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 clinical outcomes. The median age at radical prostatectomy was 62 years. Most of the specimens from the first cohort (Centre hospitalier de l'Universite de Montreal) were of Gleason score 3 + 3 = 6 (51%) while most of the specimens from the 2 other cohorts (University Health Network and Centre hospitalier universitaire de Quebec-Universite Laval) were of Gleason score 3 + 4 = 7 (51% and 52%, respectively). Most of the 483 patients were pT2 stage (44%-69%), and pT3a (22%-49%) was more frequent than pT3b (9%-12%). To investigate the prostate tissue of each patient, 2 consecutive sections of each TMA block were cut. The first section was transferred onto a glass slide to perform immunohistochemistry with H&E counterstaining for cell identification. The second section was placed on an aluminum slide, dewaxed, and then used to acquire an average of 7 Raman spectra per specimen (between 4 and 24 Raman spectra, 4 acquisitions/TMA core). Raman spectra of each cell type were then analyzed to retrieve tissue-specific molecular information and to generate classification models using machine learning technology. Models were trained and cross-validated using data from 1 institution. Accuracy, sensitivity, and specificity were 87% +/- 5%, 86% +/- 6%, and 89% +/- 8%, respectively, to differentiate PC from benign tissue, and 95% +/- 2%, 96% +/- 4%, and 94% +/- 2%, respectively, to differentiate IDC-P from PC. The trained models were then tested on Raman spectra from 2 independent institutions, reaching accuracies, sensitivities, and specificities of 84% and 86%, 84% and 87%, and 81% and 82%, respectively, to diagnose PC, and of 85% and 91%, 85% and 88%, and 86% and 93%, respectively, for the identification of IDC-P. IDC-P could further be differentiated from high-grade prostatic intraepithelial neoplasia (HGPIN), a pre-malignant intraductal proliferation that can be mistaken as IDC-P, with accuracies, sensitivities, and specificities > 95% in both training and testing cohorts. As we used stringent criteria to diagnose IDC-P, the main limitation of our study is the exclusion of borderline, difficult-to-classify lesions from our datasets. Conclusions In this study, we developed classification models for the analysis of R mu S data to differentiate IDC-P, PC, and benign tissue, including HGPIN. R mu S could be a next-generation histopathological technique used to reinforce the identification of high-risk PC patients and lead to more precise diagnosis of IDC-P.