Benign/malignant classifier of soft tissue tumors using MR imaging.
This article presents a pattern-recognition approach to the soft tissue tumors (STT) benign/malignant character diagnosis using magnetic resonance (MR) imaging applied to a large multicenter database.


OBJECTIVE
To develop and test an automatic classifier of STT into benign or malignant by using classical MR imaging findings and epidemiological information.


METHODS
A database of 430 patients (62% benign and 38% malignant) from several European multicenter registers. There were 61 different histologies (36 with benign and 25 with malignant nature). Three pattern-recognition methods (artificial neural networks, support vector machine, k-nearest neighbor) were applied to learn the discrimination between benignity and malignancy based on a defined MR imaging findings protocol. After the systems had learned by using training samples (with 302 cases), the clinical decision support system was tested in the diagnosis of 128 new STT cases.


RESULTS
An 88-92% efficacy was obtained in a not-viewed set of tumors using the pattern-recognition techniques. The best results were obtained with a back-propagation artificial neural network.


CONCLUSIONS
Benign vs. malignant STT discrimination is accurate by using pattern-recognition methods based on classical MR image findings. This objective tool will assist radiologists in STT grading.
García-Gómez
Juan M
JM
BET, Informática Médica, Universidad Politécnica de Valencia, Spain.
Vidal
César
C
Martí-Bonmatí
Luis
L
Galant
Joaquín
J
Sans
Nicolas
N
Robles
Montserrat
M
Casacuberta
Francisco
F
D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D020000:Decision Support Systems, Clinical; D005060:Europe; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016037:Single-Blind Method; D012983:Soft Tissue Neoplasms / Q000145:classification* / Q000175:diagnosis* / Q000473:pathology