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Abstract: Introduction The current workflow of prostate biopsy is in need of improvement. Optical Coherence Tomography (OCT) has emerged as a promising technology capable of providing a \'virtual\' tissue analysis in real time. We explored the technological feasibility of OCT in combination with computerised interpretation of optical signals and application of Machine-Learning algorithms for in-vivo tissue diagnosis. In this ?proof of concept? study we report the results of OCT imaging of fresh ex-vivo prostate tissue and signal processing, to identify cancer without the need for biopsy core processing. Methods OCT scans were obtained from 24 patients who underwent radical prostatectomy. Immediately after prostatectomy two postero-lateral tissue strips of approximately 15mm x 8mm x 6mm were prepared and coloured for orientation. Each strip was scanned twice from the capsular (outside) and the excision (inner) surface with an OCT microscope (EX1301, Vivosight Ltd.). Scan resolution was 4 x 4 x 50 microns. The EX1301 beam?s penetration depth is 2mm. A Bidirectional Dynamic Cortex Memory Network was trained and tested on randomly chosen samples of OCT A-scan data. Mean classification rate and standard deviation were calculated for 10 cycles of training/testing. Routine histopathology analysis was used as the reference standard. Results Of 46 strips, 24 were found to contain prostate cancer and 22 benign tissue on histopathological evaluation. Applying mathematical feature extraction to OCT signals acquired from the excision (inner) surface of the strips we could differentiate cancer from benign tissue. The mean classification rates archived for the test and training sets were 67.65% (0.70%) and 69.20% (1.49%), respectively. Conclusion The application of machine-learning techniques to OCT data sets, which were obtained from ex-vivo prostate tissue, provides encouraging results and highlights the potential for a ?virtual? biopsy approach. Further optimization and in-vivo application of this technique is in progress.