Diagnostic Study of Nodular Pulmonary Cryptococcosis Based on Radiomic Features Captured from CT Images

Title:Diagnostic Study of Nodular Pulmonary Cryptococcosis Based on Radiomic Features Captured from CT Images

Volume: 20

Author(s): Danmei Huang, Xiuting Wu, Siqi Chen, Kai Li*, Xiaobo Zhang, Xiaoyu Pan, Liang Fu, Wenjun Lin, Zefeng Li and Xuechun Guan

Affiliation:

• Department of Radiology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China

Keywords: Radiomics, Cryptococcosis, Pulmonary nodules, Prediction model, Diagnosis, CT images.

Abstract:

Background: Radiomics can quantify pulmonary nodule characteristics non-invasively by applying advanced imaging feature algorithms. Radiomic textural features derived from Computed Tomography (CT) imaging are broadly used to predict benign and malignant pulmonary nodules. However, few studies have reported on the radiomics-based identification of nodular Pulmonary Cryptococcosis (PC).

Objective: This study aimed to evaluate the diagnostic and differential diagnostic value of radiomic features extracted from CT images for nodular PC.

Methods: This retrospective analysis included 44 patients with PC (29 males, 15 females), 58 with Tuberculosis (TB) (39 males, 19 females), and 60 with Lung Cancer (LC) (20 males, 40 females) confirmed pathologically. Models 1 (PC vs. non-PC), 2 (PC vs. TB), and 3 (PC vs. LC) were established using radiomic features. Models 4 (PC vs. TB) and 5 (PC vs. LC) were established based on radiomic and CT features.

Results: Five radiomic features were predictive of PC vs. non-PC model, but accuracy and Area Under the Curve (AUC) were 0.49 and 0.472, respectively. In model 2 (PC vs. TB) involving six radiomic features, the accuracy and AUC were 0.80 and 0.815, respectively. Model 3 (PC vs. LC) with six radiomic features performed well, with AUC=0.806 and an accuracy of 0.76. Between the PC and TB groups, model 4 combining radiomics, distribution, and PI, showed AUC=0.870. In differentiating PC from LC, the combination of radiomics, distribution, PI, and RBNAV achieved AUC=0.926 and an accuracy of 0.90.

Conclusion: The prediction models based on radiomic features from CT images performed well in discriminating PC from TB and LC. The individualized prediction models combining radiomic and CT features achieved the best diagnostic performance.

Cite this article as:

Huang Danmei, Wu Xiuting, Chen Siqi, Li Kai*, Zhang Xiaobo, Pan Xiaoyu, Fu Liang, Lin Wenjun, Li Zefeng and Guan Xuechun, Diagnostic Study of Nodular Pulmonary Cryptococcosis Based on Radiomic Features Captured from CT Images, Current Medical Imaging 2024; 20 : e15734056302538 . https://dx.doi.org/10.2174/0115734056302538240522110059