Multimodal radiomics for early prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer: integration of amide proton transfer weighted imaging in radiomics
Key Points
This research aims to enhance the prediction of treatment outcomes in breast cancer using a multimodal radiomics approach.
Developed a multimodal radiomics model integrating various imaging techniques.
Compared the effectiveness of this model against traditional single-modal imaging.
Analyzed biomarkers related to treatment response after neoadjuvant chemotherapy.
The multimodal model significantly improved prediction accuracy compared to single-modal approaches.
Early identification of patients likely to achieve a complete response was achieved.
Findings support personalized therapy choices to reduce unnecessary treatments.
Abstract
Compared to single-modal imaging, our multimodal radiomics model provides more comprehensive biomarkers, thereby enabling personalized therapeutic decisions and optimizing NAC regimens to reduce overtreatment.
Like
Bookmark
Share
View Full Paper
Like
Bookmark
Share
View Full Paper
Multimodal radiomics for early prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer: integration of amide proton transfer weighted imaging in radiomics | Synapse