LNDb: A Lung Nodule Database on Computed Tomography
This work addresses the need for better databases to improve lung cancer screening tools, but it is incremental as it builds on existing databases by adding specific clinical focus.
The authors tackled the challenge of developing automatic methods for lung nodule analysis by creating LNDb, a new database that focuses on radiologist variability and local clinical reality, and found that state-of-the-art methods can match radiologist accuracy in follow-up recommendations but show decreased performance in nodule detection.
Lung cancer is the deadliest type of cancer worldwide and late detection is the major factor for the low survival rate of patients. Low dose computed tomography has been suggested as a potential screening tool but manual screening is costly, time-consuming and prone to variability. This has fueled the development of automatic methods for the detection, segmentation and characterisation of pulmonary nodules but its application to clinical routine is challenging. In this study, a new database for the development and testing of pulmonary nodule computer-aided strategies is presented which intends to complement current databases by giving additional focus to radiologist variability and local clinical reality. State-of-the-art nodule detection, segmentation and characterization methods are tested and compared to manual annotations as well as collaborative strategies combining multiple radiologists and radiologists and computer-aided systems. It is shown that state-of-the-art methodologies can determine a patient's follow-up recommendation as accurately as a radiologist, though the nodule detection method used shows decreased performance in this database.