RadxTools for assessing tumor treatment response on imaging

用于评估影像学肿瘤治疗反应的 RadxTools

• 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
• 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

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 疗法治疗后，肿瘤学家面临的最大挑战是识别无应答者（那些 残留或进行性疾病），这可能使他们能够改用替代疗法。同样如果 那些病情稳定或消退的患者被早期可靠地识别出来，患者可以避免不必要的， 高度病态的手术或活检以确认疾病。不幸的是，专家评估后治疗 成像是具有挑战性的，因为残留疾病在视觉上与良性治疗引起的变化混淆， 显像因此，迫切需要专门的放射组学（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。这些工具需要考虑到 （1）有限的精心策划的数据资源，具有深度注释的病理学验证的放射学数据集， 发现和验证用于体内治疗后表征的新成像和放射组学标记物;（2） 需要专门的放射组学工具，专门量化形态学扰动， 用于识别疾病进展的病变缩小/生长（与良性混杂因素相比），尽管存在 治疗引起的伪影（噪音加剧、对比度降低、分辨率差）;以及（3）缺乏 全面的质量控制（QC）工具，以确定哪些过多的放射组学特征都是 可辨别以及可推广到站点和扫描仪之间的变化。为了应付这些挑战， 我们提出了RadxTools，一个新的图像信息学工具包，包括三个模块：（a）RadQC，使质量 控制跨多位点成像群组的放射组学特征，（B）RadTx，包括新的放射组学工具， 捕获局部表面形态学变化和微妙的结构变形独特的肿瘤反应， 治疗后成像，以及（c）RadPathology，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体内手术前的处理后变化 显像RadxTools将在治疗后表征的背景下进行评估，用于以下用例： 区分（a）脑肿瘤的辐射效应与癌症复发;和（B）完全/部分与 直肠癌的不完全放化疗反应。宣传和传播：我们的团队有一个 成功传播信息学工具的历史（>1000次下载），包括我们最近发布的 RadTx已集成到3个信息学平台中。通过组织社区资源， 研讨会，以及发布高度策划的数据队列，我们的团队具有独特的优势，可以传播 RadxTools适用于放射组学/成像社区、专业协会和肿瘤学工作组。我们 可交付成果将包括作为5个QIN/ITCR资助平台（3D Slicer，MeVisLab， Seaport、CapTk、QIFP），以便向目标最终用户社区广泛传播， 通过本项目中的2个用例组装的带注释的学习集。