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Typical Pulmonary Carcinoid (TPC) is defined by its slow growth, frequently necessitating surgical intervention. Despite this, the long-term outcomes following tumor resection are not well understood. This study examined the factors impacting Overall Survival (OS) in patients with TPC, leveraging data from the Surveillance, Epidemiology, and End Results database spanning from 2000 to 2018. We employed Lasso-Cox analysis to identify prognostic features and developed various models using Random Forest, XGBoost, and Cox regression algorithms. Subsequently, we assessed model performance using metrics such as Area Under the Curve (AUC), calibration plot, Brier score, and Decision Curve Analysis (DCA). Among the 2687 patients, we identified five clinical features significantly affecting OS. Notably, the Random Forest model exhibited strong performance, achieving 5- and 7-year AUC values of 0.744/0.757 in the training set and 0.715/0.740 in the validation set, respectively, outperforming other models. Additionally, we developed a web-based platform aimed at facilitating easy access to the model. This study presents a machine learning model and a web-based support system for healthcare professionals, assisting in personalized treatment decisions for patients with TPC post-tumor resection. [ABSTRACT FROM AUTHOR]

Introduction: Patients presenting with brain metastases (BMs) from lung squamous cell carcinoma (LUSC) often encounter an extremely poor prognosis. A well‐developed prognostic model would assist physicians in patient counseling and therapeutic decision‐making. Methods: Patients with LUSC who were diagnosed with BMs between 2000 and 2018 were reviewed in the Surveillance, Epidemiology, and End Results (SEER) database. Using the multivariate Cox regression approach, significant prognostic factors were identified and integrated. Bootstrap resampling was used to internally validate the model. An evaluation of the performance of the model was conducted by analyzing the area under the curve (AUC) and calibration curve. Results: A total of 1812 eligible patients' clinical data was retrieved from the database. Patients' overall survival (OS) was significantly prognosticated by five clinical parameters. The nomogram achieved satisfactory discrimination capacity, with 3‐, 6‐, and 9‐month AUC values of 0.803, 0.779, and 0.760 in the training cohort and 0.796, 0.769, and 0.743 in the validation cohort. As measured by survival rate probabilities, the calibration curve agreed well with actual observations. There was also a substantial difference in survival curves between the different prognostic groups stratified by prognostic scores. For ease of access, the model was deployed on a web‐based server. Conclusions: In this study, a nomogram and a web‐based predictor were developed to assist physicians with personalized clinical decisions and treatment of patients who presented with BMs from LUSC. [ABSTRACT FROM AUTHOR]

Background: Lung adenocarcinoma is one of the most common malignant tumors of the respiratory system, ranking first in morbidity and mortality among all cancers. This study aims to establish a ferroptosis-related gene-based prognostic model to investigate the potential prognosis of lung adenocarcinoma. Methods: We obtained gene expression data with matching clinical data of lung adenocarcinoma from the The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The ferroptosis-related genes (FRGs) were downloaded from three subgroups in the ferroptosis database. Using gene expression differential analysis, univariate Cox regression, and LASSO regression analysis, seven FRGs with prognostic significance were identified. The result of multivariate Cox analysis was utilized to calculate regression coefficients and establish a risk-score formula that divided patients with lung adenocarcinoma into high-risk and low-risk groups. The TCGA results were validated using GEO data sets. Then we observe d that patients divided in the low-risk group lived longer than the overall survival (OS) of the other. Then we developed a novel nomogram including age, gender, clinical stage, TNM stage, and risk score. Results: The areas under the curves (AUCs) for 3- and 5-years OS predicted by the model were 0.823 and 0.852, respectively. Calibration plots and decision curve analysis also confirmed the excellent predictive performance of the model. Subsequently, gene function enrichment analysis revealed that the identified FRGs are important in DNA replication, cell cycle regulation, cell adhesion, chromosomal mutation, oxidative phosphorylation, P53 signaling pathway, and proteasome processes. Conclusions: Our results verified the prognostic significance of FRGs in patients with lung adenocarcinoma, which may regulate tumor progression in a variety of pathways. [ABSTRACT FROM AUTHOR]