LIONS PREY: A New Logistic Scoring System for the Prediction of Malignant Pulmonary Nodules

Author:

Doerr Fabian1ORCID,Giese Annika2,Höpker Katja3,Menghesha Hruy45,Schlachtenberger Georg6ORCID,Grapatsas Konstantinos1,Baldes Natalie1,Baldus Christian J.7,Hagmeyer Lars8,Fallouh Hazem9,Pinto dos Santos Daniel1011ORCID,Bender Edward M.12,Quaas Alexander13ORCID,Heldwein Matthias5,Wahlers Thorsten5,Hautzel Hubertus14,Darwiche Kaid15ORCID,Taube Christian16,Schuler Martin1617ORCID,Hekmat Khosro5,Bölükbas Servet1

Affiliation:

1. Department of Thoracic Surgery, West German Cancer Center, University Medical Center Essen-Ruhrlandklinik, University Duisburg-Essen, 45239 Essen, Germany

2. Department of Anesthesiology and Intensive Care Medicine, Vinzenz Pallotti Hospital Bergisch Gladbach-Bensberg, GFO-Clinics Rhein-Berg, 51429 Bergisch Gladbach, Germany

3. Clinic III for Internal Medicine, Faculty of Medicine, University Hospital Cologne, University of Cologne, 50923 Cologne, Germany

4. Department of Thoracic Surgery, Helios Clinic Bonn/Rhein-Sieg, 53123 Bonn, Germany

5. Division of Thoracic Surgery, Department of General, Thoracic and Vascular Surgery, Bonn University Hospital, 53127 Bonn, Germany

6. Department of Cardiothoracic Surgery, University Hospital of Cologne, University of Cologne, 50923 Cologne, Germany

7. Institute for Diagnostic and Interventional Radiology, University Hospital Dresden, 01307 Dresden, Germany

8. Clinic for Pneumology and Allergology, Bethanien Hospital GmbH Solingen, 42699 Solingen, Germany

9. Department of Cardiothoracic Surgery, University Hospital of Birmingham, Birmingham B15 2GW, UK

10. Department of Radiology, University Hospital Cologne, 50937 Cologne, Germany

11. Department of Radiology, Hospital of the Goethe University Frankfurt, 60590 Frankfurt am Main, Germany

12. Department of Cardiothoracic Surgery, Stanford University, Palo Alto, CA 94304, USA

13. Institute of Pathology, University of Cologne, 50923 Cologne, Germany

14. Department of Nuclear Medicine, West German Cancer Center, University Hospital Essen, University Duisburg-Essen, 45239 Essen, Germany

15. Department of Pneumology, West German Cancer Center, University Medical Center Essen-Ruhrlandklinik, University Duisburg-Essen, 45239 Essen, Germany

16. Department of Medical Oncology, West German Cancer Center, University Hospital Essen, University Duisburg-Essen, 45239 Essen, Germany

17. National Center for Tumor Diseases (NCT) West, Campus Essen, 45147 Essen, Germany

Abstract

Objectives: Classifying radiologic pulmonary lesions as malignant is challenging. Scoring systems like the Mayo model lack precision in predicting the probability of malignancy. We developed the logistic scoring system ‘LIONS PREY’ (Lung lesION Score PREdicts malignancY), which is superior to existing models in its precision in determining the likelihood of malignancy. Methods: We evaluated all patients that were presented to our multidisciplinary team between January 2013 and December 2020. Availability of pathological results after resection or CT-/EBUS-guided sampling was mandatory for study inclusion. Two groups were formed: Group A (malignant nodule; n = 238) and Group B (benign nodule; n = 148). Initially, 22 potential score parameters were derived from the patients’ medical histories. Results: After uni- and multivariate analysis, we identified the following eight parameters that were integrated into a scoring system: (1) age (Group A: 64.5 ± 10.2 years vs. Group B: 61.6 ± 13.8 years; multivariate p-value: 0.054); (2) nodule size (21.8 ± 7.5 mm vs. 18.3 ± 7.9 mm; p = 0.051); (3) spiculation (73.1% vs. 41.9%; p = 0.024); (4) solidity (84.9% vs. 62.8%; p = 0.004); (5) size dynamics (6.4 ± 7.7 mm/3 months vs. 0.2 ± 0.9 mm/3 months; p < 0.0001); (6) smoking history (92.0% vs. 43.9%; p < 0.0001); (7) pack years (35.1 ± 19.1 vs. 21.3 ± 18.8; p = 0.079); and (8) cancer history (34.9% vs. 24.3%; p = 0.052). Our model demonstrated superior precision to that of the Mayo score (p = 0.013) with an overall correct classification of 96.0%, a calibration (observed/expected-ratio) of 1.1, and a discrimination (ROC analysis) of AUC (95% CI) 0.94 (0.92–0.97). Conclusions: Focusing on essential parameters, LIONS PREY can be easily and reproducibly applied based on computed tomography (CT) scans. Multidisciplinary team members could use it to facilitate decision making. Patients may find it easier to consent to surgery knowing the likelihood of pulmonary malignancy. The LIONS PREY app is available for free on Android and iOS devices.

Publisher

MDPI AG

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