Anatomic segmentation improves prostate cancer detection with artificial neural networks analysis of 1H magnetic resonance spectroscopic imaging.
OBJECTIVE
To assess whether an artificial neural network (ANN) model is a useful tool for automatic detection of cancerous voxels in the prostate from (1)H-MRSI datasets and whether the addition of information about anatomical segmentation improves the detection of cancer.


METHODS
The Institutional Review Board approved this HIPAA-compliant study and waived informed consent. Eighteen men with prostate cancer (median age, 55 years; range, 36-71 years) who underwent endorectal MRI/MRSI before radical prostatectomy were included in this study. These patients had at least one cancer area on whole-mount histopathological map and at least one matching MRSI voxel suspicious for cancer detected. Two ANN models for automatic classification of MRSI voxels in the prostate were implemented and compared: model 1, which used only spectra as input, and model 2, which used the spectra plus information from anatomical segmentation. The models were trained, tested and validated using spectra from voxels that the spectroscopist had designated as cancer and that were verified on histopathological maps.


RESULTS
At ROC analysis, model 2 (AUC = 0.968) provided significantly better (P = 0.03) classification of cancerous voxels than did model 1 (AUC = 0.949).


CONCLUSIONS
Automatic analysis of prostate MRSI to detect cancer using ANN model is feasible. Application of anatomical segmentation from MRI as an additional input to ANN improves the accuracy of detecting cancerous voxels from MRSI.
Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York, USA; Department of Radiotherapy and Brachytherapy Planning, Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Gliwice, Poland.
Lukasz
L
Matulewicz
Jacobus F A
JF
Jansen
Louisa
L
Bokacheva
Hebert Alberto
HA
Vargas
Oguz
O
Akin
Samson W
SW
Fine
Amita
A
Shukla-Dave
James A
JA
Eastham
Hedvig
H
Hricak
Jason A
JA
Koutcher
Kristen L
KL
Zakian
D000328:Adult; D000368:Aged; D000465:Algorithms; D014408:Biomarkers, Tumor / Q000032:analysis*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D011471:Prostatic Neoplasms / Q000737:chemistry* / Q000175:diagnosis*; D066244:Proton Magnetic Resonance Spectroscopy / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
D014408:Biomarkers, Tumor
computer-aided diagnosis; magnetic resonance spectroscopic imaging; neural networks; pattern recognition; prostate cancer