ORIGINAL RESEARCH article

Front. Immunol.

Sec. B Cell Biology

Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1539924

Investigation into prognostic factors of early recurrence and progression of previously untreated diffuse large B-cell lymphoma and statistical prediction model of POD12

Provisionally accepted
Ke  LianKe Lian1Zhu  WenyaoZhu Wenyao2Zhihui  HuZhihui Hu1Fang  SuFang Su3Caixia  XuCaixia Xu4Hui  WangHui Wang5*
  • 1Shanxi Bethune Hospital, Shanxi Medical University, Taiyuan, Shanxi Province, China
  • 2The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
  • 3Shanxi Provincial Cancer Hospital, Taiyuan, Shanxi Province, China
  • 4Houma People’s Hospital, Houma, Shaanxi Province, China
  • 5Zhejiang University of Finance and Economics, Hangzhou, China

The final, formatted version of the article will be published soon.

Objective: To evaluate the incidence, prognostic value and risk factors of progression of disease within 12 months (POD12) in the patients with diffuse large B-cell lymphoma (DLBCL). Methods: Retrospective analysis of the clinical, pathological and follow-up data was carried out on 69 DLBCL cases in Shanxi Bethune hospital from January 2016 to June 2020. One-way ANOVA and multivariate Cox regression analysis were used to explore the correlation between POD12 and prognosis, and the logistic regression analysis was used to explore the risk factors of POD12, accompanied by prediction model based on the convolutional neural networks and long short-term memory (CNN-LSTM) and particle swarm optimization and general regression neural network (PSO-GRNN) models. Results: (1) POD12 is significantly correlated with PFS (P<0.001) and OS (P=0.008). (2) From the univariate logistic regression analysis corrected by the first-line chemotherapy regimen that LDH, β2-MG, Stage, ECOG, NLR and SII are as the risk factors for POD12 (P<0.1), while β2-MG and ECOG are as the independent risk factors for POD12 from multivariate logistic regression analysis (p<0.05). (3) The prediction model of POD12 is established based on LDH, β2-MG, Stage, ECOG, NLR, SII indicators. AUC is 0.846 (95% CI: 0.749~0.944, P<0.001), suggesting that the prediction model is reasonable. A prediction method of the characteristic variables of the risk of POD12 by the CNN-LSTM deep learning model based on the chaotic time series is proposed. By comparison, the CNN-LSTM and PSO-GRNN models are the most suitable to predict the risk level of the POD12 in the future.

Keywords: diffuse large B cell lymphoma, prognosis, prediction model of POD12, Early progression, risk factor

Received: 05 Dec 2024; Accepted: 07 Jul 2025.

Copyright: © 2025 Lian, Wenyao, Hu, Su, Xu and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Hui Wang, Zhejiang University of Finance and Economics, Hangzhou, China

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