P180. Lung Cancer Screening: Comparison of Established and Novel Models for Predicting Malignancy in Pulmonary Nodules
University Medical Center Essen - Ruhrlandklinik
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Dr. Doerr is a consultant for thoracic surgery at the ‘Ruhrlandklinik‘, which is affiliated to the West German Lung Centre at the Essen University Hospital. Since taking up this position in January 2023, he has been responsible for research and teaching in his department.
He completed his training in thoracic surgery at the Department for Cardiothoracic Surgery at Cologne University Hospital in February 2022. During his training, he accomplished a fellowship at the ‘Reinhard Lab’ studying new therapeutic options for SCLC and won the 2018 research award of the German Thoracic Society.
Dr. Doerr was a member of the EACTS Residents Committee from 2021 to 2024. During his term, he served as resident’s representative in the EACTS Thoracic Domain and focused on achieving synergy effects for young thoracic surgeons, especially in educational aspects. Currently, Dr. Doerr is a member of the EACTS Surgical Oncology Task Force. His term ends in October 2027.
In addition to minimally invasive oncological thoracic surgery, his clinical work focuses on the establishment of new perioperative immunotherapies for NSCLC. Furthermore, he investigates new prognostic factors for NSCLC and develops a system to predict pulmonary malignancy.
Description
Today, lung cancer screening programs for patients with a risk profile detect a large number of incidental lung lesions. The correct classification of these nodules regarding their malignancy is paramount. For incidental findings, the Brock model is commonly used today to calculate the probability of malignancy. In this study, we evaluated the predictive accuracy of the Brock model in comparison to the LIONS PREY model. Both models consider patient and nodule related parameters. In contrast to the Brock model, the LIONS PREY model takes into account prognosis-relevant risk factors such as the size progression of a nodule and tobacco consumption (see below).
Methods:
We retrospectively included all patients who underwent resection of a pulmonary lesion at our lung cancer center between August 2022 and August 2024. Patients whose clinical parameters were not fully available for calculating one of the two score systems were excluded from the study. Two study groups were formed (group M: malignant lesions; group B: benign lesions). We evaluated the predictive accuracy of the Brock model and LIONS PREY model based on calibration (observed/expected ratio), discrimination (area under ROC curve), and the total number of correctly classified patients (OCC).
Results:
During the treatment period, 776 patients were resected at our center. In this real-world setting, 424 patients (54.6%) were excluded due to unrecorded parameters. Finally, 304 patients in group M and 48 in group B were analyzed. With an OCC of 95.4%, the LIONS PREY model showed a significantly (p=0.0027) higher precision than the Brock model at 64.1%. The calibration of the LIONS PREY model (O/E ratio: 1.1) was significantly (p=0.031) superior to that of the Brock model (1.4). According to a DeLong analysis, the discriminatory power of the LIONS PREY model (AUC: 0.92; 95% CI: 0.89-0.95) was significantly (p=0.008) better than that of the Brock model (AUC: 0.62; 95% CI: 0.56-0.68).
Conclusions:
The success of a lung cancer screening program is directly linked to a reliable model to predict malignancy. Regarding predictive accuracy, the new LIONS PREY model appears superior to the established Brock model.
Authors
Fabian Doerr (1), Konstantinos Grapatsas (1), Natalie Baldes (1), Filiz Oezkan (2), Michael Forsting (3), Dirk Theegarten (4), Kaid Darwiche (2), Hubertus Hautzel (5), Martin Stuschke (6), Christian Taube (2), Marcel Wiesweg (7), Martin Schuler (7), Servet Bölükbas (1)
Institutions
(1) University Medical Center Essen, Ruhrlandklinik, Department of Thoracic Surgery, Essen, Germany, (2) University Medical Center Essen, Ruhrlandklinik, Department of Pulmonary Medicine, Essen, Germany, (3) University Medical Center Essen, Institute for Diagnostic and Interventional Radiology, Essen, Germany, (4) University Medical Center Essen, Institute of Pathology, Essen, Germany, (5) University Medical Center Essen, Department of Nuclear Medicine, Essen, Germany, (6) University Medical Center Essen, Department of Radiation Therapy, Essen, Germany, (7) University Medical Center Essen, Department of Medical Oncology, Essen, Germany