ArticleFrontiers in endocrinology2023
Comparation between novel online models and the AJCC 8th TNM staging system in predicting cancer-specific and overall survival of small cell lung cancer.
Article in Frontiers in endocrinology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Prediction of short‑term drought variation in tea plantations using a LASSO-COX-NOMOGRAM approach.Scientific reports · 2026Article
- Brief report: Artificial intelligence meets small cell lung cancer-integrating clinicopathological and wholeslide image data for prognostic prediction in SCLC.Frontiers in artificial intelligence · 2026Article
- A SEER prognostic nomogram analysis for extensive-stage small cell lung cancer: comparing the immune checkpoint inhibitors (ICIs) and non-ICIs eras.Journal of thoracic disease · 2025Article
- Predictive nomogram for risk of pulmonary infection in lung cancer patients undergoing radiochemotherapy: development and performance evaluation.American journal of cancer research · 2025Article
- Development and validation of a nomogram model for predicting lymph node metastasis in early non-small-cell lung cancer.American journal of cancer research · 2025Article
- Development and validation of a nomogram for incorporatingJournal of thoracic disease · 2024Article
- A comparative study of the efficacy and safety of PD-1/L1 inhibitor and platinum-containing dual-agent chemotherapy in patients with advanced non-small cell lung cancer resistant to EGFR-TKIs.American journal of translational research · 2024Article
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5 authors.
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Abstract
Background: Most of previous studies on predictive models for patients with small cell lung cancer (SCLC) were single institutional studies or showed relatively low Harrell concordance index (C-index) values. To build an optimal nomogram, we collected clinicopathological characteristics of SCLC patients from Surveillance, Epidemiology, and End Results (SEER) database. Methods: 24,055 samples with SCLC from 2010 to 2016 in the SEER database were analyzed. The samples were grouped into derivation cohort (n=20,075) and external validation cohort (n=3,980) based on America's different geographic regions. Cox regression analyses were used to construct nomograms predicting cancer-specific survival (CSS) and overall survival (OS) using derivation cohort. The nomograms were internally validated by bootstrapping technique and externally validated by calibration plots. C-index was computed to compare the accuracy and discrimination power of our nomograms with the 8th of version AJCC TNM staging system and nomograms built in previous studies. Decision curve analysis (DCA) was applied to explore whether the nomograms had better clinical efficiency than the 8th version of AJCC TNM staging system. Results: Age, sex, race, marital status, primary site, differentiation, T classification, N classification, M classification, surgical type, lymph node ratio, radiotherapy, and chemotherapy were chosen as predictors of CSS and OS for SCLC by stepwise multivariable regression and were put into the nomograms. Internal and external validations confirmed the nomograms were accurate in prediction. C-indexes of the nomograms were relatively satisfactory in derivation cohort (CSS: 0.761, OS: 0.761) and external validation cohort (CSS: 0.764, OS: 0.764). The accuracy of the nomograms was superior to that of nomograms built in previous studies. DCA showed the nomograms conferred better clinical efficiency than 8th version of TNM staging system. Conclusions: We developed practical nomograms for CSS (https://guowei2020.shinyapps.io/DynNom-CSS-SCLC/) and OS (https://drboidedwater.shinyapps.io/DynNom-OS-SCLC/) prediction of SCLC patients which may facilitate clinicians in individualized therapeutics.
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