Document Type
Article
Publication Date
11-4-2023
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.
Recommended Citation
Choi, Wookjin; Liu, Chia-Ju; Alam, Sadegh Riyahi; Oh, Jung Hun; Vaghjiani, Raj; Humm, John; Weber, Wolfgang; Adusumilli, Prasad; Deasy, Joseph; and Lu, Wei, "Preoperative 18F-Fdg Pet/CT and CT Radiomics for Identifying Aggressive Histopathological Subtypes in Early Stage Lung Adenocarcinoma" (2023). Department of Radiation Oncology Faculty Papers. Paper 182.
https://jdc.jefferson.edu/radoncfp/182
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
PubMed ID
38034400
Language
English
Comments
This article is the author's final published version in Computational and Structural Biotechnology Journal, Volume 21, 2023, Pages 5601 - 5608.
The published version is available at https://doi.org/10.1016/j.csbj.2023.11.008.
Copyright © 2023 The Authors