Utility of a Metabolic-Associated Nomogram to Predict the Recurrence-Free Survival of Stage I Cervical Cancer

Zhang, Yan; Lu, Huan; Zhang, Jinjin; Wang, Shixuan

doi:10.2217/fon-2020-1024

Cited by 3 publications

(3 citation statements)

References 31 publications

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“…Nomogram is a mathematical model based on multivariate regression analysis. Through this model, a variety of independent prognostic factors can be gathered together to make the predictive value more individualized and intuitive [ 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

A Research Study to Measure the Efficacy of Terminating Cervical Cancer via Customized Optimum Pathway

Zhang

et al. 2022

Journal of Healthcare Engineering

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Background. To develop a precise prognostic model of overall survival in patients with terminating cervical cancer based on surveillance, epidemiology, and end results (SEER) program. Methods. The patients were retrieved from SEER data who are diagnosed with terminating cervical cancer from 2004 to 2016. The data were performed using univariate and multivariate analyses and constructed nomograms for predicting survival. Use C-index to validate the model accuracy. Results. Totally 15839 patients diagnosed with cervical cancer were independently allocated into the training set (n = 11088) and validation set (n = 4751). The multivariate analysis results indicated that age, race, stage_T, stage_M, and stage_N were confirmed as independent risk predictors, and those factors are applied to construct this clinical model. The C-index of overall survival in the training set was 0.6816 (95% confidence intervene (CI), 0.694–0.763) and that in the validation set was 0.6931(95% CI, 0.613–0.779). All calibration curves of various factors were consistent with predicted and actual survival. Conclusion. The nomogram provides a novel method for predicting the survival of patients with terminating cervical cancer, assisting in accurate therapeutic methods for patients with primary terminating cervical cancer.

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Section: Introductionmentioning

confidence: 99%

A Research Study to Measure the Efficacy of Terminating Cervical Cancer via Customized Optimum Pathway

Zhang

et al. 2022

Journal of Healthcare Engineering

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“…Therefrom, we confirmed immunotherapy treatment effective and survival outcome discrepancy between the two METAscore groups, which was a compelling clue that METAscore could evaluate the sensitivity to antitumor immunotherapy. Incumbent data on the scoring system and the prognostic scores of CESC mainly concentrated on the perspectives of immunogenomics and genetic alteration ( Cai et al, 2020 ; Li et al, 2019 ; Wang et al, 2021 ; Zhang et al, 2021 ) . Comparatively, the METAscore developed in our study was a promising breakthrough on the immunometabolism, offering novel insights into CESC immune diversity from the metabolic landscape and highlighting that METAscore could predict sensitivity to immunotherapy.…”

Section: Discussionmentioning

confidence: 99%

Metabolism-Relevant Molecular Classification Identifies Tumor Immune Microenvironment Characterization and Immunotherapeutic Effect in Cervical Cancer

Gao

Wang

et al. 2021

Front. Mol. Biosci.

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Cervical cancer (CESC) is a gynecologic malignant tumor associated with high incidence and mortality rates because of its distinctive management complexity. Herein, we characterized the molecular features of CESC based on the metabolic gene expression profile by establishing a novel classification system and a scoring system termed as METAscore. Integrative analysis was performed on human CESC samples from TCGA dataset. Unsupervised clustering of RNA sequencing data on 2,752 formerly described metabolic genes identified three METAclusters. These METAclusters for overall survival time, immune characteristics, metabolic features, transcriptome features, and immunotherapeutic effectiveness existed distinct differences. Then we analyzed 207 DEGs among the three METAclusters and as well identified three geneclusters. Correspondingly, these three geneclusters also differently expressed among the aforementioned features, supporting the reliability of the metabolism-relevant molecular classification. Finally METAscore was constructed which emerged as an independent prognostic biomarker, related to CESC transcriptome features, metabolic features, immune characteristics, and linked to the sensitivity of immunotherapy for individual patient. These findings depicted a new classification and a scoring system in CESC based on the metabolic pattern, thereby furthering the understanding of CESC genetic signatures and aiding in the prediction of the effectiveness to anticancer immunotherapies.

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“… 21 For patients with cervical cancer, a nomogram to predict the survival of elderly patients has not been reported, no previous studies have noted the effect of competitive risk events on the probability of patients’ survival. 22 This study aimed to construct and validate a competitive risk model (CRM) nomogram to predict survival time for patients over 65 years of age diagnosed with nonmetastatic cervical cancer.…”

Section: Introductionmentioning

confidence: 99%

Competitive Risk Model Nomogram to Predict Prognosis in Patients Aged Over 65 Years with nonmetastatic Cervical Cancer: A SEER Population-Based Study

Jiao

Guo

et al. 2023

Technol Cancer Res Treat

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Objective: The prognostic factors for elderly patients with cervical cancer differ from those of younger patients. Competitive risk events could cause biases in the Cox proportional hazards (PH) model. This study aimed to construct a competitive risk model (CRM) nomogram for patients aged > 65 years with nonmetastatic cervical cancer. Methods: We retrospectively analyzed data extracted from the Surveillance, Epidemiology, and End Results (SEER) database and a total of 1856 patients from 18 cancer registries across the United States diagnosed between 2010 and 2015 were included. Kaplan–Meier analysis and log-rank tests were used to compare intergroup survival. Univariate and multivariate Cox proportional regression analyses were performed to identify independent prognostic factors. The cumulative incidence function (CIF) and Fine and Gray's test were used to determine the impact of competitive risk events on prognosis. The CRM nomogram was internally and externally validated using time-dependent receiver operator characteristic (ROC) curve (time-AUC), Brier scores, Harrell's concordance index (C-index), calibration curve, and decision curve analysis (DCA). Results: Analyses revealed that histology, age, the International Federation of Gynaecologists and Obstetricians (FIGO) stage, number of in situ malignancies, chemotherapy, radiotherapy (RT), and surgery were independent prognostic factors. The CRM nomogram accurately predicted 1-year, 3-year, and 5-year disease-specific survival (DSS). The C-indexes and Brier scores of the CRM nomogram were 0.641 and 0.094, respectively, at the 1-year cut-off in the training set. The time-AUC of the CRM nomogram at the 1-year, 3-year, and 5-year intervals in the training set were 77.6%, 77.3%, and 74.5%, respectively. The calibration curve demonstrated a favorable concordance. DCA suggested that the nomogram had a good net benefit. Therefore, the Cox model underestimated the weight of risk factors compared to CRM. Conclusions: This study presents the CRM nomogram to predict DSS in patients aged > 65 years with nonmetastatic cervical cancer. It can help clinicians implement more accurate personalized diagnostic and treatment modalities for elderly patients with cervical cancer.

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Utility of a Metabolic-Associated Nomogram to Predict the Recurrence-Free Survival of Stage I Cervical Cancer

Cited by 3 publications

References 31 publications

A Research Study to Measure the Efficacy of Terminating Cervical Cancer via Customized Optimum Pathway

A Research Study to Measure the Efficacy of Terminating Cervical Cancer via Customized Optimum Pathway

Metabolism-Relevant Molecular Classification Identifies Tumor Immune Microenvironment Characterization and Immunotherapeutic Effect in Cervical Cancer

Competitive Risk Model Nomogram to Predict Prognosis in Patients Aged Over 65 Years with nonmetastatic Cervical Cancer: A SEER Population-Based Study

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