Related Experiment Video
Updated: Jan 9, 2026
Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
Published on: June 10, 2025
Validation of Artificial Intelligence-enhanced Stimulated Raman Histopathology for Intraoperative Margin Assessment
Arif Özkan1,2,3, Karl-Moritz Schröder2,4, Peter Bronsert2,4,5,6
1Department of Urology, University of Freiburg Medical Center, Freiburg, Germany.
Background And Objective:
Stimulated Raman histology (SRH) offers promising near-real-time tissue visualization for intraoperative pathology assessment. We present preliminary results from the ROBOSPEC study, with a focus on the accuracy of results obtained via an integrated artificial intelligence (AI) tool.
Methods:
ROBOSPEC is a prospective, single-arm pilot study involving patients with prostate cancer undergoing robot-assisted radical prostatectomy (RARP). Probes from the RP specimens from the first 18 patients with intermediate-risk or high-risk prostate cancer were collected bilaterally from the dorsolateral sides of the prostate and examined with frozen section with hematoxylin and eosin staining (cryo-HE), SRH imaging (NIO laser imaging system, Invenio Imaging, Santa Clara, CA, USA). A previously published New York University AI algorithm (NYU-AI) that is based on the Inception-ResNet-v2 CNN architecture was used to generate three-color overlays to assist in interpretation. SRH images were reviewed by blinded urologists using this AI-enhanced output.
Key Findings And Limitations:
NYU-AI identified positive surgical margins in 22% of patients, with no statistically significant difference in comparison to cryo-HE (p > 0.05). Patient-based analysis yielded sensitivity and a negative predictive value (NPV) of 1.0, specificity of 0.93, and a positive predictive value of 0.75. Sample-based analysis showed similar performance, with specificity of 0.97 and identical sensitivity and NPV. These findings indicate strong diagnostic agreement between NYU-AI and conventional intraoperative pathology. Limitations of the study include the small patient cohort, the single-center design, previous training of the NYU-AI tool on prostate biopsy and periprostatic surgical-bed samples, and the lack of testing of interobserver agreement.
Conclusions And Clinical Implications:
Our preliminary findings support the potential of SRH with NYU-AI for intraoperative detection of positive surgical margins during RARP. Implementation of this technique should be further discussed after more studies have been conducted.
Patient Summary:
We looked at an artificial intelligence program using a method called stimulated Raman histology to assess the cancer status of the cutting margin during robot-assisted surgery to remove the prostate. Our preliminary results show that this method could be an alternative to the current standard as it provides accurate and faster results.
