Development and Application in Clinical Routine of Computer Aided Detection (CAD) Algorithms for the Identification of Pulmonary Nodules

Alberto Traverso


Lung Cancer is one of the main public health issues in developed countries, accounting for about 19% and 28% of cancer-related deaths in Europe and the United States of America, respectively, with a five-year survival rate of only 10-16%. Computed Tomography (CT) has been shown to be the most sensitive imaging modality for the detection of small pulmonary nodules. The identification of early stage pathological Regions of Interests (ROIs) in low dose high resolution CT scans is a very difficult and time consuming task for radiologists, because of the large number (300/500) of noisy 2D slices to be analyzed. In order to support radiologists, researchers have started developing Computer Aided Detection (CAD) algorithms to be applied to CT scans. Several studies reported an improvement in the sensitivity of radiologists when assisted by CAD systems. So far, the most common way to make CAD algorithms available in the clinical routine of health facilities is thedeployment of standalone workstations, usually equipped with a vendor-dependent Graphic User Interface (GUI). This approach presents several drawbacks, such as the high fixed cost of the software licenses, hardware and the obsolescence of both. In this paper we present a PHD project aiming at: developing and improving CADs for automatic detection of pulmonary nodules and their features, spreading the usage of CADs inside clinical practice, investigating the impact of CAD algorithms on the performance of radiodioligsts when assisted by CADs during clinical practice. In relation to the first goal we present some of the recent challenges related to the improvement of CADs, like for example the possiblity to predict the maglignancy of a nodule starting from some computed feature of the nodule itself. In the second part we analyze the big issues that have been limitating the diffusion of CADs in clinical practice and we propose a possible solution to tackle them. In the third part we propose some observer studies in order, not only to investigate the impact of CAD on the performance of radioligsts, but also to determine the most to insert CADs in clinical practice.


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Paper Citation

in Harvard Style

Traverso A. (2016). Development and Application in Clinical Routine of Computer Aided Detection (CAD) Algorithms for the Identification of Pulmonary Nodules . In Doctoral Consortium - DCBIOSTEC, ISBN , pages 24-34

in Bibtex Style

author={Alberto Traverso},
title={Development and Application in Clinical Routine of Computer Aided Detection (CAD) Algorithms for the Identification of Pulmonary Nodules},
booktitle={Doctoral Consortium - DCBIOSTEC,},

in EndNote Style

JO - Doctoral Consortium - DCBIOSTEC,
TI - Development and Application in Clinical Routine of Computer Aided Detection (CAD) Algorithms for the Identification of Pulmonary Nodules
SN -
AU - Traverso A.
PY - 2016
SP - 24
EP - 34
DO -