乳腺癌是世界范围内的女性首发恶性肿瘤，乳腺超声检查是乳腺癌筛查诊断的重要影像学方法，当前个体化医疗模式对超声影像与基因组、生物信息、临床数据融合的综合诊断提出了新挑战。本项目的目标是借助宏观影像学与分子生物学特性的综合分析运用，应用先进的影像组学分析技术研究超声多模态早期诊断乳腺癌和预后评估的新方法。.本研究将基于前期工作所建立的并不断扩充更新的超声图像数据库，获取超声多种模态图像的多层次、多参数特征，从病理组织学和分子生物学水平出发，寻找并筛选出有意义的诊断特征或特征组合，进行量化分析。在此基础上利用机器学习和数据挖掘方法，融合乳腺癌的易感基因、侵袭性关键基因信息，以及乳腺癌风险因素，建立乳腺癌的早期量化诊断和风险评估模型，并对其进行验证、临床评估和进一步优化。最终建立超声多模态乳腺癌影像组学研究的基础理论框架，获得早期诊断和预后评估的客观量化模型，提高乳腺癌早期诊断率和预后预测精度。

Breast cancer is one of malignant tumors with the highest incidence rate in women in the worldwide. Ultrasound technique has become a vital imaging method in screening and diagnosis for breast cancer. A new challenge is proposed by the current individual medical model towards the comprehensive diagnosis of the diseases by using ultrasonic imaging, genomes, biological information and clinical data. With the comprehensive analysis of the imaging and molecular and biological characteristics, applying the advanced radiomic analysis technique, the aim of this project is to explore a novel early diagnosis and prognosis evaluation method for breast cancer using multi-modal ultrasound imaging.. We will attempt to acquire multi-level and multi-parameter characteristics from multi-modal ultrasound imaging based on previous established ultrasound image database, which will be expanded and renewed. Then we will extract and select significant diagnostic features or the combination of these features from the histopathological and molecular biological levels. All of these characteristics and features will be analyzed quantitatively. Based on the above researches, the machine learning and data mining approach will be employed to comprehensively analyze the susceptible genes, key genetic information for invasion, as well as risk factors for breast cancer, to establish a model for the early quantitative diagnosis and risk evaluation, which will be validated, clinical evaluated and further optimized. Finally, we aim to establish a basic theoretical framework for the multi-modal breast cancer ultrasound radiomics study and to obtain an objective and quantitative model for the early diagnosis and prognosis evaluation. This study and this model will improve the early diagnostic rate and prediction accuracy for breast cancer.