Neoplasms

Symptoms and General Pathology

All Conditions

Skin and Connective Tissue Diseases

Neoplasm Metastasis

Breast Neoplasms

Lymphatic Metastasis

Prosthesis Failure

Breast Diseases

Skin Diseases

Neoplastic Processes

Postoperative Complications

The cohort of Sun Yat-Sen Memorial Hospital of Sun Yat-sen University is a training cohort.

The cohort of Sun Yat-sen University Cancer Center is a validation cohort.

The cohort of Tungwah Hospital of Sun Yat-Sen University is a validation cohort.

The cohort of Shunde hospital of southern medical university is a validation cohort.

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

No interventions

Herui Yao, PhD

Yunfang Yu, MD

Jie Ouyang, PhD

Qiugen Hu, PhD

Xiaohong Li, MD

Chuanmiao Xie, PhD

Nian Lu, MD

Haotian Lin, PhD

Wenben Chen, MD

Yufang Yu, MD

Yujie Tan, MD

Kai Chen, MD

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.

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

Requests for the individual data or study documents will be considered where the proposed use aligns with public good purposes, does not conflict with other requests, and the requestor is willing to sign a data access agreement. Contact is though the corresponding author.

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.

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

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

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.

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

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

Sun Yat-sen University

Tungwah Hospital of Sun Yat-Sen University

Southern Medical University, China

Zhongshan Ophthalmic Center, Sun Yat-sen University

Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

Principal Investigator

Cohort of Sun Yat-Sen Memorial Hospital of Sun Yat-sen University is the training cohort.

Cohort of Sun Yat-sen University Cancer Center is validation cohort 1.

Cohort of Tungwah Hospital of Sun Yat-Sen University is validation cohort 2.

Herui Yao, Ph. D

Chenchen Li, MD

This study is aimed to illustrate whether Radiomics combining multiparametric MRI before and after neoadjuvant chemotherapy (NACT) with clinical data is a good way to predict axillary lymph node metastasis and prognosis in invasive-breast-cancer.

This study proposes to build a clinical predictive model to predict axillary lymph node metastasis and prognosis in invasive-breast-cancer patients who received neoadjuvant chemotherapy before surgery. The model is built based on breast MRI signatures extracted and analyzed via deep machine-learning algorithm methods. Invasive breast cancer patients undergo multiparametric MRI at baseline, then undergo multiparametric MRI after received neoadjuvant chemotherapy for at least 4 cycles as planned. After the surgery, responses to neoadjuvant chemotherapy are determined according to the histopathologically examination of the surgically resected specimens. After completion of treatment procedure, patients are followed up for 5 years.

MRI Radiomics Assessing Neoadjuvant Chemotherapy in Breast Cancer to Predict Lymph Node Metastasis and Prognosis(RBC-02)

Novel Radiomics Signature on MRI Before and After Neoadjuvant Chemotherapy in Breast Cancer to Predict Axillary Lymph Node Metastasis and Prognosis (RBC-02)

The association between Radiomics of multiparametric MRI and 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.

The value of Radiomics of breast MRI in predicting responses to neoadjuvant chemotherapy, including reaching pCR and not reaching pCR.

The value of Radiomics of breast MRI in predicting pathological axillary lymph node status is defined as axillary lymph node metastasis exists or not.

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

Principal Investigation

Preoperative conventional ultrasound (US), elastic ultrasound (UE) and contrast-enhanced ultrasound (CEUS) images were analyzed. Histopathological results were used as the gold standard. The cases were randomly divided into training set and test set with a ratio of 7:3. The US image, UE image and CEUS image of the maximum long-axis section of each lesion were selected, and the region of interest (ROI) of the lesion was manually delineated.

Ultrasonic image analysis

baojie wen, doctor

The project is proposed based on multimodal ultrasonic imaging omics building used for accurate prediction of the breast cancer and axillary lymph node metastasis load artificial intelligence forecasting model, this method can dig the hidden features of ultrasonic image is not visible to the naked eye, make up the subjectivity in the process of clinical doctors in diagnosis and treatment, provide accurate, objective basis for clinical decision making.

Ultrasound Radiomics for Predicting Breast Cancer and Axillary Lymph Node Metastasis

Accurate Prediction of Breast Cancer and Axillary Lymph Node Metastasis Burden by Multi-modal Ultrasound-based Radiomics

ROC curve, sensitivity, specificity, accuracy, decision curve

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
Deep Learning Algorithms for Prediction of Lymph Node Metastasis and Prognosis in Breast Cancer MRI Radiomics (RBC-01)	NCT04003558	Breast Neoplasm... Early-stage Bre... Radiomics Axillary Lymph ... Survival, Prost...	No intervention...	18 Years - 75 Years	Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
MRI Radiomics Assessing Neoadjuvant Chemotherapy in Breast Cancer to Predict Lymph Node Metastasis and Prognosis(RBC-02)	NCT04004559	Invasive Breast... Neoadjuvant Che... Radiomics Axillary Lymph ... Prognosis	No intervention...	18 Years - 75 Years	Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Ultrasound Radiomics for Predicting Breast Cancer and Axillary Lymph Node Metastasis	NCT05768451	Breast Cancer Axillary Lymph ...	Ultrasonic imag...	18 Years - 90 Years	The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School
Deep Learning Algorithms for Prediction of Lymph Node Metastasis and Prognosis in Breast Cancer MRI Radiomics (RBC-01)	NCT04003558	Breast Neoplasm... Early-stage Bre... Radiomics Axillary Lymph ... Survival, Prost...	No intervention...	18 Years - 75 Years	Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

Spots Global Cancer Trial Database for axillary lymph node metastasis