Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Eur Radiol. 2024 Nov;34(11):7092-7103. doi: 10.1007/s00330-024-10805-5. Epub 2024 May 24.
To identify preoperative breast MR imaging and clinicopathological variables related to recurrence and develop a risk prediction model for recurrence in young women with breast cancer treated with upfront surgery.
This retrospective study analyzed 438 consecutive women with breast cancer aged 35 years or younger between January 2007 and December 2016. Breast MR images before surgery were independently reviewed by breast radiologists blinded to patient outcomes. The clinicopathological data including patient demographics, clinical features, and tumor characteristics were reviewed. Univariate and multivariate logistic regression analyses were used to identify the independent factors associated with recurrence. The risk prediction model for recurrence was developed, and the discrimination and calibration abilities were assessed.
Of 438 patients, 95 (21.7%) developed recurrence after a median follow-up of 65 months. Tumor size at MR imaging (HR = 1.158, p = 0.006), multifocal or multicentric disease (HR = 1.676, p = 0.017), and peritumoral edema on T2WI (HR = 2.166, p = 0.001) were identified as independent predictors of recurrence, while adjuvant endocrine therapy (HR = 0.624, p = 0.035) was inversely associated with recurrence. The prediction model showed good discrimination ability in predicting 5-year recurrence (C index, 0.707 in the development cohort; 0.686 in the validation cohort) and overall recurrence (C index, 0.699 in the development cohort; 0.678 in the validation cohort). The calibration plot demonstrated an excellent correlation (concordance correlation coefficient, 0.903).
A prediction model based on breast MR imaging and clinicopathological features showed good discrimination to predict recurrence in young women with breast cancer treated with upfront surgery, which could contribute to individualized risk stratification.
Our prediction model, incorporating preoperative breast MR imaging and clinicopathological features, predicts recurrence in young women with breast cancer undergoing upfront surgery, facilitating personalized risk stratification and informing tailored management strategies.
Younger women with breast cancer have worse outcomes than those diagnosed at more typical ages. The described prediction model showed good discrimination performance in predicting 5-year and overall recurrence. Incorporating better risk stratification tools in this population may help improve outcomes.
确定与复发相关的术前乳腺磁共振成像和临床病理变量，并为接受即刻手术治疗的年轻乳腺癌女性建立复发风险预测模型。
本回顾性研究分析了 2007 年 1 月至 2016 年 12 月期间连续 438 例年龄在 35 岁或以下的乳腺癌女性患者。在不了解患者结局的情况下，由乳腺放射科医师独立对术前乳腺磁共振图像进行了复查。回顾了包括患者人口统计学、临床特征和肿瘤特征在内的临床病理数据。采用单变量和多变量逻辑回归分析确定与复发相关的独立因素。建立了复发风险预测模型，并评估了其区分能力和校准能力。
在中位随访 65 个月后，438 例患者中有 95 例（21.7%）复发。磁共振成像时的肿瘤大小（HR=1.158，p=0.006）、多灶性或多中心疾病（HR=1.676，p=0.017）和 T2WI 上的瘤周水肿（HR=2.166，p=0.001）被确定为复发的独立预测因素，而辅助内分泌治疗（HR=0.624，p=0.035）与复发呈负相关。预测模型在预测 5 年复发（发展队列的 C 指数为 0.707；验证队列为 0.686）和总体复发（发展队列的 C 指数为 0.699；验证队列为 0.678）方面均具有良好的区分能力。校准图显示出极好的相关性（一致性相关系数为 0.903）。
基于乳腺磁共振成像和临床病理特征的预测模型对接受即刻手术治疗的年轻乳腺癌女性的复发具有良好的区分能力，有助于进行个体化风险分层。
我们的预测模型结合了术前乳腺磁共振成像和临床病理特征，可以预测接受即刻手术治疗的年轻乳腺癌女性的复发，有助于进行个体化风险分层并制定个体化治疗策略。
与诊断年龄更大的患者相比，年轻女性的乳腺癌结局更差。所描述的预测模型在预测 5 年和总体复发方面具有良好的区分性能。在该人群中纳入更好的风险分层工具可能有助于改善结局。
