Bao Xiao, Peng Qin, Bian Dongliang, Ni Jianjiao, Zhou Shuchang, Zhang Peng, Gu Yajia, Gong Jing, Shi Jingyun
Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.
Eur Radiol. 2025 Apr 11. doi: 10.1007/s00330-025-11563-8.
This study aims to develop a short-term spatiotemporal CT radiomics model to predict the major pathological response (MPR) to neoadjuvant chemoimmunotherapy (NCI) in NSCLC by decoding the intra- and peri-tumoral imaging phenotypes.
A total of 352 patients undergoing curative surgery following NCI for NSCLC were enrolled from two centers, forming a training cohort (n = 186), an internal validation cohort (n = 80), and an external validation cohort (n = 86). Intra- and peri-tumoral CT radiomics features were computed to capture imaging phenotypes of the tumor microenvironment. Delta radiomics features were also calculated by quantifying changes in each radiomics feature. A support vector machine classifier was utilized to develop the short-term spatiotemporal model by analyzing changes in radiomics features.
The multi-timepoint short-term spatiotemporal model, incorporating pre-treatment, post-treatment and delta radiomic features, achieved AUC values of 0.84, 0.77, and 0.75 in the training, internal validation, and external validation cohorts, respectively. These results significantly outperformed the RECIST model and pre-treatment model, with p-values < 0.05 indicating statistical significance.
This study demonstrates that short-term temporal analysis of intra- and peri-tumoral CT radiomics is a promising approach for predicting MPR to NCI in NSCLC. These findings underscore the potential of radiomics as a non-invasive tool for assessing treatment response and guiding personalized therapy in NSCLC patients.
Question Neoadjuvant chemoimmunotherapy has improved in major pathological response rate for non-small cell lung cancer (NSCLC), but it is unclear which patients will benefit most. Findings The multi-timepoint short-term spatiotemporal model based on CT pictures demonstrates high predictive performance for assessing major pathological response following neoadjuvant chemoimmunotherapy in NSCLC. Clinical relevance Short-term intra- and peri-tumoral CT radiomics is a promising approach for predicting major pathological response to neoadjuvant chemoimmunotherapy in NSCLC. These findings underscore the potential of radiomics as a non-invasive tool for assessing treatment response in NSCLC.
本研究旨在通过解析肿瘤内及肿瘤周围的影像表型，开发一种短期时空CT影像组学模型，以预测非小细胞肺癌（NSCLC）新辅助化疗免疫治疗（NCI）后的主要病理反应（MPR）。
从两个中心招募了352例接受NCI后行根治性手术的NSCLC患者，形成一个训练队列（n = 186）、一个内部验证队列（n = 80）和一个外部验证队列（n = 86）。计算肿瘤内及肿瘤周围的CT影像组学特征，以捕捉肿瘤微环境的影像表型。还通过量化每个影像组学特征的变化来计算差异影像组学特征。利用支持向量机分类器，通过分析影像组学特征的变化来开发短期时空模型。
结合治疗前、治疗后和差异影像组学特征的多时间点短期时空模型，在训练队列、内部验证队列和外部验证队列中的AUC值分别为0.84、0.77和0.75。这些结果显著优于RECIST模型和治疗前模型，p值<0.05表明具有统计学意义。
本研究表明，对肿瘤内及肿瘤周围CT影像组学进行短期时间分析是预测NSCLC患者NCI后MPR的一种有前景的方法。这些发现强调了影像组学作为一种非侵入性工具在评估NSCLC患者治疗反应和指导个性化治疗方面的潜力。
问题新辅助化疗免疫治疗提高了非小细胞肺癌（NSCLC）的主要病理反应率，但尚不清楚哪些患者将获益最大。发现基于CT图像的多时间点短期时空模型在评估NSCLC新辅助化疗免疫治疗后的主要病理反应方面具有较高的预测性能。临床意义短期肿瘤内及肿瘤周围CT影像组学是预测NSCLC新辅助化疗免疫治疗主要病理反应的一种有前景的方法。这些发现强调了影像组学作为一种非侵入性工具在评估NSCLC治疗反应方面的潜力。
