Lung cancer prediction by Deep Learning to identify benign lung nodules

Marjolein A Heuvelmans ,Carlos Arteta ,Jérôme Declerck ,W Hickes ,Hans‐Ulrich Kauczor ,Mieneke Rook ,Peter M A Van Ooijen ,Maria Tsakok ,Oyunbileg Von Stackelberg ,Timor Kadir ,P Novotný ,Carlos Francisco Silva ,Heiko Peschl ,Claus Peter Heußel ,Fergus Gleeson ,R V Rubtsov ,Lyndsey C Pickup ,Sarim Ather ,Daiwei Han ,Matthijs Oudkerk

doi:10.1016/j.lungcan.2021.01.027

Abstract

IntroductionDeep Learning has been proposed as promising tool to classify malignant nodules. Our aim was to retrospectively validate our Lung Cancer Prediction Convolutional Neural Network (LCP-CNN), which was trained on US screening data, on an independent dataset of indeterminate nodules in an European multicentre trial, to rule out benign nodules maintaining a high lung cancer sensitivity. MethodsThe LCP-CNN has been trained to generate a malignancy score for each nodule using CT data from the U.S. National Lung Screening Trial (NLST), and validated on CT scans containing 2106 nodules (205 lung cancers) detected in patients from from the Early Lung Cancer Diagnosis Using Artificial Intelligence and Big Data (LUCINDA) study, recruited from three tertiary referral centers in the UK, Germany and Netherlands. We pre-defined a benign nodule rule-out test, to identify benign nodules whilst maintaining a high sensitivity, by calculating thresholds on the malignancy score that achieve at least 99 % sensitivity on the NLST data. Overall performance per validation site was evaluated using Area-Under-the-ROC-Curve analysis (AUC). ResultsThe overall AUC across the European centers was 94.5 % (95 %CI 92.6–96.1). With a high sensitivity of 99.0 %, malignancy could be ruled out in 22.1 % of the nodules, enabling 18.5 % of the patients to avoid follow-up scans. The two false-negative results both represented small typical carcinoids. ConclusionThe LCP-CNN, trained on participants with lung nodules from the US NLST dataset, showed excellent performance on identification of benign lung nodules in a multi-center external dataset, ruling out malignancy with high accuracy in about one fifth of the patients with 5−15 mm nodules.

Highlights

Deep Learning has been proposed as promising tool to classify malignant nodules
Adequate differentiation of benign and malignant small-tointermediate sized, 5–15 mm, pulmonary nodules detected by computed tomography (CT) is a challenge for radiologists
It remains debatable whether such Deep Learning methods identify nodule characteristics specific to lung cancer, or predominantly stratify nodules based on size

Summary

Introduction

Deep Learning has been proposed as promising tool to classify malignant nodules. Our aim was to retrospectively validate our Lung Cancer Prediction Convolutional Neural Network (LCP-CNN), which was trained on US screening data, on an independent dataset of indeterminate nodules in an European multicentre trial, to rule out benign nodules maintaining a high lung cancer sensitivity. 50 % of smokers have a pulmonary nodule, [1] and 25 % have more than one, less than 1% of these nodules are malignant [1] Nodule classification for both incidentally detected and screening detected nodules are based on nodule type, size, and growth, according to Fleischner and Lung-RADSTM guidelines [2,3]. Despite their widespread adoption, these nodule management protocols still result in a rather high false-positive rate. The large nodule size results in biased data sets, as most lung cancers are generally larger than benign nodules It remains debatable whether such Deep Learning methods identify nodule characteristics specific to lung cancer, or predominantly stratify nodules based on size

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Conclusion