Peulen Heike, Mantel Frederick, Guckenberger Matthias, Belderbos José, Werner-Wasik Maria, Hope Andrew, Giuliani Meredith, Grills Inga, Sonke Jan-Jakob
Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands.
Department of Radiation Oncology, University of Wuerzburg, Wuerzburg, Germany; Department of Radiation Oncology, University Hospital Zurich, Zurich, Switzerland.
Int J Radiat Oncol Biol Phys. 2016 Sep 1;96(1):134-41. doi: 10.1016/j.ijrobp.2016.04.003. Epub 2016 Apr 12.
Fibrotic changes after stereotactic body radiation therapy (SBRT) for stage I non-small cell lung cancer (NSCLC) are difficult to distinguish from local recurrences (LR), hampering proper patient selection for salvage therapy. This study validates previously reported high-risk computed tomography (CT) features (HRFs) for detection of LR in an independent patient cohort.
From a multicenter database, 13 patients with biopsy-proven LR were matched 1:2 to 26 non-LR control patients based on dose, planning target volume (PTV), follow-up time, and lung lobe. Tested HRFs were enlarging opacity, sequential enlarging opacity, enlarging opacity after 12 months, bulging margin, linear margin disappearance, loss of air bronchogram, and craniocaudal growth. Additionally, 2 new features were analyzed: the occurrence of new unilateral pleural effusion, and growth based on relative volume, assessed by manual delineation.
All HRFs were significantly associated with LR except for loss of air bronchogram. The best performing HRFs were bulging margin, linear margin disappearance, and craniocaudal growth. Receiver operating characteristic analysis of the number of HRFs to detect LR had an area under the curve (AUC) of 0.97 (95% confidence interval [CI] 0.9-1.0), which was identical to the performance described in the original report. The best compromise (closest to 100% sensitivity and specificity) was found at ≥4 HRFs, with a sensitivity of 92% and a specificity of 85%. A model consisting of only 2 HRFs, bulging margin and craniocaudal growth, resulted in a sensitivity of 85% and a specificity of 100%, with an AUC of 0.96 (95% CI 0.9-1.0) (HRFs ≥2). Pleural effusion and relative growth did not significantly improve the model.
We successfully validated CT-based HRFs for detection of LR after SBRT for early-stage NSCLC. As an alternative to number of HRFs, we propose a simplified model with the combination of the 2 best HRFs: bulging margin and craniocaudal growth, although validation is warranted.
立体定向体部放射治疗（SBRT）治疗I期非小细胞肺癌（NSCLC）后的纤维化改变难以与局部复发（LR）相区分，这妨碍了对挽救治疗的合适患者选择。本研究在一个独立患者队列中验证了先前报道的用于检测LR的高风险计算机断层扫描（CT）特征（HRF）。
从一个多中心数据库中，根据剂量、计划靶体积（PTV）、随访时间和肺叶，将13例经活检证实为LR的患者与26例非LR对照患者按1:2进行匹配。测试的HRF包括增大的不透光区、连续增大的不透光区、12个月后增大的不透光区、边缘膨出、线性边缘消失、空气支气管造影消失以及头尾方向生长。此外，分析了2个新特征：新出现的单侧胸腔积液的发生情况，以及通过手动勾勒评估的基于相对体积的生长情况。
除空气支气管造影消失外，所有HRF均与LR显著相关。表现最佳的HRF是边缘膨出、线性边缘消失和头尾方向生长。检测LR的HRF数量的受试者操作特征分析的曲线下面积（AUC）为0.97（95%置信区间[CI]0.9 - 1.0），与原始报告中描述的性能相同。在≥4个HRF时发现了最佳折衷（最接近100%敏感性和特异性），敏感性为92%，特异性为85%。仅由2个HRF（边缘膨出和头尾方向生长）组成的模型，敏感性为85%，特异性为100%，AUC为0.96（95%CI 0.9 - 1.0）（HRF≥2）。胸腔积液和相对生长并未显著改善该模型。
我们成功验证了基于CT的HRF用于早期NSCLC患者SBRT后LR的检测。作为HRF数量的替代方法，我们提出了一个简化模型，将2个最佳HRF（边缘膨出和头尾方向生长）结合起来，尽管仍需进行验证。
