AUTHOR=Hu Jiaci , Gao Fenghua , Zhao Jin , Song Wenzhu , Wang Yanli , Zheng Yuping , Wang Lieyang , Han Weie , Ma Li , Wang Jingrong , Bai Min , Guan Tao , Xi Yanfeng , Zhang Huilai , Qiu Lixia , Su Liping TITLE=The prognostic index PRIMA-PI combined with Ki67 as a better predictor of progression of disease within 24 months in follicular lymphoma JOURNAL=Frontiers in Oncology VOLUME=13 YEAR=2023 URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2023.1090610 DOI=10.3389/fonc.2023.1090610 ISSN=2234-943X ABSTRACT=Background

Progression of disease within 24 months (POD24) is a risk factor for poor survival in follicular lymphoma (FL), and there is currently no optimal prognostic model to accurately predict patients with early disease progression. 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

This study retrospectively analyzed patients with newly diagnosed FL patients in Shanxi Provincial Cancer Hospital from January 2015 to December 2020. Data from patients undergoing immunohistochemical detection (IHC) were analyzed using χ2 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, Tianjin Cancer Hospital.

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 patient’s 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.

Conclusion

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.