Identification of intraductal carcinoma of the prostate on tissue specimens using Raman micro-spectroscopy: A diagnostic accuracy case-control study with multicohort validation

Author summaryWhy was this study done? Given its consistent association with prostate cancer (PC) recurrence, PC metastasis, and PC-specific death, the precise reporting of intraductal carcinoma of the prostate (IDC-P) is of the utmost importance. Pathologists nowadays rely mostly on morphology to differentiate intraductal lesions, with reported low interobserver agreement. Implementation of new methods in the clinical workflow would help reinforce the identification of high-risk PC patients and lead to more precise diagnosis of IDC-P. What did the researchers do and find? We used Raman micro-spectroscopy to identify the molecular composition of samples in the study of prostatic specimens. Spectral data retrieved from Raman micro-spectroscopy was analyzed using machine learning methods to generate predictive models based on biomolecular features to identify IDC-P, high-grade prostatic intraepithelial neoplasia (HGPIN), PC, and benign tissue. The tissue preparation protocol follows hospital standard operating procedures, facilitating implementation in clinical histopathology laboratories. What do these findings mean? This multicenter diagnostic accuracy case-control study showed Raman micro-spectroscopy combined with machine learning techniques could be used by pathologists to improve classification of intraductal lesions in PC. To substantiate the clinical implementation of Raman micro-spectroscopy, prospective validation studies including the full spectrum of intraductal lesions (i.e., from HGPIN to IDC-P including borderline lesions) will be necessary. Background Prostate cancer (PC) is the most frequently diagnosed cancer in North American men. Pathologists are in critical need of accurate biomarkers to characterize PC, particularly to confirm the presence of intraductal carcinoma of the prostate (IDC-P), an aggressive histopathological variant for which therapeutic options are now available. Our aim was to identify IDC-P with Raman micro-spectroscopy (R mu S) and machine learning technology following a protocol suitable for routine clinical histopathology laboratories. Methods and findings We used R mu S to differentiate IDC-P from PC, as well as PC and IDC-P from benign tissue on formalin-fixed paraffin-embedded first-line radical prostatectomy specimens (embedded in tissue microarrays [TMAs]) from 483 patients treated in 3 Canadian institutions between 1993 and 2013. The main measures were the presence or absence of IDC-P and of PC, regardless of the