https://scholars.lib.ntu.edu.tw/handle/123456789/637377
標題: | Preoperative <sup>18</sup>F-FDG PET/CT and CT radiomics for identifying aggressive histopathological subtypes in early stage lung adenocarcinoma | 作者: | Choi, Wookjin CHIA-JU LIU Alam, Sadegh Riyahi Oh, Jung Hun Vaghjiani, Raj Humm, John Weber, Wolfgang Adusumilli, Prasad S. Deasy, Joseph O. Lu, Wei |
關鍵字: | Aggressive subtypes | CT | Histopathology | Lung adenocarcinoma | Non-small cell lung cancer | PET | Preoperative | Radiomics | Surgical planning | 公開日期: | 1-一月-2023 | 卷: | 21 | 來源出版物: | Computational and Structural Biotechnology Journal | 摘要: | Lung adenocarcinoma (ADC) is the most common non-small cell lung cancer. Surgical resection is the primary treatment for early-stage lung ADC while lung-sparing surgery is an alternative for non-aggressive cases. Identifying histopathologic subtypes before surgery helps determine the optimal surgical approach. Predominantly solid or micropapillary (MIP) subtypes are aggressive and associated with a higher likelihood of recurrence and metastasis and lower survival rates. This study aims to non-invasively identify these aggressive subtypes using preoperative 18F-FDG PET/CT and diagnostic CT radiomics analysis. We retrospectively studied 119 patients with stage I lung ADC and tumors ≤ 2 cm, where 23 had aggressive subtypes (18 solid and 5 MIPs). Out of 214 radiomic features from the PET/CT and CT scans and 14 clinical parameters, 78 significant features (3 CT and 75 PET features) were identified through univariate analysis and hierarchical clustering with minimized feature collinearity. A combination of Support Vector Machine classifier and Least Absolute Shrinkage and Selection Operator built predictive models. Ten iterations of 10-fold cross-validation (10 ×10-fold CV) evaluated the model. A pair of texture feature (PET GLCM Correlation) and shape feature (CT Sphericity) emerged as the best predictor. The radiomics model significantly outperformed the conventional predictor SUVmax (accuracy: 83.5% vs. 74.7%, p = 9e-9) and identified aggressive subtypes by evaluating FDG uptake in the tumor and tumor shape. It also demonstrated a high negative predictive value of 95.6% compared to SUVmax (88.2%, p = 2e-10). The proposed radiomics approach could reduce unnecessary extensive surgeries for non-aggressive subtype patients, improving surgical decision-making for early-stage lung ADC patients. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/637377 | ISSN: | 20010370 | DOI: | 10.1016/j.csbj.2023.11.008 |
顯示於: | 醫學院附設醫院 (臺大醫院) |
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