Balagurunathan Yoganand, Choi Jung W, Thompson Zachary, Jain Michael, Locke Frederick L
Department of Machine Learning, H Lee Moffitt Cancer Center, Tampa, FL 33612, USA.
Department of Diagnostic & Interventional Radiology, H Lee Moffitt Cancer Center, Tampa, FL 33612, USA.
Cancers (Basel). 2025 May 30;17(11):1832. doi: 10.3390/cancers17111832.
: 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. : 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). : 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. : We found radiomic PC based on size and shape metrics that are able to prognosticate treatment response to CAR-T therapy.
弥漫性大B细胞淋巴瘤（DLBCL）是最常见的侵袭性疾病形式，占所有淋巴瘤病例的30%。确定哪些患者会对这些先进的细胞疗法产生反应是一个尚未解决的挑战。我们提议基于患者的影像学扫描（正电子发射断层扫描/计算机断层扫描，PET/CT）开发一种基于放射组学（定量图像指标）的特征，并使用这些指标来预测对axi-cel（阿基仑赛），即自体CD19嵌合抗原受体（CAR）T细胞（CAR-T）疗法的反应。我们精心挑选了155例接受axi-cel治疗的复发/难治性（R/R）DLBCL患者队列。利用他们的基线图像扫描（PET/CT），识别出与淋巴结/结外疾病相关的最大病变，并使用成像指标（放射组学）进行特征描述。我们使用主成分（PC）分析来降低这些跨功能类别（大小、形状和纹理）特征的维度。我们评估了基于放射组学的PC对治疗反应（1年）的预后能力，通过总生存期（OS）和无进展生存期（PFS）来衡量。我们发现，在CT/PET中，放射组学PC分别对总生存期具有预后价值（形状PC，q < 0.013/0.0108，大小PC，q < 0.003/0.0088）。相比之下，代谢肿瘤体积（MTV）具有预后价值（q < 0.0002/0.0007）。跨功能类别的放射组学PC与MTV显示出中度至弱相关性，PET和CT上获得的大小/形状/纹理-PC1的Spearman's ρ分别为0.44/0.35/0.27和0.45/0.36/0.55。我们发现基于大小和形状指标的放射组学PC能够预测对CAR-T疗法的治疗反应。
