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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

1199 participants

Study Classification

OBSERVATIONAL

Study Start Date

2011-03-23

Study Completion Date

2021-12-06

Brief Summary

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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.

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Detailed Description

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Conditions

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Breast Cancer

Study Design

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Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Study Groups

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Training cohort

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.

MRI

Intervention Type OTHER

Internal validation cohort

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.

MRI

Intervention Type OTHER

External testing cohort 1

432 from Sun Yat-sen University Cancer Center (SYSUCC; Guangzhou, China) into external testing cohort 1.

MRI

Intervention Type OTHER

External testing cohort 2

198 from Dongguan Tungwah Hospital (DTH; Dongguan, China) and Shunde Hospital of Southern Medical University (SDHSMU; Guangzhou, China) into external testing cohort 2.