Prognostic significance of pretreated serum lactate dehydrogenase level in nasopharyngeal carcinoma among Chinese population

Zhang, Mingwei; Wei, Shushan; Su, Li; Lv, Wenlong; Hong, Jinsheng

doi:10.1097/md.0000000000004494

Cited by 32 publications

(28 citation statements)

References 52 publications

(43 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, this study was examined in a single center. So, the following research should be done on a large-scale and using multicenter prospective validation of NPC groups; Third, we were just concerned with the impact of systemic inflammation–based prognostic factors on the prognosis of patients with NPC, and we did not compare the novel inflammation-based prognostic system with other traditional prognostic factors, such as Epstein-Barr Virus DNA or lactate dehydrogenase (LDH) [41,42,43]. Thus, whether the I stage is more useful in predicting OS in patients with NPC as compared to other traditional prognostic factors remains to be ascertained.…”

Section: Discussionmentioning

confidence: 99%

A Novel Inflammation-Based Stage (I Stage) Predicts Overall Survival of Patients with Nasopharyngeal Carcinoma

Chen

Liu

et al. 2016

IJMS

View full text Add to dashboard Cite

Recent studies have indicated that inflammation-based prognostic scores, such as the Glasgow Prognostic Score (GPS), modified GPS (mGPS) and C-reactive protein/Albumin (CRP/Alb) ratio, platelet–lymphocyte ratio (PLR), and neutrophil–lymphocyte ratio (NLR), have been reported to have prognostic value in patients with many types of cancer, including nasopharyngeal carcinoma (NPC). In this study, we proposed a novel inflammation-based stage, named I stage, for patients with NPC. A retrospective study of 409 newly-diagnosed cases of NPC was conducted. The prognostic factors (GPS, mGPS, CRP/Alb ratios, PLR, and NLR) were evaluated using univariate and multivariate analyses. Then, according to the results of the multivariate analyses, we proposed a I stage combination of independent risk factors (CRP/Alb ratio and PLR). The I stage was calculated as follows: patients with high levels of CRP/Alb ratio (>0.03) and PLR (>146.2) were defined as I2; patients with one or no abnormal values were defined as I1 or I0, respectively. The relationships between the I stage and clinicopathological variables and overall survival (OS) were evaluated. In addition, the discriminatory ability of the I stage with other inflammation-based prognostic scores was assessed using the AUCs (areas under the curves) analyzed by receiver operating characteristics (ROC) curves. The p value of <0.05 was considered to be significant. A total of 409 patients with NPC were enrolled in this study. Multivariate analyses revealed that only the CRP/Alb ratio (Hazard ratio (HR) = 2.093; 95% Confidence interval (CI): 1.222–3.587; p = 0.007) and PLR (HR: 2.003; 95% CI: 1.177–3.410; p = 0.010) were independent prognostic factors in patients with NPC. The five-year overall survival rates for patients with I0, I1, and I2 were 92.1% ± 2.9%, 83.3% ± 2.6%, and 63.1% ± 4.6%, respectively (p < 0.001). The I stage had a higher area under the curve value (0.670) compared with other systemic inflammation-based prognostic scores (p < 0.001). The I stage is a novel and useful predictive factor for OS in patients with NPC.

show abstract

Section: Discussionmentioning

confidence: 99%

A Novel Inflammation-Based Stage (I Stage) Predicts Overall Survival of Patients with Nasopharyngeal Carcinoma

Chen

Liu

et al. 2016

IJMS

View full text Add to dashboard Cite

show abstract

“…Beyond traditional prediction strategies, some recent studies reported that various clinical risk factors, such as hemoglobin, lactate dehydrogenase level, neutrophil-to-lymphocyte ratio, and platelet counts, were associated with poor survival (10)(11)(12)(13). However, the clinical utility of these factors was limited and unclear.…”

Section: Introductionmentioning

confidence: 99%

Radiomics Features of Multiparametric MRI as Novel Prognostic Factors in Advanced Nasopharyngeal Carcinoma

