Second primary malignancies in non-Hodgkin lymphoma: epidemiology and risk factors

Zhan, Zhumei; Guo, Wei; Wan, Xin; Bai, Ou

doi:10.1007/s00277-023-05095-8

Cited by 4 publications

(2 citation statements)

References 82 publications

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“…However, as targeted therapies begin to replace cytotoxic chemotherapy, the incidence of therapy related SPMs is likely to decline, particularly for indolent B-cell NHL. [ 18 ] Amr Ebied concluded that the risk of developing SPMs in DLBCL is significantly higher than in the general population and that the risk of developing an SMN remains significantly higher with increasing latency. [ 19 ] Similarly, another study found that the increased risk of second primary mesothelioma after radiotherapy for lymphoma was independent of multiple patient and disease characteristics and increased with earlier treatment duration and longer latency.…”

Section: Discussionmentioning

confidence: 99%

Correlation between lymphoma and second primary malignant tumor

2023

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Although studies have investigated the risk of second primary malignancies (SPMs) associated with lymphoma of various sites, limited studies have investigated risk of lymphoma with different SPMs and risk factors related to different SPMs. We conducted a retrospective cohort study to evaluate the cumulative incidence and risk factors of different secondary SPMs in patients previously diagnosed as lymphoma, and to compare the survival rates of SPMs and primary malignant tumors. Retrospective analysis was performed on data obtained from Surveillance, Epidemiology, and End Results database. Patients with an initial primary malignancy diagnosis of lymphoma between 2000 and 2019 were included in the study. The statistical analysis was conducted from March 2022 to January 2023. The development of an SPM defined as any type of malignant tumor 292,210 patients remained in final cohort, including 35,220 patients with secondary primary malignant tumor. The cumulative incidence of SPMs during 20 years of follow-up is 1.95% in combined respiratory system, 0.14% in central nervous system, is 0.82% hepatobiliary pancreatic system, is 1.31% in urinary system, is 1.92% digestive tract. Multivariate competitive risk model analysis showed that Different characteristics of lymphoma patients were associated with secondary different types of SPMS. The risk of secondary SPMs in lymphoma patients after radiotherapy and chemotherapy varies with the change of diagnosis time, diagnosis age and incubation period. Propensity score matching and Kaplan–Meier analysis showed that the survival rate of secondary tumor was significantly lower than that of matched primary malignant tumor. This study reminds us to consider the possibility of SPMs in the initial treatment of lymphoma patients, and develop a follow-up plan according to the characteristics of patients to reduce the risk of SPMs. Occurring more than 6 months after the diagnosis of lymphoma. The cumulative incidence of SPMs was estimated by Fine-Gray competing risk regression. Poisson regression was used to evaluate the therapeutic factors associated risk for SPMs in patients undergoing radiotherapy or chemotherapy. The Kaplan–Meier method was used to assess the survival outcomes of patients with SPMs.

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Section: Discussionmentioning

confidence: 99%

Correlation between lymphoma and second primary malignant tumor

2023

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“…Meanwhile, other studies have reported no difference in the incidence of SPMs between patients receiving radiotherapy and other treatments 13 . Zhumei Zhan et al believed that chemotherapy drugs could also further elevate the risk of SPMs in patients with non-Hodgkin's lymphoma 14 .…”

Section: Comparison Of Mortality Estimation Between Traditional Survi...mentioning

confidence: 99%

Construction and validation of a competing risk model for specific survival of patients with second primary malignancies after prostate cancer

Bao,

Wang,

Liu

et al. 2023

Preprint

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Objective To identify SPM death risk factors in PCa survivors and high-risk PCa patients for SPMs. With improved prostate cancer (PCa) survival, there's a growing need to study second primary malignancies (SPMs) in PCa survivors. Methods PCa patients from 2004–2015 in the SEER database were screened for SPM risk. The Fine and Gray competing risk model identified SPM mortality risk factors via univariate and multivariate analyses. A competing risk nomogram predicted 3-, 5-, and 10-year SPM mortality risk, stratifying patients by total scores for risk assessment. Model performance was assessed using the C-index, ROC curve, calibration curve, and AUC. Results SPM-diagnosed PCa patients (2004–2015) were split into a 7:3 training (n = 31,435) and validation set (n = 13,472). The nomogram included 12 factors: age, chemotherapy, radiation, Gleason Score, race, grade, marital status, tumor size, surgical site, surgery/radiation sequence, scope, and stage. C-index values were 0.70 (se: 0.001) and 0.684 (se: 0.002) in training and validation, respectively, indicating high discriminative power. The 3-, 5-, and 10-year AUCs in training were 0.75 (95% CI: 0.72–0.77), 0.73 (95% CI: 0.72–0.75), and 0.72 (95% CI: 0.7–0.73), and in validation were 0.7 (95% CI: 0.65–0.74), 0.7 (95% CI: 0.67–0.73), and 0.71 (95% CI: 0.69–0.73), respectively, showing good predictive accuracy. The calibration curve confirmed model fit. Conclusions A competing risk model predicts SPM mortality in PCa survivors, aiding high-risk patient identification and guiding survival-oriented treatment and follow-up strategies.

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