Diagnosis of breast masses from dynamic contrast-enhanced and diffusion-weighted MR: a machine learning approach.
OBJECTIVE
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is increasingly used for breast cancer diagnosis as supplementary to conventional imaging techniques. Combining of diffusion-weighted imaging (DWI) of morphology and kinetic features from DCE-MRI to improve the discrimination power of malignant from benign breast masses is rarely reported.


METHODS
The study comprised of 234 female patients with 85 benign and 149 malignant lesions. Four distinct groups of features, coupling with pathological tests, were estimated to comprehensively characterize the pictorial properties of each lesion, which was obtained by a semi-automated segmentation method. Classical machine learning scheme including feature subset selection and various classification schemes were employed to build prognostic model, which served as a foundation for evaluating the combined effects of the multi-sided features for predicting of the types of lesions. Various measurements including cross validation and receiver operating characteristics were used to quantify the diagnostic performances of each feature as well as their combination.


RESULTS
Seven features were all found to be statistically different between the malignant and the benign groups and their combination has achieved the highest classification accuracy. The seven features include one pathological variable of age, one morphological variable of slope, three texture features of entropy, inverse difference and information correlation, one kinetic feature of SER and one DWI feature of apparent diffusion coefficient (ADC). Together with the selected diagnostic features, various classical classification schemes were used to test their discrimination power through cross validation scheme. The averaged measurements of sensitivity, specificity, AUC and accuracy are 0.85, 0.89, 90.9% and 0.93, respectively.


CONCLUSIONS
Multi-sided variables which characterize the morphological, kinetic, pathological properties and DWI measurement of ADC can dramatically improve the discriminatory power of breast lesions.
School of Computer Science and Engineering, South China University of Technology, Guangzhou, People's Republic of China.
Hongmin
H
Cai
State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China ; Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China.
Yanxia
Y
Peng
Key Laboratory of Construction and Detection of Guangdong Province, Southern Medical University, Guangzhou, People's Republic of China.
Caiwen
C
Ou
Key Laboratory of Construction and Detection of Guangdong Province, Southern Medical University, Guangzhou, People's Republic of China.
Minsheng
M
Chen
State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China.
Li
L
Li
D000293:Adolescent; D000328:Adult; D000368:Aged; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D003287:Contrast Media / Q000008:administration & dosage*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008875:Middle Aged; D011859:Radiography
D003287:Contrast Media