DOI: 10.5061/dryad.cjsxksn3p
title: Data from: Identification of intraductal carcinoma of the prostate on tissue specimens using Raman micro-spectroscopy: A diagnostic accuracy case-control study with multicohort validation

description: 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 and machine
 learning technology following a protocol suitable for routine clinical
 histopathology laboratories. Methods and findings We used Raman
 micro-spectroscopy 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 three 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. Most of the 483 patients were pT2 stage
 (44–69%), and pT3a (22–49%) was more frequent than pT3b (9–12%). After
 approval of the construction of the TMAs by local ethics review board, the
 diagnostic accuracy study was approved by the Centre hospitalier de
 l’Université de Montréal (CHUM) ethics review board. Briefly, two
 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 one institution. Accuracy,
 sensitivity and specificity were respectively of
 87 ± 5%, 86 ± 6% and
 89 ± 8% to differentiate PC from benign tissue, and of
 95 ± 2%, 96 ± 4% and
 94 ± 2% respectively to differentiate IDC-P from PC. The
 trained models were then tested on data from two 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 which can be mistaken as IDC-P, with accuracies,
 sensitivities and specificities &gt;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 Raman
 micro-spectroscopy data to differentiate IDC-P, PC and benign tissue,
 including HGPIN. Raman micro-spectroscopy 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.
description: Please refer to the Readme.txt