POD24-Based prognostic signature enables personalized risk stratification in mantle cell lymphoma

Mantle cell lymphoma (MCL) exhibits significant biological and clinical heterogeneity, necessitating a refined prognostic model. According to the drawbacks of existing models which do not truly define the complexity of the disease, we used the clinical and molecular data from nine medical centers of China to validate the predictive utility of progression of disease within 24 months (POD24), and also established a novel prognostic risk model to predict the survival outcome of MCL patients. POD24 occurred in 37.7% of evaluable patients, with the median over survival being 21 months (vs. 122 months for those without POD24, P < 0.0001). The POD24-based risk model had the highest sensitivity to predict survival with the most satisfying AUC value for risk score (AUC = 0.869). In conclusion, we confirm the obviously predictive performance of POD24 and established a novel risk model combined POD24 and clinical factors. Our new prognostic model might be helpful in effectively classify MCL patients with high-risk groups in terms of survival rate, which may help in selecting high-risk MCL patients for more intensive treatment at time of relapse.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2025-03-13
DOI
https://doi.org/10.1038/s41598-025-92963-0
Primary Topic
Lymphoma Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

POD24-Based prognostic signature enables personalized risk stratification in mantle cell lymphoma

Huilai Zhang, Dongfeng Zeng, Dehui Zhou, Tao Pan et al.
Scientific Reports
Lymphoma Diagnosis and Treatment
article

POD24-Based prognostic signature enables personalized risk stratification in mantle cell lymphoma

Huilai Zhang, Dongfeng Zeng, Dehui Zhou, Tao Pan, Yizi He, Haige Ye, Yun Liang, Hui Zhou, Ling Xiao, Caiqin Wang, Rong Liang, Xiuhua Sun, Qingqing Cai
article en

Abstract

Mantle cell lymphoma (MCL) exhibits significant biological and clinical heterogeneity, necessitating a refined prognostic model. According to the drawbacks of existing models which do not truly define the complexity of the disease, we used the clinical and molecular data from nine medical centers of China to validate the predictive utility of progression of disease within 24 months (POD24), and also established a novel prognostic risk model to predict the survival outcome of MCL patients. POD24 occurred in 37.7% of evaluable patients, with the median over survival being 21 months (vs. 122 months for those without POD24, P < 0.0001). The POD24-based risk model had the highest sensitivity to predict survival with the most satisfying AUC value for risk score (AUC = 0.869). In conclusion, we confirm the obviously predictive performance of POD24 and established a novel risk model combined POD24 and clinical factors. Our new prognostic model might be helpful in effectively classify MCL patients with high-risk groups in terms of survival rate, which may help in selecting high-risk MCL patients for more intensive treatment at time of relapse.

Scientific ReportsVol. 15(1)
Central South University (CN), Army Medical University (CN), Sun Yat-sen University (CN), Dalian Medical University (CN), Chinese Academy of Medical Sciences & Peking Union Medical College (CN), Wenzhou Medical University (CN), First Affiliated Hospital of Wenzhou Medical University (CN), Tianjin Medical University Cancer Institute and Hospital (CN), Daping Hospital (CN), Second Affiliated Hospital of Dalian Medical University (CN), Sun Yat-sen University Cancer Center (CN), Institute of Hematology & Blood Diseases Hospital (CN), Third Xiangya Hospital (CN), Second Affiliated Hospital of Zhejiang University (CN), Xijing Hospital (CN), Air Force Medical University (CN)
Health Commission of Hunan Province, National Outstanding Youth Science Fund Project of National Natural Science Foundation of China
Openalex Percentile: Top 94%
Lymphoma Diagnosis and Treatment
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.