Cloud Hosting

Sung, H. et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 Countries. CA Cancer J. Clin. 71, 209249 (2021).

Article Google Scholar

Chen, S.-H., Hsiao, S.-Y., Chang, K.-Y. & Chang, J.-Y. New insights into oral squamous cell carcinoma: From clinical aspects to molecular tumorigenesis. Int J. Mol. Sci. 22, 2252 (2021).

Article CAS PubMed Central Google Scholar

Adrien, J., Bertolus, C., Gambotti, L., Mallet, A. & Baujat, B. Why are head and neck squamous cell carcinoma diagnosed so late? Influence of health care disparities and socio-economic factors. Oral Oncol. 50, 9097 (2014).

Article CAS Google Scholar

Gonzlez-Moles, M. ., Aguilar-Ruiz, M. & Ramos-Garca, P. Challenges in the early diagnosis of oral cancer, evidence gaps and strategies for improvement: A scoping review of systematic reviews. Cancers 14, 4967 (2022).

Article PubMed Central Google Scholar

Russo, D. et al. Development and validation of prognostic models for oral squamous cell carcinoma: A systematic review and appraisal of the literature. Cancers 13, 5755 (2021).

Article PubMed Central Google Scholar

Carreras-Torras, C. & Gay-Escoda, C. Techniques for early diagnosis of oral squamous cell carcinoma: Systematic review. Med. Oral. Patol. Oral. Cir. Bucal. 20, e305-315 (2015).

Article PubMed Central Google Scholar

Alabi, R. O. et al. Machine learning in oral squamous cell carcinoma: Current status, clinical concerns and prospects for future-A systematic review. Artif. Intell. Med. 115, 102060 (2021).

Article Google Scholar

Qiu L, Khormali A, & Liu K. Deep Biological Pathway Informed Pathology-Genomic Multimodal Survival Prediction. (2023) [cited 2023 Apr 3]; https://arxiv.org/abs/2301.02383

Vale-Silva, L. A. & Rohr, K. Long-term cancer survival prediction using multimodal deep learning. Sci. Rep. 11, 13505 (2021).

Article CAS PubMed Central Google Scholar

Carrillo-Perez, F. et al. Machine-learning-based late fusion on multi-omics and multi-scale data for non-small-cell lung cancer diagnosis. JPM 12, 601 (2022).

Article PubMed Central Google Scholar

Lipkova, J. et al. Artificial intelligence for multimodal data integration in oncology. Cancer Cell. 40, 10951110 (2022).

Article CAS PubMed Central Google Scholar

Steyaert, S. et al. Multimodal deep learning to predict prognosis in adult and pediatric brain tumors. Commun. Med. 3, 44 (2023).

Article PubMed Central Google Scholar

Saravi, B. et al. Artificial intelligence-driven prediction modeling and decision making in spine surgery using hybrid machine learning models. J. Personal. Med. 12, 509 (2022).

Article Google Scholar

Zuley, M.L., Jarosz, R., Kirk, S., Lee, Y., Colen, R., & Garcia, K., et al. The Cancer Genome Atlas Head-Neck Squamous Cell Carcinoma Collection (TCGA-HNSC), The Cancer Imaging Archive, 2016 (Accessed 3 Apr 2023); https://wiki.cancerimagingarchive.net/x/VYG0

Li, X. et al. Multi-omics analysis reveals prognostic and therapeutic value of cuproptosis-related lncRNAs in oral squamous cell carcinoma. Front. Genet. 13, 984911 (2022).

Article CAS PubMed Central Google Scholar

Zou, C. et al. Identification of immune-related risk signatures for the prognostic prediction in oral squamous cell carcinoma. J. Immunol. Res. 2021, 6203759 (2021).

Article PubMed Central Google Scholar

Macenko, M., Niethammer, M., Marron, J.S., Borland, D., Woosley, J.T., Xiaojun, G., et al. A method for normalizing histology slides for quantitative analysis. In 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009 (IEEE, accessed 4 Apr 2023]. P. 11071110. http://ieeexplore.ieee.org/document/5193250/

Vahadane, A. et al. Structure-preserving color normalization and sparse stain separation for histological images. IEEE Trans. Med. Imaging 35, 19621971 (2016).

Article Google Scholar

Salvi, M., Acharya, U. R., Molinari, F. & Meiburger, K. M. The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis. Comput. Biol. Med. 128, 104129 (2021).

Article Google Scholar

Carpenter, A. E. et al. Cell Profiler: Image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100 (2006).

Article PubMed Central Google Scholar

Hughey, J. J. & Butte, A. J. Robust meta-analysis of gene expression using the elastic net. Nucleic Acids Res. 43, e79 (2015).

Article PubMed Central Google Scholar

Tschodu, D. et al. Re-evaluation of publicly available gene-expression databases using machine-learning yields a maximum prognostic power in breast cancer. Sci. Rep. 13, 16402 (2023).

Article CAS PubMed Central Google Scholar

Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 2730 (2000).

Article CAS PubMed Central Google Scholar

Kanehisa, M. Toward understanding the origin and evolution of cellular organisms. Protein Sci. 28, 19471951 (2019).

Article CAS PubMed Central Google Scholar

Kanehisa, M., Furumichi, M., Sato, Y., Kawashima, M. & Ishiguro-Watanabe, M. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 51, D587D592 (2023).

Article CAS Google Scholar

Ye, H. et al. Metabolism-related bioinformatics analysis reveals that HPRT1 facilitates the progression of oral squamous cell carcinoma in vitro. J. Oncol. 2022, 116 (2022).

