Peripheral blood cell count ratios are predictive biomarkers of clinical response and prognosis for non‐surgical esophageal squamous cell carcinoma patients treated with radiotherapy

Zhi, Xiaohui; Jiang, Kan; Shen, Yue; Su, Xinyu; Wang, Ke; Ma, Yuanyuan; Zhou, Lixin

doi:10.1002/jcla.23468

Cited by 11 publications

(14 citation statements)

References 51 publications

(52 reference statements)

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“…The limitation of the seventh edition of the 2009 AJCC TNM staging criteria is that it is applicable only to patients with surgical treatment alone, but not for patients with non-surgical. n this study, the tumor staging before radiotherapy was classi ed according to the Clinical Classi cation of Esophageal Carcinoma Treated by Non-surgical Methods, the clinical prognostic value and practicability of which has been veri ed [8,9]. All serum parameters were collected with in 7 days before RT, and including neutrophil, lymphocyte, monocyte, platelet counts, and albumin levels.…”

Section: Data Collectionmentioning

confidence: 99%

Nomogram based on SIS predicting the survival of non-surgical thoracic esophageal squamous cell carcinoma treated by IMRT

Zhu

Dong

et al. 2021

Preprint

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Background: Several inflammatory markers have been reported to be associated with clinical outcomes in patients with esophageal squamous cell carcinoma (ESCC). This study was to evaluate several pre-radiotherapy serum inflammatory indicators, including the neutrophil / lymphocytes ratio (NLR), platelet / lymphocyte (PLR), systemic immune-inflammatory index (SII), systemic inflammation score(SIS), and compare which one has the highest predicted survival value. Finally, combining inflammatory markers with traditional prognostic factors, a new Nomogram model was developed to predict overall survival (OS) and progression-free survival (PFS) for ESCC patients receiving radiotherapy (RT) or chemoradiotherapy (CRT). Methods: This study retrospectively reviewed the data of 245 patients with thoracic esophageal squamous cell carcinoma (ESCC) underwent RT or CRT in the Fourth Hospital of Hebei Medical University from January 2013 to December 2015. The survival differences of these indexes were compared by the Kaplan-Meier method, and the univariate and the multivariate analyses were performed to determine these prognostic factors of overall survival (OS) and progression-free survival (PFS). Multivariate Cox proportional hazards regression models were used to create nomogram for OS and PFS.Results: 239 patients met the eligibility criteria. The estimated 1-, 3-, and 5-year OS and PFS rates were 74.6%, 36.8%, 26.5% and 58.4%, 31.3%, 20.5%, respectively, for the whole group. The difference in survival between OS and PFS was significant when univariate analysis were applied based on these inflammation-based measures. Multivariate analysis showed that tumor length, T stage, TNM stage, chemotherapy, SIS were predictive variables for OS and PFS in the multivariate model. The nomogram model established based on multivariate models of training data set had good predictive ability, the unadjusted C-index was 0.701 (95% CI, 0.662– 0.740) and 0.695 (95% CI, 0.656 - 0.734) for OS and PFS. Conclusions: This study show that SIS, as a comprehensive indicator of inflammation and nutrition, had the strongest predictive power for evaluating prognosis. Moreover, our nomogram can accurately predict OS and PFS after treatment and may provide guidance regarding adjuvant therapy and surveillance.

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Section: Data Collectionmentioning

confidence: 99%

Nomogram based on SIS predicting the survival of non-surgical thoracic esophageal squamous cell carcinoma treated by IMRT

Zhu

Dong

et al. 2021

Preprint

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“…NLR, PLR and LMR might be served as prognostic markers in patients with ESCC [29]. Peripheral blood cell count ratio was suggested to evaluate clinical response and prognosis of patients with non-surgical ESCC [30]. Serum TT may be an important factor in prognosis of ESCC patients confirmed [31].…”

Section: Experimental Datamentioning

confidence: 99%

Prediction of Postoperative Survival Level of Esophageal Cancer Patients Based on Kaplan-Meier(K-M) Survival Analysis and Gray Wolf Optimization (GWO)-BP Model

