Pulmonary Sclerosing Pneumocytoma: A Pre and Intraoperative Diagnostic Challenge. Report of Two Cases and Review of the Literature

Central and peripheral pulmonary sclerosing pneumocytomas: multi-phase CT study and comparison with Ki-67

BACKGROUND

This study aimed to evaluate the multi-phase CT findings of central and peripheral pulmonary sclerosing pneumocytomas (PSPs) and compared them with Ki-67 to reveal their neoplastic nature.

PATIENTS AND METHODS

Multi-phase CT and clinical data of 33 PSPs (15 central PSPs and 18 peripheral PSPs) were retrospectively analyzed and compared their multi-phase CT features and Ki-67 levels.

RESULTS

For quantitative indicators, central PSPs were larger than peripheral PSPs (10.39 ± 3.25 cm3 vs. 4.65 ± 2.61 cm3, P = 0.013), and tumor size was negatively correlated with acceleration index (r = -0.845, P < 0.001). The peak enhancement of central PSPs appeared in the delayed phase, with a longer time to peak enhancement (TTP, 100.81 ± 19.01 s), lower acceleration index (0.63 ± 0.17), progressive enhancement, and higher Ki-67 level. The peak enhancement of peripheral PSPs appeared in the venous phase, with the shorter TTP (62.67 ± 20.96 s, P < 0.001), higher acceleration index (0.99 ± 0.25, P < 0.001), enhancement washout, and lower Ki-67 level. For qualitative indicators, the overlying vessel sign (86.67% vs. 44.44%, P = 0.027), prominent pulmonary artery sign (73.33% vs. 27.78%, P = 0.015), and obstructive inflammation/atelectasis (26.67% vs. 0%, P = 0.033) were more common in central PSPs, while peripheral PSPs were more common with halo sign (38.89% vs. 6.67%, P = 0.046).

CONCLUSIONS

The location of PSP is a possible contributing factor to its diverse imaging-pathological findings. The tumor size, multi-phase enhancement, qualitative signs, and Ki-67 were different between central and peripheral PSPs. Combined tumor size, multi-phase findings, and Ki-67 level are helpful to reveal the nature of the borderline tumor.

Lung hamartoma resembling lung cancer: a report of three cases

Pulmonary hamartoma is a benign lung tumor. However, it is difficult to distinguish this lesion from other diseases via imaging. Three patients with pathologically confirmed pulmonary hamartoma in our department were analyzed. We believe it is necessary to combine imaging, pathology, clinical testing, and individual patient assessments to enable an earlier and more definitive diagnosis of pulmonary hamartoma. Therefore, it is necessary to analyze and summarize the clinical manifestations and imaging features of patients with pulmonary hamartoma to improve the early recognition of the disease by clinicians.

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.