Clinical trial NCT04003558: Deep Learning Algorithms for Prediction of Lymph Node Metastasis and Prognosis in Breast Cancer MRI Radiomics (RBC-01)
Official title: Deep Learning Algorithms for Prediction of Lymph Node Metastasis and Prognosis in Breast Cancer MRI Radiomics (RBC-01)
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Breast Neoplasm Female
Early-stage Breast Cancer
Radiomics
Axillary Lymph Node
Survival, Prosthesis
Keywords: Early-stage Breast Cancer
Radiomics
Axillary lymph node metastasis
Tumor microenvironment
Survival
Deep learning
Lead sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Sponsor class: OTHER
Collaborators: name: Sun Yat-sen University
name: Tungwah Hospital of Sun Yat-Sen University
name: Southern Medical University, China
name: Zhongshan Ophthalmic Center, Sun Yat-sen University
First posted: 2019-07-01
Enrollment: 1500
Sex: FEMALE
Minimum age (years): 18
Maximum age (years): 75
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients who had early stage breast cancer and completed the breast MRI examination before operation,lymph node biopsy,neoadjuvant chemotherapy,and radiotherapy.

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.

Eligibility criteria: 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

Primary outcomes:
measure: Disease free survival (DFS)
timeFrame: 5 years
Secondary outcomes:
measure: The correlation of radiomics features and tumor microenvironment
timeFrame: baseline (Completed MRI data before biopsy,surgery,neoadjuvant and radiotherapy.)
measure: Lymph node metastasis
timeFrame: Baseline
measure: Overall survival (OS)
timeFrame: 5 years
measure: Beast cancer specific motality (BCSM)
timeFrame: 5 years
measure: Recurrence free survival (RFS)
timeFrame: 5 years
Other outcomes:
Locations:
city: Dongguan
country: China
facility: Tungwah Hospital of Sun Yat-Sen University
state: Guangdong
city: Foshan
country: China
facility: Shunde hospital of southern medical university
state: Guangdong
city: Guangzhou
country: China
facility: Sun Yat-sen University Cancer Center
state: Guangdong
city: Guangzhou
country: China
facility: Zhongshan Ophthalmic Center, Sun Yat-Sen University
state: Guangdong
city: Guangzhou
country: China
facility: Sun Yat-Sen Memorial Hospital of Sun Yat-sen University
state: Guangdong
Study officials:
affiliation: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
name: Herui Yao, PhD
role: STUDY_CHAIR
affiliation: Sun Yat-sen University
name: Chuanmiao Xie, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: Tungwah Hospital of Sun Yat-Sen University
name: Jie Ouyang, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: Southern Medical University, China
name: Qiugen Hu, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: Zhongshan Ophthalmic Center, Sun Yat-sen University
name: Haotian Lin, PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Yu Y, He Z, Ouyang J, Tan Y, Chen Y, Gu Y, Mao L, Ren W, Wang J, Lin L, Wu Z, Liu J, Ou Q, Hu Q, Li A, Chen K, Li C, Lu N, Li X, Su F, Liu Q, Xie C, Yao H. Magnetic resonance imaging radiomics predicts preoperative axillary lymph node metastasis to support surgical decisions and is associated with tumor microenvironment in invasive breast cancer: A machine learning, multicenter study. EBioMedicine. 2021 Jul;69:103460. doi: 10.1016/j.ebiom.2021.103460. Epub 2021 Jul 4.