Integration of Clinical and CT-Based Radiomic Features for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Systemic Therapy in Breast Cancer

The purpose of the present study was to examine the potential of a machine learning model with integrated clinical and CT-based radiomics features in predicting pathologic complete response (pCR) to neoadjuvant systemic therapy (NST) in breast cancer. Contrast-enhanced CT was performed in 329 patien...

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Veröffentlicht in:	Cancers 2022-12, Vol.14 (24), p.6261
Hauptverfasser:	Tsai, Huei-Yi, Tsai, Tsung-Yu, Wu, Chia-Hui, Chung, Wei-Shiuan, Wang, Jo-Ching, Hsu, Jui-Sheng, Hou, Ming-Feng, Chou, Ming-Chung
Format:	Artikel
Sprache:	eng
Schlagworte:	Breast cancer Care and treatment Cell surface CT imaging Datasets Integration Learning algorithms Machine learning Methods Neoadjuvant therapy Patient outcomes Predictions Radiomics Reproducibility Statistical analysis Support vector machines Tomography Tumors
Online-Zugang:	Volltext
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