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Using Deep Learning Methods to Analyze Automated Breast Ultrasound Images to Establish a Diagnosis Therapy Assessment and Prognosis Prediction Model of Breast Cancer.

The purpose of this study is using a deep learning method to analyze the automated breast ultrasound (ABUS) imagings, establish and evaluate a diagnosis, therapy assessment and prognosis prediction model of breast cancer. The model would provide important references for further early prevention, early diagnosis and personalized treatment.

AXillary Surgery After NeoAdjuvant Treatment

The optimal surgical axillary staging technique in patients who convert from the clinically positive to clinically negative lymph node status under neoadjuvant therapy (cN+ ycN0) remains to be clarified. Different strategies (axillary lymph node dissection, sentinel node biopsy, targeted axillary dissection) are currently used in different countries. A prospective analysis …

Antler Plate on Preventing Mammary High-risk Lesions From Malignant Development

This study aims to prove whether antler disk extract can be put into clinical practice to prevent breast precancerous lesions from developing into cancer.

Transcutaneous Breast Cancer Diagnosis by Canine Odorology

Patient with an indication of interventional percutaneous procedure for characterization of a breast lesion, palpable or not, classified as category 4 or 5, according to the Breast Imaging (BI) Report and Data System of the American College of Radiology (RADS) classification detected at mammography and/or breast ultrasound examination.

SBRT for Breast Cancer Oligometastases

This is a prospective data collection of treatment outcome for newly diagnosed oligometastatic breast cancers with 1-3 bone metastases. Eligible patients will be identified from the weekly Breast MDC. Patients will receive the recommended systemic and local treatment (including metastases directed SBRT) according to our clinical practice guidelines.Patients will be …

Liquid Biopsies and Imaging in Breast Cancer

The aim of the study is to show proof of concept for combining multi-parametric MRI with liquid biopsies in addition to conventional clinical and pathologic information, to accurately predict response to neoadjuvant treatment for patients with primary breast cancer.