Evidence map›Paper›PMID 39754201›Full record

ArticleBiomarker research2025

A machine learning-based model to predict POD24 in follicular lymphoma: a study by the Chinese workshop on follicular lymphoma.

Jie Zha, Qinwei Chen, Wei Zhang, Hongmei Jing, Jingjing Ye, Huanhuan Liu, Haifeng Yu, Shuhua Yi, Caixia Li, Zhong Zheng and 17 more

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Article in Biomarker research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

27 authors.

Jie Zha *Department of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen University, Xiamen, 361003, P.R. China.
Qinwei Chen *Department of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen University, Xiamen, 361003, P.R. China.
Wei Zhang *Department of Hematology, Peking Union Medical College Hospital, Beijing, China.
Hongmei Jing *Department of Hematology, Lymphoma Research Center, Peking University Third Hospital, Beijing, China.
Jingjing YeDepartment of Hematology, Cheeloo College of Medicine, Qilu Hospital, Shandong University, Jinan, China.
Huanhuan LiuDepartment of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen University, Xiamen, 361003, P.R. China.
Haifeng YuDepartment of Lymphoma, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital), Hangzhou, China.
Shuhua YiState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Blood Diseases Hospital, Institute of Hematology, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China.
Caixia LiNational Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Zhong ZhengShanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Wei XuDepartment of Hematology, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Zhifeng LiDepartment of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen University, Xiamen, 361003, P.R. China.
Zhijuan LinDepartment of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen University, Xiamen, 361003, P.R. China.
Lingyan PingKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China.
Xiaohua HeDepartment of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China.
Liling ZhangCancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Ying XieDepartment of Hematology, Fujian Provincial Hospital, Shengli Clinical Medical College of Fujian Medical University, Fujian Medical University, Fuzhou, China.
Feili ChenLymphoma division, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
Xiuhua SunDepartment of Oncology, The Second Hospital of Dalian Medical University, Dalian, Liaoning, China.
Liping SuShanxi Hospital Affiliated to Cancer Hospital, Cancer Hospital, Shanxi Province Cancer Hospital, Chinese Academy of Medical Sciences, Shanxi Medical University, Taiyuan, Shanxi, China.
Huilai ZhangDepartment of Lymphoma, Tianjin Medical University Cancer Hospital, Tianjin, China.
Haiyan YangDepartment of Lymphoma, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital), Hangzhou, China.
Weili ZhaoShanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Lugui QiuState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Blood Diseases Hospital, Institute of Hematology, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China.
Zhiming LiDepartment of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China. lizhm@sysucc.org.cn.
Yuqin SongKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China. songyuqin622@163.com.
Bing XuDepartment of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen University, Xiamen, 361003, P.R. China. xubing@xmu.edu.cn.

Funding

Key R&D Program of Jiangsu Province BE2019798National Key R&D Program of China 2019YFC0840604National Natural Science Foundation of China U22A20290, 82170180,81800163, 82170182, 82020108003, 82100204, 82200211Natural Science Foundation of Fujian Province 2023J06054, 2020J011246, 2021J011359,2020J05307Xiamen Municipal Bureau of Science and Technology 3502Z20209003, 3502Z20224011, 3502Z20234001
6 · The paper itself

Abstract

backgroundDisease progression within 24 months (POD24) significantly impacts overall survival (OS) in patients with follicular lymphoma (FL). This study aimed to develop a robust predictive model, FLIPI-C, using a machine learning approach to identify FL patients at high risk of POD24.

methodsA cohort of 1,938 FL patients (FL1-3a) from seventeen centers nationwide in China was randomly divided into training and internal validation sets (2:1 ratio). XGBoost was utilized to construct the POD24-predicting model, which was internally validated in the validation set and externally validated in the GALLIUM cohort. Key predictors of POD24 included lymphocyte-to-monocyte ratio (LMR), lactate dehydrogenase (LDH) > ULN, low hemoglobin (Hb), elevated beta-2 microglobulin (β2-MG), maximum standardized uptake value (SUVmax), and lymph node involvement. The FLIPI-C model assigned 2 points to LMR and 1 point to each of the other variables.

resultsThe FLIPI-C model demonstrated superior accuracy (AUC) for predicting POD24 and 3-year overall survival (OS) in both the internal (AUC POD24: 0.764, OS: 0.700) and external validation cohorts (AUC POD24: 0.703, OS: 0.653), compared to existing models (FLIPI, FLIPI-2, PRIMA-PI, FLEX). Decision curve analysis confirmed the superior net benefits of FLIPI-C.

conclusionsDeveloped using a machine learning approach, the FLIPI-C model offers superior predictive accuracy and utilizes simple, widely available markers. It holds promise for informing treatment decisions and prognostic assessments in clinical practice for FL patients at high risk of POD24.

Indexed as

FLIPI-CFollicular lymphomaMachine learningOverall survivalPOD24

Identifiers

PMID39754201
PMCPMC11697473

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