scholarly journals Value of Machine Learning with Multiphases CE-MRI Radiomics for Early Prediction of Pathological Complete Response to Neoadjuvant Therapy in HER2-Positive Invasive Breast Cancer

2021 ◽  
Vol Volume 13 ◽  
pp. 5053-5062
Author(s):  
Qin Li ◽  
Qin Xiao ◽  
Jianwei Li ◽  
Zhe Wang ◽  
He Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Neoadjuvant Therapy ◽  
Invasive Breast Cancer ◽  
Pathological Complete Response ◽  
Complete Response ◽  
Early Prediction ◽  
Her2 Positive

scholarly journals Value of Machine Learning with CE-MRI Radiomics for Early Prediction of Pathological Complete Response to Neoadjuvant Therapy in HER2-positive Invasive Breast Cancer

2020 ◽  
Author(s):  
Qin Li ◽  
Qin Xiao ◽  
Jianwei Li ◽  
Zhe Wang ◽  
He Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Neoadjuvant Therapy ◽  
Invasive Breast Cancer ◽  
Pathological Complete Response ◽  
Complete Response ◽  
Early Prediction ◽  
Her2 Positive ◽  
Invasive Approach ◽  
Linear Svm

Abstract Backgrouds To assess the value of radiomics based on multiphases contrast-enhanced magnetic resonance imaging(CE-MRI) for early prediction of pathological complete response(pCR) to neoadjuvant therapy(NAT) in patients with human epithelial growth factor receptor 2(HER2) positive invasive breast cancer.Methods A total of 127 patients with newly diagnosed primary HER2 positive invasive breast cancer underwent CE-MRI before NAT and performed surgery after NAT. Radiomic features were extracted from the 1st postcontrast CE-MRI phase(CE1) and multi-phases CE-MRI(CEm). With selected features using a forward stepwise regression, 23 machine learning classifiers based on CE1 and CEm were constructed respectively. The performence of classifiers were assessed and compared. Results For the task of pCR classification, 6 radiomic features from CE1 and 6 from CEm were selected for the final machine learning models respectively. The linear SVM based on CEm outperformed the Logistic regression model using CE1 with an AUC of 0.84 vers 0.69. In mass and non-mass enhancement groups, the accuracy of linear SVM achieved 76% and 84%. Whereas in unifocal and unilateral multifocal cases, 79% and 75% accuracy were achieved by linear SVM. Conclusion Multiphases CE-MRI imaging may offer more heterogeneity information in the tumor and provide a non-invasive approach for early prediction of pCR to NAT in patients with HER2 positive invasive breast cancer.

Trastuzumab emtansine: a game changer in HER2-positive early breast cancer

2020 ◽  
Vol 16 (32) ◽  
pp. 2595-2609
Author(s):  
Max S Mano
Keyword(s):  
Breast Cancer ◽  
Early Breast Cancer ◽  
Pathological Complete Response ◽  
Growth Factor Receptor ◽  
Complete Response ◽  
Trastuzumab Emtansine ◽  
Her2 Positive ◽  
Adjuvant Trastuzumab ◽  
Epidermal Growth ◽  
Game Changer

Trastuzumab emtansine (T-DM1), given postoperatively for 14 cycles to patients with human epidermal growth factor receptor 2-positive (HER2-positive) early breast cancer (EBC) who failed to achieve a pathological complete response after standard chemotherapy and HER2 blockade, represents probably the greatest progress in the management of this aggressive form of breast cancer since the adjuvant trastuzumab pivotal trials. This article addresses the rationale behind the conception of the KATHERINE trial, T-DM1’s structure and pharmacokinetics data, clinical efficacy data of the KATHERINE trial and of other EBC trials with T-DM1, safety aspects, implications of the KATHERINE trial results to clinical practice and future perspectives in the management of HER2-positive EBC.

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