Prognostic value of lymph node metrics in lung squamous cell carcinoma: an analysis of the SEER database
Abstract Introduction Although the Tumor-Node-Metastasis (TNM) staging system is widely used for staging lung squamous cell carcinoma (LSCC), the TNM system primarily emphasizes tumor size and metastasis, without adequately considering lymph node involvement. Consequently, incorporating lymph node m...
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BMC
2024-12-01
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| Series: | World Journal of Surgical Oncology |
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| Online Access: | https://doi.org/10.1186/s12957-024-03639-4 |
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| author | Lei Liu Qiao Zhang Shuai Jin Lang Xie |
| author_facet | Lei Liu Qiao Zhang Shuai Jin Lang Xie |
| author_sort | Lei Liu |
| collection | DOAJ |
| description | Abstract Introduction Although the Tumor-Node-Metastasis (TNM) staging system is widely used for staging lung squamous cell carcinoma (LSCC), the TNM system primarily emphasizes tumor size and metastasis, without adequately considering lymph node involvement. Consequently, incorporating lymph node metastasis as an additional prognostic factor is essential for predicting outcomes in LSCC patients. Methods This retrospective study included patients diagnosed with LSCC between 2004 and 2018 and was based on data from the Surveillance, Epidemiology, and End Results (SEER) database of the National Cancer Institute. The primary endpoint of the study was cancer-specific survival (CSS), and demographic characteristics, tumor characteristics, and treatment regimens were incorporated into the predictive model. The study focused on the value of indicators related to pathological lymph node testing, including the lymph node ratio (LNR), regional node positivity (RNP), and lymph node examination count (RNE), in the prediction of cancer-specific survival in LSCC. A prognostic model was established using a multivariate Cox regression model, and the model was evaluated using the C index, Kaplan–Meier, the Akaike information criterion (AIC), decision curve analysis (DCA), continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI), and the predictive efficacy of different models was compared. Results A total of 14,200 LSCC patients (2004–2018) were divided into training and validation cohorts. The 10-year CSS rate was approximately 50%, with no significant survival differences between cohorts (p = 0.8). The prognostic analysis revealed that models incorporating LNR, RNP, and RNE demonstrated superior performance over the TNM model. The LNR and RNP models demonstrated better model fit, discrimination, and reclassification, with AUC values of 0.695 (training) and 0.665 (validation). The RNP and LNR models showed similar predictive performance, significantly outperforming the TNM and RNE models. Calibration curves and decision curve analysis confirmed the clinical utility and net benefit of the LNR and RNP models in predicting long-term CSS for LSCC patients, highlighting their value in clinical decision-making. Conclusion This study confirms that RNP status is an independent prognostic factor for CSS in LSCC, with predictive efficacy comparable to LNR, with both models enhancing survival prediction beyond TNM staging. |
| format | Article |
| id | doaj-art-73e86b8e43514b889f8924f534b93130 |
| institution | Kabale University |
| issn | 1477-7819 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | World Journal of Surgical Oncology |
| spelling | doaj-art-73e86b8e43514b889f8924f534b931302024-12-29T12:34:04ZengBMCWorld Journal of Surgical Oncology1477-78192024-12-0122111010.1186/s12957-024-03639-4Prognostic value of lymph node metrics in lung squamous cell carcinoma: an analysis of the SEER databaseLei Liu0Qiao Zhang1Shuai Jin2Lang Xie3School of Biology & Engineering (School of Health Medicine Modern Industry), Guizhou Medical UniversityMedical Department, The Second People’s Hospital of Guiyang(Jinyang Hospital)School of Biology & Engineering (School of Health Medicine Modern Industry), Guizhou Medical UniversityDepartment of Hospital Infection Management and Preventive Health Care, Zhejiang Provincial People’s Hospital Bijie HospitalAbstract Introduction Although the Tumor-Node-Metastasis (TNM) staging system is widely used for staging lung squamous cell carcinoma (LSCC), the TNM system primarily emphasizes tumor size and metastasis, without adequately considering lymph node involvement. Consequently, incorporating lymph node metastasis as an additional prognostic factor is essential for predicting outcomes in LSCC patients. Methods This retrospective study included patients diagnosed with LSCC between 2004 and 2018 and was based on data from the Surveillance, Epidemiology, and End Results (SEER) database of the National Cancer Institute. The primary endpoint of the study was cancer-specific survival (CSS), and demographic characteristics, tumor characteristics, and treatment regimens were incorporated into the predictive model. The study focused on the value of indicators related to pathological lymph node testing, including the lymph node ratio (LNR), regional node positivity (RNP), and lymph node examination count (RNE), in the prediction of cancer-specific survival in LSCC. A prognostic model was established using a multivariate Cox regression model, and the model was evaluated using the C index, Kaplan–Meier, the Akaike information criterion (AIC), decision curve analysis (DCA), continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI), and the predictive efficacy of different models was compared. Results A total of 14,200 LSCC patients (2004–2018) were divided into training and validation cohorts. The 10-year CSS rate was approximately 50%, with no significant survival differences between cohorts (p = 0.8). The prognostic analysis revealed that models incorporating LNR, RNP, and RNE demonstrated superior performance over the TNM model. The LNR and RNP models demonstrated better model fit, discrimination, and reclassification, with AUC values of 0.695 (training) and 0.665 (validation). The RNP and LNR models showed similar predictive performance, significantly outperforming the TNM and RNE models. Calibration curves and decision curve analysis confirmed the clinical utility and net benefit of the LNR and RNP models in predicting long-term CSS for LSCC patients, highlighting their value in clinical decision-making. Conclusion This study confirms that RNP status is an independent prognostic factor for CSS in LSCC, with predictive efficacy comparable to LNR, with both models enhancing survival prediction beyond TNM staging.https://doi.org/10.1186/s12957-024-03639-4Lung squamous cell carcinomaRegional nodes positiveLymph node ratioSEER databaseIntegrated discrimination improvementIntelligent retirement |
| spellingShingle | Lei Liu Qiao Zhang Shuai Jin Lang Xie Prognostic value of lymph node metrics in lung squamous cell carcinoma: an analysis of the SEER database World Journal of Surgical Oncology Lung squamous cell carcinoma Regional nodes positive Lymph node ratio SEER database Integrated discrimination improvement Intelligent retirement |
| title | Prognostic value of lymph node metrics in lung squamous cell carcinoma: an analysis of the SEER database |
| title_full | Prognostic value of lymph node metrics in lung squamous cell carcinoma: an analysis of the SEER database |
| title_fullStr | Prognostic value of lymph node metrics in lung squamous cell carcinoma: an analysis of the SEER database |
| title_full_unstemmed | Prognostic value of lymph node metrics in lung squamous cell carcinoma: an analysis of the SEER database |
| title_short | Prognostic value of lymph node metrics in lung squamous cell carcinoma: an analysis of the SEER database |
| title_sort | prognostic value of lymph node metrics in lung squamous cell carcinoma an analysis of the seer database |
| topic | Lung squamous cell carcinoma Regional nodes positive Lymph node ratio SEER database Integrated discrimination improvement Intelligent retirement |
| url | https://doi.org/10.1186/s12957-024-03639-4 |
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