Defining a Radiomic Response Phenotype: A Pilot Study using targeted therapy in NSCLC.
发表日期： 2016.09.20 来源：Sci Rep. 2016; 6: 33860.
Aerts HJ1,2,3, Grossmann P1,3, Tan Y4, Oxnard GG5 , Rizvi N6, Schwartz LH4, Zhao B4.
1. Departments of Radiation Oncology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
2. Departments of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
3. Departments of Biostatistics & Computational Biology Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
4. Departments of Radiology Columbia University College of Physicians and Surgeons and New York Presbyterian Hospital, New York, NY, USA.
5. Department of Medicine, Departments of Lowe Center for Thoracic Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
6. Department of Medicine, Division of Oncology Columbia University College of Physicians and Surgeons and New York Presbyterian Hospital, New York, NY, USA.
医学成像通过提供非侵入性的方法使肿瘤表型可视化，在肿瘤学和药物开发中起着重要作用。影像组学可以通过应用图像表征算法全面地量化这种表型，并且可以提供肿瘤大小或负担以外的重要信息。在这项研究中，我们研究了影像组学能否可以识别吉非替尼反应表型，研究了在治疗三周之前和之后的47名早期非小细胞肺癌患者的高分辨率计算机断层扫描（CT）成像。在基线扫描中，影像组学特征劳斯能量能显著预测EGFR突变状态（AUC = 0.67，p = 0.03），但体积（AUC = 0.59，p = 0.27）和直径（AUC = 0.56，p = 0.46）没有差异。虽然治疗后扫描没有预测特征（p < 0.08），但两次扫描之间的特征变化是强预测性的（显著特征AUC范围= 0.74-0.91）。技术验证表明，相关特征对于测试-重测也是非常稳定的（平均值±标准差：ICC = 0.96±0.06）。这项初步研究显示，治疗前的影像组学数据能够非侵入性地预测突变状态和相关的吉非替尼反应，证明了基于影像组学基础的表型预测潜力，以改善酪氨酸激酶抑制剂（TKIs）敏感和耐药患者群体之间的分层和反应评估。
Medical imaging plays a fundamental role in oncology and drug development, by providing a non-invasive method to visualize tumor phenotype. Radiomics can quantify this phenotype comprehensively by applying image-characterization algorithms, and may provide important information beyond tumor size or burden. In this study, we investigated if radiomics can identify a gefitinib response-phenotype, studying high-resolution computed-tomography (CT) imaging of forty-seven patients with early-stage non-small cell lung cancer before and after three weeks of therapy. On the baseline-scan, radiomic-feature Laws-Energy was significantly predictive for EGFR-mutation status (AUC = 0.67, p = 0.03), while volume (AUC = 0.59, p = 0.27) and diameter (AUC = 0.56, p = 0.46) were not. Although no features were predictive on the post-treatment scan (p < 0.08), the change in features between the two scans was strongly predictive (significant feature AUC-range = 0.74–0.91). A technical validation revealed that the associated features were also highly stable for test-retest (mean ± std: ICC = 0.96 ± 0.06). This pilot study shows that radiomic data before treatment is able to predict mutation status and associated gefitinib response non-invasively, demonstrating the potential of radiomics-based phenotyping to improve the stratification and response assessment between tyrosine kinase inhibitors (TKIs) sensitive and resistant patient populations.