国际预后指标
医学
弥漫性大B细胞淋巴瘤
内科学
比例危险模型
单变量
多元分析
肿瘤科
接收机工作特性
回顾性队列研究
单变量分析
多元统计
队列
淋巴瘤
核医学
统计
数学
作者
Jiesong Wang,Yong Sun,Meifu Lin,Qinghu Lyu,Shudan Zhai,Zheng Song,Xia Liu,Lanfang Li,Lihua Qiu,Zhengzi Qian,Xing Wan,Shiyong Zhou,Wenchen Gong,Bin Meng,Bei Yu,Jin He,Xiaofei Ye,Lei Zhu,Xianhuo Wang,Huilai Zhang
摘要
ABSTRACT Our study aimed to assess the prognostic significance of the interim National Comprehensive Cancer Network International Prognostic Index and PET‐CT‐related parameters for predicting patient outcomes and achieving precise risk stratification for diffuse large B‐cell lymphoma (DLBCL) patients. We retrospectively analyzed the clinicopathological and PET‐CT data of 498 patients diagnosed with DLBCL across three medical centers in China. 418 patients were eligible for subsequent analysis after excluding those with incomplete data and 70% of which were randomly selected as the discovery cohort, whereas the remaining 30% constituted the validation cohort. The impact of candidate factors on survival was assessed via univariate and multivariate Cox proportional hazards models. The area under the curve AUC and C‐index were calculated to assess the predictive performance of models. Univariate and multivariate Cox regression analyses identified changes in total lesion glycolysis (ΔTLG), iNCCN‐IPI, interim abdominal residual disease (iARD) status, and changes in the maximum standardized uptake value (ΔSUVmax) as independent prognostic factors. Leveraging the outcomes of the multivariate analysis, we constructed the iPET‐NCCN‐IPI prognostic model and categorized DLBCL patients into two separate prognostic risk groups based on their computed Risk Scores ( RS = 0.90×iNCCN‐IPI + 1.41×ΔTLG + 0.79×ΔSUVmax + 0.83×iARD ). The predictive performance of the model was validated by calculating the area under the receiver operating characteristic curve and the C‐index. Notably, compared with other models, the iPET‐NCCN‐IPI demonstrated superior prognostic capability. In conclusion, our study indicates that the iPET‐NCCN‐IPI stratifies DLBCL patients into two distinct prognostic risk groups and surpasses other models in prognostic predictive ability.
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