CAR-T(CAR-T)细胞疗法在非肿瘤性疾病中的应用
Chimeric antigen receptor T (CAR-T) cell therapy in non-oncological diseases.
CAR-T(CAR-T)细胞在血液系统恶性肿瘤中的应用推动了这种免疫治疗形式的显著进展。
CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
英文原题:Development of a radiomic-clinical nomogram for prediction of survival in patients with diffuse large B-cell lymphoma treated with chimeric antigen receptor T cells.
Development of a radiomic-clinical nomogram for prediction of survival in patients with diffuse large B-cell lymphoma treated with chimeric antigen receptor T cells.
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PET/CT 衍生的 R-signature 可能是接受 CAR-T 细胞治疗的 R/R DLBCL 患者的潜在预后生物标志物。此外,当 PET/CT 衍生的 R-signature 与临床因素相结合时,风险分层可进一步增强。
在目前的工作中,我们开发了一种基于18F-FDG PET/CT影像组学的模型,用于评估接受嵌合抗原受体(CAR)-T细胞治疗的复发/难治性(R/R)弥漫性大B细胞淋巴瘤(DLBCL)患者的无进展生存期(PFS)和总生存期(OS)。
当前分析共纳入61例在CAR-T 细胞输注前接受18 F-FDG PET/CT的DLBCL病例,这些患者被随机分配至训练队列(n = 42)和验证队列(n = 19)。使用LIFEx软件获取PET和CT图像的影像组学特征,随后根据PFS和OS选择最优参数构建影像组学标签(R-signatures)。随后构建并验证了影像组学模型和临床模型。
整合了R-signatures和临床风险因素的放射组学模型在PFS(C-index:0.710 vs. 0.716;AUC:0.776 vs. 0.712)和OS(C-index:0.780 vs. 0.762;AUC:0.828 vs. 0.728)方面的预后性能均优于临床模型。在验证中,两种方法预测PFS和OS的C-index分别为0.640 vs. 0.619和0.676 vs. 0.699。此外,AUC分别为0.886 vs. 0.635和0.778 vs. 0.705。校准曲线表明一致性良好,决策曲线分析提示放射组学模型的净获益高于临床模型。
In our current work, an 18 F-FDG PET/CT radiomics-based model was developed to assess the progression-free survival (PFS) and overall survival (OS) of patients with relapsed or refractory (R/R) diffuse large B-cell lymphoma (DLBCL) who received chimeric antigen receptor (CAR)-T cell therapy.
A total of 61 DLBCL cases receiving 18 F-FDG PET/CT before CAR-T cell infusion were included in the current analysis, and these patients were randomly assigned to a training cohort (n = 42) and a validation cohort (n = 19). Radiomic features from PET and CT images were obtained using LIFEx software, and radiomics signatures (R-signatures) were then constructed by choosing the optimal parameters according to their PFS and OS. Subsequently, the radiomics model and clinical model were constructed and validated.
The radiomics model that integrated R-signatures and clinical risk factors showed superior prognostic performance compared with the clinical models in terms of both PFS (C-index: 0.710 vs. 0.716; AUC: 0.776 vs. 0.712) and OS (C-index: 0.780 vs. 0.762; AUC: 0.828 vs. 0.728). For validation, the C-index of the two approaches was 0.640 vs. 0.619 and 0.676 vs. 0.699 for predicting PFS and OS, respectively. Moreover, the AUC was 0.886 vs. 0.635 and 0.778 vs. 0.705, respectively. The calibration curves indicated good agreement, and the decision curve analysis suggested that the net benefit of radiomics models was higher than that of clinical models.
PET/CT-derived R-signature could be a potential prognostic biomarker for R/R DLBCL patients undergoing CAR-T cell therapy. Moreover, the risk stratification could be further enhanced when the PET/CT-derived R-signature was combined with clinical factors.
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