Prostate cancer detection using photoacoustic imaging and deep learning

Photoacoustic tomography of intact human prostates and vascular texture analysis identify prostate cancer biopsy targets

“…Besides intensity-based analysis, prior work involving PAT of human prostates for the purpose of distinguishing malignant versus benign tissue includes applying multispectral deconvolution, frequency analysis, and deep neural nets with Greedy feature selection to a dataset of axially-sectioned, ex vivo human prostates [ [39] , [40] , [41] ]. Unfortunately, multispectral deconvolution cannot be applied here as two PAT channels are not sufficient as there are at least three endogenous PA absorbers present, i.e.…”

“…For the first time, PAT is utilized along with supervised machine learning to independently identify targets for prostate biopsy in intact human specimens. Previously, significant work was performed on an axially-sliced human prostate specimen PAT dataset looking at multispectral deconvolution, frequency analysis, and deep neural nets with Greedy feature selection [ [39] , [40] , [41] ]. While this work shows the differences between PCa and benign prostate tissue, the studies’ approaches are still based on user-selected ROIs or do not show the locations where targets would be suggested compared to ground truth histopathology slides.…”

“…The use of one PAT wavelength maximizes imaging speed and will limit added time for the procedure if clinically translated. Additionally, for the first time, we identify prostate biopsy targets with an imaging and light delivery scheme that is clinically relevant compared to previously published work [ [39] , [40] , [41] ].…”

“…Recent studies have begun applying PAT to the identification of PCa in human prostates [ 34 , [39] , [40] , [41] ]. Unfortunately, a method of identifying targets for the prostate biopsy has yet to be achieved without manual selection of regions of interest (ROI) that rely on intensity-based thresholding [ 34 , 39 ] or the use of multispectral PAT analysis [ [39] , [40] , [41] ] that would decrease the frame rate. Out of these studies, Rajanna et al used deep neural networks to learn features and then identify pixels representing PCa.…”

“…Here, we minimize the PAT channels to 1064 and 1197 nm and acquire the standard US channel. Since feature learning typically involves 10 s–100 s of features [ 40 , 42 ], we utilize the “off-the-shelf” K-means clustering feature learning of texture patches, which has been shown to be effective in single-layer networks [ 43 ], for the purpose of identifying targets for PCa biopsy.…”