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2019 | OriginalPaper | Chapter

A MRI View of Brain Tumor Outcome Prediction

Authors : Cristiana Neto, Inês Dias, Maria Santos, Victor Alves, Filipa Ferraz, João Neves, Henrique Vicente, José Neves

Published in: Creative Business and Social Innovations for a Sustainable Future

Publisher: Springer International Publishing

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Abstract

On the one hand, cancer and tumor are one of the most feared terms in today’s society. It refers to an unstable growth of cells that potentially invade the surrounding tissues and may eventually lead to edema or even death. On the other hand, the term tumor is often misleading since people assume that it is the same as cancer, but this is not necessarily true. A cancer is a particularly threatening type of tumor. The word tumor simply refers to a mass, and in particular a brain tumor is a mass located in the patient’s brain that may seriously threaten his/her life. Thus, it is crucial to study which factors may influence the outcome of a brain tumor to improve the given treatment or even make the patient more contented. Therefore, this study presents a decision support system based on Magnetic Resonance Imaging (MRI) data or knowledge (if the data is presented in context) that allows for brain tumor outcome prediction. It describes an innovative approach to cater for brain illness where Logic Programming comes in support of a computational approach based on Case Based Reasoning. An attempt is made to predict whether a patient will die or survive with or without a tumor, where the data or knowledge may be of type unknown, incomplete or even self-contradictory.

