SBIR TOPIC 417: Quantitative Radiogenomics for Precision Medicine in Breast Cancer

使用先进的医学成像技术可以为准确评估分子亚型提供一种非侵入性手段，克服了活组织检查的局限性。医学图像可以捕捉整个治疗过程中肿瘤表型及其环境的全貌，对患者的风险很低。因此，放射组学和放射基因组学的结合将在个别病变的诊断中发挥关键作用。放射组学是指从数字图像中提取并存储定量数据和共享数据库中的临床数据，以及放射基因组学，它将基因组数据和放射数据联系起来。该项目的目标是开发一个结合放射组学和放射基因组学的创新平台，用于在整个治疗过程中诊断和描述乳腺癌肿瘤，创建一个有效和强大的预后模型，以帮助临床医生为癌症患者做出个性化的治疗决策。(1)建立结合放射学和基因组学特征的放射基因组学模型，能够对MRI图像中捕获的肿瘤推断乳腺癌亚型、分期和反应标准。(2)验证该模型的诊断准确性、治疗计划的实用性及其在多个站点和供应商平台上的通用性，以显示对个性化癌症治疗的广泛影响的潜力。(3)开发直观的用户体验，支持在源MRI图像的背景下检查和解释模型输出，以支持个性化治疗决策。

The use of advanced medical imaging can provide a non-invasive means for accurately assessing molecular subtypes overcoming the limitations of biopsies. Medical images can capture a full picture of tumor phenotypes and their environments throughout treatment with a low risk to patients. Therefore, the combination of radiomics; which refers to the extraction and storage of quantitative data from digital images with clinical data in a shared database, and radiogenomics; which correlates genomic and radiomic data is poised to play a critical role in the diagnosis of individual lesions. The goal of this project is to develop an innovative platform combining radiomics and radiogenomics for diagnosing and characterizing breast cancer tumors throughout therapy, creating an efficient and robust prognostic model to aid clinicians making personalized treatment decisions for cancer patients. (1) Develop radiogenomics model combining radiomic and genomic features capable of inferring breast cancer subtype, stage, and response criteria for tumors captured within MRI images. (2) Validate the model's diagnostic accuracy, utility for treatment planning and its generality across multiple sites and vendor platforms to show potential for widespread impact on personalized cancer treatment. (3) Develop intuitive user experience supporting the inspection and interpretation of model outputs in the context of source MRI images to support personalized treatment decisions.