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.
Study Overview
Study Type
Enrollment (Actual)
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
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 Plan
How is the study designed?
Design Details
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
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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.
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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.
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External testing cohort 1
432 from Sun Yat-sen University Cancer Center (SYSUCC; Guangzhou, China) into external testing cohort 1.
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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.
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What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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DFS
Time Frame: The time from surgery to tumor recurrence, including local and/or distant recurrence, disease progression, or death, assessed up to 100 months.
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Disease-free survival
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The time from surgery to tumor recurrence, including local and/or distant recurrence, disease progression, or death, assessed up to 100 months.
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Collaborators and Investigators
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Actual)
Study Completion (Actual)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Keywords
Additional Relevant MeSH Terms
Other Study ID Numbers
- YSEC-KY-KS-2019-054-001
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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