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; Wei Xu; Zhifeng Li; Zhijuan Lin; Lingyan Ping; Xiaohua He; Liling Zhang; Ying Xie; Feili Chen; Xiuhua Sun; Liping Su; Huilai Zhang; Haiyan Yang; Weili Zhao; Lugui Qiu; Zhiming Li; Yuqin Song; Bing Xu

doi:10.1186/s40364-024-00716-4

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

Biomark Res. 2025 Jan 3;13(1):2. doi: 10.1186/s40364-024-00716-4.

Authors

Jie Zha^#^{1

2}, Qinwei Chen^#^{1

2}, Wei Zhang^#³, Hongmei Jing^#⁴, Jingjing Ye⁵, Huanhuan Liu^{1

2}, Haifeng Yu^{6

7}, Shuhua Yi⁸, Caixia Li⁹, Zhong Zheng¹⁰, Wei Xu¹¹, Zhifeng Li^{1

2}, Zhijuan Lin^{1

2}, Lingyan Ping¹², Xiaohua He^{13

14

15}, Liling Zhang¹⁶, Ying Xie¹⁷, Feili Chen¹⁸, Xiuhua Sun¹⁹, Liping Su²⁰, Huilai Zhang²¹, Haiyan Yang⁶, Weili Zhao¹⁰, Lugui Qiu⁸, Zhiming Li^{22

23

24}, Yuqin Song²⁵, Bing Xu^{26

27}

Affiliations

¹ Department of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen University, Xiamen, 361003, P.R. China.
² Key laboratory of Xiamen for diagnosis and treatment of hematological malignancy, Xiamen, China.
³ Department of Hematology, Peking Union Medical College Hospital, Beijing, China.
⁴ Department of Hematology, Lymphoma Research Center, Peking University Third Hospital, Beijing, China.
⁵ Department of Hematology, Cheeloo College of Medicine, Qilu Hospital, Shandong University, Jinan, China.
⁶ Department of Lymphoma, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital), Hangzhou, China.
⁷ Department of Lymphoma, Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, China.
⁸ State 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.
⁹ National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, The First Affiliated Hospital of Soochow University, Suzhou, China.
¹⁰ Shanghai 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.
¹¹ Department of Hematology, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
¹² Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China.
¹³ Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China.
¹⁴ State Key Laboratory of Oncology in South China, Guangzhou, China.
¹⁵ Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
¹⁶ Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
¹⁷ Department of Hematology, Fujian Provincial Hospital, Shengli Clinical Medical College of Fujian Medical University, Fujian Medical University, Fuzhou, China.
¹⁸ Lymphoma division, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
¹⁹ Department of Oncology, The Second Hospital of Dalian Medical University, Dalian, Liaoning, China.
²⁰ Shanxi Hospital Affiliated to Cancer Hospital, Cancer Hospital, Shanxi Province Cancer Hospital, Chinese Academy of Medical Sciences, Shanxi Medical University, Taiyuan, Shanxi, China.
²¹ Department of Lymphoma, Tianjin Medical University Cancer Hospital, Tianjin, China.
²² Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China. [email protected].
²³ State Key Laboratory of Oncology in South China, Guangzhou, China. [email protected].
²⁴ Collaborative Innovation Center for Cancer Medicine, Guangzhou, China. [email protected].
²⁵ Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China. [email protected].
²⁶ Department of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen University, Xiamen, 361003, P.R. China. [email protected].
²⁷ Key laboratory of Xiamen for diagnosis and treatment of hematological malignancy, Xiamen, China. [email protected].

^# Contributed equally.

Abstract

Background: Disease 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.

Methods: A 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.

Results: The 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.

Conclusions: Developed 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.

Keywords: FLIPI-C; Follicular lymphoma; Machine learning; Overall survival; POD24.

Abstract

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