Investigating the Correlation between the Cytological Grading System and CT Features in Early-Stage Lung Adenocarcinoma.

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Tác giả: Dandan Chen, Xiaodong Dai, Yazhen Han, Hui Li, Min Li, Xingfen Qi, Huiting Qiu, Lijuan Qu, Shangwen Xu, Xianzong Ye, Yeting Zeng, Zhiyong Zheng

Ngôn ngữ: eng

Ký hiệu phân loại:

Thông tin xuất bản: Switzerland : Oncology , 2025

Mô tả vật lý:

Bộ sưu tập: NCBI

ID: 685458

INTRODUCTION: The early lung adenocarcinoma detection rate has increased with the development and application of low-dose computed tomography. However, overdiagnosis and overtreatment are frequent. Here, we established a cytology grading system for adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), and minimally invasive adenocarcinoma (MIA) and correlated the grading system with computed tomography (CT) features of ground-glass nodules (GGNs) to predict their biological behavior. METHODS: We screened 166 GGNs with pathological diagnoses of AAH, AIS, and MIA from the 900th Hospital of the Joint Logistics Support Force. The Mann-Whitney U test and the multiple linear regression analysis were used to screen cytological parameters. We stratified the GGNs into low- and high-grade groups by cytological score and established a cytology grading system. The Chi-square test and multiple logistic regression analysis were used to analyze differences in CT features between the two groups. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance. RESULTS: A cytology grading system was established, and the cytological parameters included nucleoli, chromatin, and Ki-67 labeling indices. The maximum diameter growth rate of GGNs was significantly greater in the high-grade group than in the low-grade group. Vascular abnormality signs were an independent risk factor for predicting cytological grade. CONCLUSION: The study findings indicate that vascular abnormality signs are valuable predictors of the cytological grade and that the cytology grading system can effectively predict the biological behavior of GGNs, thus enabling personalized clinical decision-making to avoid overdiagnosis and overtreatment.
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