RadxTools for assessing tumor treatment response on imaging

摘要：美国每年有超过160万患者作为一线癌症接受化疗或放射治疗 心理治疗。治疗后，肿瘤学家面临的最大挑战是确定无反应者(那些有 残留或进展性疾病)，这可能使他们转向替代疗法。类似地，如果 病情稳定或消退的患者及早可靠地发现，患者可以避免不必要的和 高度病态的手术或活检以确认疾病。不幸的是，专家对治疗后的评估 成像是具有挑战性的，因为残留的疾病在视觉上与良性治疗引起的改变相混淆。 成像。因此，迫切需要专门的放射学(从成像中提取计算机化特征) 和信息学方法，以实现可靠的治疗后肿瘤评估。这样的工具将需要考虑 用于：(1)有限的精心策划的数据资源和经过深度注释的病理验证的放射数据集，用于 用于体内治疗后表征的新的成像和放射组学标记的发现和验证；(2) 需要专门的放射组学工具来具体量化形态扰动以响应 病变缩小/增长以识别进展性疾病(与良性混杂因素相比)，尽管存在 治疗引起的伪影(噪声加剧、对比度降低、分辨率差)；以及(3)缺乏 全面的质量控制(QC)工具，以确定过多的放射学特征中的哪一个既是 对于不同站点和扫描仪之间的差异，可进行区分和概括。为了应对这些挑战， 我们提出了一种新的图像信息学工具包RadxTools，它包括三个模块：(A)RadQC以实现质量 控制多部位成像队列的放射组学特征；(B)RadTx包括新的放射组学工具，该工具 捕捉肿瘤反应特有的局部表面形态计量学变化和细微结构变形 后处理成像，以及(C)RadPath Fuse，用于通过空间映射创建深度标注的学习集 治疗后从手术切除的体外组织病理标本到手术前的体内变化 成像。RadxTool将在以下使用案例的处理后表征的背景下进行评估 区分(A)放射对脑肿瘤复发的影响；以及(B)完全/部分与 直肠癌的不完全放化疗反应。交付成果和传播：我们的团队已经有了 传播信息工具的成功历史(&gt；1000下载)，包括我们最新发布的 RadTx，已集成到3个信息学平台。通过组织社区资源并有针对性地 研讨会，以及发布高度精选的数据队列，我们的团队处于独特的地位，可以传播 放射组学/成像社区、专业协会和肿瘤学工作组的RadxTools。我们的 交付成果将包括5个由QIN/ITCR资助的平台(3D Slicer、MeVisLab、 Sedeen、CapTk、QIFP)，以便广泛传播到目标最终用户社区，除了深度 通过本项目中的两个用例组装的带注释的学习集。

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.