Preoperative (18)F-FDG PET/CT and CT radiomics for identifying aggressive histopathological subtypes in early stage lung adenocarcinoma Journal Article

   

Authors:	Choi, W.; Liu, C. J.; Alam, S. R.; Oh, J. H.; Vaghjiani, R.; Humm, J.; Weber, W.; Adusumilli, P. S.; Deasy, J. O.; Lu, W.
Article Title:	Preoperative (18)F-FDG PET/CT and CT radiomics for identifying aggressive histopathological subtypes in early stage lung adenocarcinoma
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. © 2023 The Authors
Keywords:	histopathology; lung adenocarcinoma; computerized tomography; tumors; diagnosis; surgery; surgical resection; decision making; pet; non-small cell lung cancer; biological organs; ct; diseases; support vector machines; non small cell lung cancer; textures; preoperative; fdg pet; surgical planning; radiomics; radiomic; aggressive subtypes; aggressive subtype
Journal Title:	Computational and Structural Biotechnology Journal
Volume:	21
ISSN:	2001-0370
Publisher:	Research Network of Computational and Structural Biotechnology
Date Published:	2023-11-04
Start Page:	5601
End Page:	5608
Language:	English
DOI:	10.1016/j.csbj.2023.11.008
PROVIDER:	scopus
PMCID:	PMC10681940
PUBMED:	38034400
DOI/URL:	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176497306&doi=10.1016%2fj.csbj.2023.11.008&partnerID=40&md5=8f891519769dbf0a33acf80688bfbd31
Notes:	Article -- MSK Cancer Center Support Grant (P30 CA008748) acknowledged in PubMed (via Conflict of Interest Statement) and PDF -- MSK corresponding author is Wei Lu -- Source: Scopus

https://synapse.mskcc.org/synapse/works/has_related_data_chk/211375