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

SBIR 主题 417：乳腺癌精准医学的定量放射基因组学

• 批准号: 10496817
• 负责人: THOMAS TAYLOR
• 金额: $ 39.86万
• 依托单位: INFOTECH SOFT, INC.
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2022-06-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10496817
• 关键词: Address; Architecture; Bioinformatics; Biopsy; Breast Cancer Model; Cancer Patient; Characteristics; Clinic; Clinical Data; Computer software; Data; Development; Diagnosis; Environment; Evaluation; Genomics; Goals; Image; Individual; Intuition; Lesion; Magnetic Resonance Imaging; Medical Imaging; Modeling; Molecular; Oncology; Output; Patients; Performance; Phase; Phenotype; Play; Plug-in; Process; Radiogenomics; Radiology Specialty; Risk; Role; Site; Small Business Innovation Research Grant; Source; Technology; Vendor; breast lesion; cancer subtypes; deep learning; design; diagnostic accuracy; digital imaging; experience; graphical user interface; imaging platform; innovation; malignant breast neoplasm; molecular subtypes; open source; personalized cancer therapy; personalized medicine; precision medicine; prognostic model; prototype; quantitative imaging; radiomics; response; shared database; treatment planning; tumor; usability; user-friendly

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.

先进医学成像的使用可以提供一种非侵入性手段来准确评估分子亚型，克服活检的局限性。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，对患者的风险较低。因此，结合放射组学；指从数字图像中提取定量数据并将其与临床数据存储在共享数据库中，以及放射基因组学；将基因组和放射组学数据关联起来，有望在个体病变的诊断中发挥关键作用。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表征乳腺癌肿瘤，创建高效且稳健的预后模型，帮助临床医生为癌症患者做出个性化治疗决策。 (1) 开发结合放射组学和基因组特征的放射基因组学模型，能够推断乳腺癌亚型、分期和 MRI 图像中捕获的肿瘤的反应标准。 (2) 验证模型的诊断准确性、治疗计划的实用性及其跨多个站点和供应商平台的通用性，以显示对个性化癌症治疗产生广泛影响的潜力。 (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。