scholarly journals Progressive Breast Cancer Diagnosis Model Based on Multi-classifier and Multi-modal Fusion

2021 ◽  
Vol 11 (6) ◽  
pp. 387-392
Author(s):  
Jiyun Li ◽  
◽  
Chenxi Jia ◽  
Chen Qian
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Model Based ◽  
Diagnosis Model

scholarly journals A Logistic Regression Model Based on the National Mammography Database Format to Aid Breast Cancer Diagnosis

2009 ◽  
Vol 192 (4) ◽  
pp. 1117-1127 ◽  
Author(s):  
Jagpreet Chhatwal ◽  
Oguzhan Alagoz ◽  
Mary J. Lindstrom ◽  
Charles E. Kahn ◽  
Katherine A. Shaffer ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Regression Model ◽  
Cancer Diagnosis ◽  
Logistic Regression Model ◽  
Breast Cancer Diagnosis ◽  
Model Based

scholarly journals Radiomics and Machine Learning with Multiparametric Breast MRI for Improved Diagnostic Accuracy in Breast Cancer Diagnosis

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 919
Author(s):  
Isaac Daimiel Naranjo ◽  
Peter Gibbs ◽  
Jeffrey S. Reiner ◽  
Roberto Lo Gullo ◽  
Caleb Sooknanan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Diagnostic Accuracy ◽  
Cancer Diagnosis ◽  
Breast Mri ◽  
Breast Cancer Diagnosis ◽  
Multiparametric Mri ◽  
Cancer Center ◽  
Support Vector ◽  
Model Based

The purpose of this multicenter retrospective study was to evaluate radiomics analysis coupled with machine learning (ML) of dynamic contrast-enhanced (DCE) and diffusion-weighted imaging (DWI) radiomics models separately and combined as multiparametric MRI for improved breast cancer detection. Consecutive patients (Memorial Sloan Kettering Cancer Center, January 2018–March 2020; Medical University Vienna, from January 2011–August 2014) with a suspicious enhancing breast tumor on breast MRI categorized as BI-RADS 4 and who subsequently underwent image-guided biopsy were included. In 93 patients (mean age: 49 years ± 12 years; 100% women), there were 104 lesions (mean size: 22.8 mm; range: 7–99 mm), 46 malignant and 58 benign. Radiomics features were calculated. Subsequently, the five most significant features were fitted into multivariable modeling to produce a robust ML model for discriminating between benign and malignant lesions. A medium Gaussian support vector machine (SVM) model with five-fold cross validation was developed for each modality. A model based on DWI-extracted features achieved an AUC of 0.79 (95% CI: 0.70–0.88), whereas a model based on DCE-extracted features yielded an AUC of 0.83 (95% CI: 0.75–0.91). A multiparametric radiomics model combining DCE- and DWI-extracted features showed the best AUC (0.85; 95% CI: 0.77–0.92) and diagnostic accuracy (81.7%; 95% CI: 73.0–88.6). In conclusion, radiomics analysis coupled with ML of multiparametric MRI allows an improved evaluation of suspicious enhancing breast tumors recommended for biopsy on clinical breast MRI, facilitating accurate breast cancer diagnosis while reducing unnecessary benign breast biopsies.

Ductoscope Lights Up Breast Cancer Diagnosis

2007 ◽  
Vol 40 (14) ◽  
pp. 32
Author(s):  
MICHELE G. SULLIVAN
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis

Physical Activity and Survival Subsequent to Metastatic Breast Cancer Diagnosis

2010 ◽  
Author(s):  
Susan Sharp ◽  
Ashleigh Golden ◽  
Cheryl Koopman ◽  
Eric Neri ◽  
David Spiegel
Keyword(s):  
Breast Cancer ◽  
Physical Activity ◽  
Metastatic Breast Cancer ◽  
Cancer Diagnosis ◽  
Metastatic Breast ◽  
Breast Cancer Diagnosis

The significance of patient’s knowledge and behavior after breast cancer diagnosis

2019 ◽  
Vol 3 (48) ◽  
pp. 7
Author(s):  
Alina Oana Rusu-Moldovan ◽  
Maria Iuliana Gruia ◽  
Dan Mihu
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
And Behavior
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