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Deep Learning Algorithms for Prediction of Lymph Node Metastasis and Prognosis in Breast Cancer MRI Radiomics (RBC-01)

Deep Learning Algorithms for Prediction of Lymph Node Metastasis and Prognosis in Breast Cancer MRI Radiomics (RBC-01)

Status
UNKNOWN
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT04003558
Enrollment
1500
Registered
2019-07-01
Start date
2019-05-28
Completion date
2025-01-01
Last updated
2019-08-15

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Axillary Lymph Node, Breast Neoplasm Female, Early-stage Breast Cancer, Radiomics, Survival, Prosthesis

Keywords

Early-stage Breast Cancer, Radiomics, Axillary lymph node metastasis, Tumor microenvironment, Survival, Deep learning

Brief summary

This bi-directional, multicentre study aims to assess multiparametric MRI Radiomics-based prediction model for identifying metastasis lymph nodes and prognostic prediction in breast cancer.

Detailed description

Sensitivity for prediction of lymph node metastasis and survival of currently available prognostic scores in limited. This study proposes to establish a deep learning algorithms of multiparametric MRI radiomics and nomogram for identifying lymph node metastasis and prognostic prediction of breast cancer. The study will investigate the relationship between the radiomics and the tumor microenvironment. The study includes the construction of multiparametric MRI radiomics-based prediction model and the validation of the prediction model.

Interventions

OTHERNo interventions

As this is a patient registry, there are no interventions.

Sponsors

Sun Yat-sen University
CollaboratorOTHER
Tungwah Hospital of Sun Yat-Sen University
CollaboratorUNKNOWN
Southern Medical University, China
CollaboratorOTHER
Zhongshan Ophthalmic Center, Sun Yat-sen University
CollaboratorOTHER
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Lead SponsorOTHER

Study design

Observational model
COHORT
Time perspective
RETROSPECTIVE

Eligibility

Sex/Gender
FEMALE
Age
18 Years to 75 Years
Healthy volunteers
No

Inclusion criteria

* The primary lesion was diagnosed as invasive breast cancer * Patients can have regional lymph node metastasis,but no distant organ metastasis * Complete the breast MRI examination before treatment * Accept breast cancer surgery or lymph node biopsy * Eastern Cooperative Oncology Group performance status 0-2

Exclusion criteria

* Inflammatory breast cancer * Accompanied with other primary malignant tumors * Perform surgery,radiotherapy and lymph node biopsy before breast MRI examination * Patients who have neoadjuvant chemotherapy * Patients had distant and contralateral axillary lymph node metastasis * The pathologic diagnosis was extensive ductal carcinoma in situ

Design outcomes

Primary

MeasureTime frameDescription
Disease free survival (DFS)5 yearsDisease free survival (DFS), which defined as the time from the diagnosis of breast cancer to the confirmed time of metastatic disease, or death due to any other cause.

Secondary

MeasureTime frameDescription
The correlation of radiomics features and tumor microenvironmentbaseline (Completed MRI data before biopsy,surgery,neoadjuvant and radiotherapy.)Radiomics is a tool to analyze tumor microenvironment characteristics based on breast MRI images.
Lymph node metastasisBaselineThe value of Radiomics of multiparametric MRI in predicting axillary lymph node metastasis.
Overall survival (OS)5 yearsThe association between Radiomics of multiparametric MRI and overall survival (OS), which defined as the time from the beginning of diagnosis of breast cancer to the death with any causes.
Beast cancer specific motality (BCSM)5 yearsDefined as time between randomization and the time of death occur specific due to breast cancer
Recurrence free survival (RFS)5 yearsdefined as time between randomization and the time of any recurrence of ipsilateral chest, breast, regional lymph node recurrence, distant metastases, or death occurred

Countries

China

Contacts

Primary ContactHerui Yao, PhD
yaoherui@mail.sysu.edu.cn+8613500018020
Backup ContactYunfang Yu, MD
yuyf9@mail.sysu.edu.cn+8613660238987

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 22, 2026