Advertisement

ImHistNet: Learnable Image Histogram Based DNN with Application to Noninvasive Determination of Carcinoma Grades in CT Scans

  • Mohammad Arafat HussainEmail author
  • Ghassan Hamarneh
  • Rafeef Garbi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11769)

Abstract

Renal cell carcinoma (RCC) is the seventh most common cancer worldwide, accounting for an estimated 140,000 global deaths annually. Clear cell RCC (ccRCC) is the major subtype of RCC and its biological aggressiveness affects prognosis and treatment planning. An important ccRCC prognostic predictor is its ‘grade’ for which the 4-tiered Fuhrman grading system is used. Although the Fuhrman grade can be identified by percutaneous renal biopsy, recent studies suggested that such grades may be non-invasively identified by studying image texture features of the ccRCC from computed tomography (CT) data. Such image feature based identification currently mostly relies on laborious manual processes based on visual inspection of 2D image slices that are time-consuming and subjective. In this paper, we propose a learnable image histogram based deep neural network approach that can perform the Fuhrman low (I/II) and high (III/IV) grade classification for ccRCC in CT scans. Validated on a clinical CT dataset of 159 patients from the TCIA database, our method classified ccRCC low and high grades with 80% accuracy and 85% AUC.

Notes

Acknowledgement

We thank NVIDIA Corporation for supporting our research through their GPU Grant Program by donating the GeForce Titan Xp.

References

  1. 1.
    Ding, J., et al.: CT-based radiomic model predicts high grade of clear cell renal cell carcinoma. Eur. J. Radiol. 103, 51–56 (2018)CrossRefGoogle Scholar
  2. 2.
    Shu, J., et al.: Clear cell renal cell carcinoma: CT-based radiomics features for the prediction of fuhrman grade. Eur. J. Radiol. 109, 8–12 (2018)CrossRefGoogle Scholar
  3. 3.
    Ishigami, K., Leite, L.V., Pakalniskis, M.G., Lee, D.K., Holanda, D.G., Kuehn, D.M.: Tumor grade of clear cell renal cell carcinoma assessed by contrast-enhanced computed tomography. SpringerPlus 3(1), 694 (2014)CrossRefGoogle Scholar
  4. 4.
    Fuhrman, S.A., Lasky, L.C., Limas, C.: Prognostic significance of morphologic parameters in renal cell carcinoma. Am. J. Surg. Pathol. 6(7), 655–663 (1982)CrossRefGoogle Scholar
  5. 5.
    Becker, A., et al.: Critical analysis of a simplified fuhrman grading scheme for prediction of cancer specific mortality in patients with clear cell renal cell carcinoma-impact on prognosis. Eur. J. Surg. Oncol. (EJSO) 42(3), 419–425 (2016)CrossRefGoogle Scholar
  6. 6.
    Oh, S., et al.: Correlation of ct imaging features and tumor size with fuhrman grade of clear cell renal cell carcinoma. Acta Radiologica 58(3), 376–384 (2017)CrossRefGoogle Scholar
  7. 7.
    Sasaguri, K., Takahashi, N.: CT and MR imaging for solid renal mass characterization. Eur. J. Radiol. 99, 40–54 (2018)CrossRefGoogle Scholar
  8. 8.
    Huhdanpaa, H., et al.: Ct prediction of the fuhrman grade of clear cell renal cell carcinoma (RCC): towards the development of computer-assisted diagnostic method. Abdom. Imaging 40(8), 3168–3174 (2015)CrossRefGoogle Scholar
  9. 9.
    Hussain, M.A., Hamarneh, G., Garbi, R.: Noninvasive determination of gene mutations in clear cell renal cell carcinoma using multiple instance decisions aggregated CNN. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 657–665. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-00934-2_73CrossRefGoogle Scholar
  10. 10.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)CrossRefGoogle Scholar
  11. 11.
    Andrearczyk, V., Whelan, P.F.: Using filter banks in convolutional neural networks for texture classification. Pattern Recognit. Lett. 84, 63–69 (2016)CrossRefGoogle Scholar
  12. 12.
    Wang, Z., Li, H., Ouyang, W., Wang, X.: Learnable histogram: statistical context features for deep neural networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 246–262. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46448-0_15CrossRefGoogle Scholar
  13. 13.
    Clark, K., et al.: The cancer imaging archive (TCIA): maintaining and operating a public information repository. J. Digit. Imaging 26(6), 1045–1057 (2013)CrossRefGoogle Scholar
  14. 14.
    Aerts, H.J.: The potential of radiomic-based phenotyping in precision medicine: a review. JAMA Oncol. 2(12), 1636–1642 (2016)CrossRefGoogle Scholar
  15. 15.
    Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-10590-1_53CrossRefGoogle Scholar
  16. 16.
    Meng, F., Li, X., Zhou, G., Wang, Y.: Fuhrman grade classification of clear-cell renal cell carcinoma using computed tomography image analysis. J. Med. Imaging Health Inform. 7(7), 1671–1676 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mohammad Arafat Hussain
    • 1
    Email author
  • Ghassan Hamarneh
    • 2
  • Rafeef Garbi
    • 1
  1. 1.BiSICLUniversity of British ColumbiaVancouverCanada
  2. 2.Medical Image Analysis LabSimon Fraser UniversityBurnabyCanada

Personalised recommendations