Surgical excision of the whole prostate through a radical prostatectomy procedure is part of the standard of care for prostate cancer. Positive surgical margins (cancer cells having spread into surrounding nonresected tissue) occur in as many as 1 in 5 cases and strongly correlate with disease recurrence and the requirement of adjuvant treatment. Margin assessment is currently only performed by pathologists hours to days following surgery and the integration of a real-time surgical readout would benefit current prostatectomy procedures. Raman spectroscopy is a promising technology to assess surgical margins: its in vivo use during radical prostatectomy could help insure the extent of resected prostate and cancerous tissue is maximized. We thus present the design and development of a dual excitation Raman spectroscopy system (680- and 785-nm excitations) integrated to the robotic da Vinci surgical platform for in vivo use. Following validation in phantoms, spectroscopic data from 20 whole human prostates immediately following radical prostatectomy are obtained using the system. With this dataset, we are able to distinguish prostate from extra prostatic tissue with an accuracy, sensitivity, and specificity of 91%, 90.5%, and 96%, respectively. Finally, the integrated Raman spectroscopy system is used to collect preliminary spectroscopic data at the surgical margin in vivo in four patients.
Prostate cancer is the most frequent diagnosed cancers among men. When prostate cancer occurs, the cancer does not result in only one or few localized malignant tumor, but is generally spread within the whole prostate.
In order to counteract the very high level of heterogeneities exhibited by prostate tissues, we developed a method for high-resolution co-registration of Raman spectroscopy with prostate cancer diagnosis.
Raman spectra were acquired on fresh ex vivo prostate within 2 hours after radical prostatectomy using a multi-wavelength hand-held contact probe. After the measurements, the prostate was reintegrated to the usual pathological workflow: formalin fixated and paraffin embedded (FFPE), and prepared for microscope histopathological analyses. The precise reconstruction of the prostate slice with hematoxylin and eosin (H and E) tissue allows the spatial correlation of the measured area (0.2 mm2) with the correspondent histopathological information, for point-by-point diagnosis determination. The tissue was classified into groups (normal/cancer) and subgroups according to the percentage of benign glands, stroma or cancer.
Different machine learning algorithms were tested to classify the spectra with increasing levels of categorization. Preliminary results showed that Raman spectroscopy is capable of detecting prostate cancer with an accuracy >90%. In addition, high percentages of stroma (vs. glands) have been correlated with spectral signature of collagen, which is the main constituent of extracellular matrix.