Radiomics combined with clinical features in distinguishing non-calcifying tuberculosis granuloma and lung adenocarcinoma in small pulmonary nodules

PeerJ

article

Qing Dong¹  Qingqing Wen²  Nan Li³  Jinlong Tong⁴  Zhaofu Li⁵  Xin Bao⁶  Jinzhi Xu¹  Dandan Li⁷ 

[1] Department of Thoracic Surgery at No. 4 Affiliated Hospital, Harbin Medical University;Icahn School of Medicine at Mount Sinai;Department of Pathology at No. 4 Affiliated Hospital, Harbin Medical University;Department of Medical Imaging at No. 4 Affiliated Hospital, Harbin Medical University;Heilongjiang Institute of Automation;Harbin Medtech Innovative Company;Department of Radiology at Cancer Hospital, Harbin Medical University

关键词: Radiomics;    Non-calcified tuberculosis granuloma;    Lung adenocarcinoma;    Pulmonary nodules;    Clinical features;   

DOI  :  10.7717/peerj.14127

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

AimTo evaluate the performance of radiomics models with the combination of clinical features in distinguishing non-calcified tuberculosis granuloma (TBG) and lung adenocarcinoma (LAC) in small pulmonary nodules.MethodologyWe conducted a retrospective analysis of 280 patients with pulmonary nodules confirmed by surgical biopsy from January 2017 to December 2020. Samples were divided into LAC group (n = 143) and TBG group (n = 137). We assigned them to a training dataset (n = 196) and a testing dataset (n = 84). Clinical features including gender, age, smoking, CT appearance (size, location, spiculated sign, lobulated shape, vessel convergence, and pleural indentation) were extracted and included in the radiomics models. 3D slicer and FAE software were used to delineate the Region of Interest (ROI) and extract clinical features. The performance of the model was evaluated by the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC).ResultsBased on the model selection, clinical features gender, and age in the LAC group and TBG group showed a significant difference in both datasets (P < 0.05). CT appearance lobulated shape was also significantly different in the LAC group and TBG group (Training dataset, P = 0.034; Testing dataset, P = 0.030). AUC were 0.8344 (95% CI [0.7712–0.8872]) and 0.751 (95% CI [0.6382–0.8531]) in training and testing dataset, respectively.ConclusionWith the capacity to detect differences between TBG and LAC based on their clinical features, radiomics models with a combined of clinical features may function as the potential non-invasive tool for distinguishing TBG and LAC in small pulmonary nodules.

【 授权许可】

CC BY