Radiomics Nomograms of Tumors and Peritumoral Regions for the Preoperative Prediction of Spread Through Air Spaces in Lung Adenocarcinoma

Overview

Journal Transl Oncol

Specialty Oncology

Date 2020 Jul 5

PMID 32622312

Citations 28

Authors

Yaoyao Zhuo

Mingxiang Feng

Shuyi Yang

Lingxiao Zhou

Di Ge

Shaohua Lu

Lei Liu

Fei Shan

Zhiyong Zhang

Affiliations

Soon will be listed here.

Abstract

To evaluate the clinical features and radiomics nomograms of tumors and peritumoral regions for the preoperative prediction of the presence of spread through air spaces (STAS) in patients with lung adenocarcinoma. A total of 107 STAS-positive lung adenocarcinomas were selected and matched to 105 STAS-negative lung adenocarcinomas. Thin-slice CT imaging annotation and region of interest (ROI) segmentation were performed with semi-automatic in-house software. Radiomics features were extracted from all nodules and incremental distances of 5, 10, and 15 mm outside the lesion segmentation. A radiomics nomogram was established with multivariable logistic regression based on clinical and radiomics features. The maximum diameter of the solid component and mediastinal lymphadenectasis were selected as independent predictors of STAS. The radiomics nomogram of lung nodules showed especially good prediction in the training set [area under the curve (AUC), 0.98; 95% confidence interval (CI), 0.97-1.00] and test set (AUC, 0.99; 95% CI, 0.97-1.00). The radiomics nomogram of peritumoral regions also showed good prediction, but the fitting degrees of the calibration curves were not good. Our study may provide guidance for surgical methods in patients with lung adenocarcinoma.

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