Deep learning‐based classification of preclinical breast cancer tumor models using chemical exchange saturation transfer magnetic resonance imaging. (19th October 2021)

• Record Type: Journal Article
• Authors: Bie, Chongxue; Li, Yuguo; Zhou, Yang; Bhujwalla, Zaver M.; Song, Xiaolei; Liu, Guanshu; van Zijl, Peter C. M.; Yadav, Nirbhay N.
• Is Part Of: NMR in biomedicine. Volume 35:Number 2(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-10-19
• Subjects: breast cancer -- CEST MRI -- classification -- CNN -- deep learning -- saliency map; Nuclear magnetic resonance -- Periodicals; Magnetic Resonance Spectroscopy -- Periodicals; 574
• DOI: 10.1002/nbm.4626
• Languages: English
• ISSNs: 0952-3480

Abstract

Abstract : Chemical exchange saturation transfer (CEST) magnetic resonance imaging has shown promise for classifying tumors based on their aggressiveness, but CEST contrast is complicated by multiple signal sources and thus prolonged acquisition times are often required to extract the signal of interest. We investigated whether deep learning could help identify pertinent Z‐spectral features for distinguishing tumor aggressiveness as well as the possibility of acquiring only the pertinent spectral regions for more efficient CEST acquisition. Human breast cancer cells, MDA‐MB‐231 and MCF‐7, were used to establish bi‐lateral tumor xenografts in mice to represent higher and lower aggressive tumors, respectively. A convolutional neural network (CNN)‐based classification model, trained on simulated data, utilized Z‐spectral features as input to predict labels of different tissue types, including MDA‐MB‐231, MCF‐7, and muscle tissue. Saliency maps reported the influence of Z‐spectral regions on classifying tissue types. The model was robust to noise with an accuracy of more than 91.5% for low and moderate noise levels in simulated testing data (SD of noise less than 2.0%). For in vivo CEST data acquired with a saturation pulse amplitude of 2.0 μT, the model had a superior ability to delineate tissue types compared with Lorentzian difference (LD) and magnetization transfer ratio asymmetry (MTRasym ) analysis, classifying tissues to the correct types with a mean accuracy of 85.7%, sensitivity of 81.1%, and specificity of 94.0%. The model's performance did not improve substantially when using data acquired at multiple saturation pulse amplitudes or when adding LD or MTRasym spectral features, and did not change when using saliency map‐based partial or downsampled Z‐spectra. This study demonstrates the potential of CNN‐based classification to distinguish between different tumor types and muscle tissue, and speed up CEST acquisition protocols. Abstract : A CNN‐based classification model was proposed to distinguish between MDA‐MB‐231 (higher aggressive), MCF‐7 (lower aggressive) tumors, and muscle tissue in a bi‐lateral tumor xenograft model. The generated saliency map indicated the importance of Z‐spectral regions to classify tissue types. The classification model could automatically classify different tissue types voxel‐by‐voxel by using Z‐spectra features, and was helpful in speeding up CEST experimental data.