Analysis of the Vancouver lung nodule malignancy model with respect to manual and automated segmentation

Rafael Wiemker; Lilla Boroczky; Martin Bergtholdt; Tobias Klinder

doi:10.1117/12.2081917

20 March 2015 Analysis of the Vancouver lung nodule malignancy model with respect to manual and automated segmentation

Rafael Wiemker, Lilla Boroczky, Martin Bergtholdt, Tobias Klinder

Author Affiliations +

Proceedings Volume 9414, Medical Imaging 2015: Computer-Aided Diagnosis; 94140B (2015) https://doi.org/10.1117/12.2081917
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

The recently published Vancouver model for lung nodule malignancy prediction holds great promise as a practically feasible tool to mitigate the clinical decision problem of how to act on a lung nodule detected at baseline screening. It provides a formula to compute a probability of malignancy from only nine clinical and radiologic features. The feature values are provided by user interaction but in principle could also be automatically pre-filled by appropriate image processing algorithms and RIS requests. Nodule diameter is a feature with crucial influence on the predicted malignancy, and leads to uncertainty caused by inter-reader variability. The purpose of this paper is to analyze how strongly the malignancy prediction of a lung nodule found with CT screening is affected by the inter-reader variation of the nodule diameter estimation. To this aim we have estimated the magnitude of the malignancy variability by applying the Vancouver malignancy model to the LIDC-IDRI database which contains independent delineations from several readers. It can be shown that using fully automatic nodule segmentation can significantly lower the variability of the estimated malignancy, while demonstrating excellent agreement with the expert readers.

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Rafael Wiemker, Lilla Boroczky, Martin Bergtholdt, and Tobias Klinder "Analysis of the Vancouver lung nodule malignancy model with respect to manual and automated segmentation", Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94140B (20 March 2015); https://doi.org/10.1117/12.2081917

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KEYWORDS

Lung

Databases

Data modeling

Image processing

Computed tomography

3D modeling

Lung cancer

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