Predicting efficacy of neoadjuvant chemotherapy in breast cancer patients with synthetic magnetic resonance imaging method MAGiC: An observational cohort study

医学 乳腺癌 磁共振成像 肿瘤科 新辅助治疗 前瞻性队列研究 淋巴结 化疗 内科学 核医学 放射科 癌症
作者
Ting Zhan,Chenghao Yi,Yuanyuan Lang
出处
期刊:European Journal of Radiology [Elsevier]
卷期号:179: 111666-111666
标识
DOI:10.1016/j.ejrad.2024.111666
摘要

Objective MAGnetic resonance Imaging Compilation (MAGiC) is typical method of synthetic magnetic resonance imaging (MRI). The present aimed to investigate the role of MAGiC parameters of relaxation time (T1), transverse relaxation time (T2) and proton density (PD) to predict the treatment efficacy of breast cancer patients after neoadjuvant chemotherapy (NAC). Methods The present prospective cohort study enrolled 120 breast cancer patients who received NAC during 2021 to 2023. Demographic data and clinical characteristics including tumor node metastasis (TNM) stage, pathological type, molecular classification and lymph node metastasis were collected. The levels of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER-2) were measured. Patients were divided by treatment efficacy using the Miller-Payne grading as partial pathological response (pPR) group and pathological complete response (pCR). The values of MAGiC parameters of longitudinal T1, T2, and PD values were recorded. Results In all 120 patients, 73 (60.83%) cases were with pPR and 47 (39.17%) cases were with pCR after treatment. T2 values were markedly lower in pPR patients compared with pCR patients. However, no significant difference was found for T1 and PD values. No significant correlation was observed between any of MAGiC parameters and HER-2, ER or PR. ROC curve showed T2 could be used for prediction of pPR with AUC 0.780. Lymph node metastasis and low levels of T2 were found as independent risk factors for pPR after treatment. Conclusion The T2 value parameter from MAGiC is an independent risk factor for pPR following NAC in breast cancer patients, suggesting its potential as a biomarker for predicting treatment efficacy.
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