Aprendizado profundo para assistência histopatológica: um estudo de classificação de micrometástases em câncer de mama
Resumo
Algoritmos de Deep Learning para detecção de micrometástases em câncer de mama podem melhorar os padrões-ouro e a eficiência das rotinas dos patologistas. Uma análise da literatura indica que os modelos ainda precisam melhorar seu desempenho na identificação de micrometástases e células tumorais. No caso de células tumorais isoladas, as taxas de detecção ficaram abaixo de 40%. Também há oportunidades para melhorar as taxas de falsos positivos, pois muitos modelos ainda detectam nervos ou contaminações como falsas micrometástases. Neste estudo, investigamos a implementação de uma rede neural para a classificação de metástases. O objetivo é que esta rede neural de classificação seja a primeira camada de um modelo final composto por duas redes neurais - classificação e segmentação. Como resultados parciais, este trabalho obteve uma AUC = 0,998 para a tarefa de classificação nos fragmentos da lâmina.
Referências
Bandi, P., Geessink, O., Manson, Q., Van Dijk, M., Balkenhol, M., Hermsen, M., Bejnordi, B. E., Lee, B., Paeng, K., Zhong, A., et al. (2018). From detection of individual metastases to classification of lymph node status at the patient level: the camelyon17 challenge. IEEE transactions on medical imaging, 38(2):550–560.
Cardoso, F., Kyriakides, S., Ohno, S., Penault-Llorca, F., Poortmans, P., Rubio, I., Zackrisson, S., and Senkus, E. (2019). Early breast cancer: Esmo clinical practice guidelines for diagnosis, treatment and follow-up. Annals of oncology, 30(8):1194–1220.
Chen, R., Jing, Y., and Jackson, H. (2016). Identifying metastases in sentinel lymph nodes with deep convolutional neural networks. arXiv preprint arXiv:1608.01658.
Cserni, G., Maguire, A., Bianchi, S., Ryska, A., and Kovács, A. (2021). Sentinel lymph node assessment in breast cancer—an update on current recommendations. Virchows Archiv, pages 1–13.
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. Ieee.
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., et al. (2021). Artificial intelligence (ai): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57:101994.
Ehteshami Bejnordi, B., Veta, M., Johannes van Diest, P., van Ginneken, B., Karssemeijer, N., Litjens, G., van der Laak, J. A. W. M., , and the CAMELYON16 Consortium (2017). Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. JAMA, 318(22):2199–2210.
Ferlay, J., Colombet, M., Soerjomataram, I., Parkin, D. M., Piñeros, M., Znaor, A., and Bray, F. (2021). Cancer statistics for the year 2020: An overview. International journal of cancer, 149(4):778–789.
Howlader, N., Noone, A., Krapcho, M., et al. (2018). Seer cancer statistics review (csr) 1975–2014. national cancer institute website.
Lee, B. and Paeng, K. (2018). A robust and effective approach towards accurate metastasis detection and pn-stage classification in breast cancer. In International conference on medical image computing and computer-assisted intervention, pages 841–850. Springer.
Litjens, G., Bandi, P., Ehteshami Bejnordi, B., Geessink, O., Balkenhol, M., Bult, P., Halilovic, A., Hermsen, M., van de Loo, R., Vogels, R., et al. (2018a). 1399 h&e-stained sentinel lymph node sections of breast cancer patients: the camelyon dataset. GigaScience, 7(6):giy065.
Litjens, G., Bandi, P., Ehteshami Bejnordi, B., Geessink, O., Balkenhol, M., Bult, P., Halilovic, A., Hermsen, M., van de Loo, R., Vogels, R., et al. (2018b). 1399 h&e-stained sentinel lymph node sections of breast cancer patients: the camelyon dataset. GigaScience, 7(6):giy065.
Liu, Y., Gadepalli, K., Norouzi, M., Dahl, G. E., Kohlberger, T., Boyko, A., Venugopalan, S., Timofeev, A., Nelson, P. Q., Corrado, G. S., et al. (2017). Detecting cancer metastases on gigapixel pathology images. arXiv preprint arXiv:1703.02442.
Liu, Y., Kohlberger, T., Norouzi, M., Dahl, G. E., Smith, J. L., Mohtashamian, A., Olson, N., Peng, L. H., Hipp, J. D., and Stumpe, M. C. (2019). Artificial intelligence–based breast cancer nodal metastasis detection: Insights into the black box for pathologists. Archives of pathology & laboratory medicine, 143(7):859–868.
Macenko, M., Niethammer, M., Marron, J. S., Borland, D., Woosley, J. T., Guan, X., Schmitt, C., and Thomas, N. E. (2009). A method for normalizing histology slides for quantitative analysis. In 2009 IEEE international symposium on biomedical imaging: from nano to macro, pages 1107–1110. IEEE.
Siegel, R. L., Miller, K. D., Fuchs, H. E., Jemal, A., et al. (2021). Cancer statistics, 2021. Ca Cancer J Clin, 71(1):7–33.
Turki, T., Al-Sharif, A., and Taguchi, Y. (2021). End-to-end deep learning for detecting metastatic breast cancer in axillary lymph node from digital pathology images. In International Conference on Intelligent Data Engineering and Automated Learning, pages 343–353. Springer.
Valkonen, M., Kartasalo, K., Liimatainen, K., Nykter, M., Latonen, L., and Ruusuvuori, P. (2017). Metastasis detection from whole slide images using local features and random forests. Cytometry Part A, 91(6):555–565.
Wang, J., Liu, Q., Xie, H., Yang, Z., and Zhou, H. (2021). Boosted efficientnet: Detection of lymph node metastases in breast cancer using convolutional neural networks. Cancers, 13(4):661.
