Inferring FDG-PET-positivity of Lymph Node Metastases in Proven Lung Cancer from Contrast-enhanced CT Using Radiomics and Machine Learning

Overview

Journal Eur Radiol Exp

Date 2022 Sep 14

PMID 36104467

Authors

Boris Gorodetski

Philipp Hendrik Becker

Alexander Daniel Jacques Baur

Alexander Hartenstein

Julian Manuel Michael Rogasch

Christian Furth

Holger Amthauer

Bernd Hamm

Marcus Makowski

Tobias Penzkofer

Affiliations

Soon will be listed here.

Abstract

Background: We evaluated the role of radiomics applied to contrast-enhanced computed tomography (CT) in the detection of lymph node (LN) metastases in patients with known lung cancer compared to F-fluorodeoxyglucose positron emission tomography (PET)/CT as a reference.

Methods: This retrospective analysis included 381 patients with 1,799 lymph nodes (450 malignant, 1,349 negative). The data set was divided into a training and validation set. A radiomics analysis with 4 filters and 6 algorithms resulting in 24 different radiomics signatures and a bootstrap algorithm (Bagging) with 30 bootstrap iterations was performed. A decision curve analysis was applied to generate a net benefit to compare the radiomics signature to two expert radiologists as one-by-one and as a prescreening tool in combination with the respective radiologist and only the radiologists.

Results: All 24 modeling methods showed good and reliable discrimination for malignant/benign LNs (area under the curve 0.75-0.87). The decision curve analysis showed a net benefit for the least absolute shrinkage and selection operator (LASSO) classifier for the entire probability range and outperformed the expert radiologists except for the high probability range. Using the radiomics signature as a prescreening tool for the radiologists did not improve net benefit.

Conclusions: Radiomics showed good discrimination power irrespective of the modeling technique in detecting LN metastases in patients with known lung cancer. The LASSO classifier was a suitable diagnostic tool and even outperformed the expert radiologists, except for high probabilities. Radiomics failed to improve clinical benefit as a prescreening tool.

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