Dataset Information

A New Metastatic Lymph Node Classification-based Survival Predicting Model in Patients With Small Bowel Adenocarcinoma: A Derivation and Validation Study.

ABSTRACT:

Background

Current methods of lymph node (LN) staging are controversial in predicting the survival of SBA. We aimed to develop an alternative LN-classification-based nomogram to individualize SBA prognosis.

Methods

Based on the data from the Surveillance, Epidemiology, and End Results (SEER) database of patients diagnosed with SBA between 2004 and 2014, we identified the cut-off points for the number of LNs examined and the number found to be metastatic using the K-adaptive partitioning (KAPS) algorithm. Using metastatic LNs, a nomogram predicting the survival of SBA was derived, internally and externally validated, and measured by calibration curve, C-index, and decision curve analysis (DCA), and compared to the 8th TNM stage.

Results

A total of 1516 patients were included. The cut-off of 17 was the optimal examined LN number. For metastatic LN numbers, the cut-off points were 0, 2, and 8. The C-index for the nomogram was higher than the 8th TNM staging (internal: 0.734; 95% CI, 0.693 to 0.775 vs. 0.677; 95% CI, 0.652 to 0.702, P?ConclusionWe modified current N staging into a 4-level staging system based on the number of metastatic LNs: N0, no LN metastasis; N1, 1-2 metastatic LNs; N2, 3-8 metastatic LNs, and N3, >8 metastatic LNs and set the least examined LN number to 17. A nomogram based on this staging showed great clinical usability than TNM staging for predicting the survival of SBA patients.

SUBMITTER: Wu S

PROVIDER: S-EPMC6021266 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Publications

A New Metastatic Lymph Node Classification-based Survival Predicting Model in Patients With Small Bowel Adenocarcinoma: A Derivation and Validation Study.

Wu Shan S Chen Jin-Nan JN Zhang Qing-Wei QW Tang Chao-Tao CT Zhang Xin-Tian XT Tang Ming-Yu MY Li Xiao-Bo XB Ge Zhi-Zheng ZZ

EBioMedicine 20180613

<h4>Background</h4>Current methods of lymph node (LN) staging are controversial in predicting the survival of SBA. We aimed to develop an alternative LN-classification-based nomogram to individualize SBA prognosis.<h4>Methods</h4>Based on the data from the Surveillance, Epidemiology, and End Results (SEER) database of patients diagnosed with SBA between 2004 and 2014, we identified the cut-off points for the number of LNs examined and the number found to be metastatic using the K-adaptive partit ...[more]

PMID: 29908920

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Project description:BackgroundIdentifying lymph node metastasis areas during surgery for early invasive lung adenocarcinoma remains challenging. The aim of this study was to develop a nomogram mathematical model before the end of surgery for predicting lymph node metastasis in patients with early invasive lung adenocarcinoma.MethodsIn this study, we included patients with invasive lung adenocarcinoma measuring ≤ 2 cm who underwent pulmonary resection with definite pathology at Qilu Hospital of Shandong University from January 2020 to January 2022. Preoperative biomarker results, clinical features, and computed tomography characteristics were collected. The enrolled patients were randomized into a training cohort and a validation cohort in a 7:3 ratio. The training cohort was used to construct the predictive model, while the validation cohort was used to test the model independently. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors. The prediction model and nomogram were established based on the independent risk factors. Recipient operating characteristic (ROC) curves were used to assess the discrimination ability of the model. Calibration capability was assessed using the Hosmer-Lemeshow test and calibration curves. The clinical utility of the nomogram was assessed using decision curve analysis (DCA).ResultsThe overall incidence of lymph node metastasis was 13.23% (61/461). Six indicators were finally determined to be independently associated with lymph node metastasis. These six indicators were: age (P < 0.001), serum amyloid (SA) (P = 0.008); carcinoma antigen 125 (CA125) (P = 0. 042); mucus composition (P = 0.003); novel aspartic proteinase of the pepsin family A (Napsin A) (P = 0.007); and cytokeratin 5/6 (CK5/6) (P = 0.042). The area under the ROC curve (AUC) was 0.843 (95% CI: 0.779-0.908) in the training cohort and 0.838 (95% CI: 0.748-0.927) in the validation cohort. the P-value of the Hosmer-Lemeshow test was 0.0613 in the training cohort and 0.8628 in the validation cohort. the bias of the training cohort corrected C-index was 0.8444 and the bias-corrected C-index for the validation cohort was 0.8375. demonstrating that the prediction model has good discriminative power and good calibration.ConclusionsThe column line graphs created showed excellent discrimination and calibration to predict lymph node status in patients with ≤ 2 cm invasive lung adenocarcinoma. In addition, the predictive model has predictive potential before the end of surgery and can inform clinical decision making.

Dataset Information

A New Metastatic Lymph Node Classification-based Survival Predicting Model in Patients With Small Bowel Adenocarcinoma: A Derivation and Validation Study.

Background

Methods

Results

Publications

A New Metastatic Lymph Node Classification-based Survival Predicting Model in Patients With Small Bowel Adenocarcinoma: A Derivation and Validation Study.

Similar Datasets

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