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European Radiology

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01-05-2020 | Computed Tomography | Imaging Informatics and Artificial Intelligence

Marginal radiomics features as imaging biomarkers for pathological invasion in lung adenocarcinoma

Authors: Hwan-ho Cho, Geewon Lee, Ho Yun Lee, Hyunjin Park

Published in: European Radiology | Issue 5/2020

Abstract

Objectives

Lung adenocarcinomas which manifest as ground-glass nodules (GGNs) have different degrees of pathological invasion and differentiating among them is critical for treatment. Our goal was to evaluate the addition of marginal features to a baseline radiomics model on computed tomography (CT) images to predict the degree of pathologic invasiveness.

Methods

We identified 236 patients from two cohorts (training, n = 189; validation, n = 47) who underwent surgery for GGNs. All GGNs were pathologically confirmed as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IA). The regions of interest were semi-automatically annotated and 40 radiomics features were computed. We selected features using L1-norm regularization to build the baseline radiomics model. Additional marginal features were developed using the cumulative distribution function (CDF) of intratumoral intensities. An improved model was built combining the baseline model with CDF features. Three classifiers were tested for both models.

Results

The baseline radiomics model included five features and resulted in an average area under the curve (AUC) of 0.8419 (training) and 0.9142 (validation) for the three classifiers. The second model, with the additional marginal features, resulted in AUCs of 0.8560 (training) and 0.9581 (validation). All three classifiers performed better with the added features. The support vector machine showed the most performance improvement (AUC improvement = 0.0790) and the best performance was achieved by the logistic classifier (validation AUC = 0.9825).

Conclusion

Our novel marginal features, when combined with a baseline radiomics model, can help differentiate IA from AIS and MIA on preoperative CT scans.

Key Points

• Our novel marginal features could improve the existing radiomics model to predict the degree of pathologic invasiveness in lung adenocarcinoma.

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Title: Marginal radiomics features as imaging biomarkers for pathological invasion in lung adenocarcinoma
Authors: Hwan-ho Cho
Geewon Lee
Ho Yun Lee
Hyunjin Park
Publication date: 01-05-2020
Publisher: Springer Berlin Heidelberg
Keywords: Computed Tomography
Computed Tomography
Published in: European Radiology / Issue 5/2020
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-019-06581-2

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Springer Medicine

Marginal radiomics features as imaging biomarkers for pathological invasion in lung adenocarcinoma

Abstract

Objectives

Methods

Results

Conclusion

Key Points

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusion

Key Points

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