Nomograms and risk scores for predicting the risk of oral cancer in different sexes: a large-scale case-control study

Chen, Fa; Lin, Lisong; Yan, Lingjun; Liu, Fengqiong; Qiu, Yu; Wang, Jing; Hu, Zhijian; Wu, Jun; Bao, Xiaodan; Lin, Li; Wang, Rui; Cai, Guoxi; Aoyagi, Kiyoshi; Cai, Lin; He, Baochang

doi:10.7150/jca.24431

Cited by 25 publications

(26 citation statements)

References 24 publications

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“…Furthermore, the strength of associations among various clinical factors and cSCC recurrence was analyzed using the Cox proportional hazards model. The nomogram for assessment of cSCC recurrence risk was constructed using AXIN2 and Snail expression, as well as various clinicopathological characteristics such as sex, age, lesion site, tumor size, and differentiation, and evaluated with the concordance index (c-index) and a calibration plot (17). R package version 3.1.1 (The R Foundation for Statistical Computing; http://www.r-project.org) with the rms (3.5.0) and eha (2.7.6) packages was used for statistical analysis.…”

Section: Statistical Analysis χ 2 and Fisher's Exact Tests Were Usedmentioning

confidence: 99%

AXIN2 and SNAIL expression predict the risk of recurrence in cutaneous squamous cell carcinoma after Mohs micrographic surgery

et al. 2020

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Recurrence is a common complication observed during cutaneous squamous cell carcinoma (cSCC) treatment; however, biomarkers for predicting recurrence in cSCC remain unknown. The present study aimed to investigate the predictive value of axis inhibition protein 2 (AXIN2) and SNAIL expression in cSCC recurrence. AXIN2 and SNAIL expression was evaluated using immunohistochemistry in 111 cSCC tissue samples obtained from 18 patients who presented recurrence (recurrence interval, 1-91 months) and 93 patients who did not experience recurrence following Mohs micrographic surgery (MMS) during the follow-up period (156 months). Nomogram construction was performed using patients' clinicopathological characteristics and AXIN2 and SNAIL protein expression. The results demonstrated that high AXIN2 (histoscore >100) and SNAIL (histoscore >100) expression was detected in 35 and 44 cSCC tissues, respectively. Furthermore, the expression levels of AXIN2 and SNAIL were significantly associated in patients with cSCC (P=0.001). AXIN2 and SNAIL expression levels were significantly associated with tumor size (P=0.021 and P=0.044, respectively) and recurrence of cSCC (P=0.017 and P=0.042, respectively). In addition, the results of the Kaplan-Meier curve analysis revealed that recurrence-free survival was significantly associated with tumor size (P=0.025), differentiation status (P<0.001), AXIN2 expression (P=0.001) and SNAIL expression (P=0.001). Furthermore, the results of the multivariate analysis demonstrated that age (P=0.043), AXIN2 expression (P=0.001) and SNAIL expression (P=0.045) were independent risk factors for cSCC recurrence in the present cohort. A nomogram for predicting the 1-, 2-, 3-, and 5-year recurrence-free survival was developed for patients with cSCC by including independent risk factors with a concordance index of 0.75. The results suggested that high AXIN2 and SNAIL expression may be considered as potential risk factors for cSCC recurrence. This nomogram may therefore be useful to assess the probability of recurrence in patients with cSCC following MMS.

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Section: Statistical Analysis χ 2 and Fisher's Exact Tests Were Usedmentioning

confidence: 99%

AXIN2 and SNAIL expression predict the risk of recurrence in cutaneous squamous cell carcinoma after Mohs micrographic surgery

et al. 2020

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“…The traditional method of survival prediction for OPCs has been building nomograms using Cox proportional hazard (Cox) regression analysis based on available clinical and sociodemographic predictors [ 9 , 10 , 11 ]. Such models are generally based on the assumptions that each predictor is linearly associated with OPCs survival outcomes.…”

Section: Introductionmentioning

confidence: 99%

Comparison of the Tree-Based Machine Learning Algorithms to Cox Regression in Predicting the Survival of Oral and Pharyngeal Cancers: Analyses Based on SEER Database

