瘤体联合瘤周影像组学模型可预测非小细胞肺癌免疫治疗疗效OA
Combined intratumoral and peritumoral radiomics model predicts immunotherapy efficacy in non-small cell lung cancer
目的 基于CT平扫及动脉期图像分别构建瘤内、瘤周(5、10、15 mm)及瘤内联合瘤周影像组学模型,探讨其对非小细胞肺癌(NSCLC)患者免疫治疗疗效的预测价值.方法 回顾性分析2020年1月~2024年1月在蚌埠医科大学第一附属医院接受免疫治疗且治疗前行胸部增强CT的非小细胞肺癌患者127例,收集患者的影像学资料,采用多输入(平扫、动脉期及二者联合)、多ROI(瘤内及瘤周5、10、15 mm)及多模型分析策略.本研究预先设定以平扫+动脉期联合图像中提取的瘤内及瘤周15 mm区域特征构建的模型为主要分析模型.使用uAI Research Portal软件在肿瘤最大截面勾画瘤体感兴趣区(ROI),并经过平台扩增得到瘤周5、10、15 mm的ROI,进行绝对值最大归一化处理,采用最小绝对收缩与选择算子回归及最大相关最小冗余算法筛选最优影像组学特征构建影像组学模型.在预处理后通过相同的算法建立模型并绘制ROC曲线,分析训练集和测试集ROC曲线下面积(AUC)、敏感度、特异度、准确率.结果 在不同瘤周扩增条件下,基于平扫、动脉期及两者联合图像,共构建了21个影像组学模型(瘤内、瘤周及联合模型).所有模型均从2264个保留特征中经筛选后构建.其中,平扫联合动脉期影像的瘤内-瘤周15 mm联合模型性能最优,训练集的AUC值为0.8871(95%CI:0.8380~0.9362),测试集为0.7095(95%CI:0.5903~0.8286).结论 结合CT平扫与动脉期图像的瘤内及瘤周(15 mm)影像组学特征,对预测NSCLC免疫治疗反应具有一定价值.
Objective To investigate the predictive value of radiomics models based on non-contrast and arterial phase CT images of intratumoral,peritumoral(5,10,15 mm),and combined intratumoral-peritumoral regions for immunotherapy efficacy in patients with non-small cell lung cancer(NSCLC).Methods This retrospective study enrolled 127 patients with NSCLC who underwent immunotherapy and pre-treatment contrast-enhanced CT from January 2020 to January 2024 at the First Affiliated Hospital of Bengbu Medical University.A multi-input(non-contrast,arterial phase,and their combination),multi-region of interest(intratumoral,peritumoral 5,10,15 mm),and multi-model strategy was used for image analysis.The primary analytical model was pre-specified as the model constructed from intratumoral and 15 mm peritumoral features extracted from combined non-contrast and arterial phase images.Tumor regions of interest(ROIs)were delineated on the largest cross-sectional area using uAI Research Portal software.Peritumoral ROIs of 5,10,15 mm were generated via platform expansion.After absolute maximum normalization,optimal radiomic features were selected using LASSO regression and maximum relevance minimum redundancy algorithms to construct the radiomics model.Following preprocessing,models were developed using identical algorithms and ROC curves were plotted.Four metrics,including area under the curve(AUC),sensitivity,specificity,and accuracy,were analyzed for both the training and test sets.Results A total of 21 radiomics models(intratumoral,peritumoral,combined)were constructed based on non-contrast,arterial phase,and combined images under different peritumoral expansion conditions.All models were built following feature selection from an initial pool of 2264 features.The combined model using non-contrast plus arterial phase images with a 15 mm intratumoral-peritumoral region performed best,achieving an AUC of 0.8871(95%CI:0.8380-0.9362)in the training set and 0.7095(95%CI:0.5903-0.8286)in the test set.Conclusion Preliminary findings suggest that radiomics features combining intratumoral and peritumoral(15 mm)information from both non-contrast and arterial phase CT images may hold value for predicting immunotherapy response in NSCLC,potentially aiding clinical decision-making.
文欣园;胡尹迪;李轶涵;游昕楠;谢波;马宜传
蚌埠医科大学第一附属医院放射科,安徽 蚌埠 233004||蚌埠医科大学研究生院,安徽 蚌埠 233000蚌埠医科大学第一附属医院放射科,安徽 蚌埠 233004||蚌埠医科大学研究生院,安徽 蚌埠 233000蚌埠医科大学第一附属医院放射科,安徽 蚌埠 233004||蚌埠医科大学研究生院,安徽 蚌埠 233000蚌埠医科大学第一附属医院放射科,安徽 蚌埠 233004||蚌埠医科大学研究生院,安徽 蚌埠 233000蚌埠医科大学第一附属医院放射科,安徽 蚌埠 233004||蚌埠医科大学研究生院,安徽 蚌埠 233000蚌埠医科大学研究生院,安徽 蚌埠 233000
非小细胞肺癌影像组学免疫治疗瘤周区域
non-small cell lung cancerradiomicsimmunotherapyperitumoral region
《分子影像学杂志》 2026 (5)
594-603,10
安徽省临床医学研究转化专项立项项目(202304295107020072)
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