Machine learning methods for the classification of gliomas: Initial results using features extracted from MR spectroscopy.
BACKGROUND
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.


OBJECTIVE
The aim of the study is to classify gliomas into benign and malignant types using MRI data.


METHODS
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
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.
SCTIMST, Sri Chitra Tirunal Institute of Medical Sciences and Technology, Trivandrum, Kerala, India ranjithg@sctimst.ac.in.
G
G
Ranjith
SCTIMST, Sri Chitra Tirunal Institute of Medical Sciences and Technology, Trivandrum, Kerala, India.
R
R
Parvathy
NIMHANS, Sri Chitra Tirunal Institute of Medical Sciences and Technology, Trivandrum, Kerala, India.
V
V
Vikas
SCTIMST, Sri Chitra Tirunal Institute of Medical Sciences and Technology, Trivandrum, Kerala, India.
Kesavadas
K
Chandrasekharan
SCTIMST, Sri Chitra Tirunal Institute of Medical Sciences and Technology, Trivandrum, Kerala, India.
Suresh
S
Nair
D000465:Algorithms; D001185:Artificial Intelligence; D001224:Aspartic Acid / Q000031:analogs & derivatives / Q000378:metabolism; D014408:Biomarkers, Tumor / Q000378:metabolism*; D001932:Brain Neoplasms / Q000175:diagnosis* / Q000378:metabolism*; D002794:Choline / Q000378:metabolism; D003401:Creatine / Q000378:metabolism; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D005910:Glioma / Q000175:diagnosis* / Q000378:metabolism*; D006801:Humans; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D010865:Pilot Projects; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
D014408:Biomarkers, Tumor; D001224:Aspartic Acid; C000179:N-acetylaspartate; D003401:Creatine; D002794:Choline
feature extraction; machine learning; magnetic resonance spectroscopy; multilayer perceptrons; random forest; support vector machine