Liang

Wang

Sun

et al. 2021

Preprint

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Background: Esophageal squamous cell carcinoma (ESCC) is a global safety problem, especially the low 5-year survival rate of patients after surgery, and their healthy life after surgery is directly threatened.Methods: Kaplan-Meier(K-M) survival analysis is used to screen the blood indexes of patients with ESCC. The gray wolf algorithm (GWO) is introduced to optimize the weight threshold of back-propagation (BP) neural network, and a prediction model based on K-M-GWO-BP is established.Results: According to the influencing factors of postoperative survival, the postoperative survival level of patients is predicted. K-M survival analysis is used to analyze the relevant risk factors, the redundant variables are eliminated, and the whole structure of the neural network is simplified. The initial weight of BP neural network is optimized by GWO.Conclusions: BP neural network model, PSO-BP, GA-BP, SSA-BP, GWO-BP, K-M-BP, K-M-PSO-BP, K-MGA-BP, K-M-SSA-BP and K-M-GWO-BP are compared, the prediction accuracy of K-M-GWO-BP neural network model is the best.

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“…NLR, PLR and LMR might be served as prognostic markers in patients with ESCC [28]. Peripheral blood cell count ratio is suggested to evaluate clinical response and prognosis of patients with non-surgical ESCC [29]. Serum TT may be an important factor in prognosis of ESCC patients confirmed [30].…”

Section: Experimental Datamentioning

confidence: 99%

“…Therefore, the factors that may influence the survival time of patients with 17 blood indexes are tested. According to references [28][29][30][31][32], accuracy of predicting patient survival rates could be improved by adding elements.…”

Section: Experimental Datamentioning

confidence: 99%

“…By detecting the level of serum tumor markers in patients with colorectal cancer and healthy people, the early diagnosis model of colorectal cancer is established by using three machine learning algorithms, and its corresponding diagnostic value is evaluated [16]. According to the references [17], [28], [29], it is found that BP neural network shows good applicability in landslide risk assessment in different regions, and has little dependence on map scale. However, it is worth noting that more input layer and hidden layer nodes are needed to predict the survival time of patients with ESCC using BP neural network.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Survival Time of Patients With Esophageal Squamous Cell Carcinoma Based on Univariate Analysis and ASSA-BP Neural Network

et al. 2020

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Esophageal squamous cell carcinoma (ESCC) is one of the most common malignant tumors in the world. In order to find out the influencing factors, univariate Cox regression analysis is used to analyze the blood indexes to screen out the factors affecting the survival or death of patients. Spearman and Pearson correlation analysis can verify whether screening factors are related to survival. The survival curves and progression-free survival curves of 5 factors are given after the threshold is obtained by receiver operating characteristics (ROC). In order to optimize the survival accuracy of patients with ESCC, in view of the low convergence accuracy and easy to fall into local optimization of back propagation (BP) prediction network, the improved adaptive salp swarm algorithm (ASSA), genetic algorithm (GA) and back propagation (BP) neural network are combined. The initial weight and threshold of BP neural network are determined, and the ASSA-BP prediction model and GA-BP prediction model are established. In order to show the reliability and accuracy of the new model in a large range, the ASSA-BP model, GA-BP model and BP model are evaluated respectively. The ASSA-BP model is more effective in predicting the survival time of patients with ESCC, and shortens the training time and improves the prediction accuracy.

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Peripheral blood cell count ratios are predictive biomarkers of clinical response and prognosis for non‐surgical esophageal squamous cell carcinoma patients treated with radiotherapy

Cited by 11 publications

References 51 publications

Nomogram based on SIS predicting the survival of non-surgical thoracic esophageal squamous cell carcinoma treated by IMRT

Nomogram based on SIS predicting the survival of non-surgical thoracic esophageal squamous cell carcinoma treated by IMRT

Prediction of Postoperative Survival Level of Esophageal Cancer Patients Based on Kaplan-Meier(K-M) Survival Analysis and Gray Wolf Optimization (GWO)-BP Model

Prediction of Survival Time of Patients With Esophageal Squamous Cell Carcinoma Based on Univariate Analysis and ASSA-BP Neural Network

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