Automated imaging analysis tools to guide clinical decision making in brain tumor patients

自动成像分析工具指导脑肿瘤患者的临床决策

• 批准号: 10521259
• 负责人: Karthik Ramesh
• 金额: $ 4.77万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-12-06 至 2024-12-05
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10521259
• 关键词: 3-Dimensional; Address; Adjuvant Chemotherapy; Adopted; Adoption; Affect; Aftercare; Algorithms; Brain; Brain Neoplasms; Caring; Classification; Clinical; Clinical Data; Clinical Trials; Collaborations; Complex; Computer software; Control Groups; Data; Data Set; Databases; Dependence; Diagnosis; Disease; Disease Management; Disease Progression; Dose; Ensure; Environment; Evaluation; Excision; Face; Future; Glioblastoma; Goals; High Performance Computing; Image; Image Analysis; Imaging Device; Information Systems; Interobserver Variability; Intervention; Intuition; Language; Lesion; Longterm Follow-up; Magnetic Resonance Imaging; Malignant Neoplasms; Malignant neoplasm of brain; Manuals; Measurement; Medical Imaging; Methods; Monitor; Newly Diagnosed; Operative Surgical Procedures; Patient Care; Patient Monitoring; Patient imaging; Patient-Focused Outcomes; Patients; Pharmaceutical Preparations; Phase; Physicians; Play; Progression-Free Survivals; Radiation Oncologist; Radiation therapy; Radiology Specialty; Reader; Recurrence; Recurrent tumor; Reporting; Research; Research Proposals; Resources; Role; Scanning; Slice; Standardization; Structure; System; Testing; Training; Tumor Volume; United States; Universities; Validation; aggressive therapy; automated image analysis; automated segmentation; chemoradiation; clinical decision-making; cohort; convolutional neural network; deep learning; disease classification; efficacy evaluation; follow-up; imaging modality; implementation framework; improved; machine learning algorithm; multidisciplinary; network architecture; novel therapeutics; personalized medicine; prediction algorithm; predictive tools; quantitative imaging; recurrent neural network; response; segmentation algorithm; standard of care; success; tool; treatment effect; treatment strategy; tumor; tumor progression; two-dimensional; user-friendly

Project Summary Glioblastoma (GBM) is a common and aggressive form of brain cancer affecting up to 20,000 new patients in the US every year. Standard of care therapies include stereotactic surgical resection, radiation therapy, and adjuvant chemotherapy. After initial treatment, patient monitoring is guided by standard MR imaging performed at routine intervals. Despite these rigorous therapies, current median survival is only 15 months. Imaging is a central part of brain tumor management, but MRI findings in brain tumor patients can be challenging to interpret and is further confounded by interpretation variability. Accurate interpretation of imaging is particularly important during the post-treatment phase of patient care as it can help clinicians proactively manage disease through early, precise characterization of true tumor recurrence. Disease-specific structured reporting systems attempt to reduce variability in imaging results by implementing well-defined imaging criteria and standardized language. The Brain Tumor Reporting and Data System (BT-RADS), developed at Emory University, is one such framework streamlined for clinical workflows and includes quantitative criteria for more objective evaluation of follow-up imaging. While BT-RADS has had success with clinical adoptability, it still faces hurdles reducing interobserver variability and improving objective classification of disease state. This proposed study addresses an unmet need for unbiased, quantitative metrics for robust, objective interpretation of follow-up imaging for GBM patients. Where previous evaluative methods used two-dimensional, representative MRI slices for evaluating the extent of tumor, we propose to develop a deep learning segmentation tool, accurately calculating volumes of tumor after surgical resection, and trained from our expansive database of patient data with contours manually drawn by radiation oncologists. Further, we propose to develop computationally advanced software for predicting disease progression building upon the clinically developed BT- RADS criteria. Such tools would assist physicians in caring for brain tumor patients through post-treatment surveillance and guide future clinical decision making. In addition, we believe these quantitative metrics would have unbounded potential in clinical trial settings where it may be difficult to evaluate the efficacy of novel therapeutics for GBM. Therefore, these assistive tools will be tested on clinical trial data to determine if they are superior to conventional measurements alone. A fundamental goal of this proposal is ensuring the quantitative tools we develop are applicable for clinicians in their daily lives. Therefore, an effort will be made to collaborate extensively with clinicians to house the algorithms in sleek, intuitive software for physicians to utilize. Through the multidisciplinary environment, high performance computing, and clinical resources we have available, we believe this proposal will be successful in developing clinically assistive tools for unbiased, objective monitoring of GBM patients in clinical settings and evaluation of novel therapies in research settings.

项目摘要 胶质母细胞瘤（GBM）是一种常见的侵袭性脑癌， 美国每年都有患者。标准治疗包括立体定向手术切除、放射治疗 治疗和辅助化疗。初始治疗后，患者监测由标准MR成像指导 以常规间隔进行。尽管有这些严格的治疗，目前的中位生存期只有15个月。 成像是脑肿瘤治疗的核心部分，但脑肿瘤患者的MRI结果可能具有挑战性 解释，并进一步混淆了解释的可变性。对成像的准确解释尤其重要。 在患者护理的治疗后阶段非常重要，因为它可以帮助临床医生积极管理疾病 通过早期精确的肿瘤复发特征。特定疾病结构化报告系统 尝试通过实施明确定义的成像标准和标准化的 语言埃默里大学开发的脑肿瘤报告和数据系统（BT-RADS）就是这样一个系统。 简化了临床工作流程的框架，包括更客观评价 后续成像。虽然BT-RADS在临床上取得了成功，但它仍然面临着减少 观察者间变异性和改善疾病状态的客观分类。 这项拟议的研究解决了一个未满足的需求，即对稳健、客观、 GBM患者的随访成像的解释。以前的评估方法使用二维， 代表性的MRI切片用于评估肿瘤的程度，我们建议开发一种深度学习分割方法， 工具，精确计算手术切除后的肿瘤体积，并从我们庞大的数据库中进行训练 放射肿瘤学家手工绘制的患者数据轮廓。此外，我们建议发展 先进的计算软件，用于预测疾病进展的基础上，临床开发的BT- RADS标准。这些工具将有助于医生通过后期治疗来照顾脑肿瘤患者 监测和指导未来的临床决策。此外，我们认为这些量化指标将 在临床试验环境中具有无限的潜力，在临床试验环境中可能难以评价新的 GBM的治疗方法。因此，这些辅助工具将在临床试验数据上进行测试，以确定它们是否 上级单独的常规测量。 该提案的一个基本目标是确保我们开发的定量工具适用于 临床医生在日常生活中。因此，将努力与临床医生广泛合作， 这些算法被整合到一个简洁直观的软件中供医生使用。通过多学科环境，高 性能计算和我们可用的临床资源，我们相信这项建议将取得成功， 开发临床辅助工具，用于在临床环境中对GBM患者进行公正客观的监测， 在研究环境中评价新疗法。