A predictive model using MRI and clinicopathologic features for breast cancer recurrence in young women treated with upfront surgery

Eun Young Chae; Mi Ran Jung; Joo Hee Cha; Hee Jung Shin; Woo Jung Choi; Hak Hee Kim

doi:10.1007/s00330-024-10805-5

A predictive model using MRI and clinicopathologic features for breast cancer recurrence in young women treated with upfront surgery

Eur Radiol. 2024 May 24. doi: 10.1007/s00330-024-10805-5. Online ahead of print.

Authors

Eun Young Chae¹, Mi Ran Jung², Joo Hee Cha², Hee Jung Shin², Woo Jung Choi², Hak Hee Kim²

Affiliations

¹ Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea. chaeey@gmail.com.
² Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.

PMID: 38787429
DOI: 10.1007/s00330-024-10805-5

Abstract

Objectives: 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.

Methods: 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.

Results: 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).

Conclusion: 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.

Clinical relevance statement: 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.

Key points: 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.

Keywords: Breast neoplasms; Magnetic resonance imaging; Recurrence.