https://scholars.lib.ntu.edu.tw/handle/123456789/637377
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.author | Choi, Wookjin | en_US |
dc.contributor.author | CHIA-JU LIU | en_US |
dc.contributor.author | Alam, Sadegh Riyahi | en_US |
dc.contributor.author | Oh, Jung Hun | en_US |
dc.contributor.author | Vaghjiani, Raj | en_US |
dc.contributor.author | Humm, John | en_US |
dc.contributor.author | Weber, Wolfgang | en_US |
dc.contributor.author | Adusumilli, Prasad S. | en_US |
dc.contributor.author | Deasy, Joseph O. | en_US |
dc.contributor.author | Lu, Wei | en_US |
dc.date.accessioned | 2023-11-25T02:31:49Z | - |
dc.date.available | 2023-11-25T02:31:49Z | - |
dc.date.issued | 2023-01-01 | - |
dc.identifier.issn | 20010370 | - |
dc.identifier.uri | https://scholars.lib.ntu.edu.tw/handle/123456789/637377 | - |
dc.description.abstract | 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. | en_US |
dc.language.iso | en | en_US |
dc.relation.ispartof | Computational and Structural Biotechnology Journal | en_US |
dc.subject | Aggressive subtypes | CT | Histopathology | Lung adenocarcinoma | Non-small cell lung cancer | PET | Preoperative | Radiomics | Surgical planning | en_US |
dc.title | Preoperative <sup>18</sup>F-FDG PET/CT and CT radiomics for identifying aggressive histopathological subtypes in early stage lung adenocarcinoma | en_US |
dc.type | journal article | en |
dc.identifier.doi | 10.1016/j.csbj.2023.11.008 | - |
dc.identifier.scopus | 2-s2.0-85176497306 | - |
dc.identifier.url | https://api.elsevier.com/content/abstract/scopus_id/85176497306 | - |
dc.relation.journalvolume | 21 | en_US |
dc.relation.pageend | 5608 | en_US |
item.fulltext | no fulltext | - |
item.cerifentitytype | Publications | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
item.grantfulltext | none | - |
item.openairetype | journal article | - |
item.languageiso639-1 | en | - |
crisitem.author.dept | Nuclear Medicine-NTUH | - |
crisitem.author.dept | Radiology | - |
crisitem.author.orcid | 0000-0003-1641-5617 | - |
crisitem.author.parentorg | National Taiwan University Hospital | - |
crisitem.author.parentorg | College of Medicine | - |
顯示於: | 醫學院附設醫院 (臺大醫院) |
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