Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, China.
Lung Cancer and Gastrointestinal Unit, Department of Medical Oncology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.
Thorac Cancer. 2023 Oct;14(28):2869-2876. doi: 10.1111/1759-7714.15052. Epub 2023 Aug 19.
To develop a radiomics model based on chest computed tomography (CT) for the prediction of a pathological complete response (pCR) after neoadjuvant or conversion chemoimmunotherapy (CIT) in patients with non-small cell lung cancer (NSCLC).
Patients with stage IB-III NSCLC who received neoadjuvant or conversion CIT between September 2019 and July 2021 at Hunan Cancer Hospital, Xiangya Hospital, and Union Hospital were retrospectively collected. The least absolute shrinkage and selection operator (LASSO) were used to screen features. Then, model 1 (five radiomics features before CIT), model 2 (four radiomics features after CIT and before surgery) and model 3 were constructed for the prediction of pCR. Model 3 included all nine features of model 1 and 2 and was later named the neoadjuvant chemoimmunotherapy-related pathological response prediction model (NACIP).
This study included 110 patients: 77 in the training set and 33 in the validation set. Thirty-nine (35.5%) patients achieved a pCR. Model 1 showed area under the curve (AUC) = 0.65, 64% accuracy, 71% specificity, and 50% sensitivity, while model 2 displayed AUC = 0.81, 73% accuracy, 62% specificity, and 92% sensitivity. In comparison, NACIP yielded a good predictive value, with an AUC of 0.85, 81% accuracy, 81% specificity, and 83% sensitivity in the validation set.
NACIP may be a potential model for the early prediction of pCR in patients with NSCLC treated with neoadjuvant/conversion CIT.
为了预测接受新辅助或转化化疗免疫治疗（CIT）的非小细胞肺癌（NSCLC）患者的病理完全缓解（pCR），开发一种基于胸部 CT 的放射组学模型。
回顾性收集了 2019 年 9 月至 2021 年 7 月在湖南省肿瘤医院、湘雅医院和协和医院接受新辅助或转化 CIT 的 IB-III 期 NSCLC 患者。采用最小绝对收缩和选择算子（LASSO）筛选特征。然后，构建模型 1（CIT 前的 5 个放射组学特征）、模型 2（CIT 后和手术前的 4 个放射组学特征）和模型 3 用于预测 pCR。模型 3 包含模型 1 和 2 的所有 9 个特征，随后被命名为新辅助化疗免疫治疗相关病理反应预测模型（NACIP）。
本研究纳入了 110 例患者：训练集 77 例，验证集 33 例。39 例（35.5%）患者达到了 pCR。模型 1 的 AUC 为 0.65，准确性为 64%，特异性为 71%，敏感性为 50%，而模型 2 的 AUC 为 0.81，准确性为 73%，特异性为 62%，敏感性为 92%。相比之下，NACIP 在验证集中具有良好的预测价值，AUC 为 0.85，准确性为 81%，特异性为 81%，敏感性为 83%。
NACIP 可能是预测接受新辅助/转化 CIT 的 NSCLC 患者 pCR 的有潜力的模型。
