G Ranjith1, R Parvathy2, V Vikas3, Kesavadas Chandrasekharan2, Suresh Nair2. 1. SCTIMST, Sri Chitra Tirunal Institute of Medical Sciences and Technology, Trivandrum, Kerala, India ranjithg@sctimst.ac.in. 2. SCTIMST, Sri Chitra Tirunal Institute of Medical Sciences and Technology, Trivandrum, Kerala, India. 3. NIMHANS, Sri Chitra Tirunal Institute of Medical Sciences and Technology, Trivandrum, Kerala, India.
Abstract
CONTEXT: With the advent of new imaging modalities, radiologists are faced with handling increasing volumes of data for diagnosis and treatment planning. The use of automated and intelligent systems is becoming essential in such a scenario. Machine learning, a branch of artificial intelligence, is increasingly being used in medical image analysis applications such as image segmentation, registration and computer-aided diagnosis and detection. Histopathological analysis is currently the gold standard for classification of brain tumors. The use of machine learning algorithms along with extraction of relevant features from magnetic resonance imaging (MRI) holds promise of replacing conventional invasive methods of tumor classification. AIMS: The aim of the study is to classify gliomas into benign and malignant types using MRI data. SETTINGS AND DESIGN: Retrospective data from 28 patients who were diagnosed with glioma were used for the analysis. WHO Grade II (low-grade astrocytoma) was classified as benign while Grade III (anaplastic astrocytoma) and Grade IV (glioblastoma multiforme) were classified as malignant. METHODS AND MATERIALS: Features were extracted from MR spectroscopy. The classification was done using four machine learning algorithms: multilayer perceptrons, support vector machine, random forest and locally weighted learning. RESULTS: Three of the four machine learning algorithms gave an area under ROC curve in excess of 0.80. Random forest gave the best performance in terms of AUC (0.911) while sensitivity was best for locally weighted learning (86.1%). CONCLUSIONS: The performance of different machine learning algorithms in the classification of gliomas is promising. An even better performance may be expected by integrating features extracted from other MR sequences.
CONTEXT: With the advent of new imaging modalities, radiologists are faced with handling increasing volumes of data for diagnosis and treatment planning. The use of automated and intelligent systems is becoming essential in such a scenario. Machine learning, a branch of artificial intelligence, is increasingly being used in medical image analysis applications such as image segmentation, registration and computer-aided diagnosis and detection. Histopathological analysis is currently the gold standard for classification of brain tumors. The use of machine learning algorithms along with extraction of relevant features from magnetic resonance imaging (MRI) holds promise of replacing conventional invasive methods of tumor classification. AIMS: The aim of the study is to classify gliomas into benign and malignant types using MRI data. SETTINGS AND DESIGN: Retrospective data from 28 patients who were diagnosed with glioma were used for the analysis. WHO Grade II (low-grade astrocytoma) was classified as benign while Grade III (anaplastic astrocytoma) and Grade IV (glioblastoma multiforme) were classified as malignant. METHODS AND MATERIALS: Features were extracted from MR spectroscopy. The classification was done using four machine learning algorithms: multilayer perceptrons, support vector machine, random forest and locally weighted learning. RESULTS: Three of the four machine learning algorithms gave an area under ROC curve in excess of 0.80. Random forest gave the best performance in terms of AUC (0.911) while sensitivity was best for locally weighted learning (86.1%). CONCLUSIONS: The performance of different machine learning algorithms in the classification of gliomas is promising. An even better performance may be expected by integrating features extracted from other MR sequences.
Authors: Ronald M Summers; Jianhua Yao; Perry J Pickhardt; Marek Franaszek; Ingmar Bitter; Daniel Brickman; Vamsi Krishna; J Richard Choi Journal: Gastroenterology Date: 2005-12 Impact factor: 22.682
Authors: Stephen J Swensen; James R Jett; Jeff A Sloan; David E Midthun; Thomas E Hartman; Anne-Marie Sykes; Gregory L Aughenbaugh; Frank E Zink; Shauna L Hillman; Gayle R Noetzel; Randolph S Marks; Amy C Clayton; Peter C Pairolero Journal: Am J Respir Crit Care Med Date: 2002-02-15 Impact factor: 21.405
Authors: K E Warren; J A Frank; J L Black; R S Hill; J H Duyn; A A Aikin; B K Lewis; P C Adamson; F M Balis Journal: J Clin Oncol Date: 2000-03 Impact factor: 44.544
Authors: Nicolas Fayed; Barbara Oliván; Yolanda Lopez Del Hoyo; Eva Andrés; Mari Cruz Perez-Yus; Alicia Fayed; Luisa F Angel; Antoni Serrano-Blanco; Miquel Roca; Javier Garcia Campayo Journal: Neuroradiol J Date: 2019-06-19
Authors: Céline De Looze; Alan Beausang; Jane Cryan; Teresa Loftus; Patrick G Buckley; Michael Farrell; Seamus Looby; Richard Reilly; Francesca Brett; Hugh Kearney Journal: J Neurooncol Date: 2018-05-16 Impact factor: 4.130
Authors: Maike Kern; Timo A Auer; Uli Fehrenbach; Yasemin Tanyildizi; Thomas Picht; Martin Misch; Edzard Wiener Journal: Neuroradiol J Date: 2020-01-19
Authors: Biqi Zhang; Ken Chang; Shakti Ramkissoon; Shyam Tanguturi; Wenya Linda Bi; David A Reardon; Keith L Ligon; Brian M Alexander; Patrick Y Wen; Raymond Y Huang Journal: Neuro Oncol Date: 2016-06-26 Impact factor: 13.029