Deep Learning With MRI-based Multimodal-data Fusion Enhanced Postoperative Risk Stratification of Breast Cancer

Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University1,199 enrolled

Overview

Breast cancer poses a significant global health challenge, especially among women, with high rates of recurrence and distant spread despite early interventions. The timely identification of metastasis risk and accurate prediction of treatment strategies are critical for improving prognosis. However, the complex heterogeneity of breast tumors presents challenges in precise prognosis prediction. Therefore, the development of innovative methods for tumor segmentation and prognosis assessment is essential. The research conducted is a multicenter study that enrolled 1,199 non-metastatic breast cancer patients from four independent centers. Our study leverages the advancements in artificial intelligence (AI) to address this challenge. This study is the first successful application of MRI-based multimodal prediction system to precisely identify the risk of postoperative recurrence in breast cancer patients.

Study Type

OBSERVATIONAL

Enrollment

1,199

Conditions

Breast Cancer

Interventions

MRIOTHER

Eligibility

Medical Language ↔ Plain English

Inclusion Criteria: * Histologically confirmed stage I-III invasive BC * Age ≥ 18 years * The patient having undergone surgery * The existence of MRI scans Exclusion Criteria: * Lacked pathological results * Had other, simultaneous malignancies * Had MR imaging issues were excluded

Outcomes

Primary Outcomes

DFS

Disease-free survival

Time frame: The time from surgery to tumor recurrence, including local and/or distant recurrence, disease progression, or death, assessed up to 100 months.