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Proceedings Paper

Computer-aided detection of lung cancer: combining pulmonary nodule detection systems with a tumor risk prediction model
Author(s): 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
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Paper 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.

Paper Details

Date Published: 20 March 2015
PDF: 6 pages
Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94141O (20 March 2015); doi: 10.1117/12.2080955
Show Author Affiliations
Arnaud A. A. Setio, Radboud Univ. Nijmegen Medical Ctr. (Netherlands)
Colin Jacobs, Radboud Univ. Nijmegen Medical Ctr. (Netherlands)
Francesco Ciompi, Radboud Univ. Nijmegen Medical Ctr. (Netherlands)
Sarah J. van Riel M.D., Radboud Univ. Nijmegen Medical Ctr. (Netherlands)
Mathilde Marie Winkler Wille, Univ. of Copenhagen (Denmark)
Asger Dirksen, Univ. of Copenhagen (Denmark)
Eva M. van Rikxoort, Radboud Univ. Nijmegen Medical Ctr. (Netherlands)
Bram van Ginneken, Radboud Univ. Nijmegen Medical Ctr. (Netherlands)

Published in SPIE Proceedings Vol. 9414:
Medical Imaging 2015: Computer-Aided Diagnosis
Lubomir M. Hadjiiski; Georgia D. Tourassi, Editor(s)

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