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

NCT06546072 | Completed

• 1 other identifier: YSEC-KY-KS-2019-054-001
• Study Type: observational
• Target: 1,199
• Locations: 0 countries
• Sites: N/A

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.

Conditions

Breast Cancer

Keywords

deep learning

Outcome Measures

Primary Outcomes (1)

• DFS

  Disease-free survival

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

Study Arms (4)

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.

Other: MRI

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.

Other: MRI

External testing cohort 1

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

Other: MRI

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.

Other: MRI

Interventions

MRI OTHER

External testing cohort 1; External testing cohort 2; Internal validation cohort; Training cohort

Eligibility Criteria

• Age: 18 Years+
• Sex: female
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

In the study's initial phase, 1199 patients were randomly allocated at a ratio of 8:2 to training and testing datasets for automatic tumor segmentation. Subsequently, 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 for DFS prediction. The remaining patients were divided into two independent external-validation cohorts: 432 from Sun Yat-sen University Cancer Center (SYSUCC; Guangzhou, China) into external testing cohort 1, and 198 from Dongguan Tungwah Hospital (DTH; Dongguan, China) and Shunde Hospital of Southern Medical University (SDHSMU; Guangzhou, China) into external testing cohort 2.

You may qualify if:

• Histologically confirmed stage I-III invasive BC
• Age ≥ 18 years
• The patient having undergone surgery
• The existence of MRI scans

You may not qualify if:

• Lacked pathological results
• Had other, simultaneous malignancies
• Had MR imaging issues were excluded

Sponsors & Collaborators

• Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University: lead

MeSH Terms

Conditions

Breast Neoplasms

Condition Hierarchy (Ancestors)

Neoplasms by Site; Neoplasms; Breast Diseases; Skin Diseases; Skin and Connective Tissue Diseases

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: attending physician

Study Record Dates

• First Submitted: July 6, 2024
• First Posted: August 9, 2024
• Study Start: March 23, 2011
• Primary Completion: September 21, 2019
• Study Completion: December 6, 2021
• Last Updated: August 9, 2024

Record last verified: 2024-08