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

Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University1,500 enrolled

Overview

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.

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.

Study Type

OBSERVATIONAL

Enrollment

1,500

Conditions

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

Interventions

No interventionsOTHER

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

Eligibility

Sex: FEMALEMin age: 18 YearsMax age: 75 Years

Medical Language ↔ Plain English

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

Locations (5)

Tungwah Hospital of Sun Yat-Sen University

Dongguan, Guangdong, China

NOT_YET_RECRUITING

Shunde hospital of southern medical university

Foshan, Guangdong, China

RECRUITING

Sun Yat-sen University Cancer Center

Guangzhou, Guangdong, China

Outcomes

Primary Outcomes

Disease free survival (DFS)

Disease 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.

Time frame: 5 years

Secondary Outcomes

The correlation of radiomics features and tumor microenvironment

Radiomics is a tool to analyze tumor microenvironment characteristics based on breast MRI images.

Time frame: baseline (Completed MRI data before biopsy,surgery,neoadjuvant and radiotherapy.)

Lymph node metastasis

The value of Radiomics of multiparametric MRI in predicting axillary lymph node metastasis.

Time frame: Baseline

Overall survival (OS)

The 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.

Time frame: 5 years

Beast cancer specific motality (BCSM)

Defined as time between randomization and the time of death occur specific due to breast cancer

Time frame: 5 years

Recurrence free survival (RFS)

defined as time between randomization and the time of any recurrence of ipsilateral chest, breast, regional lymph node recurrence, distant metastases, or death occurred

Time frame: 5 years

Central Contacts

Herui Yao, PhD

CONTACT

+8613500018020 yaoherui@mail.sysu.edu.cn

Yunfang Yu, MD

CONTACT

+8613660238987 yuyf9@mail.sysu.edu.cn

Data from ClinicalTrials.gov

This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.