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Radiomics and deep learning models to differentiate lung adenosquamous carcinoma: A multicenter trial

Authors :
Xianjing Chu
Lishui Niu
Xianghui Yang
Shiqi He
Aixin Li
Liu Chen
Zhan Liang
Di Jing
Rongrong Zhou
Source :
iScience, Vol 26, Iss 9, Pp 107634- (2023)
Publication Year :
2023
Publisher :
Elsevier, 2023.

Abstract

Summary: Adenosquamous carcinoma (ASC) is frequently misdiagnosed or overlooked in clinical practice due to its dual histological components and potential transformation from either adenocarcinoma (ADC) or squamous cell carcinoma (SCC). Our study aimed to differentiate ASC from ADC and SCC by incorporating features of enhanced CTs and clinical characteristics to build radiomics and deep learning models. The classification models were trained in Xiangya Hospital and validated in two other independent hospitals. The areas under the receiver operating characteristic curves (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were used to estimate the performance. The optimal three-class classification model achieved a maximum AUC of 0.89 and accuracy of 0.81 in external validation sets, AUC of 0.99 and accuracy of 0.99 in the internal test set. These findings highlight the efficacy of our models in differentiating ASC, providing a non-invasive, timely, and accurate diagnostic approach before and during the treatment.

Details

Language :
English
ISSN :
25890042
Volume :
26
Issue :
9
Database :
Directory of Open Access Journals
Journal :
iScience
Publication Type :
Academic Journal
Accession number :
edsdoj.1b8e8eddab141598f75b0bea5b28da3
Document Type :
article
Full Text :
https://doi.org/10.1016/j.isci.2023.107634