CAD and Machine Learning for Breast MRI

2020 ◽  
pp. 97-111
Author(s):  
Anne L. Martel
Keyword(s):  
Machine Learning ◽  
Breast Mri

Machine learning with multiparametric breast MRI for prediction of Ki-67 and histologic grade in early-stage luminal breast cancer

2021 ◽  
Author(s):  
Sung Eun Song ◽  
Kyu Ran Cho ◽  
Yongwon Cho ◽  
Kwangsoo Kim ◽  
Seung Pil Jung ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Early Stage ◽  
Breast Mri ◽  
Histologic Grade ◽  
Luminal Breast Cancer ◽  
Ki 67

Case-based repeatability of machine learning classification performance on breast MRI

2020 ◽  
Author(s):  
Michael Vieceli ◽  
Amy Van Dusen ◽  
Karen Drukker ◽  
Hiroyuki Abe ◽  
Maryellen L. Giger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Breast Mri ◽  
Classification Performance ◽  
Machine Learning Classification ◽  
Case Based

scholarly journals A machine learning approach for differentiating malignant from benign enhancing foci on breast MRI

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Natascha C. D’Amico ◽  
Enzo Grossi ◽  
Giovanni Valbusa ◽  
Francesca Rigiroli ◽  
Bernardo Colombo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Breast Mri ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Machine learning in breast MRI

2019 ◽  
Vol 52 (4) ◽  
pp. 998-1018 ◽  
Author(s):  
Beatriu Reig ◽  
Laura Heacock ◽  
Krzysztof J. Geras ◽  
Linda Moy
Keyword(s):  
Machine Learning ◽  
Breast Mri

scholarly journals Implementation of machine learning into clinical breast MRI: Potential for objective and accurate decision-making in suspicious breast masses

PLoS ONE ◽  
2020 ◽  
Vol 15 (1) ◽  
pp. e0228446
Author(s):  
Stephan Ellmann ◽  
Evelyn Wenkel ◽  
Matthias Dietzel ◽  
Christian Bielowski ◽  
Sulaiman Vesal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Breast Mri ◽  
Breast Masses ◽  
Clinical Breast

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.

Genetic algorithm for machine learning architecture selection for breast MRI classification

2020 ◽  
Author(s):  
Peter Carras ◽  
Carina Pereira ◽  
Debosmita Biswas ◽  
Christoph Lee ◽  
Savannah Partridge ◽  
...  
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Breast Mri ◽  
Selection For

Predicting neo-adjuvant chemotherapy response from pre-treatment breast MRI using machine learning and HER2 status.

2018 ◽  
Vol 36 (15_suppl) ◽  
pp. 582-582 ◽  
Author(s):  
Nathaniel Braman ◽  
Kavya Ravichandran ◽  
Andrew Janowczyk ◽  
Jame Abraham ◽  
Anant Madabhushi
Keyword(s):  
Machine Learning ◽  
Adjuvant Chemotherapy ◽  
Breast Mri ◽  
Her2 Status ◽  
Chemotherapy Response ◽  
Pre Treatment ◽  
Neo Adjuvant Chemotherapy

scholarly journals Prediction of genetically-evaluated tumour responses to chemotherapy from breast MRI using machine learning with model selection

2021 ◽  
Vol 1780 (1) ◽  
pp. 012040
Author(s):  
Taiguang Yuan ◽  
Ze Jin ◽  
Yukiko Tokuda ◽  
Yasuto Naoi ◽  
Noriyuki Tomiyama ◽  
...  
Keyword(s):  
Machine Learning ◽  
Model Selection ◽  
Breast Mri
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