Oops, you're using an old version of your browser so some of the features on this page may not be displaying properly.

MINIMAL Requirements: Google Chrome 24+Mozilla Firefox 20+Internet Explorer 11Opera 15–18Apple Safari 7SeaMonkey 2.15-2.23

Poster Discussion 1 – Translational research

2734 - Raman microscopy for the identification of an aggressive variant of prostate cancer, intraductal carcinoma of the prostate

Date

29 Sep 2019

Session

Poster Discussion 1 – Translational research

Presenters

Dominique Trudel

Citation

Annals of Oncology (2019) 30 (suppl_5): v25-v54. 10.1093/annonc/mdz239

Authors

D. Trudel1, A. Grosset2, F. Dallaire2, T. Nguyen2, A. Kougioumoutzakis2, F. Azzi2, K. Aubertin2, F. Saad3, M. Latour4, R. Albadine4, P. Boutros5, M. Fraser5, R. Bristow6, T. Van der Kwast7, N. Benzerdjeb2, H. Hovington8, A. Bergeron9, Y. Fradet9, H. Brisson9, F. Leblond2

Author affiliations

  • 1 Pathology And Cellular Biology, Université de Montreal, H3C 3J7 - Montréal/CA
  • 2 Axe Cancer, Centre de recherche du Centre hospitalier de l'Université de Montréal, H2X 0A9 - Montréal/CA
  • 3 Urology, Hospital St. Luc du CHUM, H2X 3J4 - Montréal/CA
  • 4 Pathology, Centre Hospitalier de l'Université de Montréal (CHUM), H2X 3E4 - Montréal/CA
  • 5 Genetics, Ontario Institut for Cancer Research, M5G 0A3 - Toronto/CA
  • 6 Cancer Sciences, Cancer Research UK Manchester Institute, M20 4BX - Manchester/GB
  • 7 Pathology, Toronto General Hospital, M5G 2C4 - Toronto/CA
  • 8 Laboratoire D'uro-oncologie Expérimentale, CHU de Québec-Université Laval, G1R 3S1 - Quebec/CA
  • 9 Oncologie, CHU de Québec-Université Laval, G1R 2J6 - Québec/CA

Resources

Login to access the resources on OncologyPRO.

If you do not have an ESMO account, please create one for free.

Abstract 2734

Background

Prostate cancer (PC), initially diagnosed on biopsies by pathologists, is the most frequent cancer in North American men. However, better tools are needed for pathologists to diagnose intraductal carcinoma of the prostate (IDC), an aggressive histopathological variant of PC for which therapeutic options are now available. Indeed, no technique or biomarker is clinically available to support the diagnosis of IDC. Raman spectroscopy (RS) provides a global molecular characterisation of the tissue by analysing how photons interact with the molecules present in the tissue. Indeed, we and other groups previously used RS to detect cancer from multiple organ types, machine learning classification models being employed to process the complex Raman data.

Methods

We used Raman micro-spectroscopy (RµS) to detect IDC on tissues from 483 first-line radical prostatectomies from three Canadian institutions. Following a rapid, standardized and low-cost protocol, we acquired an average of 7 Raman spectra per patient and generated classification models using machine learning technology. Importantly, models were trained with data from one institution before independent testing on the data from the other two institutions.

Results

The three institutions included 272, 76 and 135 patients. Median age at diagnosis ranged from 61-62 years-old, with median pre-operative PSA ranging from 6.6-7.4 µg/L. Most patient had ≤3 + 4 Gleason score (60-80% of the specimens) and pT3-stage incidence was 31-55%. IDC was identified in 6-18% of the patients of each cohort. Overall, we acquired an average of 7 Raman spectra per patient. In the training cohort (N = 272), RµS identified IDC with a sensitivity of 95%, a specificity of 94% and an accuracy of 94%. Results from the testing cohort were in a similar range, with sensitivities of 88 and 92%, specificities of 83 and 91% and accuracies of 85 and 91%.

Conclusions

As clinically available biomarkers of IDC have reported sensitivities/specificities of ∼80%, we here identified IDC with accuracies ≥85%. Since our classification model was trained on a cohort and independently tested on the other two, these are likely to be close to real life experience making clinical implementation a realistic outcome.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM, continuum program) IVADO, Institut de valorisation des données.

Disclosure

All authors have declared no conflicts of interest.

This site uses cookies. Some of these cookies are essential, while others help us improve your experience by providing insights into how the site is being used.

For more detailed information on the cookies we use, please check our Privacy Policy.

Customise settings
  • Necessary cookies enable core functionality. The website cannot function properly without these cookies, and you can only disable them by changing your browser preferences.