A Support Vector Machine (SVM) Classifier Enables Prediction of Optimal Setup, Prone versus Supine, in Left Breast Cancer Patients

2009 ◽  
Vol 75 (3) ◽  
pp. S218-S219 ◽  
Author(s):  
J. Chang ◽  
X. Zhao ◽  
E. Wong ◽  
Y. Wang ◽  
S. Lymberis ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Cancer Patients ◽  
Left Breast ◽  
Support Vector ◽  
Svm Classifier ◽  
Breast Cancer Patients

scholarly journals An Immune Model to Predict Prognosis of Breast Cancer Patients Receiving Neoadjuvant Chemotherapy Based on Support Vector Machine

2021 ◽  
Vol 11 ◽  
Author(s):  
Mozhi Wang ◽  
Zhiyuan Pang ◽  
Yusong Wang ◽  
Mingke Cui ◽  
Litong Yao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Immune System ◽  
T Cell ◽  
Neoadjuvant Chemotherapy ◽  
Prediction Model ◽  
Cancer Patients ◽  
Immune Status ◽  
Support Vector ◽  
Breast Cancer Patients

Tumor microenvironment has been increasingly proved to be crucial during the development of breast cancer. The theory about the conversion of cold and hot tumor attracted the attention to the influences of traditional therapeutic strategies on immune system. Various genetic models have been constructed, although the relation between immune system and local microenvironment still remains unclear. In this study, we tested and collected the immune index of 262 breast cancer patients before and after neoadjuvant chemotherapy. Five indexes were selected and analyzed to form the prediction model, including the ratio values between after and before neoadjuvant chemotherapy of CD4+/CD8+ T cell ratio; lymphosum of T, B, and natural killer (NK) cells; CD3+CD8+ cytotoxic T cell percent; CD16+CD56+ NK cell absolute value; and CD3+CD4+ helper T cell percent. Interestingly, these characters are both the ratio value of immune status after neoadjuvant chemotherapy to the baseline. Then the prediction model was constructed by support vector machine (accuracy rate = 75.71%, area under curve = 0.793). Beyond the prognostic effect and prediction significance, the study instead emphasized the importance of immune status in traditional systemic therapies. The result provided new evidence that the dynamic change of immune status during neoadjuvant chemotherapy should be paid more attention.

Predominance of RAD21 and ERBB2 amplification and progesterone receptor positivity in tumors of the right breast support breast cancer lateralization.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e12557-e12557
Author(s):  
Zachary Spigelman ◽  
Jo-Ellen Murphy
Keyword(s):  
Breast Cancer ◽  
Progesterone Receptor ◽  
Cancer Patients ◽  
Breast Tumors ◽  
Left Breast ◽  
Breast Cancers ◽  
Her2 Expression ◽  
Breast Cancer Patients ◽  
The Right ◽  
Chi Square Analysis

e12557 Background: Biologic lateralization broadly impacts breast cancer. Malignancies originating in the left breast compared to the right breast tend to be more frequent, larger and of poorer prognosis. Left breast tumors respond differently to HER2-neu signaling and have lateralized Ki67 expression. In a prior study a right-left asymmetry in the neutrophil/lymphocyte ratio (NLR) of breast cancers was identified (ASCO 2018, e13094). As a follow-up, retrospective analysis of results from comprehensive genomic profiling (CGP) of right and left side breast cancer specimens was performed to determine a potential genomic etiology for the observed NLR lateralization. Methods: Tumors from 43 consecutive breast cancer patients underwent analysis for all classes of genomic alterations by hybrid capture-based CGP (Foundation Medicine). The CGP results from the 25 left- and 18 right-sided breast cancer samples were analyzed along with the histologic grade and status of estrogen receptor (ER), progesterone receptor (PR), and HER2 expression. Results: In this cohort of advanced breast cancer patients (stage 3-4), no statistically significant differences in lateralization were identified based on patient age, tumor stage, or frequency of ER or Her2 expression (Table). A predominance of PR positivity (p=0.14 chi square analysis) and amplifications in the ERBB2 (p=0.37) and RAD21 (p=0.08) genes were detected in right side tumors. Conclusions: Together with the prior study, trends in asymmetry based on genomic, pathologic, and immunohistologic differences have been detected in breast cancers, including an increased incidence of ERBB2 and RAD21 amplification in right-side breast tumors in this cohort. The predominance of lower PR positivity in the left breast tumors may be due to preferential hypermethylation, consistent with reports that it mediates biologic lateralization changes, downregulates PR expression, and alters amplification rates. Epigenetic methylation, may contribute to asymmetric breast cancer biology and have implications for therapeutic strategy. Further study is warranted.[Table: see text]

scholarly journals Dosimetry of the left anterior descending coronary artery in left breast cancer patients treated with postoperative external radiotherapy

2018 ◽  
Vol 23 (2) ◽  
pp. 91-96 ◽  
Author(s):  
Adela Poitevin-Chacón ◽  
Jessica Chávez-Nogueda ◽  
Rubí Ramos Prudencio ◽  
Alejandro Calvo Fernández ◽  
Alejandro Rodríguez Laguna ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Coronary Artery ◽  
Cancer Patients ◽  
Left Breast ◽  
External Radiotherapy ◽  
Breast Cancer Patients

scholarly journals EP-1297 Heart of the Matter: A study of 112 left breast cancer patients treated with DIBH

2019 ◽  
Vol 133 ◽  
pp. S710
Author(s):  
J. Windsor ◽  
T. Ramanarasiah ◽  
M. Burke ◽  
J. Mitchell
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Left Breast ◽  
Breast Cancer Patients

A NEW CAD SYSTEM FOR BREAST CANCER CLASSIFICATION USING DISCRIMINATION POWER ANALYSIS OF WAVELET’S COEFFICIENTS AND SUPPORT VECTOR MACHINE

2020 ◽  
Vol 20 (06) ◽  
pp. 2050036
Author(s):  
NASSER EDINNE BENHASSINE ◽  
ABDELNOUR BOUKAACHE ◽  
DJALIL BOUDJEHEM
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Power Analysis ◽  
Region Growing ◽  
Pectoral Muscle ◽  
Support Vector ◽  
Svm Classifier ◽  
Discrete Wavelet ◽  
Discrimination Power ◽  
Cad System

The Computer-Aided Diagnostic (CAD) system is an important tool that helps radiologists to provide a second opinion for the early detection of breast cancer and therefore, aids to reduce the mortality rates. In this work, we try to develop a new (CAD) system to classify mammograms into benign or malignant. The proposed system consists of three main steps. The preprocessing stage consists of noise filtering, elimination of unwanted objects and suppressing the pectoral muscle. The Seeded Region Growing (SRG) segmentation technique is applied in a triangular region that contains the pectoral muscle to localize it and extract the region of interest (ROI). The features extraction step is performed by applying the discrete wavelet transform (DWT) to each obtained ROI, and the most discriminating coefficients are selected using the discrimination power analysis (DPA) method. Finally, the classification is carried out by the support vector machine (SVM), artificial neural networks (ANN), random forest (RF) and Naive Bayes (NB) classifiers. The evaluation of the proposed system on the mini-MIAS database shows its effectiveness compared to other recently published CAD systems, and a classification accuracy of about 99.41% with the SVM classifier was obtained.

Sign in / Sign up

Export Citation Format

Share Document