Dataset Information

Development of a novel biomarker model for predicting preoperative lymph node metastatic extent in esophageal squamous cell carcinoma1.

ABSTRACT: The number and range of lymph node metastasis (LNM) are critical prognostic factors in esophageal squamous cell carcinoma (ESCC). Preoperative serum biomarkers are reported to be associated with LNM. However, whether these markers can precisely predict the extent of LNM is not known. The aim of this study was to evaluate the predictive value of preoperative serum SCC-Ag, Cyfra21-1, CEA, CA19-9 and CA72-4 for LNM number and range by retrospectively investigating 577 ESCC patients undergone esophagectomy from 2007-2010. In this study, the positive rate of SCC-Ag and CA19-9 were associated with pN stage. Significant differences were found in CEA and CA19-9 between pN0-1 stage patients and pN2-3 stage patients. However, in subgroup analysis (patients with pN0-1), significant difference was found only in SCC-Ag between pN0 and pN1 stage patients (P=0.003). Middle thoracic ESCC patients were Chosen to analyze the correlation between the range of LNM and biomarkers. SCC-Ag was correlated with paraesophageal and paracardial lymph nodes, but not correlated with subcarinal and left gastric artery lymph nodes. Interestingly, the results of CEA were opposite to that of SCC-Ag. CA19-9 was associated with subcarinal and paracardial LNM (P=0.000, P=0.038). Based on the results, a model incorporated SCC-Ag, CEA and CA19-9 was constructed. The rate of patients with pN2-3 stage was 15.4% and 54.4% in group 1 and 4 of our model. In summary, SCC-Ag was associated with early lymph node metastatic stage, and CEA and CA19-9 have a close relationship with advanced lymph node metastatic stage. The model combining SCC-Ag, CEA and CA19-9 might help identify the preoperative extent of LNM for a subgroup of ESCC patients that can be benefited from neoadjuvant therapy.

SUBMITTER: Ma Z

PROVIDER: S-EPMC5739679 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Development of a novel biomarker model for predicting preoperative lymph node metastatic extent in esophageal squamous cell carcinoma<sup>1</sup>.

Ma Zhao Z Wu Xianxian X Xu Bo B Jiang Hongjing H Tang Peng P Yue Jie J Ma Mingquan M Chen Chuangui C Zhang Hongdian H Yu Zhentao Z

Oncotarget 20171111 62

The number and range of lymph node metastasis (LNM) are critical prognostic factors in esophageal squamous cell carcinoma (ESCC). Preoperative serum biomarkers are reported to be associated with LNM. However, whether these markers can precisely predict the extent of LNM is not known. The aim of this study was to evaluate the predictive value of preoperative serum SCC-Ag, Cyfra21-1, CEA, CA19-9 and CA72-4 for LNM number and range by retrospectively investigating 577 ESCC patients undergone esopha ...[more]

PMID: 29285292

Similar Datasets

Project description:PURPOSE:To build and validate a radiomics-based nomogram for the prediction of pre-operation lymph node (LN) metastasis in esophageal cancer. PATIENTS AND METHODS:A total of 197 esophageal cancer patients were enrolled in this study, and their LN metastases have been pathologically confirmed. The data were collected from January 2016 to May 2016; patients in the first three months were set in the training cohort, and patients in April 2016 were set in the validation cohort. About 788 radiomics features were extracted from computed tomography (CT) images of the patients. The elastic-net approach was exploited for dimension reduction and selection of the feature space. The multivariable logistic regression analysis was adopted to build the radiomics signature and another predictive nomogram model. The predictive nomogram model was composed of three factors with the radiomics signature, where CT reported the LN number and position risk level. The performance and usefulness of the built model were assessed by the calibration and decision curve analysis. RESULTS:Thirteen radiomics features were selected to build the radiomics signature. The radiomics signature was significantly associated with the LN metastasis (P<0.001). The area under the curve (AUC) of the radiomics signature performance in the training cohort was 0.806 (95% CI: 0.732-0.881), and in the validation cohort it was 0.771 (95% CI: 0.632-0.910). The model showed good discrimination, with a Harrell's Concordance Index of 0.768 (0.672 to 0.864, 95% CI) in the training cohort and 0.754 (0.603 to 0.895, 95% CI) in the validation cohort. Decision curve analysis showed our model will receive benefit when the threshold probability was larger than 0.15. CONCLUSION:The present study proposed a radiomics-based nomogram involving the radiomics signature, so the CT reported the status of the suspected LN and the dummy variable of the tumor position. It can be potentially applied in the individual preoperative prediction of the LN metastasis status in esophageal cancer patients.

Project description:IntroductionThe axillary lymph node (ALN) status of breast cancer patients is an important prognostic indicator. The use of primary breast mass features for the prediction of ALN status is rare. Two nomograms based on preoperative ultrasound (US) images of breast tumors and ALNs were developed for the prediction of ALN status.MethodsA total of 743 breast cancer cases collected from 2016 to 2019 at the Second Affiliated Hospital of Harbin Medical University were randomly divided into a training set (n = 523) and a test set (n = 220). A primary tumor feature model (PTFM) and ALN feature model (ALNFM) were separately generated based on tumor features alone, and a combination of features was used for the prediction of ALN status. Logistic regression analysis was used to construct the nomograms. A receiver operating characteristic curve was plotted to obtain the area under the curve (AUC) to evaluate accuracy, and bias-corrected AUC values and calibration curves were obtained by bootstrap resampling for internal and external verification. Decision curve analysis was applied to assess the clinical utility of the models.ResultsThe AUCs of the PTFM were 0.69 and 0.67 for the training and test sets, respectively, and the bias-corrected AUCs of the PTFM were 0.67 and 0.67, respectively. Moreover, the AUCs of the ALNFM were 0.86 and 0.84, respectively, and the bias-corrected AUCs were 0.85 and 0.81, respectively. Compared with the PTFM, the ALNFM showed significantly improved prediction accuracy (p < 0.001). Both the calibration and decision curves of the ALNFM nomogram indicated greater accuracy and clinical practicality. When the US tumor size was ≤21.5 mm, the Spe was 0.96 and 0.92 in the training and test sets, respectively. When the US tumor size was greater than 21.5 mm, the Sen was 0.85 in the training set and 0.87 in the test set. Our further research showed that when the US tumor size was larger than 35 mm, the Sen was 0.90 in the training set and 0.93 in the test set.ConclusionThe ALNFM could effectively predict ALN status based on US images especially for different US tumor size.

Dataset Information

Development of a novel biomarker model for predicting preoperative lymph node metastatic extent in esophageal squamous cell carcinoma1.

Publications

Development of a novel biomarker model for predicting preoperative lymph node metastatic extent in esophageal squamous cell carcinoma<sup>1</sup>.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure