RadxTools for assessing tumor treatment response on imaging

用于评估影像学肿瘤治疗反应的 RadxTools

基本信息

批准号：
10477947
负责人：
Pallavi Tiwari
金额：
$ 36.57万
依托单位：
CASE WESTERN RESERVE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10477947
关键词：
3-Dimensional Accounting Address Aftercare Alternative Therapies American American College of Radiology Imaging Network Benchmarking Benign Biophysics Biopsy Brain Neoplasms Cancer Patient Clinical Clinical Pathology Communities Complication Data Data Set Detection Development Diagnosis Diagnostic Imaging Disease Eastern Cooperative Oncology Group Educational workshop Evaluation Funding Growth Histopathology Image In complete remission Informatics Learning Lesion Magnetic Resonance Imaging Malignant neoplasm of brain Measures Mesorectal Morphologic artifacts Morphology Neoadjuvant Therapy Noise Oncologist Oncology Operative Surgical Procedures Optics Pathologic Pathology Patients Phenotype Positioning Attribute Professional Organizations Progressive Disease Quality Control Radiation Radiation necrosis Radiation therapy Radiology Specialty RadxTools Recording of previous events Rectal Cancer Recurrence Recurrent tumor Residual Tumors Residual state Resolution Resources Site Societies Southwest Oncology Group Specimen Surface The Cancer Imaging Archive Tissues Treatment Protocols Tumor Markers Validation Variant base cancer recurrence cancer therapy chemoradiation clinical imaging cohort computerized data resource digital feature extraction follow-up imaging informatics in vivo in vivo imaging informatics tool novel open source photonics prototype radiation effect radiological imaging radiomics responders and non-responders response routine imaging side effect success tool treatment effect treatment response tumor visual information working group

项目摘要

ABSTRACT: Over 1.6 million patients in the U.S. annually undergo chemo- or radiation- as first-line cancer therapy. After therapy, the most significant challenge for oncologists is identifying non-responders (those with residual or progressive disease), which could allow them to be switched to alternative therapies. Similarly, if those with stable or regressing disease were identified early and reliably, patients could avoid unnecessary and highly morbid surgeries or biopsies for disease confirmation. Unfortunately, expert assessment of post-treatment imaging is challenging, as residual disease is visually confounded with benign treatment-induced changes on imaging. There is hence a critical need for dedicated radiomic (computerized feature extraction from imaging) and informatics approaches to enable reliable post-treatment tumor assessment. Such tools will need to account for: (1) Limited well-curated data resources with deeply annotated pathology-validated radiographic datasets, for discovery and validation of new imaging and radiomic markers for post-treatment characterization in vivo; (2) Need for specialized radiomics tools that specifically quantify morphological perturbations in response to shrinkage/growth of the lesion for identifying progressive disease (versus benign confounders), despite presence of treatment-induced artifacts (exacerbated noise, reduced contrast, poor resolution); and (3) Lack of comprehensive quality control (QC) tools to identify which of a plethora of radiomic features are both discriminable as well as generalizable to variations between sites and scanners. To address these challenges, we propose RadxTools, a new image informatics toolkit comprising three modules: (a) RadQC to enable quality control of radiomics features across multi-site imaging cohorts, (b) RadTx comprising new radiomics tools which capture local surface morphometric changes and subtle structural deformations unique to tumor response on post-treatment imaging, and (c) RadPathFuse for creating deeply annotated learning sets by spatially mapping post-treatment changes from ex vivo surgically excised histopathology specimens onto pre-operative in vivo imaging. RadxTools will be evaluated in the context of post-treatment characterization for use cases in distinguishing (a) radiation effects from cancer recurrence for brain tumors; and (b) complete/partial vs incomplete chemoradiation response for rectal cancers. Deliverables and Dissemination: Our team has had a successful history of disseminating informatics tools (>1000 downloads), including our most recent release of RadTx which has been integrated into 3 informatics platforms. By organizing community resources and targeted workshops, as well as releasing highly curated data cohorts, our team is uniquely positioned to disseminate RadxTools to the radiomics/imaging community, professional societies, and oncology working groups. Our deliverables will include tool prototypes as modules within 5 QIN/ITCR-funded platforms (3D Slicer, MeVisLab, Sedeen, CapTk, QIFP) for widespread dissemination to targeted end-user communities, in addition to deeply annotated learning sets assembled through the 2 use-cases in this project.

摘要：美国每年有超过 160 万患者接受化疗或放疗作为一线癌症治疗治疗。治疗后，肿瘤学家面临的最重大挑战是识别无反应者（那些有反应的人）残留或进展性疾病），这可以让他们转向替代疗法。同样，如果那些病情稳定或消退的患者能够及早可靠地被识别出来，患者可以避免不必要的和用于确诊疾病的高发病率手术或活组织检查。不幸的是，专家对治疗后的评估成像具有挑战性，因为残余疾病在视觉上与良性治疗引起的变化混淆成像。因此，迫切需要专用放射组学（从成像中提取计算机特征）和信息学方法，以实现可靠的治疗后肿瘤评估。此类工具需要考虑用于：(1) 有限的精心策划的数据资源，具有经过深入注释的病理学验证的放射线照相数据集，用于发现和验证新的成像和放射组学标记，用于体内治疗后表征； (2) 需要专门的放射组学工具来专门量化形态学扰动以响应尽管存在病变，但仍可通过病变的缩小/生长来识别进行性疾病（与良性混杂因素相比）治疗引起的伪影（噪声加剧、对比度降低、分辨率差）； (3) 缺乏全面的质量控制 (QC) 工具，用于识别众多放射组学特征中的哪一个既是可区分并可推广到站点和扫描仪之间的差异。为了应对这些挑战，我们提出 RadxTools，一个新的图像信息学工具包，包含三个模块：(a) RadQC 以确保质量控制多位点成像队列的放射组学特征，(b) RadTx 包含新的放射组学工具，捕获肿瘤反应特有的局部表面形态变化和微妙的结构变形治疗后成像，以及 (c) RadPathFuse，用于通过空间映射创建深度注释的学习集从离体手术切除的组织病理学标本到术前体内的治疗后变化成像。 RadxTools 将在以下用例的治疗后表征的背景下进行评估区分 (a) 脑肿瘤的辐射效应与癌症复发； (b) 完整/部分 vs 直肠癌放化疗反应不完全。可交付成果和传播：我们的团队已经传播信息学工具的成功历史（> 1000次下载），包括我们最新发布的 RadTx 已集成到 3 个信息学平台中。通过组织社区资源并有针对性研讨会，以及发布精心策划的数据队列，我们的团队具有独特的优势来传播 RadxTools 面向放射组学/影像学界、专业协会和肿瘤学工作组。我们的可交付成果将包括作为 5 个 QIN/ITCR 资助平台（3D Slicer、MeVisLab、 Sedeen、CapTk、QIFP），除了深入了解之外，还可以广泛传播到目标最终用户社区通过本项目中的 2 个用例组装的带注释的学习集。