Liu Jianjing, Sui Chunxiao, Bian Haiman, Li Yue, Wang Ziyang, Fu Jie, Qi Lisha, Chen Kun, Xu Wengui, Li Xiaofeng
Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.
Front Oncol. 2024 Jul 26;14:1425837. doi: 10.3389/fonc.2024.1425837. eCollection 2024.
This study aimed to establish and evaluate the value of integrated models involving F-FDG PET/CT-based radiomics and clinicopathological information in the prediction of pathological complete response (pCR) to neoadjuvant therapy (NAT) for non-small cell lung cancer (NSCLC).
A total of 106 eligible NSCLC patients were included in the study. After volume of interest (VOI) segmentation, 2,016 PET-based and 2,016 CT-based radiomic features were extracted. To select an optimal machine learning model, a total of 25 models were constructed based on five sets of machine learning classifiers combined with five sets of predictive feature resources, including PET-based alone radiomics, CT-based alone radiomics, PET/CT-based radiomics, clinicopathological features, and PET/CT-based radiomics integrated with clinicopathological features. Area under the curves (AUCs) of receiver operator characteristic (ROC) curves were used as the main outcome to assess the model performance.
The hybrid PET/CT-derived radiomic model outperformed PET-alone and CT-alone radiomic models in the prediction of pCR to NAT. Moreover, addition of clinicopathological information further enhanced the predictive performance of PET/CT-derived radiomic model. Ultimately, the support vector machine (SVM)-based PET/CT radiomics combined clinicopathological information presented an optimal predictive efficacy with an AUC of 0.925 (95% CI 0.869-0.981) in the training cohort and an AUC of 0.863 (95% CI 0.740-0.985) in the test cohort. The developed nomogram involving radiomics and pathological type was suggested as a convenient tool to enable clinical application.
The F-FDG PET/CT-based SVM radiomics integrated with clinicopathological information was an optimal model to non-invasively predict pCR to NAC for NSCLC.
本研究旨在建立并评估基于F-FDG PET/CT的放射组学与临床病理信息的综合模型在预测非小细胞肺癌（NSCLC）新辅助治疗（NAT）的病理完全缓解（pCR）中的价值。
本研究共纳入106例符合条件的NSCLC患者。在感兴趣体积（VOI）分割后，提取了2016个基于PET的和2016个基于CT的放射组学特征。为选择最佳机器学习模型，基于五组机器学习分类器与五组预测特征资源构建了总共25个模型，包括仅基于PET的放射组学、仅基于CT的放射组学、基于PET/CT的放射组学、临床病理特征以及基于PET/CT的放射组学与临床病理特征相结合。采用受试者操作特征（ROC）曲线下面积（AUC）作为评估模型性能的主要指标。
在预测NAT的pCR方面，基于PET/CT的混合放射组学模型优于仅基于PET和仅基于CT的放射组学模型。此外，添加临床病理信息进一步提高了基于PET/CT的放射组学模型的预测性能。最终，基于支持向量机（SVM）的PET/CT放射组学结合临床病理信息在训练队列中呈现出最佳预测效能，AUC为0.925（95%CI 0.869-0.981），在测试队列中AUC为0.863（95%CI 0.740-0.985）。所开发的包含放射组学和病理类型的列线图被认为是一种便于临床应用的工具。
基于F-FDG PET/CT的SVM放射组学与临床病理信息相结合是无创预测NSCLC新辅助化疗pCR的最佳模型。
