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.

高级医学成像的使用可以提供一种非侵入性手段，以精确评估克服活检局限性的分子亚型。医学图像可以在整个治疗过程中捕获肿瘤表型及其环境的全部图像，对患者的风险较低。因此，放射素学的组合；这是指从数字图像中提取和存储共享数据库中的临床数据和放射基因组学中的定量数据；将基因组和放射线数据相关联在诊断单个病变中起关键作用。该项目的目的是开发一个创新的平台，将放射组学和放射基因组学结合在一起，以诊断和表征整个治疗过程中的乳腺癌肿瘤，从而创建一个有效且稳健的预后模型，以帮助临床医生为癌症患者做出个性化治疗决策。 （1）开发了放射基因组学模型，结合了能够推断MRI图像中捕获的肿瘤的乳腺癌亚型，阶段和反应标准的放射线和基因组特征。 （2）验证该模型的诊断准确性，治疗计划的实用性以及其在多个站点和供应商平台上的一般性，以显示对个性化癌症治疗的广泛影响的潜力。 （3）发展直观的用户经验，支持在源MRI图像的背景下对模型输出的检查和解释，以支持个性化的治疗决策。