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

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.

Sex

Female

Minimum age

18 Years

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

• DERIVEDYu Y, Ren W, Mao L, Ouyang W, Hu Q, Yao Q, Tan Y, He Z, Ban X, Hu H, Lin R, Wang Z, Chen Y, Wu Z, Chen K, Ouyang J, Li T, Zhang Z, Liu G, Chen X, Li Z, Duan X, Wang J, Yao H. MRI-based multimodal AI model enables prediction of recurrence risk and adjuvant therapy in breast cancer. Pharmacol Res. 2025 Jun;216:107765. doi: 10.1016/j.phrs.2025.107765. Epub 2025 May 7. PMID 40345352