CT Cavitary Features in Lung Cancer to Differentiate Between Squamous Cell Carcinoma and Adenocarcinoma

Introduction: Cavitary lung cancer is frequently found in both adenocarcinoma and squamous cell carcinoma (SCC) and the differentiation between these two types of cancers is becoming more important as the role of molecular targeted therapy is increasingly used. Objectives: To evaluate the use of CT cavitary features to differentiate between adenocarcinoma and SCC lung cancer. Methods: A retrospective study of cavitary lung lesions seen on CT scan in patients prior to treatment for pathologically proven adenocarcinoma or SCC between January 2010 and April 2021 was conducted. The chest CT findings of the tumors with cavitation, including the cavitary wall pattern, diameter of the tumor and cavity, tumoral wall thickness, location, number, intra-tumoral bronchiectasis, associated calcification, emphysema, honeycomb appearance, satellite nodules, tree-in-bud pattern, and pleural tagging, were evaluated. Results: A total of 54 patients were included in the study (74&percent; adenocarcinoma and 26&percent; SCC). The mean tumoral diameter (mean Dt), mean cavitary diameter (mean Dc), and Dc/Dt ratio tended to be greater in SCCs (p = 0.001, p = 0.002, and p = 0.030 respectively). Multiple cavities were significantly more encountered in SCCs (p = 0.021). Neither the pattern of the cavitary wall nor the rest of the other associated CT findings could help differentiate between SCC and adenocarcinoma. Conclusions: Some features such as mean Dt, mean Dc, Dc/Dt ratio, and prevalence of multiple cavities tended to be greater in SCCs compared to adenocarcinomas.

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Publication Details

Journal
Asian Medical Journal and Alternative Medicine
Published
2025-04-11
DOI
https://doi.org/10.70933/2773-9465.1005
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00

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article

CT Cavitary Features in Lung Cancer to Differentiate Between Squamous Cell Carcinoma and Adenocarcinoma

Nisa Muangman, Krittachat Butnian, Chanikarn Khanutwong
Asian Medical Journal and Alternative Medicine
Radiomics and Machine Learning in Medical Imaging
article

CT Cavitary Features in Lung Cancer to Differentiate Between Squamous Cell Carcinoma and Adenocarcinoma

Nisa Muangman, Krittachat Butnian, Chanikarn Khanutwong
article en

Abstract

Introduction: Cavitary lung cancer is frequently found in both adenocarcinoma and squamous cell carcinoma (SCC) and the differentiation between these two types of cancers is becoming more important as the role of molecular targeted therapy is increasingly used. Objectives: To evaluate the use of CT cavitary features to differentiate between adenocarcinoma and SCC lung cancer. Methods: A retrospective study of cavitary lung lesions seen on CT scan in patients prior to treatment for pathologically proven adenocarcinoma or SCC between January 2010 and April 2021 was conducted. The chest CT findings of the tumors with cavitation, including the cavitary wall pattern, diameter of the tumor and cavity, tumoral wall thickness, location, number, intra-tumoral bronchiectasis, associated calcification, emphysema, honeycomb appearance, satellite nodules, tree-in-bud pattern, and pleural tagging, were evaluated. Results: A total of 54 patients were included in the study (74&percent; adenocarcinoma and 26&percent; SCC). The mean tumoral diameter (mean Dt), mean cavitary diameter (mean Dc), and Dc/Dt ratio tended to be greater in SCCs (p = 0.001, p = 0.002, and p = 0.030 respectively). Multiple cavities were significantly more encountered in SCCs (p = 0.021). Neither the pattern of the cavitary wall nor the rest of the other associated CT findings could help differentiate between SCC and adenocarcinoma. Conclusions: Some features such as mean Dt, mean Dc, Dc/Dt ratio, and prevalence of multiple cavities tended to be greater in SCCs compared to adenocarcinomas.

Asian Medical Journal and Alternative MedicineVol. 25(1)
Faculty of Medicine Siriraj Hospital, Mahidol University
Good health and well-being
Openalex Percentile: Top 85%
Radiomics and Machine Learning in Medical Imaging
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