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  4. CT-Based Radiomic Analysis for Preoperative Prediction of Tumor Invasiveness in Lung Adenocarcinoma Presenting as Pure Ground-Glass Nodule
 
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CT-Based Radiomic Analysis for Preoperative Prediction of Tumor Invasiveness in Lung Adenocarcinoma Presenting as Pure Ground-Glass Nodule

Journal
Cancers
Journal Volume
14
Journal Issue
23
Date Issued
2022-11-29
Author(s)
Kao, Tzu-Ning
MIN-SHU HSIEH  
WEI-LI CHEN  
Yang, Chi-Fu Jeffrey
Chuang, Ching-Chia
XU-HENG CHIANG  
Chen, Yi-Chang
Lee, Yi-Hsuan
HSAO-HSUN HSU  
CHUNG-MING CHEN  
MONG-WEI LIN  
JIN-SHING CHEN  
DOI
10.3390/cancers14235888
URI
https://scholars.lib.ntu.edu.tw/handle/123456789/627200
URL
https://api.elsevier.com/content/abstract/scopus_id/85143597084
Abstract
It remains a challenge to preoperatively forecast whether lung pure ground-glass nodules (pGGNs) have invasive components. We aimed to construct a radiomic model using tumor characteristics to predict the histologic subtype associated with pGGNs. We retrospectively reviewed clinicopathologic features of pGGNs resected in 338 patients with lung adenocarcinoma between 2011-2016 at a single institution. A radiomic prediction model based on forward sequential selection and logistic regression was constructed to differentiate adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma. The study cohort included 133 (39.4%), 128 (37.9%), and 77 (22.8%) patients with AIS, MIA, and invasive adenocarcinoma (acinar 55.8%, lepidic 33.8%, papillary 10.4%), respectively. The majority (83.7%) underwent sublobar resection. There were no nodal metastases or tumor recurrence during a mean follow-up period of 78 months. Three radiomic features-cluster shade, homogeneity, and run-length variance-were identified as predictors of histologic subtype and were selected to construct a prediction model to classify the AIS/MIA and invasive adenocarcinoma groups. The model achieved accuracy, sensitivity, specificity, and AUC of 70.6%, 75.0%, 70.0%, and 0.7676, respectively. Applying the developed radiomic feature model to predict the histologic subtypes of pGGNs observed on CT scans can help clinically in the treatment selection process.
Subjects
ground-glass nodule; invasiveness; lung adenocarcinoma; lung cancer surgery; radiomic feature analysis
SDGs

[SDGs]SDG3

Publisher
MDPI
Type
journal article

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