旨在确定术前乳腺 MR 成像和与复发相关的临床病理变量，并开发接受前期手术治疗的年轻乳腺癌女性复发的风险预测模型。这项回顾性研究分析了 2007 年 1 月至 2007 年 1 月至 2007 年 4 月期间连续 438 名年龄在 35 岁或以下的乳腺癌女性。 2016 年 12 月。手术前的乳房 MR 图像由对患者结果不知情的乳腺放射科医生独立审查。回顾了临床病理数据，包括患者人口统计、临床特征和肿瘤特征。使用单变量和多变量逻辑回归分析来确定与复发相关的独立因素。建立复发风险预测模型，并评估判别和校准能力。 438例患者中，中位随访65个月后，95例（21.7%）出现复发。 MR成像时的肿瘤大小（HR = 1.158，P = 0.006），多焦点或多中性疾病（HR = 1.676，P = 0.017），T2WI上的肿瘤疾病（HR = 2.166，p = 0.001）被确定为独立的复发预测指标，而辅助内分泌治疗（HR = 0.624，p = 0.035）与复发呈负相关。该预测模型在预测 5 年复发（C 指数，开发队列中为 0.707；验证队列中为 0.686）和总体复发（C 指数，开发队列中为 0.699；验证队列中为 0.678）方面表现出良好的辨别能力。校准图显示出良好的相关性（一致性相关系数，0.903）。基于乳腺 MR 成像和临床病理特征的预测模型显示出良好的辨别力，可以预测接受前期手术治疗的年轻乳腺癌女性的复发，这可能有助于个体化风险分层我们的预测模型结合了术前乳房 MR 成像和临床病理特征，可以预测接受前期手术的年轻乳腺癌女性的复发，促进个性化风险分层并为定制管理策略提供信息。患有乳腺癌的年轻女性的结果比在更典型的情况下诊断的女性要差年龄。所描述的预测模型在预测 5 年复发和总体复发方面表现出良好的区分性能。在该人群中采用更好的风险分层工具可能有助于改善结果。© 2024。作者，获得欧洲放射学会的独家许可。

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.© 2024. The Author(s), under exclusive licence to European Society of Radiology.