医学
弥漫性大B细胞淋巴瘤
内科学
国际预后指标
无进展生存期
无线电技术
弗雷明翰风险评分
预测值
肿瘤科
淋巴瘤
总体生存率
放射科
疾病
作者
Jakoba J. Eertink,Gerben J.C. Zwezerijnen,Martijn W. Heymans,Simone Pieplenbosch,Sanne E. Wiegers,Ulrich Dührsen,Andreas Hüttmann,Lars Kurch,Christine Hanoun,Pieternella J. Lugtenburg,Sally F. Barrington,N. George Mikhaeel,Luca Ceriani,Emanuele Zucca,Sándor Czibor,Tamás Györke,Martine E.D. Chamuleau,Otto S. Hoekstra,Henrica C. W. de Vet,Ronald Boellaard
出处
期刊:Blood
[Elsevier BV]
日期:2023-03-31
卷期号:141 (25): 3055-3064
被引量:29
标识
DOI:10.1182/blood.2022018558
摘要
The objective of this study is to externally validate the clinical positron emission tomography (PET) model developed in the HOVON-84 trial and to compare the model performance of our clinical PET model using the international prognostic index (IPI). In total, 1195 patients with diffuse large B-cell lymphoma (DLBCL) were included in the study. Data of 887 patients from 6 studies were used as external validation data sets. The primary outcomes were 2-year progression-free survival (PFS) and 2-year time to progression (TTP). The metabolic tumor volume (MTV), maximum distance between the largest lesion and another lesion (Dmaxbulk), and peak standardized uptake value (SUVpeak) were extracted. The predictive values of the IPI and clinical PET model (MTV, Dmaxbulk, SUVpeak, performance status, and age) were tested. Model performance was assessed using the area under the curve (AUC), and diagnostic performance, using the positive predictive value (PPV). The IPI yielded an AUC of 0.62. The clinical PET model yielded a significantly higher AUC of 0.71 (P < .001). Patients with high-risk IPI had a 2-year PFS of 61.4% vs 51.9% for those with high-risk clinical PET, with an increase in PPV from 35.5% to 49.1%, respectively. A total of 66.4% of patients with high-risk IPI were free from progression or relapse vs 55.5% of patients with high-risk clinical PET scores, with an increased PPV from 33.7% to 44.6%, respectively. The clinical PET model remained predictive of outcome in 6 independent first-line DLBCL studies, and had higher model performance than the currently used IPI in all studies.
科研通智能强力驱动
Strongly Powered by AbleSci AI