Abstract A014: Raman microscopy to assess biochemical recurrence risk after radical prostatectomy
Bibliographic record
Abstract
Abstract Background: There is an urgent need for pathologists to better define patients with high-risk prostate cancer. One of the promising tools is Raman micro-spectroscopy, also known as Raman microscopy, a nondestructive and label-free imaging technique based on light scattered after reflection. Our group has recently developed a rapid standardized protocol for the preparation of formalin-fixed, paraffin-embedded (FFPE) diagnostic tissues suitable for Raman microscopy. The objective of this study was to evaluate the potential of Raman microscopy to assess the prognosis of prostate cancer patients with FFPE tissues from radical prostatectomy. Methods: Patients treated by first-line radical prostatectomy between 1994 and 2004 at Centre hospitalier de l’Université de Montréal (CHUM) were included in this study. FFPE prostate cancer tissues from surgery were used for the construction of tissue microarrays (TMAs). To enable Raman microscopy, TMA sections of 4 µm were placed on low-cost aluminum slides. The rapid dewaxing protocol of the hospital was used (8 minutes), followed by 20 minutes of vacuum drying. All Raman spectra were acquired with the Renishaw inVia confocal Raman microscope equipped with a 785-nm line focus laser. After removing background contributions (e.g., autofluorescence) for each spectrum with Wire 4.4 software, a custom toolbox in MATLAB was used to predict biochemical recurrence. Chemometric methods and calculated ratios were used for the analysis of Raman microscopy images. Results: A total of 320 Raman spectra from 80 patients were analyzed from prostate cancer TMAs, representing 25 patients with biochemical recurrence within 18 months after radical prostatectomy and 55 without. Using a Support Vector Machine (SVM) technique and correlation feature selection for classification, our results with Raman microscopy identified biochemical recurrence with an accuracy of 83.7%, a sensitivity of 84.0% and a specificity of 83.6%. Raman peak assignment of features was used to investigate the molecular differences between these two patient groups. We found that the molecular constituents of RNA and phosphorylated proteins were more important in prostate cancer with biochemical recurrence. In contrast, Raman peaks of the phospholipid head of cell membranes, DNA, and collagen were more intense in prostate cancer without biochemical recurrence. For the visualization of these different molecular constituents of prostate cancer, we developed two methods of Raman microscopy imaging. The first method involved analysis of chemometric data (i.e., extraction of chemical information) to identify the whole tissue (phenylalanine), nuclei (DNA), and red blood cells (hemoglobin), followed by background removal. The images of our chemometric analysis created a virtual staining of hematoxylin and eosin (H&E). The second method involved testing several ratios of Raman peaks associated with proteins, lipids, DNA and RNA. Calculated ratios distinguished specific structures of the prostate tissue, such as the cancerous and normal glands, by different colors. Conclusions: This is the first study demonstrating the potential of Raman microscopy for the prediction of biochemical recurrence within 18 months following radical prostatectomy for prostate cancer. Raman microscopy imaging of tissues is a promising method for the recognition of specific structures, which could help pathologists in the accuracy of diagnosis. The accessibility of this technology to clinicians could be useful for patient follow-up and treatment strategies. Citation Format: Andrée-Anne Grosset, Catherine St-Pierre, Karl St-Arnaud, Kelly Aubertin, Michael Jermyn, Frédéric Leblond, Dominique Trudel. Raman microscopy to assess biochemical recurrence risk after radical prostatectomy [abstract]. In: Proceedings of the AACR Special Conference: Prostate Cancer: Advances in Basic, Translational, and Clinical Research; 2017 Dec 2-5; Orlando, Florida. Philadelphia (PA): AACR; Cancer Res 2018;78(16 Suppl):Abstract nr A014.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".