Discussion
pulmonary tuberculoma, tumor, PTB, lung tuberculoma, calcification, lung adenocarcinoma, fibrosis, adenocarcinomas, lung cancer, tuberculosis disease infection, benign granulomatous lesions, granulomas, lung squamous cell carcinoma, diabetes
TUMOR, LUNG ADENOCARCINOMA, DISEASE, FIBROSIS, TUBERCULOSIS, ADENOCARCINOMAS, LUNG CANCER, OF LUNG ADENOCARCINOMA, CAVITY, GRANULOMAS, INACTIVE PULMONARY TUBERCULOSIS, LUNG SQUAMOUS CELL CARCINOMA, DIABETES
According to this retrospective study, there were some significant differences between PNMA and PTB patients with solitary pulmonary solid nodules in terms of qualitative and quantitative clinical characteristics. We selected seven clinical variables to build our clinical prediction model to differentiate PNMA from PTB, including smoking history, diabetes history, lesion located in the lower lobe, satellite lesions, cavity or vacuole, plain CT value and ΔCTV [In this study, smoking was an independent factor to distinguish PNMA and PTB. In a previous study, cigarette smoking increases not only the risk of progression to active pulmonary tuberculosis disease but also the risk of acquiring new tuberculosis disease infection [In histology, PNMA is characterized by mucin-rich tumor cells, fibrosis, with central fibrosis and alveolar spaces filled with mucin [CT scanning, especially CT dynamic contrast-enhanced scanning, is a valuable tool in the diagnosis of lung cancer and tuberculosis. The CT features of lung adenocarcinoma and pulmonary tuberculoma have received much attention in the literature, but PNMA and PTB have received fewer reports [To explore a much more effective method to differentiate PNMA from PTB, we extracted five independent radiomic features associated with PNMA and PTB, including flatness, cluster shade, minimum, median and skewness. These parameters belong to the Form Factor Parameters, Texture Parameters, and Histogram Parameters. Flatness is a form factor parameter that is independent of the gray-level intensity distribution in the ROI. Cluster Shade, as one of the Texture Parameters, is the task of grouping a group of objects so that objects in the same group (cluster shade) are more similar to each other (in a sense) than objects in other groups (cluster shade). The larger the Cluster Shade value, the more asymmetrical it is. The minimum belongs to the histogram parameter, which represents the minimum pixel value of an image (of the lesion). The median is also a histogram parameter that represents the median pixel value of an image (of the lesion). Another histogram parameter is skewness, which reflects the degree of asymmetry in the histogram distribution, and if the predictive value has been effective, the absolute values of the skewness would have been higher. All features above were obtained via the conversion of images to higher-dimensional data. They allowed high-throughput mining of quantitative imaging features from general medical images, followed by an automated analysis to assist clinical decision-making. Previous studies revealed that radiomics features from CT could preoperatively differentiate lung adenocarcinoma from lung tuberculoma in patients with pulmonary solitary solid nodules and could also distinguish adenocarcinomas from granulomas. To our knowledge, this is the first study to differentiate PNMA from PTB based on radiomic features. In this study, the model based on these radiomic features also illustrated an effective role in preoperatively differentiating PNMA from PTB. According to our ROC analyses, the AUC values for the radiomics model were 0.840 and 0.960 in the training and test groups, respectively. The clinical-radiomics combined model (ROC-AUC: 0.940–0.990) was significantly better than the clinical model and radiomics model. Furthermore, the decision curve analysis also demonstrated that the combined model performed significantly better than the clinical model and radiomic model in predicting outcomes. Decision curve analysis offers important information beyond the standard performance metrics of discrimination and calibration and could be used to evaluate the clinical impact, indicating that there was a higher chance of success. In this study, not only an internal validation was carried out, but an external validation was also carried out on this basis. At the same time, the ROC curve analysis showed that the AUC of the clinical-radiomics combined model was greater than 0.9000 in both the internal validation and external validation. This suggested that the clinical-radiomics combined model had a good discrimination degree and a strong ability to distinguish PNMA from PTB.Our study had several limitations that must be considered. First, it was a retrospective study with a relatively small sample size. Second, due to the study's inclusion of only patients who had pathologic results after surgery, selection bias cannot be ignored. Additionally, only PNMA and PTB were observed without calcification, so the results should be interpreted cautiously. Third, our study only evaluated the relationship between PNMA and PTB, and other types of lung nodules need to be further explored, such as lung squamous cell carcinoma and other benign granulomatous lesions. To guide clinical practice, the model will be validated in a multicenter, prospective, large-scale study in the future and further optimized.In conclusion, in our present study, we established a model to differentiate PNMA from PTB by using preoperative clinicopathological features, radiomic features, and clinical-radiomic features for the first time. The clinical-radiomic model established that we established also showed good predictive value and had potential value in clinical practice.