Rydzewski Nicholas R, Yadav Poonam, Musunuru Hima Bindu, Condit Kevin M, Francis David, Zhao Shuang G, Baschnagel Andrew M
Department of Human Oncology, University of Wisconsin Hospital and Clinics, Madison, Wisconsin.
Carbone Cancer Center, University of Wisconsin Hospital and Clinics, Madison, Wisconsin.
Adv Radiat Oncol. 2021 Dec 29;7(3):100884. doi: 10.1016/j.adro.2021.100884. eCollection 2022 May-Jun.
Our purpose was to determine whether bone density and bone-derived radiomic metrics in combination with dosimetric variables could improve risk stratification of rib fractures after stereotactic body radiation therapy (SBRT) for early-stage non-small cell lung cancer (NSCLC).
A retrospective analysis was conducted of patients with early-stage NSCLC treated with SBRT. Dosimetric data and rib radiomic data extracted using PyRadiomics were used for the analysis. A subset of patients had bone density scans that were used to create a predicted bone density score for all patients. A 10-fold cross validated approach with 10 resamples was used to find the top univariate logistic models and elastic net regression models that predicted for rib fracture.
A total of 192 treatment plans were included in the study with a rib fracture rate of 16.1%. A predicted bone density score was created from a multivariate model with vertebral body Hounsfield units and patient weight, with an R-squared of 0.518 compared with patient dual-energy x-ray absorptiometry T-scores. When analyzing all patients, a low predicted bone density score approached significance for increased risk of rib fracture ( = .07). On competing risk analysis, when stratifying patients based on chest wall V30 Gy and bone density score, those with a V30 Gy ≥30 cc and a low bone density score had a significantly higher risk of rib fracture compared with all other patients ( < .001), with a predicted 2-year risk of rib fracture of 28.6% (95% confidence interval, 17.2%-41.1%) and 4.9% (95% confidence interval, 2.3%-9.0%), respectively. Dosimetric variables were the primary drivers of fracture risk. A multivariate elastic net regression model including all dosimetric variables was the best predictor of rib fracture (area under the curve [AUC], 0.864). Bone density variables (AUC, 0.618) and radiomic variables (AUC, 0.617) have better predictive power than clinical variables that exclude bone density (AUC, 0.538).
Radiomic features, including a bone density score that includes vertebral body Hounsfield units and radiomic signatures from the ribs, can be used to stratify risk of rib fracture after SBRT for NSCLC.
我们的目的是确定骨密度和骨衍生的放射组学指标与剂量学变量相结合是否能改善早期非小细胞肺癌(NSCLC)立体定向体部放射治疗(SBRT)后肋骨骨折的风险分层。
对接受SBRT治疗的早期NSCLC患者进行回顾性分析。使用PyRadiomics提取的剂量学数据和肋骨放射组学数据用于分析。一部分患者进行了骨密度扫描,用于为所有患者创建预测骨密度评分。采用10重交叉验证方法和10次重采样来寻找预测肋骨骨折的最佳单变量逻辑模型和弹性网回归模型。
该研究共纳入192个治疗计划,肋骨骨折发生率为16.1%。通过包含椎体Hounsfield单位和患者体重的多变量模型创建了预测骨密度评分,与患者双能X线吸收法T评分相比,决定系数R²为0.518。在分析所有患者时,低预测骨密度评分接近肋骨骨折风险增加的显著性水平(P = 0.07)。在竞争风险分析中,根据胸壁V30 Gy和骨密度评分对患者进行分层时,V30 Gy≥30 cc且骨密度评分低的患者与所有其他患者相比,肋骨骨折风险显著更高(P < 0.001),预测的2年肋骨骨折风险分别为28.6%(95%置信区间,17.2% - 41.1%)和4.9%(95%置信区间,2.3% - 9.0%)。剂量学变量是骨折风险的主要驱动因素。包含所有剂量学变量的多变量弹性网回归模型是肋骨骨折的最佳预测模型(曲线下面积[AUC],0.864)。骨密度变量(AUC,0.618)和放射组学变量(AUC,0.