Behavior-Oriented Nomogram for the Stratification of Lower-Grade Gliomas to Improve Individualized Treatment

Wei, Ruo-Lun; Zhang, Liwei; Li, Jianguo; Yang, Fengdong; Xue, Yue; Wei, Xinting

doi:10.3389/fonc.2020.538133

Cited by 6 publications

(6 citation statements)

References 13 publications

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“…The C-index values range from 0.5 to 1, and is positively correlated with the predictive performance of the model. When the value is greater than 0.7, the results demonstrated that the model has a reliable discriminant ability [ 14 ]. For the verification of the prediction model, the verification queue is utilized for internal verification, and the cases collected by our hospital are used for independent external verification, with bootstrap resampling used to create the calibration curve.…”

Section: Methodsmentioning

confidence: 99%

Development and validation of a prognostic nomogram for early stage non-small cell lung cancer: a study based on the SEER database and a Chinese cohort

et al. 2022

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Objective This study aimed to construct a nomogram to effectively predict the overall survival (OS) of patients with early-stage non-small-cell lung cancer (NSCLC). Methods For the training and internal validation cohorts, a total of 26,941 patients with stage I and II NSCLC were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. A nomogram was constructed based on the risk factors affecting prognosis using a Cox proportional hazards regression model. And 505 patients were recruited from Jiaxing First Hospital for external validation. The discrimination and calibration of the nomogram were evaluated by C-index and calibration curves. Results A Nomogram was created after identifying independent prognostic factors using univariate and multifactorial factor analysis. The C-index of this nomogram was 0.726 (95% CI, 0.718–0.735) and 0.721 (95% CI, 0.709–0.734) in the training cohort and the internal validation cohort, respectively, and 0.758 (95% CI, 0.691–0.825) in the external validation cohort, which indicates that the model has good discrimination. Calibration curves for 1-, 3-, and 5-year OS probabilities showed good agreement between predicted and actual survival. In addition, DCA analysis showed that the net benefit of the new model was significantly higher than that of the TNM staging system. Conclusion We developed and validated a survival prediction model for patients with non-small cell lung cancer in the early stages. This new nomogram is superior to the traditional TNM staging system and can guide clinicians to make the best clinical decisions.

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Section: Methodsmentioning

confidence: 99%

Development and validation of a prognostic nomogram for early stage non-small cell lung cancer: a study based on the SEER database and a Chinese cohort

et al. 2022

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“…Patients with early‐onset PCa have gradually become a distinctive subset among PCa patients due to the significantly increased incidence 31 . Accurately and effectively predicting the prognosis of cancer patients is of great significance for clinical treatment and guideline formulation 32,33 . Patient prognosis is reflected not only by several pathological indicators included in the TNM classification system but also by social and other clinicopathological factors.…”

Section: Discussionmentioning

confidence: 99%

“… 31 Accurately and effectively predicting the prognosis of cancer patients is of great significance for clinical treatment and guideline formulation. 32 , 33 Patient prognosis is reflected not only by several pathological indicators included in the TNM classification system but also by social and other clinicopathological factors. Therefore, with the concept of precision medicine proposed, the TNM classification system was unable to meet the needs of clinicians.…”

Section: Discussionmentioning

confidence: 99%

Nomogram for predicting the overall survival of patients with early‐onset prostate cancer: A population‐based retrospective study

Qiao

Zheng

et al. 2022

Cancer Medicine

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Background The incidence of early‐onset prostate cancer (PCa) has increased significantly over the past few decades. It is necessary to develop a prognostic nomogram for the prediction of overall survival (OS) in early‐onset PCa patients. Methods A total of 23,730 early‐onset PCa patients (younger than 55 years old) between 2010 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were enrolled for the current study, and randomly separated into the training cohort and the validation cohort. 361 eligible early‐onset PCa patients from The Cancer Genome Atlas‐Prostate Adenocarcinoma (TCGA‐PRAD) cohort were obtained as the external validation cohort. Independent predictors were selected by univariate and multivariate Cox regression analysis, and a prognostic nomogram was constructed for 1‐, 3‐, and 5‐year OS. The accurate and discriminative abilities of the nomogram were evaluated by the concordance index (C‐index), receiver operating characteristic curve (ROC), calibration plot, net reclassification index (NRI), and integrated discrimination improvement (IDI). Results Multivariate Cox analysis showed that race, marital status, TNM stage, prostate‐specific antigen, Gleason score, and surgery were significantly associated with poor prognosis of PCa. A nomogram consisting of these variables was established, which had higher C‐indexes than the TNM system (training cohort: 0.831 vs. 0.746, validation cohort: 0.817 vs. 0.752). Better AUCs of the nomogram than the TNM system at 1, 3, and 5 years were found in both the training cohort and the validation cohort. The 3‐year and 5‐year AUCs of the nomogram in the TCGA‐PRAD cohort were 0.723 and 0.679, respectively. The calibration diagram, NRI, and IDI also showed promising prognostic value in OS. Conclusions We developed an effective prognostic nomogram for OS prediction in early‐onset PCa patients, which will further assist both the precise clinical treatment and the assessment of long‐term outcomes.

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“…Accurate prognosis prediction is an important component of the individualized treatment of tumours [22] .…”

Section: Discussionmentioning

confidence: 99%

Prognostic Estimation Model for Oligodendroglioma: An mRNA-Sequence Data-Based Analysis

Zhu

Shen

Yang

et al. 2022

Preprint

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Object: In contrast with the previous diagnostic strategy, which relied only on histopathologic evidence, the integrated diagnosis of oligodendroglioma based on the 5th edition of World Health Organization Classification of Tumors of the Central Nervous System (WHO CNS 5) criteria requires the codeletion of chromosome arms 1p and 19q and isocitrate dehydrogenase gene (IDH1 or IDH2) mutation (mt). The existing prognostic indicators may not be completely suitable for oligodendroglioma patients based on the new diagnostic criteria. We aimed to identify a prognostic prediction model for oligodendrogliomas based on the WHO CNS5 classification.Methods: We collected 175 glioma samples to investigate significant changes in mRNAs using the Chinese Glioma Genome Atlas (CGGA) database and to establish a prediction model for prognosis by Least Absolute Shrinkage and Selection Operator (LASSO) and Cox logistic analysis.Results: Eighty-eight differentially expressed RNAs (DERNAs) were identified between the long survival group and the short survival group. Seven RNAs were selected to calculate risk scores. Risk level, age and Primary-or-Recurrent Status (PRS) type were used as factors for the prognostic model.Conclusion: An individualized prognostic model for oligodendroglioma patients based on the WHO CNS5 criteria was established. The predictive ability of this model was validated in a validation cohort, which demonstrated its predictive accuracy. In the future, more pathological evidence is needed to support our predictive model to further classify oligodendrogliomas.

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Behavior-Oriented Nomogram for the Stratification of Lower-Grade Gliomas to Improve Individualized Treatment

Cited by 6 publications

References 13 publications

Development and validation of a prognostic nomogram for early stage non-small cell lung cancer: a study based on the SEER database and a Chinese cohort

Development and validation of a prognostic nomogram for early stage non-small cell lung cancer: a study based on the SEER database and a Chinese cohort

Nomogram for predicting the overall survival of patients with early‐onset prostate cancer: A population‐based retrospective study

Prognostic Estimation Model for Oligodendroglioma: An mRNA-Sequence Data-Based Analysis

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