Classificação Hierárquica de Células de Câncer de Mama Usando Imagens MIBI e Aprendizado de Máquina
Resumo
O câncer de mama é uma das principais causas de mortalidade feminina mundial, com diagnóstico frequentemente tardio. A detecção precoce é crucial para evitar estágios avançados da doença e melhorar o prognóstico. Subtipos agressivos, como o carcinoma triplo-negativo (TNBC), demandam terapias personalizadas baseadas no perfil tumoral. A análise do microambiente tumoral é desafiadora devido à complexidade na identificação dos componentes do tecido. Avanços em visão computacional e aprendizado profundo têm permitido o desenvolvimento de métodos para segmentação e classificação de imagens. Este estudo propõe um método para classificar células quanto ao tipo geral e à identidade imune. A abordagem foi testada em imagens de TNBC e apresentou resultados promissores de AUC para a classificação de células em tipos gerais (0,9998) e imunes (0,9994).Referências
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Lima, Thiago et al. (2023). “Automatic classification of pulmonary nodules in computed tomography images using pre-trained networks and bag of features”. Em: Multimedia Tools and Applications 82.27, pp. 42977–42993. ISSN: 1573-7721.
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Luz et al. (2023). “Malignant breast lesions detection in histopathological images based on the combination of bioinspired texture descriptors and deep features”. Em: Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 11.5, pp. 1889–1896. ISSN: 2168-1171.
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Ptacek, Jason et al. (2020). “Multiplexed ion beam imaging (MIBI) for characterization of the tumor microenvironment across tumor types”. Em: Laboratory Investigation 100.8, pp. 1111–1123. ISSN: 0023-6837.
Shrestha, Prem, Nicholas Kuang e Ji Yu (2023). “Efficient end-to-end learning for cell segmentation with machine generated weak annotations”. Em: Communications Biology 6.1. ISSN: 2399-3642.
Studebaker, Gerald A. (1985). “A “Rationalized” Arcsine Transform”. Em: Journal of Speech, Language, and Hearing Research 28.3, pp. 455–462. ISSN: 1558-9102.
Zagami, Paola e Lisa Anne Carey (2022). “Triple negative breast cancer: Pitfalls and progress”. Em: NPJ breast cancer 8.1, p. 95.
Amitay, Yael et al. (2023). “CellSighter: a neural network to classify cells in highly multiplexed images”. Em: Nature Communications 14.1. ISSN: 2041-1723.
Anderson, Nicole M. e M. Celeste Simon (2020). “The tumor microenvironment”. Em: Current Biology 30.16, R921–R925. ISSN: 0960-9822.
Bajaj, Sweta et al. (2024). “Automated Single Cell Phenotyping of Time-of-Flight Secondary Ion Mass Spectrometry Tissue Images”. Em: Journal of the American Society for Mass Spectrometry 35.12, pp. 3126–3134. ISSN: 1879-1123.
Bortolomeazzi, Michele et al. (2022). “A SIMPLI (Single-cell Identification from MultiPLexed Images) approach for spatially-resolved tissue phenotyping at single-cell resolution”. Em: Nature Communications 13.1. ISSN: 2041-1723.
Derakhshan, Fatemeh e Jorge S Reis-Filho (2022). “Pathogenesis of triple-negative breast cancer”. Em: Annual Review of Pathology: Mechanisms of Disease 17.1, pp. 181–204.
Fei, Nanyi et al. (2021). “Z-Score Normalization, Hubness, and Few-Shot Learning”. Em: 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, pp. 142–151.
Hong, Jin-Hyuk e Sung-Bae Cho (2008). “A probabilistic multi-class strategy of one-vs.-rest support vector machines for cancer classification”. Em: Neurocomputing 71.16–18, pp. 3275–3281. ISSN: 0925-2312.
Keren, Leeat et al. (2019). “MIBI-TOF: A multiplexed imaging platform relates cellular phenotypes and tissue structure”. Em: Science Advances 5.10. ISSN: 2375-2548.
Lahiri, Aditya et al. (2023). “Drug Target Identification in Triple Negative Breast Cancer Stem Cell Pathways: A Computational Study of Gene Regulatory Pathways Using Boolean Networks”. Em: IEEE Access 11, pp. 56672–56690. ISSN: 2169-3536.
Li, Yun et al. (2022). “Recent advances in therapeutic strategies for triple-negative breast cancer”. Em: Journal of hematology & oncology 15.1, p. 121.
Lima, Thiago et al. (2023). “Automatic classification of pulmonary nodules in computed tomography images using pre-trained networks and bag of features”. Em: Multimedia Tools and Applications 82.27, pp. 42977–42993. ISSN: 1573-7721.
Luz, Daniel S. et al. (2022). “Automatic detection metastasis in breast histopathological images based on ensemble learning and color adjustment”. Em: Biomedical Signal Processing and Control 75, p. 103564. ISSN: 1746-8094.
Luz et al. (2023). “Malignant breast lesions detection in histopathological images based on the combination of bioinspired texture descriptors and deep features”. Em: Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 11.5, pp. 1889–1896. ISSN: 2168-1171.
Pang, Minxing et al. (2024). “CelloType: a unified model for segmentation and classification of tissue images”. Em: Nature Methods 22.2, pp. 348–357. ISSN: 1548-7105.
Powers, David M. W. (2020). “Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation”. Em.
Ptacek, Jason et al. (2020). “Multiplexed ion beam imaging (MIBI) for characterization of the tumor microenvironment across tumor types”. Em: Laboratory Investigation 100.8, pp. 1111–1123. ISSN: 0023-6837.
Shrestha, Prem, Nicholas Kuang e Ji Yu (2023). “Efficient end-to-end learning for cell segmentation with machine generated weak annotations”. Em: Communications Biology 6.1. ISSN: 2399-3642.
Studebaker, Gerald A. (1985). “A “Rationalized” Arcsine Transform”. Em: Journal of Speech, Language, and Hearing Research 28.3, pp. 455–462. ISSN: 1558-9102.
Zagami, Paola e Lisa Anne Carey (2022). “Triple negative breast cancer: Pitfalls and progress”. Em: NPJ breast cancer 8.1, p. 95.
Publicado
09/06/2025
Como Citar
LUZ, Daniel de Sousa; MATHEW, Mano Joseph; LIMA, Thiago José Barbosa; VELOSO E SILVA, Romuere Rodrigues; CARVALHO FILHO, Antonio Oseas de; ARAÚJO, Flávio Henrique Duarte de.
Classificação Hierárquica de Células de Câncer de Mama Usando Imagens MIBI e Aprendizado de Máquina. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 25. , 2025, Porto Alegre/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 461-472.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2025.7290.