Haag

Lynch

et al. 2020

Cancers

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This study aims to demonstrate the use of the tree-based machine learning algorithms to predict the 3- and 5-year disease-specific survival of oral and pharyngeal cancers (OPCs) and compare their performance with the traditional Cox regression. A total of 21,154 individuals diagnosed with OPCs between 2004 and 2009 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Three tree-based machine learning algorithms (survival tree (ST), random forest (RF) and conditional inference forest (CF)), together with a reference technique (Cox proportional hazard models (Cox)), were used to develop the survival prediction models. To handle the missing values in predictors, we applied the substantive model compatible version of the fully conditional specification imputation approach to the Cox model, whereas we used RF to impute missing data for the ST, RF and CF models. For internal validation, we used 10-fold cross-validation with 50 iterations in the model development datasets. Following this, model performance was evaluated using the C-index, integrated Brier score (IBS) and calibration curves in the test datasets. For predicting the 3-year survival of OPCs with the complete cases, the C-index in the development sets were 0.77 (0.77, 0.77), 0.70 (0.70, 0.70), 0.83 (0.83, 0.84) and 0.83 (0.83, 0.86) for Cox, ST, RF and CF, respectively. Similar results were observed in the 5-year survival prediction models, with C-index for Cox, ST, RF and CF being 0.76 (0.76, 0.76), 0.69 (0.69, 0.70), 0.83 (0.83, 0.83) and 0.85 (0.84, 0.86), respectively, in development datasets. The prediction error curves based on IBS showed a similar pattern for these models. The predictive performance remained unchanged in the analyses with imputed data. Additionally, a free web-based calculator was developed for potential clinical use. In conclusion, compared to Cox regression, ST had a lower and RF and CF had a higher predictive accuracy in predicting the 3- and 5-year OPCs survival using SEER data. The RF and CF algorithms provide non-parametric alternatives to Cox regression to be of clinical use for estimating the survival probability of OPCs patients.

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“…Particularly, integrating predictive risk factors into an established tool is helpful for physicians to make clinical decisions. 10…”

Section: Introductionmentioning

confidence: 99%

Predictive Risk Factors for Early Seizures in Traumatic Brain Injury

Parmontree

Tunthanathip

Doungngern

et al. 2019

Journal of Neurosciences in Rural Practice

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Background Early posttraumatic seizure (PTS) is a significant cause of unfavorable outcomes in traumatic brain injury (TBI). This study was aimed to investigate the incidence and determine a predictive model for early PTS. Materials and Methods A prospective cohort study of 484 TBI patients was conducted. All patients were evaluated for seizure activities within 7 days after the injury. Risk factors for early PTS were identified using univariate analysis. The candidate risk factors with p < 0.1 were selected into multivariable logistic regression analysis to identify predictors of early PTS. The fitting model and the power of discrimination with the area under the receiver operating characteristic (AUROC) curve were demonstrated. The nomogram for prediction of early PTS was developed for individuals. Results There were 27 patients (5.6%) with early PTS in this study. The final model illustrated chronic alcohol use (odds ratio [OR]: 4.06, 95% confidence interval [CI]: 1.64–10.07), epidural hematoma (OR: 3.98, 95% CI: 1.70–9.33), and Glasgow Coma Scale score 3–8 (OR: 3.78, 95% CI: 1.53–9.35) as predictors of early PTS. The AUROC curve was 0.77 (95% CI: 0.66–0.87). Conclusions The significant predictors for early PTS were chronic alcohol use, epidural hematoma, and severe TBI. Our nomogram was considered as a reliable source for prediction.

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Nomograms and risk scores for predicting the risk of oral cancer in different sexes: a large-scale case-control study

Cited by 25 publications

References 24 publications

AXIN2 and SNAIL expression predict the risk of recurrence in cutaneous squamous cell carcinoma after Mohs micrographic surgery

AXIN2 and SNAIL expression predict the risk of recurrence in cutaneous squamous cell carcinoma after Mohs micrographic surgery

Comparison of the Tree-Based Machine Learning Algorithms to Cox Regression in Predicting the Survival of Oral and Pharyngeal Cancers: Analyses Based on SEER Database

Predictive Risk Factors for Early Seizures in Traumatic Brain Injury

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