AUTHOR=Ji Huijun , Liu Qianqian , Chen Yingxiu , Gu Mengyao , Chen Qi , Guo Shaolan , Ning Shangkun , Zhang Juntao , Li Wan-Hu 

TITLE=Combined model of radiomics and clinical features for differentiating pneumonic-type mucinous adenocarcinoma from lobar pneumonia: An exploratory study

JOURNAL=Frontiers in Endocrinology

VOLUME=Volume 13 - 2022

YEAR=2023

URL=https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2022.997921

DOI=10.3389/fendo.2022.997921

ISSN=1664-2392

ABSTRACT=Abstract
Purpose:  
The aim of this study is to differentiate between pneumonic-type mucinous adenocarcinoma (PTMA) and lobar pneumonia (LP) by pretreatment CT radiomics and clinical or radiological parameters. 
Methods: 
199 eligible patients (LP=138, PTMA=61) were evaluated in this retrospective study and were divided into training (n=140) and validation cohorts (n=59). Radiomics features were extracted from plain CT images. The radiomics model and combined nomogram model were established by multivariate logistic regression analysis and the clinical utility was assessed. The performance of the constructed radiomics model was evaluated using the area under curve (AUC) of receiver operating characteristic (ROC) curve. 
Results: 
Fourteen radiomic features based on lung CT images were constructed to distinguish between PTMA and LP, and its differential performance was good with an area under the curve (AUC) of 0.90 (95% CI，0.83-0.96) in the training cohort and 0.88 (95% CI, 0.79–0.97) in the validation cohort. Based on the radiomic signature and clinical features, a combined nomogram was developed and showed excellent differential ability with highest AUC of 0.94 (95 %CI,0.90–0.98) in the training cohort and 0.91 (95 %CI,0.84–0.99) in the validation cohort, which performed better than the clinical model significantly only.The Hosmer-Lemeshow test also showed good performance for the logistic regression model in the training cohort (P = 0.765) and validation cohort (P = 0.064). Good alignment with the calibration curve indicated the good performance of the nomogram. The clinical decision curve analysis demonstrates the clinical application value of the nomogram prediction model.
Conclusions: 
A quantitative nomogram based on clinical risk factors and radiomic features of CT images can be used to distinguish pneumonic-type mucinous adenocarcinoma and lobar pneumonia with excellent predictive ability, which can provide appropriate therapy decision support for clinicians, especially in situation where the differential diagnosis is difficult.