Prediction of malignancy by a radiomic signature from contrast agent-free diffusion MRI in suspicious breast lesions found on screening mammography.
To assess radiomics as a tool to determine how well lesions found suspicious on breast cancer screening X-ray mammography can be categorized into malignant and benign with unenhanced magnetic resonance (MR) mammography with diffusion-weighted imaging and T2-weighted sequences.



From an asymptomatic screening cohort, 50 women with mammographically suspicious findings were examined with contrast-enhanced breast MRI (ceMRI) at 1.5T. Out of this protocol an unenhanced, abbreviated diffusion-weighted imaging protocol (ueMRI) including T2-weighted, (T2w), diffusion-weighted imaging (DWI), and DWI with background suppression (DWIBS) sequences and corresponding apparent diffusion coefficient (ADC) maps were extracted. From ueMRI-derived radiomic features, three Lasso-supervised machine-learning classifiers were constructed and compared with the clinical performance of a highly experienced radiologist: 1) univariate mean ADC model, 2) unconstrained radiomic model, 3) constrained radiomic model with mandatory inclusion of mean ADC.



The unconstrained and constrained radiomic classifiers consisted of 11 parameters each and achieved differentiation of malignant from benign lesions with a .632 + bootstrap receiver operating characteristics (ROC) area under the curve (AUC) of 84.2%/85.1%, compared to 77.4% for mean ADC and 95.9%/95.9% for the experienced radiologist using ceMRI/ueMRI.



In this pilot study we identified two ueMRI radiomics classifiers that performed well in the differentiation of malignant from benign lesions and achieved higher performance than the mean ADC parameter alone. Classification was lower than the almost perfect performance of a highly experienced breast radiologist. The potential of radiomics to provide a training-independent diagnostic decision tool is indicated. A performance reaching the human expert would be highly desirable and based on our results is considered possible when the concept is extended in larger cohorts with further development and validation of the technique.



1 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:604-616.
Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Sebastian
S
Bickelhaupt
Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Daniel
D
Paech
Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.|Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany.
Philipp
P
Kickingereder
Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Franziska
F
Steudle
Radiological Clinic at the ATOS Clinic Heidelberg, Heidelberg, Germany.
Wolfgang
W
Lederer
Radiology Center Mannheim (RZM), Mannheim, Germany.
Heidi
H
Daniel
Junior Group Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Michael
M
Götz
Junior Group Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Nils
N
Gählert
Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Diana
D
Tichy
Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Manuel
M
Wiesenfarth
Department of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Frederik B
FB
Laun
Junior Group Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Klaus H
KH
Maier-Hein
Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Heinz-Peter
HP
Schlemmer
Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
David
D
Bonekamp
D000368:Aged; D001706:Biopsy; D001940:Breast / Q000000981:diagnostic imaging; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D003287:Contrast Media / Q000737:chemistry*; D038524:Diffusion Magnetic Resonance Imaging; D055088:Early Detection of Cancer; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D007091:Image Processing, Computer-Assisted; D008327:Mammography; D008875:Middle Aged; D010865:Pilot Projects; D011446:Prospective Studies; D011871:Radiology; D012189:Retrospective Studies; D014965:X-Rays
D003287:Contrast Media
DWIBS; apparent diffusion coefficient; diffusion-weighted imaging with background suppression; magnetic resonance; mammography; radiomics