Neoadjuvant Chemotherapy Efficacy in Breast Cancer: Insights from Magnetic Resonance Imaging Compilation (MAGIC)
Year: 2025
Type: article
Study design: Observational Study
Abstract: RATIONALE AND OBJECTIVES: This study aimed to investigate the role of Magnetic Resonance Imaging Compilation (MAGIC) technology in diagnosing pathologically complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer with mass enhancement (ME) and non-mass enhancement (NME) lesions. MATERIALS AND METHODS: A total of 101 breast cancer patients from November 2021 to June 2024 were retrospectively analyzed and divided into the non-pCR group and the pCR group according to the Miller-Payne grade. Magnetic Resonance (MR) parameters before the first NAC and the last preoperative MR examination (post-parameters) were recorded, respectively. To compare the changes in MR parameters before and after NAC in the two types of breast cancer. RESULTS: One hundred and one breast cancer patients with 114 lesions. 36.63% (37/101) of patients achieved pCR, and 68.42% (78/114) were ME lesions. The T1, T2, and PD values decreased, and the ADC value increased significantly after NAC in both ME and NME lesions. However, only post-T1, post-T2, and ADC values (excluding PD) effectively differentiated pCR from non-pCR in ME lesions, whereas no significant differences were observed in NME lesions. Notably, MAGIC technology combined with DWI significantly enhanced diagnostic accuracy for pCR in ME lesions (all P values < 0.05), but this approach showed limited utility in NME breast cancer. CONCLUSION: For ME breast cancer, MAGIC technology serves as an important tool for clinical decision-making by effectively predicting pCR and, in combination with DWI, improving the accuracy of diagnosis.
Source: Academic Radiology
Authors: Honghong Wu, Zebo Huang, Jie Wang
Institutions: Zhejiang Cancer Hospital, Nanjing Medical University
Language: English
Topic: MRI in cancer diagnosis
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,510
Funder: Nanjing Medical University
Award: G3267031750
OpenAlex ID: W4410025343
