Deep Learning-Assisted Segmentation, Multiparametric MRI Radiomics, and Clinical Features for Recurrence Risk Stratification in Young-Age Breast Cancer: A Prospective Pilot Study

Abstract

Purpose: To develop an exploratory prognostic model integrating multiparametric MRI radiomics and clinical features for recurrence risk stratification in young-age breast cancer (YABC). Materials and Methods: In this prospective single-institution cohort, women under 40 years with invasive breast cancer were enrolled between March 2017 and August 2019 and followed for at least 5 years. Tumor and contralateral fibroglandular tissue were segmented on multiparametric breast MRI using a deep learning-assisted model. Radiomic features were extracted and reduced via principal component analysis, and then integrated with clinical variables. Recurrence-free survival (RFS) was compared between risk groups using Kaplan–Meier analysis and the log-rank test. Internal assessment used 1000 bootstrap resamples to evaluate selection stability and optimism-corrected performance. Results: Fifty women (mean age, 34.8 ± 3.6 years) were included, and 11 patients (22%) had recurrence during a median follow-up of 64.5 months. Triple-negative subtype and lower T1_cancer3 showed strong exploratory associations, whereas lower ADC_FGT4 and larger pathologic tumor size showed weaker associations. Risk groups differed in RFS (log-rank p = 0.0022). Bootstrap analysis showed moderate selection stability, with preserved discrimination but limited calibration stability after optimism correction. Conclusions: MRI radiomics combined with clinical factors may provide complementary prognostic information for recurrence risk stratification in YABC, warranting validation in larger independent cohorts.