Computed tomography–based radiomics for identifying pulmonary cryptococcosis mimicking lung cancer

• Published in: Medical physics (Lancaster) 2022-09, Vol.49 (9), p.5943-5952
• Main Authors: Zhang, Yongchang, Chu, Zhigang, Yu, Jianqun, Chen, Xiaoyi, Liu, Jing, Xu, Jingxu, Huang, Chencui, Peng, Liqing
• Format: Article
• Language: English
• Subjects: computed tomography; lung cancer; pulmonary cryptococcosis; radiomics
• ISSN: 0094-2405; 2473-4209
• DOI: 10.1002/mp.15789

Background Pulmonary cryptococcosis (PC) is an invasive pulmonary fungal disease, and nodule/mass‐type PC may mimic lung cancer (LC) in imaging appearance. Thus, an accurate diagnosis of nodule/mass‐type PC is beneficial for appropriate management. However, the differentiation of nodule/mass‐type PC from LC through computed tomography (CT) is still challenging. Purpose To develop and externally test a CT‐based radiomics model for differentiating nodule/mass‐type PC from LC. Methods In this retrospective study, patients with nodule/mass‐type PC or LC who underwent non‐enhanced chest CT were included: Institution 1 was for the training set, and institutions 2 and 3 were for the external test set. Large quantities of radiomics features were extracted. The radiomics score (Rad‐score) was calculated using the linear discriminant analysis, and a subsequent fivefold cross‐validation was performed. A combined model was developed by incorporating Rad‐score and clinical factors. Finally, the models were tested with an external test set and compared using the area under the receiver operating characteristic curve (AUC). Results A total of 168 patients (45 with PC and 123 with LC) were in the training set, and 72 (36 with PC and 36 with LC) were in the external test set. Of the 81 patients with PC, 30 were immunocompromised (37%). Rad‐score, comprising 18 features, had an AUC of 0.844 after fivefold cross‐validation, which was lower than that (AUC = 0.943, p = 0.003) of the combined model integrating Rad‐score, age, lobulation, pleural retraction, and patches. In the external test set, Rad‐score and the combined model obtained good predictive performance (AUC = 0.824 for Rad‐score, and 0.869 for the combined model). Moreover, the combined model outperformed the clinical model in the cross‐validation and external test (0.943 vs. 0.810, p