Department of Radiation Oncology, University of California Davis School of Medicine, Sacramento, CA.
Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA.
Clin Lung Cancer. 2017 Nov;18(6):e425-e431. doi: 10.1016/j.cllc.2017.05.014. Epub 2017 May 25.
Over 50% of patients who receive stereotactic body radiotherapy (SBRT) develop radiographic evidence of radiation-induced lung injury. Radiomics is an emerging approach that extracts quantitative features from image data, which may provide greater value and a better understanding of pulmonary toxicity than conventional approaches. We aimed to investigate the potential of computed tomography-based radiomics in characterizing post-SBRT lung injury.
A total of 48 diagnostic thoracic computed tomography scans (acquired prior to SBRT and at 3, 6, and 9 months post-SBRT) from 14 patients were analyzed. Nine radiomic features (ie, 7 gray level co-occurrence matrix [GLCM] texture features and 2 first-order features) were investigated. The ability of radiomic features to distinguish radiation oncologist-defined moderate/severe lung injury from none/mild lung injury was assessed using logistic regression and area under the receiver operating characteristic curve (AUC). Moreover, dose-response curves (DRCs) for radiomic feature changes were determined as a function of time to investigate whether there was a significant dose-response relationship.
The GLCM features (logistic regression P-value range, 0.012-0.262; AUC range, 0.643-0.750) outperformed the first-order features (P-value range, 0.100-0.990; AUC range, 0.543-0.661) in distinguishing lung injury severity levels. Eight of 9 radiomic features demonstrated a significant dose-response relationship at 3, 6, and 9 months post-SBRT. Although not statistically significant, the GLCM features showed clear separations between the 3- or 6-month DRC and the 9-month DRC.
Radiomic features significantly correlated with radiation oncologist-scored post-SBRT lung injury and showed a significant dose-response relationship, suggesting the potential for radiomics to provide a quantitative, objective measurement of post-SBRT lung injury.
超过 50%接受立体定向体放射治疗（SBRT）的患者出现放射性肺损伤的影像学证据。放射组学是一种新兴的方法，它可以从图像数据中提取定量特征，与传统方法相比，它可能提供更大的价值和对肺毒性的更好理解。我们旨在研究基于计算机断层扫描的放射组学在描述 SBRT 后肺损伤中的潜力。
对 14 名患者的 48 例诊断性胸部 CT 扫描（在 SBRT 前和 SBRT 后 3、6 和 9 个月采集）进行了分析。研究了 9 个放射组学特征（即 7 个灰度共生矩阵[GLCM]纹理特征和 2 个一阶特征）。使用逻辑回归和受试者工作特征曲线（ROC）下面积（AUC）评估放射组学特征区分放射肿瘤学家定义的中重度肺损伤与无/轻度肺损伤的能力。此外，还确定了放射组学特征变化的剂量-反应曲线（DRC），作为随时间推移的函数，以研究是否存在显著的剂量-反应关系。
GLCM 特征（逻辑回归 P 值范围，0.012-0.262；AUC 范围，0.643-0.750）在区分肺损伤严重程度水平方面优于一阶特征（P 值范围，0.100-0.990；AUC 范围，0.543-0.661）。9 个放射组学特征中有 8 个在 SBRT 后 3、6 和 9 个月时表现出显著的剂量-反应关系。尽管没有统计学意义，但 GLCM 特征在 3 个月或 6 个月的 DRC 与 9 个月的 DRC 之间显示出明显的分离。
放射组学特征与放射肿瘤学家评分的 SBRT 后肺损伤显著相关，并表现出显著的剂量-反应关系，这表明放射组学有可能提供 SBRT 后肺损伤的定量、客观测量。
