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

Completed

• Conditions: Breast Cancer
• Interventions: Other: MRI

• Registration Number: NCT06546072
• Lead Sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

Brief Summary

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.

Detailed Description

Not available

Study Timeline

• Study Start: 2011-03-23
• Primary Completion: 2019-09-21
• Study Completion: 2021-12-06
• Study First Submitted: 2024-07-06
• Status Verified: 2024-08
• Study First Submitted QC: 2024-08-08
• Last Update Submitted: 2024-08-08
• Study First Posted: 2024-08-09
• Last Update Posted: 2024-08-09

Recruitment & Eligibility

• Status: COMPLETED
• Sex: Female
• Target Recruitment: 1199

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

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Internal validation cohort	MRI	We randomly assigned 569 patients from Sun Yat-sen Memorial Hospital of Sun Yat-sen University (SYSMH; Guangzhou, China) at a ratio of 3:1 to training (n = 456) and internal-validation (n = 113) cohorts.
Training cohort	MRI	We randomly assigned 569 patients from Sun Yat-sen Memorial Hospital of Sun Yat-sen University (SYSMH; Guangzhou, China) at a ratio of 3:1 to training (n = 456) and internal-validation (n = 113) cohorts.
External testing cohort 1	MRI	432 from Sun Yat-sen University Cancer Center (SYSUCC; Guangzhou, China) into external testing cohort 1.
External testing cohort 2	MRI	198 from Dongguan Tungwah Hospital (DTH; Dongguan, China) and Shunde Hospital of Southern Medical University (SDHSMU; Guangzhou, China) into external testing cohort 2.

Primary Outcome Measures

Name	Time	Method
DFS	The time from surgery to tumor recurrence, including local and/or distant recurrence, disease progression, or death, assessed up to 100 months.	Disease-free survival