Identification of intraductal carcinoma of the prostate on tissue specimens using Raman micro-spectroscopy: A diagnostic accuracy case–control study with multicohort validation
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Andrée-Anne Grosset aff001; Frédérick Dallaire aff001; Tien Nguyen aff001; Mirela Birlea aff001; Jahg Wong aff001; François Daoust aff001; Noémi Roy aff001; André Kougioumoutzakis aff001; Feryel Azzi aff001; Kelly Aubertin aff001; Samuel Kadoury aff001; Mathieu Latour aff003; Roula Albadine aff003; Susan Prendeville aff007; Paul Boutros aff008; Michael Fraser aff008; Rob G. Bristow aff013; Theodorus van der Kwast aff013; Michèle Orain aff014; Hervé Brisson aff014; Nazim Benzerdjeb aff001; Hélène Hovington aff014; Alain Bergeron aff014; Yves Fradet aff014; Bernard Têtu aff014; Fred Saad aff001; Frédéric Leblond aff001; Dominique Trudel aff001
Působiště autorů:
Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
aff001; Institut du cancer de Montréal, Montreal, Quebec, Canada
aff002; Department of Pathology and Cellular Biology, Université de Montréal, Montreal, Quebec, Canada
aff003; Department of Computer Engineering and Software Engineering, Polytechnique Montréal, Montreal, Quebec, Canada
aff004; Department of Engineering Physics, Polytechnique Montréal, Montreal, Quebec, Canada
aff005; Department of Pathology, Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
aff006; Laboratory Medicine Program, University Health Network, Toronto, Ontario, Canada
aff007; Informatics & Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
aff008; Department of Human Genetics, University of California, Los Angeles, Los Angeles, California, United States of America
aff009; Department of Urology, University of California, Los Angeles, Los Angeles, California, United States of America
aff010; Institute for Precision Health, University of California, Los Angeles, Los Angeles, California, United States of America
aff011; Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, California, United States of America
aff012; Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
aff013; Oncology Division, Centre de recherche du Centre hospitalier universitaire de Québec–Université Laval, Quebec City, Quebec, Canada
aff014; Centre de recherche sur le cancer, Université Laval, Quebec City, Quebec, Canada
aff015; Department of Surgery, Université Laval, Quebec City, Quebec, Canada
aff016
Vyšlo v časopise:
Identification of intraductal carcinoma of the prostate on tissue specimens using Raman micro-spectroscopy: A diagnostic accuracy case–control study with multicohort validation. PLoS Med 17(8): e32767. doi:10.1371/journal.pmed.1003281
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pmed.1003281
Souhrn
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 (RμS) and machine learning technology following a protocol suitable for routine clinical histopathology laboratories.
Methods and findings
We used RμS 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’Université de Montréal) 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 Québec–Université 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 RμS data to differentiate IDC-P, PC, and benign tissue, including HGPIN. RμS 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.
Klíčová slova:
Biomarkers – Cancer detection and diagnosis – Lesions – Lymphocytes – Machine learning – Pathologists – Prostate cancer – Prostate gland
Zdroje
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