Construction and validation of a clinical differentiation model between peripheral lung cancer and solitary pulmonary tuberculosis.
Lung Cancer
; 193: 107851, 2024 Jul.
Article
de En
| MEDLINE
| ID: mdl-38905954
ABSTRACT
OBJECTIVE:
To establish and validate a clinical model for differentiating peripheral lung cancer (PLC) from solitary pulmonary tuberculosis (SP-TB) based on clinical and imaging features. MATERIALS ANDMETHODS:
Retrospectively, 183 patients (100 PLC, 83 SP-TB) in our hospital were randomly divided into a training group and an internal validation group (ratio 73), and 100 patients (50 PLC, 50 SP-TB) in Sichuan Provincial People's Hospital were identified as an external validation group. The collected qualitative and quantitative variables were used to determine the independent feature variables for distinguishing between PLC and SP-TB through univariate logistic regression, multivariate logistic regression. Then, traditional logistic regression models and machine learning algorithm models (decision tree, random forest, xgboost, support vector machine, k-nearest neighbors, light gradient boosting machine) were established using the independent feature variables. The model with the highest AUC value in the internal validation group was used for subsequent analysis. The receiver operating characteristic curve (ROC), calibration curve, and decision curves analysis (DCA) were used to assess the model's discrimination, calibration, and clinical usefulness.RESULT:
Age, smoking history, maximum diameter of lesion, lobulation, spiculation, calcification, and vascular convergence sign were independent characteristic variables to differentiate PLC from SP-TB. The logistic regression model had the highest AUC value of 0.878 for the internal validation group, based on which a quantitative visualization nomogram was constructed to discriminate the two diseases. The area under the ROC curve (AUC) of the model in the training, internal validation, and external validation groups were 0.915 (95 % CI 0.866-0.965), 0.878 (95 % CI 0.784-0.971), and 0.912 (95 % CI 0.855-0.969), respectively, and the calibration curves fitted well. Decision curves analysis (DCA) confirmed the good clinical benefit of the model.CONCLUSION:
The model constructed based on clinical and imaging features can accurately differentiate between PLC and SP-TB, providing potential value for developing reasonable clinical plans.Mots clés
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Tuberculose pulmonaire
/
Tumeurs du poumon
Limites:
Adult
/
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Langue:
En
Journal:
Lung Cancer
Sujet du journal:
NEOPLASIAS
Année:
2024
Type de document:
Article
Pays d'affiliation:
Chine
Pays de publication:
Irlande