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Literature
1.
go back to reference Rathi, V., Palani, S.: Brain tumor MRI image classification with feature selection and extraction using linear discriminant analysis. CoRR. abs/1208.2128 (2012) Rathi, V., Palani, S.: Brain tumor MRI image classification with feature selection and extraction using linear discriminant analysis. CoRR. abs/1208.2128 (2012)
3.
go back to reference Papadopoulos, M., Saadoun, S., Binder, D., Manley, G., Krishna, S., Verkman, A.: Molecular mechanisms of brain tumor edema. Neuroscience 129(4), 1011–1020 (2004)CrossRef Papadopoulos, M., Saadoun, S., Binder, D., Manley, G., Krishna, S., Verkman, A.: Molecular mechanisms of brain tumor edema. Neuroscience 129(4), 1011–1020 (2004)CrossRef
4.
go back to reference Singh, S., Clarke, I., Terasaki, M., Bonn, V., Hawkins, C., Squire, J., Dirks, P.: Identification of a cancer stem cell in human brain tumors. Can. Res. 63, 5821–5828 (2003) Singh, S., Clarke, I., Terasaki, M., Bonn, V., Hawkins, C., Squire, J., Dirks, P.: Identification of a cancer stem cell in human brain tumors. Can. Res. 63, 5821–5828 (2003)
5.
go back to reference Kakas, A., Kowalski, R., Toni, F.: The role of abduction in logic programming. In: Gabbay, D., Hogger, C., Robinson, I. (eds.) Handbook of Logic in Artificial Intelligence and Logic Programming, vol. 5, pp. 235–324. Oxford University Press, Oxford (1998) Kakas, A., Kowalski, R., Toni, F.: The role of abduction in logic programming. In: Gabbay, D., Hogger, C., Robinson, I. (eds.) Handbook of Logic in Artificial Intelligence and Logic Programming, vol. 5, pp. 235–324. Oxford University Press, Oxford (1998)
6.
go back to reference Pereira, L., Anh, H.: Evolution prospection. In: Nakamatsu, K. (ed.) New Advances in Intelligent Decision Technologies—Results of the First KES International Symposium IDT 2009, Studies in Computational Intelligence, vol. 199, pp. 51–64. Springer, Berlin (2009) Pereira, L., Anh, H.: Evolution prospection. In: Nakamatsu, K. (ed.) New Advances in Intelligent Decision Technologies—Results of the First KES International Symposium IDT 2009, Studies in Computational Intelligence, vol. 199, pp. 51–64. Springer, Berlin (2009)
7.
go back to reference Neves, J., Machado, J., Analide, C., Abelha, A., Brito, L.: The halt condition in genetic programming. In: Neves, J., Santos, M.F., Machado, J. (eds.) Progress in Artificial Intelligence. LNAI, vol. 4874, pp. 160–169. Springer, Berlin (2007)CrossRef Neves, J., Machado, J., Analide, C., Abelha, A., Brito, L.: The halt condition in genetic programming. In: Neves, J., Santos, M.F., Machado, J. (eds.) Progress in Artificial Intelligence. LNAI, vol. 4874, pp. 160–169. Springer, Berlin (2007)CrossRef
8.
go back to reference Neves, J.: A logic interpreter to handle time and negation in logic databases. In: Muller, R., Pottmyer, J. (eds.) Proceedings of the 1984 Annual Conference of the ACM on the 5th Generation Challenge, pp. 50–54. Association for Computing Machinery, New York (1984) Neves, J.: A logic interpreter to handle time and negation in logic databases. In: Muller, R., Pottmyer, J. (eds.) Proceedings of the 1984 Annual Conference of the ACM on the 5th Generation Challenge, pp. 50–54. Association for Computing Machinery, New York (1984)
9.
go back to reference Machado J., Abelha A., Novais P., Neves J., Neves J.: Quality of service in healthcare units. In Bertelle, C., Ayesh, A. (eds.) Proceedings of the ESM 2008, pp. 291–298. Eurosis—ETI Publication, Ghent (2008) Machado J., Abelha A., Novais P., Neves J., Neves J.: Quality of service in healthcare units. In Bertelle, C., Ayesh, A. (eds.) Proceedings of the ESM 2008, pp. 291–298. Eurosis—ETI Publication, Ghent (2008)
10.
go back to reference Lucas, P.: Quality checking of medical guidelines through logical abduction. In: Coenen, F., Preece, A., Mackintosh A. (eds) Proceedings of AI-2003 (Research and Developments in Intelligent Systems XX), pp. 309–321. Springer, London (2003)CrossRef Lucas, P.: Quality checking of medical guidelines through logical abduction. In: Coenen, F., Preece, A., Mackintosh A. (eds) Proceedings of AI-2003 (Research and Developments in Intelligent Systems XX), pp. 309–321. Springer, London (2003)CrossRef
11.
go back to reference Fernandes, F., Vicente, H., Abelha, A., Machado, J., Novais, P., Neves J.: Artificial neural networks in diabetes control. In: Proceedings of the 2015 Science and Information Conference (SAI 2015), pp. 362–370, IEEE Edition, Los Alamitos (2015) Fernandes, F., Vicente, H., Abelha, A., Machado, J., Novais, P., Neves J.: Artificial neural networks in diabetes control. In: Proceedings of the 2015 Science and Information Conference (SAI 2015), pp. 362–370, IEEE Edition, Los Alamitos (2015)
13.
go back to reference Fedorov, A., Beichel, R., Kalpathy-Cramer, J., Finet, J., Fillion-Robin, J.-C., Pujol, S., Bauer, C., Jennings, D., Fennessy, F.M., Sonka, M., Buatti, J., Aylward, S.R., Miller, J.V., Pieper, S., Kikinis, R.: 3D Slicer as an image computing platform for the quantitative imaging network. Magn. Reson. Imaging 30, 1323–1341 (2012)CrossRef Fedorov, A., Beichel, R., Kalpathy-Cramer, J., Finet, J., Fillion-Robin, J.-C., Pujol, S., Bauer, C., Jennings, D., Fennessy, F.M., Sonka, M., Buatti, J., Aylward, S.R., Miller, J.V., Pieper, S., Kikinis, R.: 3D Slicer as an image computing platform for the quantitative imaging network. Magn. Reson. Imaging 30, 1323–1341 (2012)CrossRef
15.
go back to reference Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun. 7, 39–59 (1994) Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun. 7, 39–59 (1994)
16.
go back to reference Richter, M.M., Weber, R.O.: Case-Based Reasoning: A Textbook. Springer, Berlin (2013)CrossRef Richter, M.M., Weber, R.O.: Case-Based Reasoning: A Textbook. Springer, Berlin (2013)CrossRef
17.
go back to reference Esteves, M., Vicente, H., Gomes, S., Abelha, A., Santos, M.F., Machado, J., Neves, J., Neves, J.: Waiting time screening in diagnostic medical imaging—a case-based view. In: Tan, Y., Shi, Y. (eds.) Data Mining and Big Data. Lecture Notes on Computer Science, vol. 9714, pp. 296–308. Springer International Publishing, Cham (2016) Esteves, M., Vicente, H., Gomes, S., Abelha, A., Santos, M.F., Machado, J., Neves, J., Neves, J.: Waiting time screening in diagnostic medical imaging—a case-based view. In: Tan, Y., Shi, Y. (eds.) Data Mining and Big Data. Lecture Notes on Computer Science, vol. 9714, pp. 296–308. Springer International Publishing, Cham (2016)
18.
go back to reference Figueiredo, M., Esteves, L., Neves, J., Vicente, H.: A data mining approach to study the impact of the methodology followed in chemistry lab classes on the weight attributed by the students to the lab work on learning and motivation. Chem. Educ. Res. Pract. 17, 156–171 (2016)CrossRef Figueiredo, M., Esteves, L., Neves, J., Vicente, H.: A data mining approach to study the impact of the methodology followed in chemistry lab classes on the weight attributed by the students to the lab work on learning and motivation. Chem. Educ. Res. Pract. 17, 156–171 (2016)CrossRef
19.
go back to reference Haykin, S.: Neural Networks and Learning Machines. Pearson Education, Upper Saddle River (2009) Haykin, S.: Neural Networks and Learning Machines. Pearson Education, Upper Saddle River (2009)
20.
go back to reference Florkowski, C.M.: Sensitivity, specificity, receiver-operating characteristic (ROC) curves and likelihood ratios: communicating the performance of diagnostic tests. Clin. Biochem. Rev. 29(Suppl 1), S83–S87 (2008) Florkowski, C.M.: Sensitivity, specificity, receiver-operating characteristic (ROC) curves and likelihood ratios: communicating the performance of diagnostic tests. Clin. Biochem. Rev. 29(Suppl 1), S83–S87 (2008)
21.
go back to reference Hajian-Tilaki, K.: Receiver operating characteristic (roc) curve analysis for medical diagnostic test evaluation. Caspian J. Intern. Med. 4, 627–635 (2013) Hajian-Tilaki, K.: Receiver operating characteristic (roc) curve analysis for medical diagnostic test evaluation. Caspian J. Intern. Med. 4, 627–635 (2013)
Metadata
Title
A MRI View of Brain Tumor Outcome Prediction
Authors
Cristiana Neto
Inês Dias
Maria Santos
Victor Alves
Filipa Ferraz
João Neves
Henrique Vicente
José Neves
Copyright Year
2019
DOI
https://doi.org/10.1007/978-3-030-01662-3_1