Nomogram incorporating clinicopathological parameters to predict the survival of patients with mantle cell lymphoma

Zhu, Yuandong; Xu, Wenxian; Zheng, Xiao; Zheng, Zhuojun

doi:10.1136/jim-2018-000837

Cited by 3 publications

(3 citation statements)

References 28 publications

(50 reference statements)

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“…A nomogram is a visual format of a statistical predictive model that permits improved predictive accuracy for clinical outcomes compared with the former prognostic scales through calculating the cumulative effect of each independent variable [23]. A wide variety of nomograms were previously developed for malignacies and some were indicated for DLBCL [18,19,[24][25][26][27][28][29][30], incorporating clinicopathological parameters to predict the survival of patients ( Supplementary Table). Although some of these nomograms were dedicated to incorporating NLR and inflammatory cytokines into nomogram construction for accurate survival prediction [28][29][30], they were all indicated for de novo patients with front-line treatment.…”

Section: Discussionmentioning

confidence: 99%

Prognostic Nomogram and Predictive Factors in Refractory or Relapsed Diffuse Large B-Cell Lymphoma Patients Failing Front-Line R-CHOP Regimens

et al. 2020

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Background:The clinical course of relapsed or refractory (r/r) diffuse large B-cell lymphoma (DLBCL) is variable, with limited efficacy data of second-line treatment in a post-rituximab real-world context. Hence, we explored the predictors and constructed a nomogram for risk stratification in this population. Patients and methods: Among 296 r/r DLBCL patients pretreated with R-CHOP (rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone) at the Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College between 2006 and 2017, 231 were included for nomogram construction. After randomization, we constructed the prognostic nomogram in the primary cohort (n=161) based on a multivariate analysis and confirmed it in the validation cohort (n=70). Additionally, we explored predictive factors for second-line therapy using a ordinal regression analysis. Results: Four independent prognostic factors including rituximab in the second-line setting, initial Eastern Cooperative Oncology Group (ECOG) performance status (PS), response to front-line treatment, and invasion on progression/recurrence were used to construct the nomogram. The nomogram had a C index of 0.70 with AUC values of 0.73 and 0.71 for the primary and validation cohorts, respectively. Subsequently, three risk groups (low, intermediate, and high) were determined with median overall survival (OS) of 38.0, 25.0, and 7.0 months, respectively. Additionally, we conducted a multivariate ordinal regression analysis and identified prior response to front-line treatment (odds ratio=4.50, 95% CI: 1.84-11.27, p=0.001) and bulky disease at diagnosis (odds ratio=0.36, 95% CI: 0.182-0.702, p=0.003) were two independent factors for second-line treatment efficacy. Conclusions: The established predictors for treatment efficacy and the novel nomogram for survival in r/r DLBCL patients could potentially be applied for risk stratification and treatment guidance in the post-rituximab era.

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Section: Discussionmentioning

confidence: 99%

Prognostic Nomogram and Predictive Factors in Refractory or Relapsed Diffuse Large B-Cell Lymphoma Patients Failing Front-Line R-CHOP Regimens

et al. 2020

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“…The occurrence and progression of MCL exhibit complex biological behaviors, and patient survival and prognosis are in uenced by numerous factors. The low incidence rate and heterogeneity of MCL pose challenges in establishing an optimal prognosis model [21]. Previous studies on prognostic factors in MCL patients have noted unsatisfactory nal treatment outcomes [22].…”

Section: Discussionmentioning

confidence: 99%

Clinical significance of Cyclin D1 by complete quantification detection in mantle cell lymphoma: Positive indicator in prognosis

