Li Yue, Liu Jun, Li Hong-Xuan, Cai Xu-Wei, Li Zhi-Gang, Ye Xiao-Dan, Teng Hao-Hua, Fu Xiao-Long, Yu Wen
Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.
Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Front Oncol. 2021 Feb 19;10:615167. doi: 10.3389/fonc.2020.615167. eCollection 2020.
After neoadjuvant chemoradiotherapy (NCRT) in locally advanced esophageal squamous cell cancer (ESCC), roughly 40% of the patients may achieve pathologic complete response (pCR). Those patients may benefit from organ-saving strategy if the probability of pCR could be correctly identified before esophagectomy. A reliable approach to predict pathological response allows future studies to investigate individualized treatment plans.
All eligible patients treated in our center from June 2012 to June 2019 were retrospectively collected. Radiomics features extracted from pre-/post-NCRT CT images were selected by univariate logistic and LASSO regression. A radiomics signature (RS) developed with selected features was combined with clinical variables to construct RS+clinical model with multivariate logistic regression, which was internally validated by bootstrapping. Performance and clinical usefulness of RS+clinical model were assessed by receiver operating characteristic (ROC) curves and decision curve analysis, respectively.
Among the 121 eligible patients, 51 achieved pCR (42.1%) after NCRT. Eighteen radiomics features were selected and incorporated into RS. The RS+clinical model has improved prediction performance for pCR compared with the clinical model (corrected area under the ROC curve, 0.84 vs. 0.70). At the 60% probability threshold cutoff (i.e., the patient would opt for observation if his probability of pCR was >60%), net 13% surgeries could be avoided by RS+clinical model, equivalent to implementing organ-saving strategy in 31.37% of the 51 true-pCR cases.
The model built with CT radiomics features and clinical variables shows the potential of predicting pCR after NCRT; it provides significant clinical benefit in identifying qualified patients to receive individualized organ-saving treatment plans.
在局部晚期食管鳞状细胞癌（ESCC）中进行新辅助放化疗（NCRT）后，大约40%的患者可能实现病理完全缓解（pCR）。如果在食管切除术之前能够正确识别pCR的可能性，这些患者可能从器官保留策略中获益。一种可靠的预测病理反应的方法可以让未来的研究去探索个体化治疗方案。
回顾性收集2012年6月至2019年6月在本中心接受治疗的所有符合条件的患者。通过单变量逻辑回归和LASSO回归从NCRT前后的CT图像中提取的放射组学特征被选中。利用选定特征开发的放射组学特征（RS）与临床变量相结合，通过多变量逻辑回归构建RS + 临床模型，并通过自举法进行内部验证。分别通过受试者操作特征（ROC）曲线和决策曲线分析评估RS + 临床模型的性能和临床实用性。
在121例符合条件的患者中，51例在NCRT后实现了pCR（42.1%）。18个放射组学特征被选中并纳入RS。与临床模型相比，RS + 临床模型对pCR的预测性能有所提高（校正后的ROC曲线下面积，0.84对0.70）。在60%概率阈值截断点（即，如果患者的pCR概率>60%，他将选择观察），RS + 临床模型可以避免13%的手术，相当于在51例真正pCR病例中的31.37%实施器官保留策略。
利用CT放射组学特征和临床变量构建的模型显示出预测NCRT后pCR的潜力；它在识别合格患者以接受个体化器官保留治疗方案方面提供了显著的临床益处。
