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

Springer Science and Business Media LLC - Tập 51 Số 1 - 2021
Lucas Glaucio da Silva1, Waleska Rayanne Sizinia da Silva Monteiro1, Tiago Medeiros de Aguiar Moreira2, Maria Aparecida Esteves Rabelo1, Emílio Augusto Campos Pereira de Assis3,1, Gustavo Torres de Souza4,2
1Faculty of Medical and Health Sciences of Juiz de Fora, Juiz de Fora, Brazil
2Department of Biology - Genetics - Federal University of Juiz de Fora, Juiz de Fora, Brazil
3Animal Reproduction Laboratory - Brazilian Agricultural Research Corporation – Dairy Cattle, Laboratory of Animal Reproduction, Juiz de Fora, Brazil
4Center for Investigation and Diagnosis of Pathological Anatomy, Juiz de Fora, Brazil

Tóm tắt

AbstractHistopathology 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 promissing 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.

Từ khóa


Tài liệu tham khảo

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

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

G. Aresta, T. Araújo, 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

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

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

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

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

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

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

N. Dimitriou, O. Arandjelović, P.D. Caie, Deep learning for whole slide image analysis: An overview. Front. Med. 6 (2019). https://doi.org/10.3389/fmed.2019.00264

D. Hanahan, R.A. Weinberg, The hallmarks of cancer. Cell 100, 57–70 (2000). https://doi.org/10.1016/S0092-8674(00)81683-9

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

P. Waliszewski, F. Wagenlehner, S. Gattenlöhner, 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

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

Thông tin
Thông tin xuất bản

Springer Science and Business Media LLC

Tập 51 Số 1

Thông tin tác giả