Zhang

Tian

Dong

et al. 2017

Clinical Cancer Research

427

351

View full text Add to dashboard Cite

Purpose: To identify MRI-based radiomics as prognostic factors in patients with advanced nasopharyngeal carcinoma (NPC).Experimental Design: One-hundred and eighteen patients (training cohort: n ¼ 88; validation cohort: n ¼ 30) with advanced NPC were enrolled. A total of 970 radiomics features were extracted from T2-weighted (T2-w) and contrast-enhanced T1-weighted (CET1-w) MRI. Least absolute shrinkage and selection operator (LASSO) regression was applied to select features for progression-free survival (PFS) nomograms. Nomogram discrimination and calibration were evaluated. Associations between radiomics features and clinical data were investigated using heatmaps.Results: The radiomics signatures were significantly associated with PFS. A radiomics signature derived from joint CET1-w and T2-w images showed better prognostic performance than signatures derived from CET1-w or T2-w images alone. One radiomics nomogram combined a radiomics signature from joint CET1-w and T2-w images with the TNM staging system. This nomogram showed a significant improvement over the TNM staging system in terms of evaluating PFS in the training cohort (C-index, 0.761 vs. 0.514; P < 2.68 Â 10 À9 ). Another radiomics nomogram integrated the radiomics signature with all clinical data, and thereby outperformed a nomogram based on clinical data alone (C-index, 0.776 vs. 0.649; P < 1.60 Â 10 À7 ). Calibration curves showed good agreement. Findings were confirmed in the validation cohort. Heatmaps revealed associations between radiomics features and tumor stages. Conclusions: Multiparametric MRI-based radiomics nomograms provided improved prognostic ability in advanced NPC. These results provide an illustrative example of precision medicine and may affect treatment strategies.

show abstract

“…40,41 After evaluating 14 803 patients with an NC diagnosis, Zhang et al reported that high LDH levels before treatment were associated with poor overall, progression-free and distant metastasis-free survival. 42 In this study, significant variables were determined as age, weight loss, initial NLR, initial LDH, initial hemoglobin, radiotherapy duration, tumor diameter, number of cases undergoing concurrent chemotherapy, and T and N stages. Machine learning is a subfield of artificial intelligence that involves development of algorithms that obtain model and parameters best representing the available data.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Prognosis in Nasopharyngeal Cancer Using Machine Learning

Akçay

Etiz

Çelik

et al. 2020

Technol Cancer Res Treat

View full text Add to dashboard Cite

Background and Aim: Although the prognosis of nasopharyngeal cancer largely depends on a classification based on the tumor-lymph node metastasis staging system, patients at the same stage may have different clinical outcomes. This study aimed to evaluate the survival prognosis of nasopharyngeal cancer using machine learning. Settings and Design: Original, retrospective. Materials and Methods: A total of 72 patients with a diagnosis of nasopharyngeal cancer who received radiotherapy ± chemotherapy were included in the study. The contribution of patient, tumor, and treatment characteristics to the survival prognosis was evaluated by machine learning using the following techniques: logistic regression, artificial neural network, XGBoost, support-vector clustering, random forest, and Gaussian Naive Bayes. Results: In the analysis of the data set, correlation analysis, and binary logistic regression analyses were applied. Of the 18 independent variables, 10 were found to be effective in predicting nasopharyngeal cancer-related mortality: age, weight loss, initial neutrophil/lymphocyte ratio, initial lactate dehydrogenase, initial hemoglobin, radiotherapy duration, tumor diameter, number of concurrent chemotherapy cycles, and T and N stages. Gaussian Naive Bayes was determined as the best algorithm to evaluate the prognosis of machine learning techniques (accuracy rate: 88%, area under the curve score: 0.91, confidence interval: 0.68-1, sensitivity: 75%, specificity: 100%). Conclusion: Many factors affect prognosis in cancer, and machine learning algorithms can be used to determine which factors have a greater effect on survival prognosis, which then allows further research into these factors. In the current study, Gaussian Naive Bayes was identified as the best algorithm for the evaluation of prognosis of nasopharyngeal cancer.

show abstract

Prognostic significance of pretreated serum lactate dehydrogenase level in nasopharyngeal carcinoma among Chinese population

Cited by 32 publications

References 52 publications

A Novel Inflammation-Based Stage (I Stage) Predicts Overall Survival of Patients with Nasopharyngeal Carcinoma

A Novel Inflammation-Based Stage (I Stage) Predicts Overall Survival of Patients with Nasopharyngeal Carcinoma

Radiomics Features of Multiparametric MRI as Novel Prognostic Factors in Advanced Nasopharyngeal Carcinoma

Evaluation of Prognosis in Nasopharyngeal Cancer Using Machine Learning

Contact Info

Product

Resources

About