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Prediction of tumor doubling time of lung adenocarcinoma using radiomic margin characteristics

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Title
Prediction of tumor doubling time of lung adenocarcinoma using radiomic margin characteristics
Author(s)
Hyun Jung Yoon; Hyunjin Park; Ho Yun Lee; Insuk Sohn; Joonghyun Ahn; Seung-Hak Lee
Publication Date
2020-09
Journal
Thoracic Cancer, v.11, no.9, pp.2600 - 2609
Publisher
WILEY-BLACKWELL
Abstract
© 2020 The Authors. Thoracic Cancer published by China Lung Oncology Group and John Wiley & Sons Australia, Ltd. Background: Because shape or irregularity along the tumor perimeter can result from interactions between the tumor and the surrounding parenchyma, there could be a difference in tumor growth rate according to tumor margin or shape. However, no attempt has been made to evaluate the correlation between margin or shape features and tumor growth. Methods: We evaluated 52 lung adenocarcinoma (ADC) patients who had at least two computed tomographic (CT) examinations before curative resection. Volume-based doubling times (DTs) were calculated based on CT scans, and patients were divided into two groups according to the growth pattern (GP) of their ADCs (gradually growing tumors [GP I] vs. growing tumors with a temporary decrease in DT [GP II]). CT radiomic features reflecting margin characteristics were extracted, and radiomic features reflective of tumor DT were selected. Results: Among the 52 patients, 41 (78.8%) were assigned to GP I and 11 (21.2%) to GP II. Of the 94 radiomic features extracted, eccentricity, surface-to-volume ratio, LoG uniformity (σ = 3.5), and LoG skewness (σ = 0.5) were ultimately selected for tumor DT prediction. Selected radiomic features in GP I were surface-to-volume ratio, contrast, LoG uniformity (σ = 3.5), and LoG skewness (σ = 0.5), similar to those for total subjects, whereas the radiomic features in GP II were solidity, energy, and busyness. Conclusions: This study demonstrated the potential of margin-related radiomic features to predict tumor DT in lung ADCs. Key points: Significant findings of the study: We found a relationship between margin-related radiomic features and tumor doubling time. What this study adds: Margin-related radiomic features can potentially be used as noninvasive biomarkers to predict tumor doubling time in lung adenocarcinoma and inform treatment strategies
URI
https://pr.ibs.re.kr/handle/8788114/8485
DOI
10.1111/1759-7714.13580
ISSN
1759-7706
Appears in Collections:
Center for Neuroscience Imaging Research (뇌과학 이미징 연구단) > 1. Journal Papers (저널논문)
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