The prognostic index prima‐pi combined with ki67 as a better predictor of progression of disease within 24 months in follicular lymphoma

Introduction: Follicular lymphoma (FL) is one of the common sbutypes of non-Hodgkin's lymphoma (NHL). The disease progresses slowly and has a long median survival but is difficult to treat and prone to recurrent relapse. Notably, disease progression within 24 months after first-line treatment (POD24) has been found to be a risk factor for poor survival in follicular lymphoma, but there is no optimal prognostic model to accurately predict patients with early disease progression. Given the technical complexity as well as the cost, bio-clinical prediction is currently difficult to be popularized in the clinical setting, so simple and efficient tumor microenvironment assays are a current research hotspot. How to combine traditional prognostic models with new indicators to establish a new prediction system to predict the early progression of FL patients more accurately is a future research direction. Methods: The study retrospectively analyzed patients with newly diagnosed FL patients in Shanxi Provincial Cancer Hospital from January 2015 to December 2020. Date from patients undergoing immunohistochemical detection (IHC) were analyzed using chi-square test and multivariate Logistic regression. Also, we built a nomogram model based on the results of LASSO regression analysis of POD24, which was validated in both the training set and validation set, and additional external validation was performed using a dataset (n = 74) from another center. Results: The multivariate Logistic regression results suggest that high-risk PRIMA-PI group, Ki-67 high expression represent risk factors for POD24 (p < 0.05). Next, PRIMA-PI and Ki67 were combined to build a new model, namely, PRIMA-PIC to reclassify high and low-risk groups. The result showed that the new clinical prediction model constructed by PRIMA-PI with ki67 has a high sensitivity to the prediction of POD24. Compared to PRIMA-PI, PRIMA-PIC also has better discrimination in predicting patients' progression-free survival (PFS) and overall survival (OS). In addition, we built nomogram models based on the results of LASSO regression (histological grading, NK cell percentage, PRIMA-PIC risk group) in the training set, which were validated using internal validation set and external validation set, we found that C-index and calibration curve showed good performance. Conclusions: As such, the new predictive model-based nomogram established by PRIMA-PI and Ki67 could well predict the risk of POD24 in FL patients, which boasts clinical practical value. Keyword: diagnostic and prognostic biomarkers No conflicts of interests pertinent to the abstract.