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Open Access 01-12-2024 | Computed Tomography | Research

The value of CT shape quantification in predicting pathological classification of lung adenocarcinoma

Authors: Mingjie guo, Zhan Cao, Zhichao Huang, Shaowen Hu, Yafei Xiao, Qianzhou Ding, Yalong Liu, Xiaokang An, Xianjie Zheng, Shuanglin Zhang, Guoyu Zhang

Published in: BMC Cancer | Issue 1/2024

Abstract

Objective

To evaluate whether quantification of lung GGN shape is useful in predicting pathological categorization of lung adenocarcinoma and guiding the clinic.

Methods

98 patients with primary lung adenocarcinoma were pathologically confirmed and CT was performed preoperatively, and all lesions were pathologically ≤ 30 mm in size. On CT images, we measured the maximum area of the lesion’s cross-section (MA). The longest diameter of the tumor (LD) was marked with points A and B, and the perpendicular diameter (PD) was marked with points C and D, which was the longest diameter perpendicular to AB. and D, which was the longest diameter perpendicular to AB. We took angles A and B as big angle A (BiA) and small angle A (SmA). We measured the MA, LD, and PD, and for analysis we derived the LD/PD ratio and the BiA/SmA ratio. The data were analysed using the chi-square test, t-test, ROC analysis, and binary logistic regression analysis.

Results

Precursor glandular lesions (PGL) and microinvasive adenocarcinoma (MIA) were distinguished from invasive adenocarcinoma (IAC) by the BiA/SmA ratio and LD, two independent factors (p = 0.007, p = 0.018). Lung adenocarcinoma pathological categorization was indicated by the BiA/SmA ratio of 1.35 and the LD of 11.56 mm with sensitivity of 81.36% and 71.79%, respectively; specificity of 71.79% and 74.36%, respectively; and AUC of 0.8357 (95% CI: 0.7558–0.9157, p < 0.001), 0.8666 (95% CI: 0.7866–0.9465, p < 0.001), respectively. In predicting the pathological categorization of lung adenocarcinoma, the area under the ROC curve of the BiA/SmA ratio combined with LD was 0.9231 (95% CI: 0.8700-0.9762, p < 0.001), with a sensitivity of 81.36% and a specificity of 89.74%.

Conclusions

Quantification of lung GGN morphology by the BiA/SmA ratio combined with LD could be helpful in predicting pathological classification of lung adenocarcinoma.

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Title: The value of CT shape quantification in predicting pathological classification of lung adenocarcinoma
Authors: Mingjie guo
Zhan Cao
Zhichao Huang
Shaowen Hu
Yafei Xiao
Qianzhou Ding
Yalong Liu
Xiaokang An
Xianjie Zheng
Shuanglin Zhang
Guoyu Zhang
Publication date: 01-12-2024
Publisher: BioMed Central
Keywords: Computed Tomography
Computed Tomography
Published in: BMC Cancer / Issue 1/2024
Electronic ISSN: 1471-2407
DOI: https://doi.org/10.1186/s12885-023-11802-5

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