Correction to: Prognostic significance of serum inflammatory markers in esophageal cancer

Powell, Arfon; Eley, Catherine; Chin, Carven; Coxon, Alexandra; Christian, Adam; Lewis, Wyn G.; Collaborative, South East Wales Oesophagogastric Cancer

doi:10.1007/s10388-021-00836-y

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“…Previous clinical studies have preliminarily con rmed the predictive value of some clinical pathological biomarkers in predicting recurrence, metastasis, and overall survival rates after ESCC surgery, such as tumor size, venous invasion, differentiation status, and tumor lymph node metastasis (TNM) [25][26][27] . However, these predictive biomarkers have high detection costs and may not be applicable to the complex prognosis of elderly ESCC patients.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Machine Learning Methods for Predicting 3-Year Survival in Elderly Esophageal squamous cancer Patients Based on Oxidative Stress

Xie,

Huang,

Gao

et al. 2024

Preprint

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Background There is currently a lack of machine learning model studies exploring the relationship between oxidative stress score (OSS) and the prognosis of elderly Esophageal squamous cancer(ESCC) patients. MethodsThis study included elderly ESCC patients who underwent curative resection surgery from January 2013 to December 2020. Machine learning strategies including decision tree (DT), random forest (RF), and support vector machine (SVM) were employed to construct a predictive model for 3-year overall survival (OS) for elder ESCC base on OSS. ResultsPatients were divided into derivation cohort and validation cohort, and consisted of 340 and 145 patients, respectively. 8 important features which were the most important factors influencing 3-year OS (pathological N stage, pathological T stage, tumor histological type, vascular invasion, CEA, OSS, CA 19-9, and the amount of bleeding) were included in training the RF, DT and SVM. In the derivation cohort, the RF model exhibited the highest predictive performance with an AUC of 0.975(0.962-0.987), while the DT model is 0.784(0.739-0.830) and the SVM is 0.879(0.843-0.916). In the external validation cohort showed the similar trend . Conclusion The random forest clinical prediction model constructed based on OSS can effectively predict the prognosis of elderly ESCC patients after curative surgery.

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Section: Discussionmentioning

confidence: 99%

Comparison of Machine Learning Methods for Predicting 3-Year Survival in Elderly Esophageal squamous cancer Patients Based on Oxidative Stress

Xie,

Huang,

Gao

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Correction to: Prognostic significance of serum inflammatory markers in esophageal cancer

Abstract: In the original publication of the article, under the section “Clinicopathological characteristics”, the thresholds

Cited by 1 publication

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Comparison of Machine Learning Methods for Predicting 3-Year Survival in Elderly Esophageal squamous cancer Patients Based on Oxidative Stress

Comparison of Machine Learning Methods for Predicting 3-Year Survival in Elderly Esophageal squamous cancer Patients Based on Oxidative Stress

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