Radiomics of NSCLC

 描述（由申请人提供）：“放射组学”是从射线照相图像中提取和分析可开采的定量数据的过程。放射组学的首要假设是描述大小、形状和纹理的图像特征反映了潜在的肿瘤病理生理学，因此可以开发并鉴定为用于预测、证实或反应监测的生物标志物。放射组学旨在使用标准护理图像，允许开发和管理统计功效所需的大型数据集。在该奖项的第一个周期中，我们解决了“放射组学管道”中所有步骤的挑战，即（1）定义采集和图像重建对放射组学数据质量的影响;（2）策划以保持高数据质量;（3）鉴定半自动分割工具;（4）放射组学特征的统计鉴定;（5）放射组学特征的统计鉴定。（5）开发数据库共享工具，以便进行快速行动方案假设检验;（6）开发信息学方法，并将其应用于这些数据集。有了这个管道，我们从CT图像中识别并验证了准确预测手术或化疗-放疗肺癌患者生存率的特定特征。在这一竞争性的延续中，我们打算在此之前的工作基础上，将放射组学纳入非小细胞肺癌（NSCLC）患者术后管理的决策支持系统。NSCLC是全球癌症死亡的主要原因，因此，即使是决策支持的增量改进也会对患者的生活产生深远的影响。我们将使用并扩展我们已经开发的放射组学框架，以解决肺癌护理中一个引人注目的焦点问题：是否用辅助化疗（AT）治疗术后患者。几乎所有NSCLC手术候选人都接受高质量的诊断CT。早期NSCLC患者通常采用肺叶切除术和纵隔淋巴结切除术进行切除。其中，高达35%的人将在5年内经历远处复发。AT可以减少复发，但是否治疗的决定并不是微不足道的，因为AT与显著的发病率甚至死亡率相关。目前，这一决定仅因癌症分期而不充分。没有预测模型可以准确识别哪些患者复发的可能性最高，因此需要最积极的辅助随访。我们的提案将通过开发“复发风险”评分来解决这一重要的临床问题，该评分结合了来自两家机构的3，600多名患者的放射组学、临床和基因组数据。这些数据和分析工具将通过定量成像网络（QIN）框架内为此目的开发的独特工具进行共享。

 DESCRIPTION (provided by applicant): "Radiomics" is the process of extracting and analyzing mineable, quantitative data from radiographic images. The overarching hypothesis of Radiomics is that image features describing size, shape, and texture reflect underlying tumor pathophysiology and hence, can be developed and qualified as biomarkers for prediction, prognostication or response monitoring. Radiomics is designed to use standard-of-care images, allowing the development and curation of large data sets that are needed for statistical power. In the first cycle of this award, we addressed challenges to all steps in the "radiomic pipeline", viz (1) defining the impact of acquisition and image reconstruction on the quality of radiomics data; (2) curating to maintain high data quality; (3) qualifying semi-automated segmentation tools; (4) statistical qualification of radiomic features; (5) developing database sharing tools to allow rapi hypothesis testing; and (6) developing and applying informatics approaches to these datasets. With this pipeline, we identified and validated specific features from CT images that accurately predicted survival in lung cancer patients treated with surgery or chemo-radiation. In this competing continuation, we intend to build on this prior work to incorporate radiomics into a decision support system for post-surgery management of non-small cell lung cancer (NSCLC) patients. NSCLC is the leading cause of cancer deaths worldwide and hence, even incremental improvements in decision support can have a profound impact on patients' lives. We will use and extend the radiomics framework that we have developed to address a compelling and focused question in lung cancer care: whether to treat post-surgery patients with adjuvant chemotherapy (AT). Virtually all NSCLC surgical candidates receive high-quality diagnostic CTs. Early stage NSCLC patients are commonly resected with lobectomy and mediastinal lymph node removal. Of these, up to 35 percent will experience distant recurrence within 5 years. Recurrence can be reduced with AT, yet the decision whether or not to treat is not trivial, as AT is associated with significant morbidities and even mortality. This decision is currently ill-informed by cancer stage alone. There are no predictive models that can accurately identify which patients have the highest likelihood of recurrence, thus requiring most aggressive adjuvant follow-up. Our proposal will address this important clinical problem by development of a "Risk-of-Recurrence" score with a combination of radiomic, clinical and genomic data curated from over 3,600 patients from two institutions. These data and analytical tools will be shared via unique tools developed for this purpose within the framework of the Quantitative Imaging Network (QIN).