- -

Going deeper through the Gleason scoring scale: An automatic end-to-end system for histology prostate grading and cribriform pattern detection

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

  • Estadisticas de Uso

Going deeper through the Gleason scoring scale: An automatic end-to-end system for histology prostate grading and cribriform pattern detection

Show full item record

Silva-Rodríguez, J.; Colomer, A.; Sales, MA.; Molina, R.; Naranjo Ornedo, V. (2020). Going deeper through the Gleason scoring scale: An automatic end-to-end system for histology prostate grading and cribriform pattern detection. Computer Methods and Programs in Biomedicine. 195:1-18. https://doi.org/10.1016/j.cmpb.2020.105637

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/166264

Files in this item

Item Metadata

Title: Going deeper through the Gleason scoring scale: An automatic end-to-end system for histology prostate grading and cribriform pattern detection
Author: Silva-Rodríguez, Julio Colomer, Adrián Sales, María A. Molina, Rafael Naranjo Ornedo, Valeriana
UPV Unit: Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions
Universitat Politècnica de València. Instituto del Transporte y Territorio - Institut del Transport i Territori
Issued date:
Abstract:
[EN] Background and Objective: Prostate cancer is one of the most common diseases affecting men worldwide. The Gleason scoring system is the primary diagnostic and prognostic tool for prostate cancer. Further-more, recent ...[+]
Subjects: Prostate cancer , Gleason , Cribriform , Whole side images , Convolutional neural networks , Deep learning
Copyrigths: Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
Source:
Computer Methods and Programs in Biomedicine. (issn: 0169-2607 )
DOI: 10.1016/j.cmpb.2020.105637
Publisher:
Elsevier
Publisher version: https://doi.org/10.1016/j.cmpb.2020.105637
Project ID:
info:eu-repo/grantAgreement/MINECO//DPI2016-77869-C2-1-R/ES/SISTEMA DE INTERPRETACION DE IMAGENES HISTOPATOLOGICAS PARA LA DETECCION DE CANCER DE PROSTATA/
Thanks:
This work was supported by the Spanish Ministry of Economy and Competitiveness through project DPI2016-77869. The Titan V used for this research was donated by the NVIDIA Corporation.
Type: Artículo

References

Gordetsky, J., & Epstein, J. (2016). Grading of prostatic adenocarcinoma: current state and prognostic implications. Diagnostic Pathology, 11(1). doi:10.1186/s13000-016-0478-2

Epstein, J. I., Egevad, L., Amin, M. B., Delahunt, B., Srigley, J. R., & Humphrey, P. A. (2016). The 2014 International Society of Urological Pathology (ISUP) Consensus Conference on Gleason Grading of Prostatic Carcinoma. American Journal of Surgical Pathology, 40(2), 244-252. doi:10.1097/pas.0000000000000530

Sharma, M., & Miyamoto, H. (2018). Percent Gleason pattern 4 in stratifying the prognosis of patients with intermediate-risk prostate cancer. Translational Andrology and Urology, 7(S4), S484-S489. doi:10.21037/tau.2018.03.20 [+]
Gordetsky, J., & Epstein, J. (2016). Grading of prostatic adenocarcinoma: current state and prognostic implications. Diagnostic Pathology, 11(1). doi:10.1186/s13000-016-0478-2

Epstein, J. I., Egevad, L., Amin, M. B., Delahunt, B., Srigley, J. R., & Humphrey, P. A. (2016). The 2014 International Society of Urological Pathology (ISUP) Consensus Conference on Gleason Grading of Prostatic Carcinoma. American Journal of Surgical Pathology, 40(2), 244-252. doi:10.1097/pas.0000000000000530

Sharma, M., & Miyamoto, H. (2018). Percent Gleason pattern 4 in stratifying the prognosis of patients with intermediate-risk prostate cancer. Translational Andrology and Urology, 7(S4), S484-S489. doi:10.21037/tau.2018.03.20

Kweldam, C. F., van der Kwast, T., & van Leenders, G. J. (2018). On cribriform prostate cancer. Translational Andrology and Urology, 7(1), 145-154. doi:10.21037/tau.2017.12.33

Remotti, H. (2012). Tissue Microarrays: Construction and Use. Pancreatic Cancer, 13-28. doi:10.1007/978-1-62703-287-2_2

