Towards a Virtual Biopsy: An improved multimodal imaging biomarker to guide treatment decisions in neuro-oncology by combining advanced tissue microstructure imaging with deep learning

走向虚拟活检：一种改进的多模态成像生物标志物，通过将先进的组织微观结构成像与深度学习相结合来指导神经肿瘤学的治疗决策

• 批准号: 10325327
• 负责人: Nathan Scott White
• 金额: $ 25.47万
• 依托单位: CORTECHS LABS, INC.
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-22 至 2024-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10325327
• 关键词: 3-Dimensional; Apoptosis; Biological Markers; Biology; Biophysics; Biopsy; Brain; Brain Neoplasms; Cell Proliferation; Clinical; Computer software; Data; Data Set; Derivation procedure; Detection; Diagnosis; Diagnostic radiologic examination; Diffusion Magnetic Resonance Imaging; Discipline; Disease; Edema; Engineering; Ensure; Feedback; Generations; Glioma; Goals; Hybrids; Image; Imaging Device; Imaging Techniques; In complete remission; Institution; Label; Machine Learning; Magnetic Resonance Imaging; Malignant - descriptor; Malignant neoplasm of brain; Manuals; Maps; Measures; Modeling; Monitor; Multimodal Imaging; Necrosis; Normal tissue morphology; Operative Surgical Procedures; Outcome; Patient Care; Patients; Pattern; Performance; Phase; Prediction of Response to Therapy; Probability; Production; Prognosis; Progression-Free Survivals; Progressive Disease; Protocols documentation; Radiation Injuries; Radiation therapy; Radiology Specialty; Reporting; Reproducibility; Restriction Spectrum Imaging; Retrospective cohort; Risk; Site; Software Tools; Stable Disease; Standardization; Technology; Tissues; Training; Translating; Tumor Volume; Validation; angiogenesis; base; biomarker performance; brain tumor imaging; cancer clinical trial; chemotherapy; clinical decision support; clinical decision-making; cloud platform; contrast enhanced; convolutional neural network; data standards; deep learning; imaging biomarker; improved; interest; multimodality; neural network classifier; neuro-oncology; novel; novel therapeutics; outcome prediction; partial response; patient stratification; potential biomarker; predict clinical outcome; predictive marker; prognostic; quantitative imaging; radiologist; rate of change; recruit; research clinical testing; serial imaging; software development; spectrograph; support tools; tissue biomarkers; tissue injury; treatment response; tumor; tumor microenvironment; tumor progression; usability; validation studies; virtual biopsy; water diffusion

Project Summary/Abstract Despite advances in surgery, radiotherapy, and chemotherapy, the prognosis for neuro- oncology patients remains poor, with a mean survival of 12-15 months for high-grade gliomas. One major reason for poor survival is that it remains difficult to accurately assess tumor progression and treatment-related changes on standard imaging. This limits necessary information for guiding biopsies or resecting malignant tissue. In addition, new therapies can cause radiological patterns that obfuscate the underlying course of the disease. As a result, there is a critical need for new quantitative imaging tools to evaluate brain tumors and their progression. The goal of this proposal is to develop novel quantitative imaging biomarkers using the combination of advanced tissue microstructure imaging and deep learning to accurately discriminate tumor from non-tumor tissue, measure tumor progression and treatment response, and predict clinical outcomes. This approach utilizes restricted spectrum imaging (RSI), an advanced diffusion-weighted imaging technique that models the restricted diffusion of water to improve tumor conspicuity. Phase I of this proposal will develop a hybrid multimodal, RSI-based biomarker for brain cancer, quantify the biomarker in abnormal sub-regions, and demonstrate the biomarker’s performance in predicting clinical outcomes. Phase II of this proposal will develop and deploy commercial-grade software to the CorTechs Labs cloud platform, demonstrate its clinical usability and utility, and generate the materials required for a 510K FDA submission. The AI technology developed through this proposal will ultimately serve as a clinical decision support tool to improve clinician performance in diagnosing and evaluating brain tumors and predicting tumor response to treatment.

项目总结/摘要 尽管在手术、放疗和化疗方面取得了进展，但神经系统肿瘤的预后仍不乐观。 肿瘤患者仍然很差，高级别患者的平均生存期为12-15个月。 神经胶质瘤存活率低的一个主要原因是， 在标准成像上评估肿瘤进展和治疗相关变化。这 限制了引导活检或切除恶性组织的必要信息。在 此外，新的治疗方法可能会导致放射学模式，使潜在的 病程。因此，迫切需要新的定量成像 评估脑肿瘤及其进展的工具。本提案的目的是 开发新的定量成像生物标志物，使用先进的 组织微结构成像和深度学习，以准确区分肿瘤和 非肿瘤组织，测量肿瘤进展和治疗反应，并预测 临床结果。这种方法利用受限频谱成像（RSI）， 一种先进的扩散加权成像技术， 水以提高肿瘤的显著性。该提案的第一阶段将开发一种混合动力汽车， 多模式，基于RSI的脑癌生物标志物，定量异常 子区域，并证明生物标志物在预测临床 结果。本提案的第二阶段将开发和部署商业级软件 到CorTechs Labs云平台，展示其临床可用性和实用性，并 生成510 K FDA提交所需的材料。AI技术 通过该提案开发的最终将作为临床决策支持工具 提高临床医生在诊断和评估脑肿瘤方面的表现， 预测肿瘤对治疗的反应。