Computer-aided detection of lung cancer: combining pulmonary nodule detection systems with a tumor risk prediction model

Arnaud A. A. Setio; Colin Jacobs; Francesco Ciompi; Sarah J. van Riel M.D.; Mathilde Marie Winkler Wille; Asger Dirksen; Eva M. van Rikxoort; Bram van Ginneken

doi:10.1117/12.2080955

20 March 2015 Computer-aided detection of lung cancer: combining pulmonary nodule detection systems with a tumor risk prediction model

Arnaud A. A. Setio, Colin Jacobs, Francesco Ciompi, Sarah J. van Riel M.D., Mathilde Marie Winkler Wille, Asger Dirksen, Eva M. van Rikxoort, Bram van Ginneken

Author Affiliations +

Proceedings Volume 9414, Medical Imaging 2015: Computer-Aided Diagnosis; 94141O (2015) https://doi.org/10.1117/12.2080955
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

Computer-Aided Detection (CAD) has been shown to be a promising tool for automatic detection of pulmonary nodules from computed tomography (CT) images. However, the vast majority of detected nodules are benign and do not require any treatment. For effective implementation of lung cancer screening programs, accurate identification of malignant nodules is the key. We investigate strategies to improve the performance of a CAD system in detecting nodules with a high probability of being cancers. Two strategies were proposed: (1) combining CAD detections with a recently published lung cancer risk prediction model and (2) the combination of multiple CAD systems. First, CAD systems were used to detect the nodules. Each CAD system produces markers with a certain degree of suspicion. Next, the malignancy probability was automatically computed for each marker, given nodule characteristics measured by the CAD system. Last, CAD degree of suspicion and malignancy probability were combined using the product rule. We evaluated the method using 62 nodules which were proven to be malignant cancers, from 180 scans of the Danish Lung Cancer Screening Trial. The malignant nodules were considered as positive samples, while all other findings were considered negative. Using a product rule, the best proposed system achieved an improvement in sensitivity, compared to the best individual CAD system, from 41.9% to 72.6% at 2 false positives (FPs)/scan and from 56.5% to 88.7% at 8 FPs/scan. Our experiment shows that combining a nodule malignancy probability with multiple CAD systems can increase the performance of computerized detection of lung cancer.

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Arnaud A. A. Setio, Colin Jacobs, Francesco Ciompi, Sarah J. van Riel M.D., Mathilde Marie Winkler Wille, Asger Dirksen, Eva M. van Rikxoort, and Bram van Ginneken "Computer-aided detection of lung cancer: combining pulmonary nodule detection systems with a tumor risk prediction model", Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94141O (20 March 2015); https://doi.org/10.1117/12.2080955

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