the discovery of knowledge from the data: detecting hidden patterns and finding unknown relations. However, these techniques face several challenges with real-world data. Particularly, dealing with inconsistencies, errors, noise, and missing values requires appropriate preprocessing and data preparation procedures. In this article, we investigate the impact of preprocessing to provide high-quality data for classification techniques. A wide range of preprocessing and data preparation methods are studied, and a set of preprocessing steps was leveraged to obtain appropriate classification results. The preprocessing is done on a real-world breast cancer dataset of the Reza Radiation Oncology Center in Mashhad with various features and a great percentage of null values, and the results are reported in this article. To evaluate the impact of the preprocessing steps on the results of classification algorithms, this case study was divided into the following 3 experiments: Breast cancer recurrence prediction without data preprocessing Breast cancer recurrence prediction by error removal Breast cancer recurrence prediction by error removal and filling null values Then, in each experiment, dimensionality reduction techniques are used to select a suitable subset of features for the problem at hand. Breast cancer recurrence prediction models are constructed using the 3 widely used classification algorithms, namely, naïve Bayes, k-nearest neighbor, and sequential minimal optimization. The evaluation of the experiments is done in terms of accuracy, sensitivity, F-measure, precision, and G-mean measures. Our results show that recurrence prediction is significantly improved after data preprocessing, especially in terms of sensitivity, F-measure, precision, and G-mean measures.

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