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Prostate cancer remains one of the leading malignancies affecting men globally, with early detection being pivotal for successful intervention and treatment1. Historically, the diagnosis of prostate cancer has relied heavily on histopathological evaluation of prostate biopsy samples, most commonly through Hematoxylin and Eosin (H&E) staining2. While effective, traditional methods of prostate biopsy analysis typically result in a waiting period of several days to weeks, depending on the city or country, due to procedural and logistical factors. These delays arise from multiple labor-intensive steps, including formalin fixation, paraffin embedding, sectioning, and H&E staining. Thus, the cumulative processing time, compounded by the high volume of biopsy samples can delay diagnosis and treatment planning.
Recent advances in imaging technologies particularly stimulated Raman scattering (SRS) microscopy, can transform diagnostic practices by providing a robust imaging method that is background-free and easily interpretable3,4. SRS utilizes two laser beams, the pump beam (ωp) and the Stokes beam (ωs), which interact with the sample. When the frequency difference (Δω = ωp −ωs) matches a specific molecular vibrational frequency (Ω), the signal is amplified due to stimulated Raman gain or loss. This process enhances the contrast of molecular vibrations, allowing for highly sensitive imaging of tissues3,4. SRS enables the detection of molecular vibrations associated with CH2 stretching vibrations (2,845 cm-1), correlating with lipids, and CH3 stretching vibrations (2,930 cm-1), linked to proteins and DNA4. The detection of the SRS signal typically involves a high-frequency modulation transfer scheme, allowing for precise isolation of the weak vibrational signals from background noise.
SRS microscopy has optical sectioning capabilities that enable precise three-dimensional imaging without the need for physical tissue sectioning. This is achieved by aligning and focusing the pump and Stokes laser beams at a diffraction-limited spot within the sample, where specific molecular vibrations are excited. The confocal nature of SRS, derived from its quadratic dependence on the lasers' intensity, ensures that signals are confined to the focal plane, excluding out-of-focus contributions and providing highly localized chemical information5,6. This depth-resolved imaging preserves tissue integrity by eliminating mechanical slicing, reducing processing time, and maintaining the biological and molecular context of the sample.
Building on the principles of SRS, stimulated Raman histology (SRH) utilizes this molecular vibrational data to create pseudo-H&E images of fresh, unstained tissue in real time, thus providing clinicians with faster and more efficient diagnostic tools7,8. This capability to generate high-quality images has made SRH an indispensable tool for research and potential clinical applications9. Recently, intraoperative margin assessment using SRH provided near-real-time pathologic feedback during partial gland ablation and radical prostatectomy, allowing for immediate treatment adjustments10,11.
The SRH imager leverages intrinsic molecular vibrations to provide insights into tissue composition. SRH can be used to effectively differentiate cancerous from benign prostate tissues by analyzing CH2 and CH3 vibrational properties7,12. The SRH imager captures these vibrations and produces pseudo-H&E images that enhance nuclear contrast, serving as a rapid alternative to conventional histopathology and delivering high-quality images in 2-8 min, depending on tissue size7,8.
The effectiveness of SRH for the rapid pathologic examination of unprocessed prostate biopsies was evaluated. Pathologists were trained to interpret SRH images from prostate biopsy cores obtained ex vivo from prostatectomy specimens8. The SRH scanning method was optimized to enable the acquisition of high-resolution images within minutes, significantly reducing the time required compared to traditional histopathology. Biopsy samples containing a mix of benign and malignant histology served as a training set for the pathologists, who then performed a blinded evaluation of a separate set of biopsies imaged by SRH and processed by H&E staining to serve as ground truth. The results showed that the mean pathologist accuracy for identifying prostate cancer was 95.7%, with good concordance in detecting clinically significant cancers, indicating that SRH can effectively support near-real-time diagnosis8.
In subsequent studies, artificial intelligence (AI) was integrated to further enhance diagnostic accuracy, efficiency, and ease of implementation. This AI model employs deep learning techniques to analyze vibrational and morphological features captured by SRH, enabling automated classification of prostate biopsy samples into benign, cancerous, and non-diagnostic regions, significantly streamlining the pathological assessment process9. The SRH-AI integration demonstrated impressive accuracy, achieving 96.5% in prostate cancer detection, with sensitivity and specificity rates of 96.3% and 96.6%, respectively9. By integrating AI with SRH, diagnostic performance matching that of experienced pathologists was achieved.
Through the extensive evaluation of SRH with pathologists and the development of an AI model integrated into the SRH imager, the capabilities of this technology have been significantly advanced. This protocol outlines the detailed steps for preparing prostate biopsy samples, imaging them using the SRH imager, and analyzing the data with AI-assisted tools. By following these steps, researchers and clinicians can leverage this novel technique to enhance prostate cancer detection, research, and treatment.