Robot-assisted biopsy sampling for online Raman spectroscopy cancer confirmation in the operating room

• Published in: International journal for computer assisted radiology and surgery 2024-06, Vol.19 (6), p.1103-1111
• Main Authors: Grajales, David, Le, William T., Tran, Trang, David, Sandryne, Dallaire, Frédérick, Ember, Katherine, Leblond, Frédéric, Ménard, Cynthia, Kadoury, Samuel
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Biopsy; Biopsy - methods; Brain; Brain Neoplasms - diagnosis; Brain Neoplasms - pathology; Breast Neoplasms - pathology; Cancer; Classification; Computer Imaging; Computer Science; Female; Health Informatics; Humans; Imaging; Machine learning; Male; Medicine; Medicine & Public Health; Operating Rooms; Original Article; Pattern Recognition and Graphics; Prostate; Prostatic Neoplasms - diagnosis; Prostatic Neoplasms - pathology; Radiology; Raman spectroscopy; Real time; Robot arms; Robotic Surgical Procedures - methods; Robotics; Robots; Spectrum analysis; Spectrum Analysis, Raman - methods; Support vector machines; Surgery; Vision; Workflow
• ISSN: 1861-6429; 1861-6410; 1861-6429
• DOI: 10.1007/s11548-024-03100-7

Purpose Cancer confirmation in the operating room (OR) is crucial to improve local control in cancer therapies. Histopathological analysis remains the gold standard, but there is a lack of real-time in situ cancer confirmation to support margin confirmation or remnant tissue. Raman spectroscopy (RS), as a label-free optical technique, has proven its power in cancer detection and, when integrated into a robotic assistance system, can positively impact the efficiency of procedures and the quality of life of patients, avoiding potential recurrence. Methods A workflow is proposed where a 6-DOF robotic system (optical camera + MECA500 robotic arm) assists the characterization of fresh tissue samples using RS. Three calibration methods are compared for the robot, and the temporal efficiency is compared with standard hand-held analysis. For healthy/cancerous tissue discrimination, a 1D-convolutional neural network is proposed and tested on three ex vivo datasets (brain, breast, and prostate) containing processed RS and histopathology ground truth. Results The robot achieves a minimum error of 0.20 mm (0.12) on a set of 30 test landmarks and demonstrates significant time reduction in 4 of the 5 proposed tasks. The proposed classification model can identify brain, breast, and prostate cancer with an accuracy of 0.83 (0.02), 0.93 (0.01), and 0.71 (0.01), respectively. Conclusion Automated RS analysis with deep learning demonstrates promising classification performance compared to commonly used support vector machines. Robotic assistance in tissue characterization can contribute to highly accurate, rapid, and robust biopsy analysis in the OR. These two elements are an important step toward real-time cancer confirmation using RS and OR integration.