Radiomics signature: a biomarker for the preoperative discrimination of lung invasive adenocarcinoma manifesting as a ground-glass nodule

Abstract

Objectives

To identify the radiomics signature allowing preoperative discrimination of lung invasive adenocarcinomas from non-invasive lesions manifesting as ground-glass nodules.

Methods

This retrospective primary cohort study included 160 pathologically confirmed lung adenocarcinomas. Radiomics features were extracted from preoperative non-contrast CT images to build a radiomics signature. The predictive performance and calibration of the radiomics signature were evaluated using intra-cross (n=76), external non-contrast-enhanced CT (n=75) and contrast-enhanced CT (n=84) validation cohorts. The performance of radiomics signature and CT morphological and quantitative indices were compared.

Results

355 three-dimensional radiomics features were extracted, and two features were identified as the best discriminators to build a radiomics signature. The radiomics signature showed a good ability to discriminate between invasive adenocarcinomas and non-invasive lesions with an accuracy of 86.3%, 90.8%, 84.0% and 88.1%, respectively, in the primary and validation cohorts. It remained an independent predictor after adjusting for traditional preoperative factors (odds ratio 1.87, p < 0.001) and demonstrated good calibration in all cohorts. It was a better independent predictor than CT morphology or mean CT value.

Conclusions

The radiomics signature showed good predictive performance in discriminating between invasive adenocarcinomas and non-invasive lesions. Being a non-invasive biomarker, it could assist in determining therapeutic strategies for lung adenocarcinoma.

Key Points

• The radiomics signature was a non-invasive biomarker of lung invasive adenocarcinoma.

• The radiomics signature outweighed CT morphological and quantitative indices.

• A three-centre study showed that radiomics signature had good predictive performance.