Departments of Radiation Oncology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Departments of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Sci Rep. 2016 Sep 20;6:33860. doi: 10.1038/srep33860.
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.
医学影像学在肿瘤学和药物开发中起着至关重要的作用，它提供了一种非侵入性的方法来可视化肿瘤表型。通过应用图像特征化算法，放射组学可以全面地量化这种表型，并可能提供超出肿瘤大小或负担的重要信息。在这项研究中，我们研究了放射组学是否可以识别吉非替尼反应表型，对 47 名早期非小细胞肺癌患者在治疗前和治疗后 3 周的高分辨率计算机断层扫描（CT）成像进行了研究。在基线扫描中，Laws-Energy 放射组学特征与 EGFR 突变状态显著相关（AUC=0.67，p=0.03），而体积（AUC=0.59，p=0.27）和直径（AUC=0.56，p=0.46）则没有。尽管在治疗后扫描中没有特征具有预测性（p>0.08），但两次扫描之间特征的变化具有很强的预测性（显著特征 AUC 范围为 0.74-0.91）。技术验证表明，相关特征的测试-重测也具有高度稳定性（平均值±标准差：ICC=0.96±0.06）。这项初步研究表明，治疗前的放射组学数据能够无创地预测突变状态和相关的吉非替尼反应，证明了基于放射组学的表型分析在酪氨酸激酶抑制剂（TKI）敏感和耐药患者群体中的分层和反应评估方面具有潜在应用价值。