Google Scholar

Ferreira, A.-K. et al. Survival and prognostic factors in patients with oral squamous cell carcinoma. Med. Oral. Patol. Oral. Cir. Bucal. 26, e387e392 (2021).

Article Google Scholar

Asio, J., Kamulegeya, A. & Banura, C. Survival and associated factors among patients with oral squamous cell carcinoma (OSCC) in Mulago hospital, Kampala, Uganda. Cancers Head Neck. 3, 9 (2018).

Article PubMed Central Google Scholar

Girod, A., Mosseri, V., Jouffroy, T., Point, D. & Rodriguez, J. Women and squamous cell carcinomas of the oral cavity and oropharynx: Is there something new?. J. Oral Maxillof. Surg. 67, 19141920 (2009).

Article Google Scholar

Wong, K., Rostomily, R. & Wong, S. Prognostic gene discovery in glioblastoma patients using deep learning. Cancers 11, 53 (2019).

Article CAS PubMed Central Google Scholar

Hsich, E., Gorodeski, E. Z., Blackstone, E. H., Ishwaran, H. & Lauer, M. S. Identifying important risk factors for survival in patient with systolic heart failure using random survival forests. Circ. Cardiovasc. Qual. Outcomes 4, 3945 (2011).

Article Google Scholar

Ishwaran, H., Kogalur, U. B., Gorodeski, E. Z., Minn, A. J. & Lauer, M. S. High-dimensional variable selection for survival data. J. Am. Stat. Assoc. 105, 20517 (2010).

Article MathSciNet CAS Google Scholar

Ishwaran, H., Kogalur, U. B., Chen, X. & Minn, A. J. Random survival forests for high-dimensional data. Stat. Anal. Data Min. ASA Data Sci. J. 2011(4), 11532 (2011).

Article MathSciNet Google Scholar

Katzman, J. L. et al. Deepsurv: personalized treatment recommender system using a cox proportional hazards deep neural network. BMC Med. Res. Methol. 18, 187202 (2018).

Google Scholar

Sargent, D. J. Comparison of artificial neural networks with other statistical approaches. Cancer 91, 16361642 (2001).

Article CAS Google Scholar

Xiang, A., Lapuerta, P., Ryutov, A., Buckley, J. & Azen, S. Comparison of the performance of neural network methods and Cox regression for censored survival data. Comput. Stat. Data Anal. 34, 24357 (2000).

Article Google Scholar

Nie, Z., Zhao, P., Shang, Y. & Sun, B. Nomograms to predict the prognosis in locally advanced oral squamous cell carcinoma after curative resection. BMC Cancer 21, 372 (2021).

Article PubMed Central Google Scholar

Nojavanasghari, B., Gopinath, D., Koushik, J., Baltruaitis, T., & Morency, L. P. Deep multimodal fusion for persuasiveness prediction. In Proceedings of the 18th ACM International Conference on Multimodal Interaction. 284288 (2016).

Kampman, O., Barezi, E. J., Bertero, D., & Fung, P. Investigating audio, video, and text fusion methods for end-to-end automatic personality prediction. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics vol. 2.606611 (2018).

Wang, Z., Li, R., Wang, M. & Li, A. Gpdbn: Deep bilinear network integrating both genomic data and pathological images for breast cancer prognosis prediction. Bioinformatics 27, 29632970 (2021).

Article Google Scholar

Subramanian, V., Syeda-Mahmood, T., & Do, M. N. Multimodal fusion using sparse cca for breast cancer survival prediction. In Proceedings of IEEE 18th International Symposium on Biomedical Imaging (ISBI).14291432 (2021).

Mai, S., Hu, H., & Xing, S. Modality to modality translation: An adversarial representation learning and graph fusion network for multimodal fusion. In Proceedings of the AAAI Conference on Artificial Intelligence 164172 (2020).

Mobadersany, P. et al. Predicting cancer outcomes from histology and genomics. Predicting cancer outcomes from histology and genomics using convolutional networks. Proc. Natl. Acad. Sci. 115, 29702979 (2018).

Article Google Scholar

Wang, C. et al. A cancer survival prediction method based on graph convolutional network. IEEE Trans. Nanobiosci. 19, 117126 (2020).

Article Google Scholar

Zadeh, A., Chen, M., Poria, S., Cambria, E., & Morency, L. P Tensor fusion network for multimodal sentiment analysis. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 11031114 (2017).

Chen, R. J. et al. Pathomic fusion: An integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis. IEEE Trans. Med. Imaging 41, 757770 (2022).

Article PubMed Central Google Scholar

Kim, J. H., On, K. W., Lim, W., Kim, J., Ha, J. W., & Zhang, B. T. Hadamard product for low-rank bilinear pooling. In Proceedings of International Conference on Learning Representations, 114 (2017)

Liu, Z., Shen, Y., Lakshminarasimhan, V. B., Liang, P. P., Zadeh, A., & Morency, L. P. Efficient low-rank multimodal fusion with modality-specific factors. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. 22472256 (2021)

Li, R., Wu, X., Li, A. & Wang, M. Hfbsurv: Hierarchical multimodal fusion with factorized bilinear models for cancer survival prediction. Bioinformatics 38, 25872594 (2022).

Article CAS PubMed Central Google Scholar

Read more here:
Multimodal artificial intelligence-based pathogenomics improves survival prediction in oral squamous cell carcinoma ... - Nature.com