A prognostic framework for predicting lung signet ring cell carcinoma via a machine learning based cox proportional hazard model.
Cox proportional hazard model
Lung signet ring cell carcinoma
Machine learning
Nomogram
SEER
Journal
Journal of cancer research and clinical oncology
ISSN: 1432-1335
Titre abrégé: J Cancer Res Clin Oncol
Pays: Germany
ID NLM: 7902060
Informations de publication
Date de publication:
25 Jul 2024
25 Jul 2024
Historique:
received:
05
08
2023
accepted:
08
07
2024
medline:
26
7
2024
pubmed:
26
7
2024
entrez:
25
7
2024
Statut:
epublish
Résumé
Signet ring cell carcinoma (SRCC) is a rare type of lung cancer. The conventional survival nomogram used to predict lung cancer performs poorly for SRCC. Therefore, a novel nomogram specifically for studying SRCC is highly required. Baseline characteristics of lung signet ring cell carcinoma were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression and random forest analysis were performed on the training group data, respectively. Subsequently, we compared results from these two types of analyses. A nomogram model was developed to predict 1-year, 3-year, and 5-year overall survival (OS) for patients, and receiver operating characteristic (ROC) curves and calibration curves were used to assess the prediction accuracy. Decision curve analysis (DCA) was used to assess the clinical applicability of the proposed model. For treatment modalities, Kaplan-Meier curves were adopted to analyze condition-specific effects. We obtained 731 patients diagnosed with lung signet ring cell carcinoma (LSRCC) in the SEER database and randomized the patients into a training group (551) and a validation group (220) with a ratio of 7:3. Eight factors including age, primary site, T, N, and M.Stage, surgery, chemotherapy, and radiation were included in the nomogram analysis. Results suggested that treatment methods (like surgery, chemotherapy, and radiation) and T-Stage factors had significant prognostic effects. The results of ROC curves, calibration curves, and DCA in the training and validation groups demonstrated that the nomogram we constructed could precisely predict survival and prognosis in LSRCC patients. Through deep verification, we found the constructed model had a high C-index, indicating that the model had a strong predictive power. Further, we found that all surgical interventions had good effects on OS and cancer-specific survival (CSS). The survival curves showed a relatively favorable prognosis for T0 patients overall, regardless of the treatment modality. Our nomogram is demonstrated to be clinically beneficial for the prognosis of LSRCC patients. The surgical intervention was successful regardless of the tumor stage, and the Cox proportional hazard (CPH) model had better performance than the machine learning model in terms of effectiveness.
Identifiants
pubmed: 39052087
doi: 10.1007/s00432-024-05886-0
pii: 10.1007/s00432-024-05886-0
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
364Subventions
Organisme : the Science and Technology Special Project of Zhanjiang
ID : (2022A01034)
Organisme : The Guangdong Provincial Department of Education Research Project
ID : (2022KTSCX)
Informations de copyright
© 2024. The Author(s).
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