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.
CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
英文原题:Radiomic features of PET/CT imaging of large B cell lymphoma lesions predicts CAR T cell therapy efficacy.
Radiomic features of PET/CT imaging of large B cell lymphoma lesions predicts CAR T cell therapy efficacy.
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我们的研究发现,PET 影像上基于形状的影像组学特征可预测治疗结局(1 年生存),并对总生存期具有预后价值。
复发/难治性弥漫性大B细胞淋巴瘤(DLBCL)可通过CD19靶向自体CAR-T 细胞疗法阿基仑赛(axi-cel)有效治疗。基于诊断影像的特征可能有助于识别对这一先进免疫疗法产生临床应答的患者。
本研究旨在基于正电子发射断层扫描/计算机断层扫描(PET/CT)建立影像组学特征标志物,包括代谢肿瘤负荷,以预测难治/复发DLBCL患者接受CAR-T 后的持久应答。
回顾性分析155例接受axi-cel CAR-T 治疗的复发/难治DLBCL患者。依据淋巴结或结外部位评估疾病累及情况。124例至少有一个淋巴结病灶,94例至少有一个结外病灶(两个亚队列部分重叠)。分别使用306项定量影像指标表征PET及CT病灶区域。采用主成分(PC)分析降低特征维度,类别包括大小(n=38)、形状(n=9)和纹理(n=259)。使用筛选出的特征建立1年生存预测模型,并通过Kaplan-Meier(KM)曲线检验其对总生存期(OS)和无进展生存期(PFS)的预后价值。
PET上最大结外病灶的形状PC特征可预测1年生存(AUC 0.68,95% CI:0.43–0.94),并具有OS/PFS预后价值(p<0.018)。代谢肿瘤体积(MTV)是独立预测因素,AUC为0.74(95% CI:0.58–0.87)。联合这些特征后,预测效能提高,AUC为0.78(95% CI:0.70–0.87)。此外,形状PC特征与总MTV无相关性(Spearman相关系数0.359,p值0.001)。
本研究发现,PET影像中的形状影像组学特征可预测治疗结局(1年生存)并预测总生存期。研究还发现了与大小无关、预测效能可与MTV相比的影像组学指标;这些指标可提供互补信息,提高治疗结局预测能力。
Relapsed and refractory Diffuse large B cell lymphoma (DLBCL) can be successfully treated with axicabtagene ciloleucel (axi-cel), a CD19-directed autologous chimeric antigen receptor T cell (CAR-T) therapy. Diagnostic image-based features could help identify the patients who would clinically respond to this advanced immunotherapy.
The aim of this study was to establish a radiomic image feature-based signature derived from positron emission tomography/computed tomography (PET/CT), including metabolic tumor burden, which can predict a durable response to CAR-T therapy in refractory/relapsed DLBCL.
We conducted a retrospective review of 155 patients with relapsed/refractory DLBCL treated with axi-cel CAR-T therapy. The patients' disease involvement was evaluated based on nodal or extranodal sites. A sub-cohort of these patients with at least one nodal lesion (n=124) was assessed, while an overlapping sub-cohort (n=94) had at least one extranodal lesion. The lesion regions were characterized using 306 quantitative imaging metrics for PET images and CT images independently. Principal component (PC) analysis was performed to reduce the dimensionality in feature-based functional categories: size (n=38), shape (n=9), and texture (n=259). The selected features were used to build prediction models for survival at 1 year and tested for prognosis to overall/progression-free survival (OS/PFS) using a Kaplan-Meier (KM) plot.
The Shape-based PC features of the largest extranodal lesion on PET were predictive of 1-year survival (AUC 0.68 [0.43,0.94]) and prognostic of OS/PFS (p<0.018). Metabolic tumor volume (MTV) was an independent predictor with an area under the curve (AUC) of 0.74 [0.58, 0.87]. Combining these features improved the predictor performance (AUC of 0.78 [0.7, 0.87]). Additionally, the Shape-based PC features were unrelated to total MTV (Spearman's of 0.359, p 0.001).
Our study found that shape-based radiomic features on PET imaging were predictive of treatment outcome (1-year survival) and prognostic of overall survival. We also found non-size-based radiomic predictors that had comparable performance to MTV and provided complementary information to improve the predictability of treatment outcomes.
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