KHOUJA, M. H., BAEKELANDT, M., SARAB, A., NESLAND, J. M., & HOLM, R. (2010). Limitations of tissue microarrays compared with whole tissue sections in survival analysis. Oncology Letters, 1(5), 827-831. doi:10.3892/ol_00000145

Gertych, A., Ing, N., Ma, Z., Fuchs, T. J., Salman, S., Mohanty, S., … Knudsen, B. S. (2015). Machine learning approaches to analyze histological images of tissues from radical prostatectomies. Computerized Medical Imaging and Graphics, 46, 197-208. doi:10.1016/j.compmedimag.2015.08.002

Ren, J., Sadimin, E., Foran, D. J., & Qi, X. (2017). Computer aided analysis of prostate histopathology images to support a refined Gleason grading system. Medical Imaging 2017: Image Processing. doi:10.1117/12.2253887

Esteban, Á. E., López-Pérez, M., Colomer, A., Sales, M. A., Molina, R., & Naranjo, V. (2019). A new optical density granulometry-based descriptor for the classification of prostate histological images using shallow and deep Gaussian processes. Computer Methods and Programs in Biomedicine, 178, 303-317. doi:10.1016/j.cmpb.2019.07.003

Lucas, M., Jansen, I., Savci-Heijink, C. D., Meijer, S. L., de Boer, O. J., van Leeuwen, T. G., … Marquering, H. A. (2019). Deep learning for automatic Gleason pattern classification for grade group determination of prostate biopsies. Virchows Archiv, 475(1), 77-83. doi:10.1007/s00428-019-02577-x

Arvaniti, E., Fricker, K. S., Moret, M., Rupp, N., Hermanns, T., Fankhauser, C., … Claassen, M. (2018). Automated Gleason grading of prostate cancer tissue microarrays via deep learning. Scientific Reports, 8(1). doi:10.1038/s41598-018-30535-1

G. Nir, S. Hor, D. Karimi, L. Fazli, B.F. Skinnider, P. Tavassoli, D. Turbin, C.F. Villamil, G. Wang, R.S. Wilson, K.A. Iczkowski, M.S. Lucia, P.C. Black, P. Abolmaesumi, S.L. Goldenberg, S.E. Salcudean, Automatic grading of prostate cancer in digitized histopathology images: Learning from multiple experts, 2018. 10.1016/j.media.2018.09.005

Nir, G., Karimi, D., Goldenberg, S. L., Fazli, L., Skinnider, B. F., Tavassoli, P., … Salcudean, S. E. (2019). Comparison of Artificial Intelligence Techniques to Evaluate Performance of a Classifier for Automatic Grading of Prostate Cancer From Digitized Histopathologic Images. JAMA Network Open, 2(3), e190442. doi:10.1001/jamanetworkopen.2019.0442

García, G., Colomer, A., & Naranjo, V. (2019). First-Stage Prostate Cancer Identification on Histopathological Images: Hand-Driven versus Automatic Learning. Entropy, 21(4), 356. doi:10.3390/e21040356

Ma, Y., Jiang, Z., Zhang, H., Xie, F., Zheng, Y., Shi, H., … Shi, J. (2018). Generating region proposals for histopathological whole slide image retrieval. Computer Methods and Programs in Biomedicine, 159, 1-10. doi:10.1016/j.cmpb.2018.02.020

Li, W., Li, J., Sarma, K. V., Ho, K. C., Shen, S., Knudsen, B. S., … Arnold, C. W. (2019). Path R-CNN for Prostate Cancer Diagnosis and Gleason Grading of Histological Images. IEEE Transactions on Medical Imaging, 38(4), 945-954. doi:10.1109/tmi.2018.2875868

Openseadragon, (http://openseadragon.github.io/), Accessed: 10-07-2018.

Cohen, J. (1968). Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit. Psychological Bulletin, 70(4), 213-220. doi:10.1037/h0026256

Swets, J. A. (1988). Measuring the Accuracy of Diagnostic Systems. Science, 240(4857), 1285-1293. doi:10.1126/science.3287615

Kweldam, C. F., Nieboer, D., Algaba, F., Amin, M. B., Berney, D. M., Billis, A., … van Leenders, G. J. L. H. (2016). Gleason grade 4 prostate adenocarcinoma patterns: an interobserver agreement study among genitourinary pathologists. Histopathology, 69(3), 441-449. doi:10.1111/his.12976

[-]

recommendations

 

This item appears in the following Collection(s)

Show full item record