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:The predictive power of baseline metabolic and volumetric [(18)F]FDG PET parameters with different thresholds for early therapy failure and mortality risk in DLBCL patients undergoing CAR-T-cell therapy.
The predictive power of baseline metabolic and volumetric [(18)F]FDG PET parameters with different thresholds for early therapy failure and mortality risk in DLBCL patients undergoing CAR-T-cell therapy.
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本研究表明,使用不同阈值的代谢肿瘤负荷自动勾画方法在结果上并无显著差异。因此,当前采用 SUV > 4.0 固定绝对阈值的临床标准似乎是一个可行的选择。
[18F]FDG 成像是复发或治疗难治性 DLBCL 患者接受 CAR-T 细胞治疗时患者管理不可或缺的一部分。特定影像参数预测效能的计算方法仍不明确。通过这项回顾性研究,我们试图评估基线代谢参数和肿瘤负荷的预测效能,这些参数通过不同阈值方法自动分割计算,用于预测 DLBCL 患者早期治疗失败和死亡风险。
共纳入18例成年患者,他们在2018年12月至2023年10月期间接受了CAR-T 细胞治疗,并至少在治疗前进行了一次、治疗后30天和90天内进行了两次[18F]FDG PET扫描。我们在VOI内进行了单击自动分割,并提取SUV参数,通过应用基于以下概念的阈值来计算MTV和TLG:固定绝对阈值SUV max > 4.0、相对绝对阈值SUV max等值线 > 40 %、背景阈值即肝脏SUV值加其2个SD值,以及仅肝脏SUV值。
对于早期治疗失败,基线代谢参数如SUV max、SUV peak和SUV mean往往比基线代谢负荷具有更大的预测能力。然而,无论使用何种阈值方法,基线代谢负荷在预测死亡风险方面均更优。
[ 18 F]FDG imaging is an integral part of patient management in CAR-T-cell therapy for recurrent or therapy-refractory DLBCL. The calculation methods of predictive power of specific imaging parameters still remains elusive. With this retrospective study, we sought to evaluate the predictive power of the baseline metabolic parameters and tumor burden calculated with automated segmentation via different thresholding methods for early therapy failure and mortality risk in DLBCL patients.
Eighteen adult patients were enrolled, who underwent CAR-T-cell therapy accompanied by at least one pretherapeutic and two posttherapeutic [ 18 F]FDG PET scans within 30 and 90 days between December 2018 and October 2023. We performed single-click automatic segmentation within VOIs in addition to extracting the SUV parameters to calculate the MTVs and TLGs by applying thresholds based on the concepts of a fixed absolute threshold with an SUV max > 4.0, a relative absolute threshold with an isocontour of > 40 % of the SUV max , a background threshold involving the addition of the liver SUV value and its 2 SD values, and only the liver SUV value.
For early therapy failure, baseline metabolic parameters such as the SUV max , SUV peak and SUV mean tended to have greater predictive power than did the baseline metabolic burden. However, the baseline metabolic burden was superior in the prediction of mortality risk regardless of the thresholding method used.
This study revealed that automated delineation methods of metabolic tumor burden using different thresholds do not differ in outcome substantially. Therefore, the current clinical standard with a fixed absolute threshold value of SUV > 4.0 seems to be a feasible option.
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