Multimodal Data-Driven Intelligent Systems for Breast Cancer Prediction

Authors

DOI:

https://doi.org/10.4108/eetpht.10.6424

Keywords:

Deep neural networks, Multimodality, Breast cancer, Genome data

Abstract

Cancer, a malignant disease, results from abnormalities in the body cells that lead to uncontrolled growth and division, surpassing healthy growth and stability. In the case of breast cancer, this uncontrolled growth and division occurs in breast cells. Early identification of breast cancer is key to lowering mortality rates. Several new developments in artificial intelligence predictive models show promise for assisting decision-making. The primary goal of the proposed study is to build an efficient Breast Cancer Intelligent System using a multimodal dataset. The aim is to to establish Computer-Aided Diagnosis for breast cancer by integrating various data.

This study uses the TCGA "The Cancer Genome Atlas Breast Invasive Carcinoma Collection" (TCGA-BRCA) dataset, which is part of an ongoing effort to create a community integrating cancer phenotypic and genotypic data. The TCGA- BRCA dataset includes: Clinical Data, RNASeq Gene Data, Mutation Data, and Methylation Data. Both clinical and genomic data are used in this study for breast cancer diagnosis. Integrating multiple data modalities enhances the robustness and precision of diagnostic and prognostic models in comparison with conventional techniques. The approach offers several advantages over unimodal models due to its ability to integrate diverse data sources. Additionally, these models can be employed to forecast the likelihood of a patient developing breast cancer in the near future, providing a valuable tool for early intervention and treatment planning.

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References

World health organization cancer. (2018). Fact Sheet-Cancer. Available at: https://www.who.int/health-topics/cancer

https://www.livemint.com/news/india/icmr-data-shows-unequal-toll-of-cancer-on-women-11670349329355.html

https://www.industryarc.com/PressRelease/2625/Oncology-Market-Research.html

Mertz, S., Mayer, M., Paonessa, D., Papadopoulos, E., Alessandro, F., Peccatori, K. S., ... & Spence, D. (2016). Breast Cancer Center Survey: Cancer center management, support, and perception of mBC patient needs across 582 healthcare professionals

https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)32381-3/fulltext

Bini, S. A. (2018). Artificial intelligence, machine learning, deep learning, and cognitive computing: what do these terms mean and how will they impact health care?. The Journal of arthroplasty, 33(8), 2358-2361.

Karthik, S., Perumal, R. S., & Mouli, P. C. (2018). Breast cancer classification using deep neural networks. In Knowledge computing and its applications (pp. 227-241). Springer, Singapore.

Stahlschmidt, S. R., Ulfenborg, B., & Synnergren, J. (2022). Multimodal deep learning for biomedical data fusion: a review. Briefings in Bioinformatics, 23(2), bbab569. https://doi.org/10.1093/bib/bbab569

https://jina.ai/news/what-is-multimodal-deep-learning-and-what-are-the-applications/

Van't Veer, L. J., Dai, H., Van De Vijver, M. J., He, Y. D., Hart, A. A., Mao, M., ... & Friend, S. H. (2002). Gene expression profiling predicts clinical outcome of breast cancer. nature, 415(6871), 530-536.

Yap, M. H., Pons, G., Marti, J., Ganau, S., Sentis, M., Zwiggelaar, R., ... & Marti, R. (2017). Automated breast ultrasound lesions detection using convolutional neural networks. IEEE journal of biomedical and health informatics, 22(4), 1218-1226.

Al-Antari, M. A., Al-Masni, M. A., Choi, M. T., Han, S. M., & Kim, T. S. (2018). A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification. International journal of medical informatics, 117, 44-54.

Sun, D., Li, A., Tang, B., & Wang, M. (2018). Integrating genomic data and pathological images to effectively predict breast cancer clinical outcome. Computer methods and programs in biomedicine, 161, 45-53.

Gevaert, O., Smet, F. D., Timmerman, D., Moreau, Y., & Moor, B. D. (2006). Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks. Bioinformatics, 22(14), e184-e190.

Sun, D., Wang, M., & Li, A. (2018). A multimodal deep neural network for human breast cancer prognosis prediction by integrating multi-dimensional data. IEEE/ACM transactions on computational biology and bioinformatics, 16(3), 841-850.

Khademi, M., & Nedialkov, N. S. (2015, December). Probabilistic graphical models and deep belief networks for prognosis of breast cancer. In 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) (pp. 727-732). IEEE.

Qian, X., Pei, J., Zheng, H., Xie, X., Yan, L., Zhang, H., ... & Shung, K. K. (2021). Prospective assessment of breast cancer risk from multimodal multiview ultrasound images via clinically applicable deep learning. Nature biomedical engineering, 5(6), 522-532.

