A MRI View of Brain Tumor Outcome Prediction

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.

[1]  Peter J. F. Lucas,et al.  Quality Checking of Medical Guidelines through Logical Abduction , 2003, SGAI Conf..

[2]  José Neves,et al.  The Halt Condition in Genetic Programming , 2007, EPIA Workshops.

[3]  Michael M. Richter,et al.  Case-Based Reasoning , 2013, Springer Berlin Heidelberg.

[4]  V. P. Gladis Pushpa Rathi,et al.  Brain tumor MRI image classification with feature selection and extraction using linear discriminant analysis , 2012, ArXiv.

[5]  Agnar Aamodt,et al.  Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches , 1994, AI Commun..

[6]  Milan Sonka,et al.  3D Slicer as an image computing platform for the Quantitative Imaging Network. , 2012, Magnetic resonance imaging.

[7]  M. C. Papadopoulos,et al.  Molecular mechanisms of brain tumor edema , 2004, Neuroscience.

[8]  José Neves,et al.  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 , 2016 .

[9]  Cynthia Hawkins,et al.  Identification of a cancer stem cell in human brain tumors. , 2003, Cancer research.

[10]  Michael M. Richter,et al.  Case-Based Reasoning: A Textbook , 2013 .

[11]  Manuel Filipe Santos,et al.  Waiting Time Screening in Diagnostic Medical Imaging - A Case-Based View , 2016, DMBD.

[12]  José Neves,et al.  Quality of service in healthcare units , 2010, Int. J. Comput. Aided Eng. Technol..

[13]  Paulo Novais,et al.  Artificial neural networks in diabetes control , 2015, 2015 Science and Information Conference (SAI).

[14]  Simon Haykin,et al.  Neural Networks and Learning Machines , 2010 .

[15]  K. Hajian‐Tilaki,et al.  Receiver Operating Characteristic (ROC) Curve Analysis for Medical Diagnostic Test Evaluation. , 2013, Caspian journal of internal medicine.