Abstract
Renal cell carcinoma (RCC) is the seventh most common cancer worldwide, accounting for an estimated 140,000 global deaths annually. An important RCC prognostic predictor is its ‘stage’ for which the tumor-node-metastasis (TNM) staging system is used. Although TNM staging is performed by radiologists via pre-surgery volumetric medical image analysis, a recent study suggested that such staging may be performed by studying the image features of the RCC from computed tomography (CT) data. Currently TNM staging mostly relies on laborious manual processes based on visual inspection of 2D CT image slices that are time-consuming and subjective; a recent study reported about \(\sim \)25% misclassification in their patient pools. Recently, we proposed a learnable image histogram based deep neural network approach (ImHistNet) for RCC grading, which is capable of learning textural features directly from the CT images. In this paper, using a similar architecture, we perform the stage low (I/II) and high (III/IV) classification for RCC in CT scans. Validated on a clinical CT dataset of 159 patients from the TCIA database, our method classified RCC low and high stages with about 83% accuracy.
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Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2016. CA: Cancer J. Clin. 66(1), 7–30 (2016)
Ding, J., et al.: CT-based radiomic model predicts high grade of clear cell renal cell carcinoma. Eur. J. Radiol. 103, 51–56 (2018)
Escudier, B., et al.: Renal cell carcinoma: ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Ann. Oncol. 27(suppl 5), v58–v68 (2016)
Janssen, M., et al.: Survival outcomes in patients with large (\(\ge \) 7cm) clear cell renal cell carcinomas treated with nephron-sparing surgery versus radical nephrectomy: results of a multicenter cohort with long-term follow-up. PLoS ONE 13(5), e0196427 (2018)
AAlAbdulsalam, A.K., Garvin, J.H., Redd, A., Carter, M.E., Sweeny, C., Meystre, S.M.: Automated extraction and classification of cancer stage mentions from unstructured text fields in a central cancer registry. AMIA Summits Transl. Sci. Proc. 2018, 16 (2018)
Bradley, A., MacDonald, L., Whiteside, S., Johnson, R., Ramani, V.: Accuracy of preoperative CT T staging of renal cell carcinoma: which features predict advanced stage? Clin. Radiol. 70(8), 822–829 (2015)
Tan, H.J., Norton, E.C., Ye, Z., Hafez, K.S., Gore, J.L., Miller, D.C.: Long-term survival following partial vs radical nephrectomy among older patients with early-stage kidney cancer. JAMA 307(15), 1629–1635 (2012)
Shah, P.H., et al.: Partial nephrectomy is associated with higher risk of relapse compared with radical nephrectomy for clinical stage T1 renal cell carcinoma pathologically up staged to T3a. J. Urol. 198(2), 289–296 (2017)
Hussain, M.A., Hamarneh, G., Garbi, R.: ImHistNet: Learnable image histogram based DNN with application to noninvasive determination of carcinoma grades in CT scans. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 1–8. Springer (2019). https://doi.org/10.1007/978-3-030-32226-7_15
Clark, K., et al.: The cancer imaging archive (TCIA): maintaining and operating a public information repository. J. Digit. Imaging 26(6), 1045–1057 (2013)
Aerts, H.J.: The potential of radiomic-based phenotyping in precision medicine: a review. JAMA Oncol. 2(12), 1636–1642 (2016)
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_53
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_73
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We thank NVIDIA Corporatoin for supporting our research through their GPU Grant Program by donating the GeForce Titan Xp.
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Hussain, M.A., Hamarneh, G., Garbi, R. (2019). Renal Cell Carcinoma Staging with Learnable Image Histogram-Based Deep Neural Network. In: Suk, HI., Liu, M., Yan, P., Lian, C. (eds) Machine Learning in Medical Imaging. MLMI 2019. Lecture Notes in Computer Science(), vol 11861. Springer, Cham. https://doi.org/10.1007/978-3-030-32692-0_61
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DOI: https://doi.org/10.1007/978-3-030-32692-0_61
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