Binder, A., Bockmayr, M., Hägele, M., Wienert, S., Heim, D., Hellweg, K., ... & Klauschen, F. (2021). Morphological and molecular breast cancer profiling through explainable machine learning. Nature Machine Intelligence, 3(4), 355-366.

Liu, T., Huang, J., Liao, T., Pu, R., Liu, S., & Peng, Y. (2022). A hybrid deep learning model for predicting molecular subtypes of human breast cancer using multimodal data. Irbm, 43(1), 62-74.

Arya, N., & Saha, S. (2021). Multimodal advanced deep learning architectures for breast cancer survival prediction. Knowledge-Based Systems, 221, 106965.

Lingle, W., Erickson, B. J., Zuley, M. L., Jarosz, R., Bonaccio, E., Filippini, J., Net, J. M., Levi, L., Morris, E. A., Figler, G. G., Elnajjar, P., Kirk, S., Lee, Y., Giger, M., & Gruszauskas, N. (2016). The Cancer Genome Atlas Breast Invasive Carcinoma Collection (TCGA-BRCA) (Version 3) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2016.AB2NAZRP

Kanimozhi, G., & Shanmugavadivu, P. (2021). Optimized DEEP neural networks architecture model for breast cancer diagnosis. cancer, 3, 4.

Farhangfar, A., Kurgan, L., & Dy, J. (2008). Impact of imputation of missing values on classification error for discrete data. Pattern Recognition, 41(12), 3692-3705.

Jerez JM, Molina I, García-LaencinaPJ, Alba E, Ribelles N, Martín M, Franco L. Missing data imputation using statistical and machine learning methods in a real breast cancer problem. Artificial intelligence in medicine. 2010 Oct 1;50(2):105-15.

Yu, L., Zhou, R., Chen, R., & Lai, K. K. (2022). Missing data preprocessing in credit classification: One-hot encoding or imputation?. Emerging Markets Finance and Trade, 58(2), 472-482.

Kanimozhi, G., Shanmugavadivu, P., & Rani, M. M. S. (2020). Machine Learning‐Based Recommender System for Breast Cancer Prognosis. Recommender System with Machine Learning and Artificial Intelligence: Practical Tools and Applications in Medical, Agricultural and Other Industries, 121-140.

Ali, P. J. M., Faraj, R. H., Koya, E., Ali, P. J. M., & Faraj, R. H. (2014). Data normalization and standardization: a technical report. Mach Learn Tech Rep, 1(1), 1-6.

Mohsen, H., El-Dahshan, E. S. A., El-Horbaty, E. S. M., & Salem, A. B. M. (2018). Classification using deep learning neural networks for brain tumors. Future Computing and Informatics Journal, 3(1), 68-71.

Ganesan, K., Pichai, S., Kavitha, M. S., & Takahashi, M. (2022). Data imputation in deep neural network to enhance breast cancer detection. International Journal of Imaging Systems and Technology, 32(6), 2094-2106.

Van Laarhoven, T. (2017). L2 regularization versus batch and weight normalization. arXiv preprint arXiv:1706.05350.

Zhang, G., Wang, C., Xu, B., & Grosse, R. (2018). Three mechanisms of weight decay regularization. arXiv preprint arXiv:1810.12281.

Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research, 15(1), 1929-1958.

Xiao, Yawen; Wu, Jun; Lin, Zongli; Zhao, Xiaodong (2017). A Deep Learning-based Multi-model Ensemble Method for Cancer Prediction. Computer Methods and Programs in Biomedicine, 153, 1-9

https://medium.com/mlearning-ai/apply-machine-learning-algorithms-for-genomics-data-classification-132972933723

Yu, Z., Wang, Z., Yu, X., & Zhang, Z. (2020). RNA-seq-based breast cancer subtypes classification using machine learning approaches. Computational intelligence and neuroscience, 2020.

Visa, S., Ramsay, B., Ralescu, A. L., & Van Der Knaap, E. (2011). Confusion Matrix-based Feature Selection. MAICS, 710, 120-127

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Published

25-06-2024 — Updated on 25-06-2024

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How to Cite

1.
Pichai S, Kanimozhi G, Rani MMS, Riyaz N. Multimodal Data-Driven Intelligent Systems for Breast Cancer Prediction. EAI Endorsed Trans Perv Health Tech [Internet]. 2024 Jun. 25 [cited 2024 Dec. 12];10. Available from: https://publications.eai.eu/index.php/phat/article/view/6424