Image-guided Raman spectroscopy navigation system to improve transperineal prostate cancer detection. Part 2: in-vivo tumor-targeting using a classification model combining spectral and MRI-radiomics features

• Published in: Journal of biomedical optics 2022-09, Vol.27 (9), p.095004-095004
• Main Authors: Grajales, David, Picot, Fabien, Shams, Roozbeh, Dallaire, Frédérick, Sheehy, Guillaume, Alley, Stephanie, Barkati, Maroie, Delouya, Guila, Carrier, Jean-Francois, Birlea, Mirela, Trudel, Dominique, Leblond, Frédéric, Ménard, Cynthia, Kadoury, Samuel
• Format: Article
• Language: English
• Subjects: Accuracy; Biopsy; Brachytherapy; Cancer therapies; Classification; Computer vision; Fiber optics; Hypodermic needles; Inelastic scattering; Magnetic resonance imaging; Medical imaging; Navigation systems; Optics; Patients; Prostate cancer; Radiation therapy; Radiomics; Raman spectra; Raman spectroscopy; Registration; Spectroscopy; Spectrum analysis; Support vector machines; Tomography; Tumors; Ultrasonic imaging; Visualization
• ISSN: 1083-3668; 1560-2281
• DOI: 10.1117/1.JBO.27.9.095004

Significance: The diagnosis and treatment of prostate cancer (PCa) are limited by a lack of intraoperative information to accurately target tumors with needles for biopsy and brachytherapy. An innovative image-guidance technique using optical devices could improve the diagnostic yield of biopsy and efficacy of radiotherapy. Aim: To evaluate the performance of multimodal PCa detection using biomolecular features from in-situ Raman spectroscopy (RS) combined with image-based (radiomics) features from multiparametric magnetic resonance images (mpMRI). Approach: In a prospective pilot clinical study, 18 patients were recruited and underwent high-dose-rate brachytherapy. Multimodality image fusion (preoperative mpMRI with intraoperative transrectal ultrasound) combined with electromagnetic tracking was used to navigate an RS needle in the prostate prior to brachytherapy. This resulting dataset consisted of Raman spectra and co-located radiomics features from mpMRI. Feature selection was performed with the constraint that no more than 10 features were retained overall from a combination of inelastic scattering spectra and radiomics. These features were used to train support vector machine classifiers for PCa detection based on leave-one-patient-out cross-validation. Results: RS along with biopsy samples were acquired from 47 sites along the insertion trajectory of the fiber-optics needle: 26 were confirmed as benign or grade group  =  1, and 21 as grade group >1, according to histopathological reports. The combination of the fingerprint region of the RS and radiomics showed an accuracy of 83% (sensitivity  =  81  %   and a specificity  =  85  %  ), outperforming by more than 9% models trained with either spectroscopic or mpMRI data alone. An optimal number of features was identified between 6 and 8 features, which have good potential for discriminating grade group ≥1  /  grade group 1  /  grade group ≤1 (accuracy  =  91  %  ). Conclusions: In-situ Raman spectroscopy combined with mpMRI radiomics features can lead to highly accurate PCa detection for improved in-vivo targeting of biopsy sample collection and radiotherapy seed placement.