Background: Diffuse large B-cell lymphomas (DLBCLs) are the most common, aggressive disease form that accounts for 30% of all lymphoma cases. Identifying patients who will respond to these advanced cell-based therapies is an unaddressed challenge.Methods: We propose to develop a radiomics- (quantitative image metric) based signature on the patients’ imaging scans (positron emission tomography/computed tomography, PET/CT) and use these metrics to prognosticate response to axi-cel (axicabtagene ciloleucel), autologous CD19 chimeric antigen receptor (CAR) T-cell (CAR-T) therapy. We curated a cohort of 155 patients with relapsed/refractory (R/R) DLBCL who were treated with axi-cel. Using their baseline image scan (PET/CT), the largest lesions related to nodal/extra-nodal disease were identified and characterized using imaging metrics (radiomics). We used principal component (PC) analysis to reduce the dimensionality of these features across the functional categories (size, shape, and texture). We evaluated the prognostic ability of radiomic-based PC to treatment response (1-year), measured by overall survival (OS) and progression-free survival (PFS).Results: We found that radiomic PC was prognostic of overall survival (Shape-PC, q < 0.013/0.0108, Size-PC, q < 0.003/0.0088), in CT/PET, respectively. In comparison, the metabolic tumor volume (MTV) was prognostic (q < 0.0002/0.0007). The radiomic PCs across the functional categories showed moderate to weak correlation with MTV, Spearman’s ρ of 0.44/0.35/0.27, and 0.45/0.36/0.55 for Size/Shape/Texture-PC1 obtained on PET and CT, respectively.Conclusions: We found radiomic PC based on size and shape metrics that are able to prognosticate treatment response to CAR-T therapy.
背景：弥漫性大B细胞淋巴瘤（DLBCL）是最常见的侵袭性淋巴瘤类型，占所有淋巴瘤病例的30%。识别哪些患者会对先进的细胞疗法产生反应，仍是一个尚未解决的挑战。 方法：我们提出基于患者影像扫描（正电子发射断层扫描/计算机断层扫描，PET/CT）开发一种放射组学（定量影像指标）特征，并利用这些指标来预测对阿基仑赛（axicabtagene ciloleucel，一种自体CD19嵌合抗原受体T细胞疗法）的治疗反应。我们收集了155例接受阿基仑赛治疗的复发/难治性DLBCL患者队列。利用其基线影像扫描（PET/CT），识别出与淋巴结/结外病变相关的最大病灶，并通过影像指标（放射组学）进行特征提取。我们采用主成分分析对功能类别（大小、形状和纹理）的特征进行降维处理。评估了基于放射组学的主成分对治疗反应（1年）的预后能力，通过总生存期和无进展生存期进行衡量。 结果：我们发现，在CT/PET影像中，放射组学主成分对总生存期具有预后价值（形状主成分，q < 0.013/0.0108；大小主成分，q < 0.003/0.0088）。相比之下，代谢肿瘤体积也具有预后意义（q < 0.0002/0.0007）。各功能类别的放射组学主成分与代谢肿瘤体积呈中度至弱相关，PET和CT获取的大小/形状/纹理第一主成分的斯皮尔曼相关系数ρ分别为0.44/0.35/0.27和0.45/0.36/0.55。 结论：我们发现基于大小和形状指标的放射组学主成分能够预测对CAR-T疗法的治疗反应。
Radiomic Features Prognosticate Treatment Response in CAR-T Cell Therapy
肿瘤(癌症)患者之家