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An evaluation of image descriptors combined with clinical data for breast cancer diagnosis

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose   

Breast cancer computer-aided diagnosis (CADx) may utilize image descriptors, demographics, clinical observations, or a combination. CADx performance was compared for several image features, clinical descriptors (e.g. age and radiologist’s observations), and combinations of both kinds of data. A novel descriptor invariant to rotation, histograms of gradient divergence (HGD), was developed to deal with round-shaped objects, such as masses. HGD was compared with conventional CADx features.

Method

HGD and 11 conventional image descriptors were evaluated using cases from two publicly available mammography data sets, the digital database for screening mammography (DDSM) and the breast cancer digital repository (BCDR), with 1,762 and 362 instances, respectively. Three experiments were done for each data set according to the type of lesion (i.e., all lesions, masses, and calcifications), resulting in six scenarios. For each scenario, 100 training and test sets were generated via resampling without replacement and five machine learning classifiers were used to assess the diagnostic performance of the descriptors.

Results

Clinical descriptors outperformed image descriptors in the DDSM sample (three out of six scenarios), and combining the two kind of descriptors was advantageous in five out of six scenarios. HGD was the best descriptor (or comparable to best) in 8 out of 12 scenarios, demonstrating promising capabilities to describe masses.

Conclusions

The combination of clinical data and image descriptors was advantageous in most mammography CADx scenarios. A new descriptor based on the divergence of the gradient (HGD) was demonstrated to be a feasible predictor of breast masses’ diagnosis.

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Notes

  1. BCDR-F01 from BCDR is now available for download at http://bcdr.inegi.up.pt.

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Acknowledgments

The authors would like expressing their gratitude to the Department of Radiology at Hospital São João Porto, Portugal, who provided the data and assisted in the validation of the data sets used in this research. Prof. Guevara acknowledges POPH—QREN—Tipologia 4.2—Promotion of scientific employment funded by the ESF and MCTES, Portugal. Finally, the authors acknowledge TM Deserno, Dept. of Medical Informatics, RWTH Aachen, Germany, for providing the PNG images of the DDSM database.

Conflict of Interest

The authors declare that they have no conflict of interest. Ethical standards All experiments were performed with public data from previous studies, and therefore, no ethical violations may result from the experiments reported here.

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Authors and Affiliations

  1. Instituto de Engenharia Mecânica e Gestão Industrial, Universidade do Porto, Rua Dr Roberto Frias, 400, 4200-465 , Porto, Portugal

    Daniel C. Moura & Miguel A. Guevara López

  2. Faculdade Engenharia, Departamento de Engenharia Informática, Universidade do Porto, Porto, Portugal

    Daniel C. Moura

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  1. Daniel C. Moura
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Correspondence to Daniel C. Moura.

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Moura, D.C., Guevara López, M.A. An evaluation of image descriptors combined with clinical data for breast cancer diagnosis. Int J CARS 8, 561–574 (2013). https://doi.org/10.1007/s11548-013-0838-2

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  • DOI: https://doi.org/10.1007/s11548-013-0838-2

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