Wilms’ Tumor in Childhood: Can Pattern Recognition Help for Classification?
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Wilms’ tumor or nephroblastoma is a kidney tumor and the most common renal malignancy in childhood. Clinicians assume that these tumors develop from embryonic renal precursor cells - sometimes via nephrogenic rests or nephroblastomatosis. In Europe, chemotherapy is carried out prior to surgery, which downstages the tumor. This results in various pathological subtypes with differences in their prognosis and treatment.
First, we demonstrate that the classical distinction between nephroblastoma and its precursor lesion is error prone with an accuracy of 0.824. We tackle this issue with appropriate texture features and improve the classification accuracy to 0.932.
Second, we are the first to predict the development of nephroblastoma under chemotherapy. We use a bag of visual model and show that visual clues are present that help to approximate the developing subtype.
Last but not least, we provide our data set of 54 kidneys with nephroblastomatosis in conjunction with 148 Wilms’ tumors.
J. Weickert has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 741215, ERC Advanced Grant INCOVID).
- 9.Hötker, A.M., et al.: Diffusion-weighted MRI in the assessment of nephroblastoma: results of a multi-center trial (2018, Submitted)Google Scholar
- 16.Pastore, G., Znaor, A., Spreafico, F., Graf, N., Pritchard-Jones, K., Steliarova-Foucher, E.: Malignant renal tumours incidence and survival in European children (1978–1997): report from the Automated Childhood Cancer Information System project. Eur. J. Cancer 42(13), 2103–2114 (2006)CrossRefGoogle Scholar
- 17.Reinhard, H., et al.: Outcome of relapses of nephroblastoma in patients registered in the SIOP/GPOH trials and studies. Oncol. Rep. 20(2), 463–467 (2008)Google Scholar
- 20.Soomro, M.H., et al.: Haralick’s texture analysis applied to colorectal T2-weighted MRI: a preliminary study of significance for cancer evolution. In: Proceedings of 13th International Conference on Biomedical Engineering, pp. 16–19. IEEE (2017)Google Scholar
- 24.Zayed, N., Elnemr, H.A.: Statistical analysis of Haralick texture features to discriminate lung abnormalities. J. Biomed. Imaging 2015, 12 (2015)Google Scholar