Background/Objectives: T cell receptor fusion constructs (TRuCs), a next generation engineered T cell therapy, hold great promise. To accelerate the clinical development of these therapies, improving patient selection is a crucial pathway forward. Methods: We retrospectively analyzed 23 mesothelioma patients (85 target tumors) treated in a phase 1/2 single arm clinical trial (NCT03907852). Five imaging sites were involved, the settings for the evaluations were Blinded Independent Central Reviews (BICRs) with double reads. The reproducibility of 3416 radiomics and delta-radiomics (Δradiomics) was assessed. The univariate analysis evaluated correlations at the target tumor level with (1) tumor diameter response; (2) tumor volume response, according to the Quantitative Imaging Biomarker Alliance; and (3) the mean standard uptake value (SUV) response, as defined by the positron emission tomography response criteria in solid tumors (PERCISTs). A random forest model predicted the response of the target pleural tumors. Results: Tumor anatomical distribution was 55.3%, 17.6%, 14.1%, and 10.6% in the pleura, lymph nodes, peritoneum, and soft tissues, respectively. Radiomics/Δradiomics reproducibility differed across tumor localizations. Radiomics were more reproducible than Δradiomics. In the univariate analysis, none of the radiomics/Δradiomics correlated with any response criteria. With an accuracy ranging from 0.75 to 0.9, three radiomics/Δradiomics were able to predict the response of target pleural tumors. Pivotal studies will require a sample size of 250 to 400 tumors. Conclusions: The prediction of responding target pleural tumors can be achieved using a machine learning-based radiomics/Δradiomics analysis. Tumor-specific reproducibility and the average values indicated that using tumor models to create an effective patient model would require combining several target tumor models.
背景/目的：T细胞受体融合构建体（TRuCs）作为新一代工程化T细胞疗法，展现出巨大潜力。为加速此类疗法的临床开发，优化患者选择是关键路径。方法：我们回顾性分析了参与1/2期单臂临床试验（NCT03907852）的23例间皮瘤患者（共85个靶病灶）。研究涉及五个影像中心，采用双读法的盲态独立中心阅片进行评估。对3416个影像组学及Δ影像组学特征的可重复性进行评估。单变量分析在靶病灶层面评估了以下相关性：（1）肿瘤直径应答；（2）根据定量影像生物标志物联盟标准评估的肿瘤体积应答；（3）基于实体瘤正电子发射断层扫描应答标准定义的平均标准摄取值应答。采用随机森林模型预测胸膜靶病灶的应答情况。结果：肿瘤解剖分布显示胸膜占55.3%，淋巴结17.6%，腹膜14.1%，软组织10.6%。不同部位肿瘤的影像组学/Δ影像组学可重复性存在差异，其中影像组学特征的可重复性优于Δ影像组学。单变量分析显示，所有影像组学/Δ影像组学特征均与任何应答标准无相关性。通过三个影像组学/Δ影像组学特征可预测胸膜靶病灶应答，准确度范围为0.75-0.9。关键性研究需要250-400个肿瘤样本量。结论：基于机器学习的影像组学/Δ影像组学分析可实现胸膜靶病灶应答预测。肿瘤特异性可重复性及平均值分析表明，建立有效的患者模型需要整合多个靶病灶模型。
肿瘤(癌症)患者之家