Applied Microscopy

  • 제51권
  • /
  • Pages.6.1-6.9
  • /
  • 2021
  • /
  • 2287-5123(pISSN)
  • /
  • 2287-4445(eISSN)
한국현미경학회 (Korean Society of Microscopy)
권호 표지
DOI QR코드

DOI QR Code

Fractal dimension analysis as an easy computational approach to improve breast cancer histopathological diagnosis

  • 투고 : 2021.03.17
  • 심사 : 2021.04.20
  • 발행 : 2021.12.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Histopathology is a well-established standard diagnosis employed for the majority of malignancies, including breast cancer. Nevertheless, despite training and standardization, it is considered operator-dependent and errors are still a concern. Fractal dimension analysis is a computational image processing technique that allows assessing the degree of complexity in patterns. We aimed here at providing a robust and easily attainable method for introducing computer-assisted techniques to histopathology laboratories. Slides from two databases were used: A) Breast Cancer Histopathological; and B) Grand Challenge on Breast Cancer Histology. Set A contained 2480 images from 24 patients with benign alterations, and 5429 images from 58 patients with breast cancer. Set B comprised 100 images of each type: normal tissue, benign alterations, in situ carcinoma, and invasive carcinoma. All images were analyzed with the FracLac algorithm in the ImageJ computational environment to yield the box count fractal dimension (Db) results. Images on set A on 40x magnification were statistically different (p = 0.0003), whereas images on 400x did not present differences in their means. On set B, the mean Db values presented promising statistical differences when comparing. Normal and/or benign images to in situ and/or invasive carcinoma (all p < 0.0001). Interestingly, there was no difference when comparing normal tissue to benign alterations. These data corroborate with previous work in which fractal analysis allowed differentiating malignancies. Computer-aided diagnosis algorithms may beneficiate from using Db data; specific Db cut-off values may yield ~ 99% specificity in diagnosing breast cancer. Furthermore, the fact that it allows assessing tissue complexity, this tool may be used to understand the progression of the histological alterations in cancer.

키워드

과제정보

The authors would like to acknowledge SUPREMA institute for promoting the research and maintenance of the students as well as CNPq for further funding.

