Radiomics nomograms of tumors and peritumoral regions for the preoperative prediction of spread through air spaces in lung adenocarcinoma

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.

Conflict of interest statement

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.

Figures

Fig. 1

A: Recruitment pathway in this study. B: Workflow of image processing. Aug: August; Jan: January; STAS+: positive STAS; STAS-: negative STAS; F: female; M: male.

Fig. 2

A: Lung nodule segmentation. Extracting peritumoral regions: incremental distances of 5 mm (B), 10 mm (C), and 15 mm (D) outside the nodule segmentation. The center point of the lesion was determined according to CT imaging annotation, and a spherical shape was fitted with the maximum distance from the center point to the edge of the lesion as a radius. Finally, amplification was performed on the basis of this sphere.

Fig. 3

Radiomics feature selection. The least absolute shrinkage and selection operator (A) included choosing the regularization parameter λ (B) and determining the number of features. A total of seven radiomics features were chosen (C).

Fig. 4

Construction, performance and validation of the radiomics nomogram. A: The radiomics nomogram was developed using seven selected radiomics parameters and two clinical features. ROC curves of the nomogram and clinical model in the training (B) and test (C) sets. The calibration curves of the radiomics nomogram in the training (D) and test (E) sets.

Fig. S1

Radiomics signature construction. Radscores were compared from positive STAS and negative STAS on training (A) and test (B) set. ROC analysis was used to evaluate the performance of the model on training (C) and test (D) set. 0: negative STAS; 1: positive STAS.

Fig. S2

Decision curve analysis for radiomics and clinical model to evaluate the clinical usefulness of the model.

Fig. S3

Radiomics parameters selected from the 5 mm (A), 10 mm (B) and 15 mm (C) peritumoral regions.

Fig. S4

The calibration curves of radiomics nomogram in training and test set of the 5 mm (A–B), 10 mm (C–D) and 15 mm (E–F) peritumoral regions.