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Prostate cancer is the most common cancer among men. An accurate diagnosis of its severity at detection plays a major role in improving their survival. Recently, machine learning models using biomarkers identified from Raman micro-spectroscopy discriminated intraductal carcinoma of the prostate (IDC-P) from cancer tissue with a ≥85  %   detection accuracy and differentiated high-grade prostatic intraepithelial neoplasia (HGPIN) from IDC-P with a ≥97.8  %   accuracy. To improve the classification performance of machine learning models identifying different types of prostate cancer tissue using a new dimensional reduction technique. A radial basis function (RBF) kernel support vector machine (SVM) model was trained on Raman spectra of prostate tissue from a 272-patient cohort (Centre hospitalier de l'Université de Montréal, CHUM) and tested on two independent cohorts of 76 patients [University Health Network (UHN)] and 135 patients (Centre hospitalier universitaire de Québec-Université Laval, CHUQc-UL). Two types of engineered features were used. Individual intensity features, i.e., Raman signal intensity measured at particular wavelengths and novel Raman spectra fitted peak features consisting of peak heights and widths. Combining engineered features improved classification performance for the three aforementioned classification tasks. The improvements for IDC-P/cancer classification for the UHN and CHUQc-UL testing sets in accuracy, sensitivity, specificity, and area under the curve (AUC) are (numbers in parenthesis are associated with the CHUQc-UL testing set): +4  %   (+8  %  ), +7  %   (+9  %  ), +2  %   (6%), +9 (+9) with respect to the current best models. Discrimination between HGPIN and IDC-P was also improved in both testing cohorts: +2.2  %   (+1.7  %  ), +4.5  %   (+3.6  %  ), +0  %   (+0  %  ), +2.3 (+0). While no global improvements were obtained for the normal versus cancer classification task [+0  %   (-2  %  ), +0  %   (-3  %  ), +2  %   (-2  %  ), +4 (+3)], the AUC was improved in both testing sets. Combining individual intensity features and novel Raman fitted peak features, improved the classification performance on two independent and multicenter testing sets in comparison to using only individual intensity features.

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.

Lung cancer is the most frequently diagnosed cancer overall and the deadliest cancer in North America. Early diagnosis through current bronchoscopy techniques is limited by poor diagnostic yield and low specificity, especially for lesions located in peripheral pulmonary locations. Even with the emergence of robotic-assisted platforms, bronchoscopy diagnostic yields remain below 80%. The aim of this study was to determine whether in situ single-point fingerprint (800 to 1700 cm − 1) Raman spectroscopy coupled with machine learning could detect lung cancer within an otherwise heterogenous background composed of normal tissue and tissue associated with benign conditions, including emphysema and bronchiolitis. A Raman spectroscopy probe was used to measure the spectral fingerprint of normal, benign, and cancer lung tissue in 10 patients. Each interrogated specimen was characterized by histology to determine cancer type, i.e., small cell carcinoma or non-small cell carcinoma (adenocarcinoma and squamous cell carcinoma). Biomolecular information was extracted from the fingerprint spectra to identify biomolecular features that can be used for cancer detection. Supervised machine learning models were trained using leave-one-patient-out cross-validation, showing lung cancer could be detected with a sensitivity of 94% and a specificity of 80%. This proof of concept demonstrates fingerprint Raman spectroscopy is a promising tool for the detection of lung cancer during diagnostic procedures and can capture biomolecular changes associated with the presence of cancer among a complex heterogeneous background within less than 1 s. [ABSTRACT FROM AUTHOR]

Objective: Biopsies for prostate cancer (PCa) are prone to Gleason score (GS) upgrading after radical prostatectomy (RP) due to undersampling. This discrepancy can be reduced with a novel label-free and non-destructive tissue spectral analysis technique, Raman spectroscopy (RS). Our pilot study consists in identifying PCa through RS in ex vivo prostate. Method: We included patients undergoing RP for PCa. A 4mm prostate section is sampled <2 hours post-RP. Approximately 40 spectral acquisitions are performed with a multi-wavelength hand-held Raman probe that has a diameter of 500 microns. Each acquisition point is inked and the section is processed for histological analysis by H&E staining. Each point is associated to spectral acquisitions by creating a correlation area (CA) that compensated for positional variations by adding a 1000 microns buffer zone. Preliminary histological data components for each CA included benign, benign hyperplasia, cancer, cancer variant and GS. Spectral acquisitions are analyzed by Raman-specific machine learning methods in order to identify cancer. Data and results: Our pilot study included 7 patients, totaling 220 acquisitions. 74 CA were fully delimitated in a PCa nodule and 146 in benign tissue. After processing through machine learning algorithms, RS achieved a 76% sensitivity, 99% specificity and 91% accuracy for cancer detection with the leave-one-out procedure. Conclusion: Preliminary data suggests that Raman spectroscopy in ex vivo RP specimens shows high accuracy for PCa detection. Our results are being fully explored in a 50 patient study that will additionally propose novel algorithms for GS stratification and cancer density testing. [ABSTRACT FROM AUTHOR]

Objective: The integration of complementary analyzes to help pathologists determine the diagnosis and prognosis of cancer patients is important. Raman microscopy is a promising tool, based on the analysis of how light is scattered after reflection on a surface. This non-destructive and label-free technique indicates the molecular content of the tissue. However, current Raman microscopy studies implicate parameters that are incompatible with the clinical workflow, such as frozen tissue, expensive slides and/or 15µm sections of formalin-fixed paraffin-embedded (FFPE) tissues. The objective of this study was to evaluate the potential of Raman microscopy as a clinical tool. Methods: The rapid standardized clinical protocol for the preparation of FFPE tissues of our hospital (CHUM) and low-cost aluminum slides were used. Tissue microarrays containing FFPE prostate tissues from 80 patients were included in this study. A confocal Raman microscope was used for the acquisition of Raman spectra. Data and results: A total of 160 Raman spectra from benign prostatic tissues and 320 Raman spectra from prostate cancer were acquired. Prostate tissue classification with artificial intelligence, specifically with Support Vector Machine (SVM) technique, identified benign tissue, cancer, grade groups and biochemical recurrence within 18 months with accuracy, sensitivity and specificity greater than 83%. Conclusions: This is the first study demonstrating the potential of Raman microscopy using a rapid standardized clinical protocol for the preparation of FFPE tissues. The evaluation of the diagnosis and prognosis of patients by this technique should be exploited by pathologists for precise patient management. [ABSTRACT FROM AUTHOR]

Extracellular vesicles, secreted spontaneously or in response to stress by all cell types, are proposed as alternative biotherapies to cellular therapies and to synthetic nanomedicines. Their logistical advantages (storage, stability, availability, tolerance), their ability to cross biological barriers, to deliver their contents (proteins, lipids and nucleic acids) in order to modify their target cells, as well as their immunomodulatory and regenerative activities, are of growing interest for a very wide spectrum of diseases. Here we review the challenges to bring these biotherapies to the clinic and discuss some promising applications in cancer and regenerative medicine., (© 2021 médecine/sciences – Inserm.)