참고문헌

  1. M. Adel, A. Kotb, O. Farag, et al., in 2019 8th International Conference on Modern Circuits and Systems Technologies (MOCAST). Breast cancer diagnosis using image processing and machine learning for Elastography images (IEEE, 2019), pp. 1-4. https://doi.org/10.1109/MOCAST.2019.8741846
  2. J. Angel Arul Jothi, V. Mary Anita Rajam, A survey on automated cancer diagnosis from histopathology images. Artif. Intell. Rev. 48, 31-81 (2017). https://doi.org/10.1007/s10462-016-9494-6
  3. G. Aresta, T. Araujo, S. Kwok, et al., BACH: Grand challenge on breast cancer histology images. Med. Image Anal. 56, 122-139 (2019). https://doi.org/10.1016/j.media.2019.05.010
  4. B. Braverman, M. Tambasco, Scale-specific multifractal medical image analysis. Comput. Math Methods Med. 2013, 1-11 (2013). https://doi.org/10.1155/2013/262931
  5. J.M. Bueno-de-Mesquita, D.S.A. Nuyten, J. Wesseling, et al., The impact of inter-observer variation in pathological assessment of node-negative breast cancer on clinical risk assessment and patient selection for adjuvant systemic treatment. Ann. Oncol. 21, 40-47 (2010). https://doi.org/10.1093/annonc/mdp273
  6. F. Cardoso, L. Cataliotti, A. Costa, et al., European breast cancer conference manifesto on breast centres/units. Eur. J. Cancer 72, 244-250 (2017). https://doi.org/10.1016/j.ejca.2016.10.023
  7. A. Chan, J.A. Tuszynski, Automatic prediction of tumour malignancy in breast cancer with fractal dimension. R. Soc. Open Sci. 3, 160558 (2016). https://doi.org/10.1098/rsos.160558
  8. S.S. Cross, D.W.K. Cotton, The fractal dimension may be a useful morphometric discriminant in histopathology. J. Pathol. 166, 409-411 (1992). https://doi.org/10.1002/path.1711660414
  9. P.F.F. de Arruda, M. Gatti, F.N.F. Junior, et al., Quantification of fractal dimension and Shannon's entropy in histological diagnosis of prostate cancer. BMC Clin. Pathol. 13, 6 (2013). https://doi.org/10.1186/1472-6890-13-6
  10. N. Dimitriou, O. Arandjelovic, P.D. Caie, Deep learning for whole slide image analysis: An overview. Front. Med. 6 (2019). https://doi.org/10.3389/fmed.2019.00264
  11. D. Hanahan, R.A. Weinberg, The hallmarks of cancer. Cell 100, 57-70 (2000). https://doi.org/10.1016/S0092-8674(00)81683-9
  12. D. Hanahan, R.A. Weinberg, Hallmarks of cancer: The next generation. Cell 144, 646-674 (2011). https://doi.org/10.1016/j.cell.2011.02.013
  13. N. Harbeck, F. Penault-Llorca, J. Cortes, et al., Breast cancer. Nat. Rev. Dis. Prim. 5, 66 (2019). https://doi.org/10.1038/s41572-019-0111-2
  14. Q. Hu, H.M. Whitney, M.L. Giger, A deep learning methodology for improved breast cancer diagnosis using multiparametric MRI. Sci. Rep. 10, 10536 (2020). https://doi.org/10.1038/s41598-020-67441-4
  15. O. Iizuka, F. Kanavati, K. Kato, et al., Deep learning models for Histopathological classification of gastric and colonic epithelial Tumours. Sci. Rep. 10, 1504 (2020). https://doi.org/10.1038/s41598-020-58467-9
  16. L. Irwig, P. Macaskill, N. Houssami, Evidence relevant to the investigation of breast symptoms: The triple test. Breast 11, 215-220 (2002). https://doi.org/10.1054/brst.2001.0409
  17. W. Klonowski, R. Stepien, P. Stepien, Simple fractal method of assessment of histological images for application in medical diagnostics. Nonlinear Biomed. Phys. 4, 7 (2010). https://doi.org/10.1186/1753-4631-4-7
  18. D. Komura, S. Ishikawa, Machine learning methods for Histopathological image analysis. Comput. Struct. Biotechnol. J. 16, 34-42 (2018). https://doi.org/10.1016/j.csbj.2018.01.001
  19. H. Li, M.L. Giger, B.Q. Huynh, N.O. Antropova, Deep learning in breast cancer risk assessment: Evaluation of convolutional neural networks on a clinical dataset of full-field digital mammograms. J. Med. Imaging 4, 1 (2017). https://doi.org/10.1117/1.JMI.4.4.041304
  20. S. Maipas, A. Nonni, E. Politi, et al., The goodness-of-fit of the fractal dimension as a diagnostic factor in breast cancer. Cureus. (2018). https://doi.org/10.7759/cureus.3630
  21. Z. Mohammadzadeh, R. Safdari, M. Ghazisaeidi, et al., Advances in optimal detection of cancer by image processing; experience with lung and breast cancers. Asian Pac. J. Cancer Prev. 16, 5613-5618 (2015). https://doi.org/10.7314/APJCP.2015.16.14.5613
  22. S.R. Nayak, J. Mishra, G. Palai, Analysing roughness of surface through fractal dimension: A review. Image Vis. Comput. 89, 21-34 (2019). https://doi.org/10.1016/j.imavis.2019.06.015
  23. P.L. Nguyen, D. Schultz, A.A. Renshaw, et al., The impact of pathology review on treatment recommendations for patients with adenocarcinoma of the prostate. Urol. Oncol. Semin. Orig. Investig. 22, 295-299 (2004). https://doi.org/10.1016/S1078-1439(03)00236-9
  24. K. Rabe, O.L. Snir, V. Bossuyt, et al., Interobserver variability in breast carcinoma grading results in prognostic stage differences. Hum. Pathol. 94, 51-57 (2019). https://doi.org/10.1016/j.humpath.2019.09.006
  25. L. Shen, L.R. Margolies, J.H. Rothstein, et al., Deep learning to improve breast cancer detection on screening mammography. Sci. Rep. 9, 12495 (2019). https://doi.org/10.1038/s41598-019-48995-4
  26. H.-P. Sinn, H. Kreipe, A brief overview of the WHO classification of breast tumors, 4th edition, focusing on issues and updates from the 3rd edition. Breast Care 8, 149-154 (2013). https://doi.org/10.1159/000350774
  27. F.A. Spanhol, L.S. Oliveira, C. Petitjean, L. Heutte, A dataset for breast cancer Histopathological image classification. IEEE Trans. Biomed. Eng. 63, 1455-1462 (2016). https://doi.org/10.1109/TBME.2015.2496264
  28. M. Tambasco, M. Eliasziw, A.M. Magliocco, Morphologic complexity of epithelial architecture for predicting invasive breast cancer survival. J. Transl. Med. 8, 140 (2010). https://doi.org/10.1186/1479-5876-8-140
  29. P. Waliszewski, F. Wagenlehner, S. Gattenlohner, W. Weidner, On the relationship between tumor structure and complexity of the spatial distribution of cancer cell nuclei: A fractal geometrical model of prostate carcinoma. Prostate 75, 399-414 (2015). https://doi.org/10.1002/pros.22926
  30. B. Wei, Z. Han, X. He, Y. Yin, in 2017 IEEE 2nd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA). Deep learning model based breast cancer histopathological image classification (IEEE, 2017), pp. 348-353