Ogbonna Chioma P, Breen William G, Le Noach Pierre, Rajagopalan Srinivasan, Hostetter Logan J, Maldonado Fabien, Bartholmai Brian J, Merrell Kenneth W, Peikert Tobias
Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine.
Department of Radiology, and.
Ann Am Thorac Soc. 2025 Aug;22(8):1236-1243. doi: 10.1513/AnnalsATS.202410-1047OC.
Stereotactic body radiation therapy (SBRT) represents an effective therapeutic strategy for early-stage non-small cell lung cancer (NSCLC); however, local and systemic recurrences represent ongoing challenges. Computed tomography (CT) radiomics-based risk models can potentially be used to predict the risk of local recurrence on pretreatment CT scans. Development of a radiomics model to predict local recurrence after SBRT in patients with NSCLC. This single-institution study includes a retrospective case-control training set (20 patients with local recurrence and 40 control subjects) and an independent validation set (198 consecutive cases) of patients with early-stage NSCLC treated with SBRT. Tumors were semiautomatically segmented, and 102 quantitative radiomic features, including texture, landscape, spatial, nodule shape, and nodule surface features, were extracted. These features were included in three separate multivariable models to predict the risk of recurrence on the basis of pre-SBRT, post-SBRT, and the difference between the pre-SBRT and post-SBRT scans (Delta model). The pre-SBRT model was subsequently validated in an independent validation set. Thirteen independent variables were selected for the models using the Boruta algorithm. The sensitivity, specificity, and area under the curve of the pre-SBRT, post-SBRT, and Delta models were 85%, 90%, and 0.91; 85%, 92.5%, and 0.92; and 85%, 92.5%, and 0.94, respectively. The pre-SBRT model was validated in the independent validation set (area under the curve, 0.89; confidence interval, 0.83-0.92), because this model was believed to be the most useful to assist in individualized treatment planning. Radiomic analysis facilitated the development of three high-performing models predicting local recurrence using either pre-SBRT CT, post-SBRT CT, or the change between these two. We successfully validated the most clinically relevant model, the pre-SBRT model. Although this model needs further validation, it may facilitate individualized surveillance, treatment planning, and selection of adjuvant therapy.
立体定向体部放射治疗（SBRT）是早期非小细胞肺癌（NSCLC）的一种有效治疗策略；然而，局部和全身复发仍是持续存在的挑战。基于计算机断层扫描（CT）的放射组学风险模型有可能用于预测治疗前CT扫描的局部复发风险。开发一种放射组学模型以预测NSCLC患者SBRT后的局部复发。这项单机构研究包括一个回顾性病例对照训练集（20例局部复发患者和40例对照受试者）以及一个接受SBRT治疗的早期NSCLC患者的独立验证集（198例连续病例）。对肿瘤进行半自动分割，并提取102个定量放射组学特征，包括纹理、形态、空间、结节形状和结节表面特征。这些特征被纳入三个单独的多变量模型，以根据SBRT前、SBRT后以及SBRT前与SBRT后扫描之间的差异（Delta模型）预测复发风险。随后在一个独立验证集中对SBRT前模型进行验证。使用Boruta算法为模型选择了13个独立变量。SBRT前、SBRT后和Delta模型的敏感性、特异性和曲线下面积分别为85%、90%和0.91；85%、92.5%和0.92；以及85%、92.5%和0.94。SBRT前模型在独立验证集中得到验证（曲线下面积，0.89；置信区间，0.83 - 0.92），因为该模型被认为对协助个体化治疗计划最有用。放射组学分析有助于开发三个使用SBRT前CT、SBRT后CT或两者之间变化来预测局部复发的高性能模型。我们成功验证了最具临床相关性的模型，即SBRT前模型。尽管该模型需要进一步验证，但它可能有助于个体化监测、治疗计划和辅助治疗的选择。