Wollmann, T. and Rohr, K. (2017). Automatic breast cancer grading in lymph nodes using a deep neural network. arXiv preprint arXiv:1707.07565.
Zhang, W., Zhu, C., Liu, J., Wang, Y., and Jin, M. (2019). Cancer metastasis detection through multiple spatial context network. In Proceedings of the 2019 8th International Conference on Computing and Pattern Recognition, pages 221–225.
Cardoso, F., Kyriakides, S., Ohno, S., Penault-Llorca, F., Poortmans, P., Rubio, I., Zackrisson, S., and Senkus, E. (2019). Early breast cancer: Esmo clinical practice guidelines for diagnosis, treatment and follow-up. Annals of oncology, 30(8):1194–1220.
Chen, R., Jing, Y., and Jackson, H. (2016). Identifying metastases in sentinel lymph nodes with deep convolutional neural networks. arXiv preprint arXiv:1608.01658.
Cserni, G., Maguire, A., Bianchi, S., Ryska, A., and Kovács, A. (2021). Sentinel lymph node assessment in breast cancer—an update on current recommendations. Virchows Archiv, pages 1–13.
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. Ieee.
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., et al. (2021). Artificial intelligence (ai): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57:101994.
Ehteshami Bejnordi, B., Veta, M., Johannes van Diest, P., van Ginneken, B., Karssemeijer, N., Litjens, G., van der Laak, J. A. W. M., , and the CAMELYON16 Consortium (2017). Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. JAMA, 318(22):2199–2210.
Ferlay, J., Colombet, M., Soerjomataram, I., Parkin, D. M., Piñeros, M., Znaor, A., and Bray, F. (2021). Cancer statistics for the year 2020: An overview. International journal of cancer, 149(4):778–789.
Howlader, N., Noone, A., Krapcho, M., et al. (2018). Seer cancer statistics review (csr) 1975–2014. national cancer institute website.
Lee, B. and Paeng, K. (2018). A robust and effective approach towards accurate metastasis detection and pn-stage classification in breast cancer. In International conference on medical image computing and computer-assisted intervention, pages 841–850. Springer.
Litjens, G., Bandi, P., Ehteshami Bejnordi, B., Geessink, O., Balkenhol, M., Bult, P., Halilovic, A., Hermsen, M., van de Loo, R., Vogels, R., et al. (2018a). 1399 h&e-stained sentinel lymph node sections of breast cancer patients: the camelyon dataset. GigaScience, 7(6):giy065.
Litjens, G., Bandi, P., Ehteshami Bejnordi, B., Geessink, O., Balkenhol, M., Bult, P., Halilovic, A., Hermsen, M., van de Loo, R., Vogels, R., et al. (2018b). 1399 h&e-stained sentinel lymph node sections of breast cancer patients: the camelyon dataset. GigaScience, 7(6):giy065.
Liu, Y., Gadepalli, K., Norouzi, M., Dahl, G. E., Kohlberger, T., Boyko, A., Venugopalan, S., Timofeev, A., Nelson, P. Q., Corrado, G. S., et al. (2017). Detecting cancer metastases on gigapixel pathology images. arXiv preprint arXiv:1703.02442.
Liu, Y., Kohlberger, T., Norouzi, M., Dahl, G. E., Smith, J. L., Mohtashamian, A., Olson, N., Peng, L. H., Hipp, J. D., and Stumpe, M. C. (2019). Artificial intelligence–based breast cancer nodal metastasis detection: Insights into the black box for pathologists. Archives of pathology & laboratory medicine, 143(7):859–868.
Macenko, M., Niethammer, M., Marron, J. S., Borland, D., Woosley, J. T., Guan, X., Schmitt, C., and Thomas, N. E. (2009). A method for normalizing histology slides for quantitative analysis. In 2009 IEEE international symposium on biomedical imaging: from nano to macro, pages 1107–1110. IEEE.
Siegel, R. L., Miller, K. D., Fuchs, H. E., Jemal, A., et al. (2021). Cancer statistics, 2021. Ca Cancer J Clin, 71(1):7–33.
Turki, T., Al-Sharif, A., and Taguchi, Y. (2021). End-to-end deep learning for detecting metastatic breast cancer in axillary lymph node from digital pathology images. In International Conference on Intelligent Data Engineering and Automated Learning, pages 343–353. Springer.
Valkonen, M., Kartasalo, K., Liimatainen, K., Nykter, M., Latonen, L., and Ruusuvuori, P. (2017). Metastasis detection from whole slide images using local features and random forests. Cytometry Part A, 91(6):555–565.
Wang, J., Liu, Q., Xie, H., Yang, Z., and Zhou, H. (2021). Boosted efficientnet: Detection of lymph node metastases in breast cancer using convolutional neural networks. Cancers, 13(4):661.
Wollmann, T. and Rohr, K. (2017). Automatic breast cancer grading in lymph nodes using a deep neural network. arXiv preprint arXiv:1707.07565.
Zhang, W., Zhu, C., Liu, J., Wang, Y., and Jin, M. (2019). Cancer metastasis detection through multiple spatial context network. In Proceedings of the 2019 8th International Conference on Computing and Pattern Recognition, pages 221–225.
Publicado
27/06/2023
Como Citar
KUHN, Gabriela; ZEISER, Felipe André; ROEHE, Adriana; COSTA, Cristiano André da; RAMOS, Gabriel de Oliveira.
Aprendizado profundo para assistência histopatológica: um estudo de classificação de micrometástases em câncer de mama. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 23. , 2023, São Paulo/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 407-418.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2023.230093.
