Analysis of the F-18 FDG PET/CT features of pulmonary sclerosing pneumocytoma

Increased 68 Ga-FAPI Uptake in Pulmonary Sclerosing Pneumocytoma

ABSTRACT

Pulmonary sclerosing pneumocytoma is a rare benign neoplasm arising from the primitive respiratory epithelium. Here, we report 68 Ga-FAPI PET/CT findings of pulmonary sclerosing pneumocytoma in a 55-year-old woman. The images showed a solitary pulmonary mass in the left lower lobe with intense 68 Ga-FAPI uptake. Our case illustrates that the sclerosing pneumocytoma should be taken into consideration as one of the differential diagnoses in lung nodules/masses with intense 68 Ga-FAPI uptake.

Analysis of the value of enhanced CT combined with texture analysis in the differential diagnosis of pulmonary sclerosing pneumocytoma and atypical peripheral lung cancer: a feasibility study

BACKGROUND

As a rare benign lung tumour, pulmonary sclerosing pneumocytoma (PSP) is often misdiagnosed as atypical peripheral lung cancer (APLC) on routine imaging examinations. This study explored the value of enhanced CT combined with texture analysis to differentiate between PSP and APLC.

METHODS

Forty-eight patients with PSP and fifty patients with APLC were retrospectively enrolled. The CT image features of the two groups of lesions were analysed, and MaZda software was used to evaluate the texture of CT venous phase thin-layer images. Independent sample t-test, Mann-Whitney U tests or χ² tests were used to compare between groups. The intra-class correlation coefficient (ICC) was used to analyse the consistency of the selected texture parameters. Spearman correlation analysis was used to evaluate the differences in texture parameters between the two groups. Based on the statistically significant CT image features and CT texture parameters, the independent influencing factors between PSP and APLC were analysed by multivariate logistic regression. Extremely randomized trees (ERT) was used as the classifier to build models, and the models were evaluated by the five-fold cross-validation method.

RESULTS

Logistic regression analysis based on CT image features showed that calcification and arterial phase CT values were independent factors for distinguishing PSP from APLC. The results of logistic regression analysis based on CT texture parameters showed that WavEnHL_s-1 and Perc.01% were independent influencing factors to distinguish the two. Compared with the single-factor model (models A and B), the classification accuracy of the model based on image features combined with texture parameters was 0.84 ± 0.04, the AUC was 0.84 ± 0.03, and the sensitivity and specificity were 0.82 ± 0.13 and 0.87 ± 0.12, respectively.

CONCLUSION

Enhanced CT combined with texture analysis showed good diagnostic value for distinguishing PSP and APLC, which may contribute to clinical decision-making and prognosis evaluation.