Zha Jie, Chen Qinwei, Zhang Wei, Jing Hongmei, Ye Jingjing, Liu Huanhuan, Yu Haifeng, Yi Shuhua, Li Caixia, Zheng Zhong, Xu Wei, Li Zhifeng, Lin Zhijuan, Ping Lingyan, He Xiaohua, Zhang Liling, Xie Ying, Chen Feili, Sun Xiuhua, Su Liping, Zhang Huilai, Yang Haiyan, Zhao Weili, Qiu Lugui, Li Zhiming, Song Yuqin, Xu Bing
Department of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen University, Xiamen, 361003, P.R. China.
Key laboratory of Xiamen for diagnosis and treatment of hematological malignancy, Xiamen, China.
Biomark Res. 2025 Jan 3;13(1):2. doi: 10.1186/s40364-024-00716-4.
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.
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.
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.
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.
24个月内疾病进展（POD24）对滤泡性淋巴瘤（FL）患者的总生存期（OS）有显著影响。本研究旨在采用机器学习方法开发一种强大的预测模型FLIPI-C，以识别有POD24高风险的FL患者。
来自中国全国17个中心的1938例FL患者（FL1-3a）队列被随机分为训练集和内部验证集（比例为2:1）。利用XGBoost构建POD24预测模型，该模型在验证集中进行内部验证，并在GALLIUM队列中进行外部验证。POD24的关键预测因素包括淋巴细胞与单核细胞比率（LMR）、乳酸脱氢酶（LDH）>正常上限（ULN）、低血红蛋白（Hb）、β2微球蛋白（β2-MG）升高、最大标准化摄取值（SUVmax）和淋巴结受累情况。FLIPI-C模型给LMR赋2分，给其他每个变量赋一分。
与现有模型（FLIPI、FLIPI-2、PRIMA-PI、FLEX）相比，FLIPI-C模型在内部验证队列（POD24的AUC：0.764，OS：0.700）和外部验证队列（POD24的AUC：0.703，OS：0.653）中均显示出预测POD24和3年总生存期（OS）的更高准确性（AUC）。决策曲线分析证实了FLIPI-C的更高净效益。
FLIPI-C模型采用机器学习方法开发，具有更高的预测准确性，并利用简单、广泛可用的标志物。它有望为临床实践中POD24高风险的FL患者的治疗决策和预后评估提供参考。
