Lung cancer prediction by Deep Learning to identify benign lung nodules. (April 2021)

• Record Type: Journal Article
• Authors: Heuvelmans, Marjolein A.; van Ooijen, Peter M.A.; Ather, Sarim; Silva, Carlos Francisco; Han, Daiwei; Heussel, Claus Peter; Hickes, William; Kauczor, Hans-Ulrich; Novotny, Petr; Peschl, Heiko; Rook, Mieneke; Rubtsov, Roman; von Stackelberg, Oyunbileg; Tsakok, Maria T.; Arteta, Carlos; Declerck, Jerome; Kadir, Timor; Pickup, Lyndsey; Gleeson, Fergus; Oudkerk, Matthijs
• Is Part Of: Lung cancer. Volume 154(2021)
• Page Start: 1
• Page End: 4
• Publication Date: 2021-04
• Subjects: AI arteficial intelligence -- AUC area-under-the-ROC-curve analysis -- CT computed tomography -- LCP-CNN lung cancer prediction convolutional neural network -- NLST National Lung Screening Trial; Lung cancer -- Screening -- Pulmonary nodule -- Deep learning; Lungs -- Cancer -- Periodicals; Lung Neoplasms -- Abstracts; Lung Neoplasms -- Periodicals; Poumons -- Cancer -- Périodiques; Lungs -- Cancer; Periodicals; Electronic journals; Electronic journals; 616.99424
• DOI: 10.1016/j.lungcan.2021.01.027
• Languages: English
• ISSNs: 0169-5002

Abstract

Highlights: Our LCP-CNN was trained to rule out benign lung nodules with high accuracy. Excellent performance on identification of benign nodules in an external dataset (sensitivity 99 %). Malignancy could be ruled out in about 20 % of patients with 5−15 mm nodules using the LCP-CNN. Abstract: 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. Methods: The 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). Results: The 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. Conclusion: The 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.