Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using a deep learning (DL) method
ConclusionThis study established a deep learning model to predict PCR status after neoadjuvant therapy by combining pre ‐NAC and post‐NAC MRI data. The model performed better than using pre‐NAC data only, and also performed better than using post‐NAC data only.Key pointsSignificant findings of the study.It achieved an AUC of 0.968 for pCR prediction. It showed a significantly greater AUC than using pre ‐NAC data only.What this study addsThis study established a deep learning model to predict PCR status after neoadjuvant therapy by combining pre ‐NAC and post‐NAC MRI data.
Source: Thoracic Cancer - Category: Cancer & Oncology Authors: Yu ‐Hong Qu,
Hai‐Tao Zhu,
Kun Cao,
Xiao‐Ting Li,
Meng Ye,
Ying‐Shi Sun Tags: ORIGINAL ARTICLE Source Type: research
More News: Breast Cancer | Cancer | Cancer & Oncology | Chemotherapy | Learning | Men | Neoadjuvant Chemotherapy Therapy | Neoadjuvant Therapy | Radiology | Study | Training | Universities & Medical Training