Yang,

Song,

Yin

et al. 2024

Preprint

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Background: The positive expression of Cyclin D1 in immunohistochemical (IHC) staining serves as the cornerstone for diagnosing mantle cell lymphoma (MCL). However, existing literature does not conclusively establish whether the expression ratio and staining intensity significantly influence diagnostic outcomes or patient prognosis. In this retrospective study, the correlation between comprehensive Cyclin D1 quantification and the prognosis of MCL patients was studied. Methods:The Cyclin D1 protein level was assessed in 120 formalin-fixed paraffin-embedded samples from mantle cell lymphoma (MCL) patients using the quantitative dot blot (QDB) analysis technique. R language software was employed for statistical analysis to determine the optimal threshold with statistical significance. Additionally, Kaplan-Meier method was utilized to evaluate the relationship between the absolute level of Cyclin D1 protein and overall survival (OS) of patients. Furthermore, the Chi-square test was applied to analyze the causes of single and multiple fractures, with a significance level of p < 0.05. Finally, the Log-rank test was used to compare two survival curves, where a significance level of p < 0.05 was considered statistically significant. Results: At the optimized cutoff of 0.46 nmol/g, univariate analysis revealed a positive correlation between Cyclin D1 protein level and patient survival (OS). Specifically, in the subgroup with complete quantification of Cyclin D1 higher than the cutoff, the 5-year OS was 18%, whereas in the subgroup with complete quantification of Cyclin D1 lower than the cutoff, the 5-year OS was 4.8% (Log-rank test, P=0.017). This indicates that patients with Cyclin D1 levels above the cutoff had significantly better overall survival compared to those below the cutoff. Additionally, in the Pearson distribution test, Ki-67 emerged as an independent prognostic factor for the complete quantification of Cyclin D1. Notably, Cyclin D1 complete quantification results remained unaffected by factors such as gender, age, LDH level, Ann Arbor stage, Ki-67, IPI, MIPI, and MIPI-c (Chi-square test, p > 0.05).

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“…3,4 However, most studies on the construction of predictive models have focused on a specific pathological type or site in the gastrointestinal tract. [6][7][8][9][10][11][12][13] Therefore, in this study, PINHL was designated as a separate entity. 14 Today, the Ann Arbor and Lugano stages are used to evaluate the progression of lymphoma.…”

Section: Introductionmentioning

confidence: 99%

Development and External Validation of a Nomogram to Predict Cancer-Specific Survival in Patients with Primary Intestinal Non-Hodgkin Lymphomas

Zhang¹,

Liu²,

Tao³

et al. 2021

CMAR

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Purpose: Primary intestinal non-Hodgkin lymphoma (PINHL) is a biologically and clinically heterogeneous disease. Few individual prediction models are available to establish prognoses for PINHL patients. Herein, a novel nomogram was developed and verified to predict long-term cancer-specific survival (CSS) rates in PINHL patients, and a convenient online risk calculator was created using the nomogram. Materials and Methods: Data on PINHL patients from January 1, 2004, to December 31, 2015, obtained from the Surveillance, Epidemiology, and End Results (SEER) database (n = 2372; training cohort), were analyzed by Cox regression to identify independent prognostic parameters for CSS. The nomogram was internally and externally validated in a SEER cohort (n = 1014) and a First Affiliated Hospital of Guangzhou University of Chinese Medicine (FAHGUCM) cohort (n = 37), respectively. Area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA) were used to evaluate nomogram performance. Results: Five independent predictors were identified, namely, age, marital status, Ann Arbor Stage, B symptoms, and histologic type. The nomogram showed good performance in discrimination and calibration, with C-indices of 0.772 (95% CI: 0.754-0.790), 0.763 (95% CI: 0.734-0.792), and 0.851 (95% CI: 0.755-0.947) in the training, internal validation, and external validation cohorts, respectively. The calibration curve indicated that the nomogram was accurate, and DCA showed that the nomogram had a high clinical application value. AUC values indicated that the prediction accuracy of the nomogram was higher than that of Ann Arbor Stage (training cohort: 0.804 vs 0.630; internal validation cohort: 0.800 vs 0.637; external validation cohort: 0.811 vs 0.598), and Kaplan-Meier curves indicated the same. Conclusion: A nomogram was developed to assist clinicians in predicting the survival of PINHL patients and in making optimal treatment decisions. An online calculator based on the nomogram was made available at https://cuifenzhang.shinyapps.io/DynNomapp/.

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Nomogram incorporating clinicopathological parameters to predict the survival of patients with mantle cell lymphoma

Cited by 3 publications

References 28 publications

Prognostic Nomogram and Predictive Factors in Refractory or Relapsed Diffuse Large B-Cell Lymphoma Patients Failing Front-Line R-CHOP Regimens

Prognostic Nomogram and Predictive Factors in Refractory or Relapsed Diffuse Large B-Cell Lymphoma Patients Failing Front-Line R-CHOP Regimens

Clinical significance of Cyclin D1 by complete quantification detection in mantle cell lymphoma: Positive indicator in prognosis

Development and External Validation of a Nomogram to Predict Cancer-Specific Survival in Patients with Primary Intestinal Non-Hodgkin Lymphomas

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