Galli Eugenio, Guarneri Andrea, Sorà Federica, Viscovo Marcello, Pansini Ilaria, Maiolo Elena, Alma Eleonora, Annunziata Salvatore, Sica Simona, Leccisotti Lucia, Hohaus Stefan
Dipartimento di Scienze di Laboratorio Ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
Unità di Medicina Nucleare, Dipartimento di Diagnostica per Immagini e Radioterapia Oncologica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
Hematol Oncol. 2025 Jan;43(1):e70029. doi: 10.1002/hon.70029.
Disease burden is a critical determinant of outcomes in CAR-T therapy for B-cell lymphomas, and one of the most widely used techniques for its assessment is Total Metabolic Tumor Volume (TMTV) measured via [F]FDG PET/CT. Biological parameters may further refine the risk profile. We analyzed baseline [F]FDG PET/CT scans from 40 patients treated with CAR-T, using an AI-based automated segmentation algorithm to determine TMTV. Our analysis identified that a baseline TMTV greater than 48.4 cm³ and elevated LDH independently predicted progression-free survival (PFS) after CAR-T therapy (HR 4.28, p = 0.007, and HR 8.20, p < 0.001, respectively). We then proposed a 0-to-2 risk score, assigning one point each for elevated TMTV and elevated LDH. All patients with a score of two experienced a PFS of less than 90 days following CAR-T infusion. Among the remaining patients, those with 0 points versus 1 point demonstrated a 3-month PFS of 100% versus 85%, a 6-month PFS of 92% versus 53%, and a 12-month PFS of 83% versus 53%, respectively. Importantly, patients with high baseline TMTV who achieved a TMTV reduction to less than 1.99 cm³ by day 30 had a PFS of 66%, significantly better compared to those who did not achieve this reduction. AI-guided TMTV assessment, combined with LDH levels, provides a rapid and sensitive method for risk stratification at the bedside, which could help optimize patient management prior to CAR-T therapy.
疾病负担是B细胞淋巴瘤CAR-T治疗结果的关键决定因素，评估疾病负担最广泛使用的技术之一是通过[F]FDG PET/CT测量的总代谢肿瘤体积（TMTV）。生物学参数可能会进一步细化风险特征。我们分析了40例接受CAR-T治疗患者的基线[F]FDG PET/CT扫描，使用基于人工智能的自动分割算法来确定TMTV。我们的分析发现，基线TMTV大于48.4 cm³和乳酸脱氢酶（LDH）升高分别独立预测CAR-T治疗后的无进展生存期（PFS）（风险比[HR]分别为4.28，p = 0.007和HR 8.20，p < 0.001）。然后我们提出了一个0至2分的风险评分，TMTV升高和LDH升高各得1分。所有得2分的患者在CAR-T输注后PFS均小于90天。在其余患者中，得0分与得1分的患者3个月PFS分别为100%和85%，6个月PFS分别为92%和53%，12个月PFS分别为83%和53%。重要的是，基线TMTV高且在第30天时TMTV降低至小于1.99 cm³的患者PFS为66%，与未实现这种降低的患者相比明显更好。人工智能引导的TMTV评估与LDH水平相结合，提供了一种快速且灵敏的床旁风险分层方法，这有助于在CAR-T治疗前优化患者管理。
