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• W4366605609 abstract "Abstract The purpose of this study was to investigate the performances of the tumor response prediction prior to neoadjuvant chemotherapy based on quantitative ultrasound, tumour core-margin, texture derivative analyses, and molecular parameters in a large cohort of patients (n = 208) with locally advanced breast cancer and combined them to best determine tumour responses with machine learning approach. Two multi-features response prediction algorithms using a k-nearest neighbour and support vector machine were developed with leave-one out and hold-out cross-validation methods to evaluate the performance of the response prediction models. In a leave-one out approach, the quantitative ultrasound-texture analysis based model attained a good classification performance with 80% of accuracy and AUC of 0.83. Including molecular subtype in the model improve the performance to 83% of accuracy and 0.87 of AUC. Due to limited number of sample in the training process, a model developed with a hold-out approach exhibited slightly higher bias error in classification performance. The most relevant features selected in predicting the response groups are core-to-margin, texture derivative, and molecular subtype. These results imply that that tumour-margin, baseline texture-derivative analysis methods combined with molecular subtype can be potentially used for the prediction of ultimate treatment response in patients prior to neoadjuvant chemotherapy." @default.
• W4366605609 created "2023-04-23" @default.
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• W4366605609 date "2023-04-21" @default.
• W4366605609 modified "2023-10-12" @default.
• W4366605609 title "A Priori Prediction of Breast Cancer Response to Neoadjuvant Chemotherapy using Quantitative Ultrasound, Texture Derivative and Molecular Subtype" @default.
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• W4366605609 doi "https://doi.org/10.21203/rs.3.rs-2787380/v1" @default.
• W4366605609 hasPublicationYear "2023" @default.
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