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基于CT的瘤周和瘤内放射组学作为不同肿瘤类型对免疫检查点抑制剂非典型反应的治疗前预测指标:一项初步多中心研究

CT-Based Peritumoral and Intratumoral Radiomics as Pretreatment Predictors of Atypical Responses to Immune Checkpoint Inhibitor Across Tumor Types: A Preliminary Multicenter Study.

作者信息

He Shuai, Feng Yuqing, Lin Qi, Wang Lihua, Wei Lijun, Tong Jing, Zhang Yuwei, Liu Ying, Ye Zhaoxiang, Guo Yan, Yu Tao, Luo Yahong

机构信息

Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China.

Department of Oncology, The Fifth People's Hospital of Shenyang, Shenyang, China.

出版信息

Front Oncol. 2021 Oct 18;11:729371. doi: 10.3389/fonc.2021.729371. eCollection 2021.

Abstract

OBJECTIVE

To develop and validate a new strategy based on radiomics features extracted from intra- and peritumoral regions on CT images for the prediction of atypical responses to the immune checkpoint inhibitor (ICI) in cancer patients.

METHODS

In total, 135 patients derived from five hospitals with pathologically confirmed malignancies receiving ICI were included in this retrospective study. Atypical responses including pseudoprogression (PsP) and hyperprogression disease (HPD) were identified as their definitions. A subgroup of standard progression disease (sPD) in 2018 was also involved in this study. Based on pretreatment CT imaging, a total of 107 features were extracted from intra- and peri-tumoral regions, respectively. The least absolute shrinkage and selection operator (Lasso) algorithm was used for feature selection, and multivariate logistic analysis was used to develop radiomics signature (RS). Finally, a total of nine RSs, derived from intra-tumoral, peri-tumoral, and combination of both regions, were built respectively to distinguish PsP . HPD, PsP . sPD, and HPD . sPD. The performance of the RSs was evaluated with discrimination, calibration, and clinical usefulness.

RESULTS

No significant difference was found when compared in terms of clinical characteristics of PsP, HPD, and sPD. RS based on combined regions outperformed those from either intra-tumoral or peri-tumoral alone, yielding an AUC (accuracy) of 0.834 (0.827) for PsP . HPD, 0.923 (0.868) for PsP . sPD, and 0.959 (0.894) for HPD . sPD in the training datasets, and 0.835 (0.794) for PsP . HPD, 0.919 (0.867) for PsP . sPD, and 0.933 (0.842) for HPD . sPD in the testing datasets. The combined RS showed good fitness (Hosmer-Lemeshow test p > 0.05) and provided more net benefit than the treat-none or treat-all scheme by decision curve analysis in both training and testing datasets.

CONCLUSION

Pretreatment radiomics are helpful to predict atypical responses to ICI across tumor types. The combined RS outperformed those from either intra- or peri-tumoral alone which may provide a more comprehensive characterization of atypical responses to ICI.

摘要

目的

开发并验证一种基于从CT图像的肿瘤内和瘤周区域提取的放射组学特征的新策略,用于预测癌症患者对免疫检查点抑制剂(ICI)的非典型反应。

方法

本回顾性研究共纳入了来自五家医院的135例经病理证实患有恶性肿瘤且接受ICI治疗的患者。将包括假性进展(PsP)和超进展性疾病(HPD)在内的非典型反应按照其定义进行识别。2018年标准进展性疾病(sPD)的一个亚组也参与了本研究。基于治疗前的CT成像,分别从肿瘤内和瘤周区域提取了总共107个特征。使用最小绝对收缩和选择算子(Lasso)算法进行特征选择,并使用多变量逻辑分析来构建放射组学特征(RS)。最后,分别构建了总共九个来自肿瘤内、瘤周以及两者区域组合的RS,以区分PsP.HPD、PsP.sPD和HPD.sPD。通过区分度、校准度和临床实用性对RS的性能进行评估。

结果

在PsP、HPD和sPD的临床特征方面进行比较时,未发现显著差异。基于组合区域的RS优于单独来自肿瘤内或瘤周区域的RS,在训练数据集中,对于PsP.HPD的曲线下面积(准确性)为0.834(0.827),对于PsP.sPD为0.923(0.868),对于HPD.sPD为0.959(0.894);在测试数据集中,对于PsP.HPD为0.835(0.794),对于PsP.sPD为0.919(0.867),对于HPD.sPD为0.933(0.842)。组合RS显示出良好的拟合度(Hosmer-Lemeshow检验p>0.05),并且在训练和测试数据集中通过决策曲线分析比不治疗或全部治疗方案提供了更多的净效益。

结论

治疗前的放射组学有助于预测不同肿瘤类型对ICI的非典型反应。组合RS优于单独来自肿瘤内或瘤周区域的RS,这可能为ICI的非典型反应提供更全面的特征描述。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5b63/8560023/ff991e94361f/fonc-11-729371-g001.jpg

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