Preoperative prediction of lymphovascular and visceral pleural invasion of lung adenocarcinoma based on 18F-FDG PET radiomics
10.3760/cma.j.cn321828-20230313-00056
- VernacularTitle:18F-FDG PET影像组学在术前预测肺腺癌脉管浸润及脏层胸膜侵犯中的应用价值
- Author:
Xiaohui SUN
1
;
Zhipeng LIU
;
Dazhuang YANG
;
Jun ZHANG
Author Information
1. 南京医科大学附属泰州人民医院核医学科，泰州 225300
- Keywords:
Lung neoplasms;
Adenocarcinoma;
Neoplasm invasiveness;
Radiomics;
Positron-emission tomography;
Fluorodeoxyglucose F18
- From:
Chinese Journal of Nuclear Medicine and Molecular Imaging
2024;44(2):74-79
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To evaluate the predictive value of 18F-FDG PET-based radiomics models for lymphovascular invasion (LVI) and visceral pleural invasion (VPI) in lung adenocarcinoma (LAC) prior to surgery. Methods:Eighty-seven patients with LAC (42 males, 45 females, age: (64.6±9.0) years; 90 lesions) pathologically confirmed in the Affiliated Taizhou People′s Hospital of Nanjing Medical University between August 2018 and August 2022 were retrospectively included. Based on the radiomics features extracted from PET images, the machine learning models were constructed by using the support vector machine (SVM), logical regression (LR), decision tree (DT), and K-nearest neighbor (KNN) algorithm. Stratified sampling (Python′s StratifiedkFold function) was employed to divide the data into training set and test set at a ratio of 8∶2. The model stability was assessed using the 50% discount cross-validation. The ROC curve was drawn, and the AUC was calculated to evaluate the value of radiomics models in predicting LVI and VPI in LAC. Delong test was used to compare AUCs of different models.Results:The radiomics models (SVM, LR, DT, KNN) based on PET images showed good predictive value for LVI and VPI in patients with LAC. For LVI, the AUCs were 0.91, 0.90, 0.91, 0.91 in the training set, and were 0.85, 0.87, 0.77, 0.78 in the test set; for VPI, the AUCs were 0.86, 0.86, 0.84, 0.81 in the training set, and were 0.82, 0.80, 0.69, 0.78 in the test set. The F1 scores of the SVM model were the best (0.59 and 0.66 for predicting LVI and VPI respectively). The Delong test showed that there were no significant differences in AUCs among the four models ( z values: from -1.46 to 1.71, all P>0.05). Conclusions:The machine learning models based on 18F-FDG PET radiomics features are effective in predicting LVI and VPI in patients with LAC prior to surgery. These models can assist clinicians in stratifying the risk of LAC and making informed clinical decisions. The SVM model has the best performance in predicting LVI and VPI.
