Short summary

The overall objective of this study is to develop, train, and validate an artificially intelligent algorithm capable of detecting potentially malignant pulmonary findings on LDCT in the above mentioned screening population, capable of aiding clinicians in triage. In order to achieve this overall objective the study will be divided into three consecutive substudies, which will be described in detail in the following.

Rationale

Lung cancer is a significant global health concern and the leading cause of cancer-related mortality worldwide. The majority of lung cancer cases are diagnosed in more advanced stages, highlighting the urgent need for improved detection methods to enhance survival rates. For this reason the integration of artificial intelligence (AI) or deep learning (DL) as an adjunct diagnostic tool in future lung cancer screening is believed to be of significant importance to overcome the practical and economic challenges.

Description of the cohort

Former lung cancer patients, patients affiliated with the department of pulmonary medicine OUH, DANCAVAS study population.

Data and biological material

Thoracic CT-scans

Collaborating researchers and departments

Department of Radiology, Odense Universitets Hospital

• Benjamin Rasmussen

Department of Pulmonary Medicine Odense Universitets Hospital

• Christian Borbjerg Laursen

Center Of Artificial Intelligence - CAI-X

SDU Robotics, Mærsk Mc-Kinney Møller Instituttet

• Rajeeth Savarimuthu