Machine Learning Models for the Diagnosis and Prognosis Prediction of High-Grade B-Cell Lymphoma - Kong, Hui; Zhu, Haojie; Zheng, Xiaoyun; Jiang, Meichen; Chen, Lushan; Lan, Lingqiong; Ren, Jinhua; Luo, Xiaofeng; Zheng, Jing; Zheng, Zhihong; Chen, Zhizhe; Hu, Jianda; et al. (Frontiers in Immunology, 2022)
High-grade B-cell lymphomas (HGBL) is a newly introduced category of rare and heterogeneous invasive B-cell lymphoma (BCL), which is diagnosed depending on fluorescent in situ hybridization (FISH), an expensive and laborious analysis. In order to identify HGBL minimizing work up and costs, a total of 187 newly diagnosed BCL patients were enrolled in a cohort study. As a result, the overall survival (OS) and progression-free survival (PFS) for the HGBL were inferior to those for the non-HGBL. HGBL (n = 35) were more likely to have a high-grade histomorphology appearance, extranodal involvement, bone marrow involvement, and whole-body maximum standardized uptake (SUVmax). The machine learning classification models indicated histomorphology appearance, Ann Arbor stage, LDH, and IPI risk group were independent risk factors for diagnosing HGBL. Patients with the high IPI risk group, CD10 positive, extranodal involvement, high LDH, high WBC, bone marrow involvement, old age, advanced Ann Arbor stage, and high SUVmax had a higher risk of dying in one year. In addition, these models prompt the clinical features with which the patients should be recommended to receive FISH diagnosis. Furthermore, this study supports that first-line treatment with R-CHOP has dismal efficacy in HGBL. A novel induction therapeutic regimen is still urgently needed to ameliorate the poor outcome of HGBL patients.