1
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Ember K, Dallaire F, Plante A, Sheehy G, Guiot MC, Agarwal R, Yadav R, Douet A, Selb J, Tremblay JP, Dupuis A, Marple E, Urmey K, Rizea C, Harb A, McCarthy L, Schupper A, Umphlett M, Tsankova N, Leblond F, Hadjipanayis C, Petrecca K. In situ brain tumor detection using a Raman spectroscopy system-results of a multicenter study. Sci Rep 2024; 14:13309. [PMID: 38858389 PMCID: PMC11164901 DOI: 10.1038/s41598-024-62543-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Accepted: 05/17/2024] [Indexed: 06/12/2024] Open
Abstract
Safe and effective brain tumor surgery aims to remove tumor tissue, not non-tumoral brain. This is a challenge since tumor cells are often not visually distinguishable from peritumoral brain during surgery. To address this, we conducted a multicenter study testing whether the Sentry System could distinguish the three most common types of brain tumors from brain tissue in a label-free manner. The Sentry System is a new real time, in situ brain tumor detection device that merges Raman spectroscopy with machine learning tissue classifiers. Nine hundred and seventy-six in situ spectroscopy measurements and colocalized tissue specimens were acquired from 67 patients undergoing surgery for glioblastoma, brain metastases, or meningioma to assess tumor classification. The device achieved diagnostic accuracies of 91% for glioblastoma, 97% for brain metastases, and 96% for meningiomas. These data show that the Sentry System discriminated tumor containing tissue from non-tumoral brain in real time and prior to resection.
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Affiliation(s)
- Katherine Ember
- Polytechnique Montréal, Montreal, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, Canada
| | - Frédérick Dallaire
- Polytechnique Montréal, Montreal, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, Canada
| | - Arthur Plante
- Polytechnique Montréal, Montreal, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, Canada
| | - Guillaume Sheehy
- Polytechnique Montréal, Montreal, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, Canada
| | - Marie-Christine Guiot
- Division of Neuropathology, Department of Pathology, Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Frédéric Leblond
- Polytechnique Montréal, Montreal, Canada.
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, Canada.
- Institut du Cancer de Montréal, Montreal, Canada.
| | - Constantinos Hadjipanayis
- Mount Sinai Hospital, New York, NY, USA.
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
| | - Kevin Petrecca
- Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada.
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2
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Dai B, Han D, Miao Y, Zhou Y, Hajiarbabi M, Wang Y, Butch CJ, Cai H, Hu J. Accurate categorization and rapid pathological diagnosis correction with Micro-Raman technique in human lung adenocarcinoma infiltration level. Transl Lung Cancer Res 2024; 13:885-900. [PMID: 38736487 PMCID: PMC11082714 DOI: 10.21037/tlcr-24-168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Accepted: 04/01/2024] [Indexed: 05/14/2024]
Abstract
Background In the context of surgical interventions for lung adenocarcinoma (LADC), precise determination of the extent of LADC infiltration plays a pivotal role in shaping the surgeon's strategic approach to the procedure. The prevailing diagnostic standard involves the expeditious intraoperative pathological diagnosis of areas infiltrated by LADC. Nevertheless, current methodologies rely on the visual interpretation of tissue images by proficient pathologists, introducing an error margin of up to 15.6%. Methods In this study, we investigated the utilization of Micro-Raman technique on isolated specimens of human LADC with the objective of formulating and validating a workflow for the pathological diagnosis of LADC featuring diverse degrees of infiltration. Our strategy encompasses a thorough pathological characterization of LADC, spanning different tissue types and levels of infiltration. Through the integration of Raman spectroscopy with advanced deep learning models for simultaneous diagnosis, this approach offers a swift, precise, and clinically relevant means of analysis. Results The diagnostic performance of the convolutional neural network (CNN) model, coupled with the microscopic Raman technique, was found to be exceptional and consistent, surpassing the traditional support vector machine (SVM) model. The CNN model exhibited an area under the curve (AUC) value of 96.1% for effectively distinguishing normal tissue from LADC and an impressive 99.0% for discerning varying degrees of infiltration in LADCs. To comprehensively assess its clinical utility, Raman datasets from patients with intraoperative rapid pathologic diagnostic errors were utilized as test subjects and input into the established CNN model. The results underscored the substantial corrective capacity of the Micro-Raman technique, revealing a misdiagnosis correction rate exceeding 96% in all cases. Conclusions Ultimately, our discoveries highlight the Micro-Raman technique's potential to augment the intraoperative diagnostic precision of LADC with varying levels of infiltration. And compared to the traditional SVM model, the CNN model has better generalization ability in diagnosing different infiltration levels. This method furnishes surgeons with an objective groundwork for making well-informed decisions concerning subsequent surgical plans.
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Affiliation(s)
- Bo Dai
- Department of Biomedical Engineering, College of Engineering and Applied Sciences, State Key Laboratory of Analytical Chemistry for Life Science, Nanjing University, Nanjing, China
- Department of Thoracic Surgery, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Dong Han
- Department of Biomedical Engineering, College of Engineering and Applied Sciences, State Key Laboratory of Analytical Chemistry for Life Science, Nanjing University, Nanjing, China
| | - Yufei Miao
- Department of Biomedical Engineering, College of Engineering and Applied Sciences, State Key Laboratory of Analytical Chemistry for Life Science, Nanjing University, Nanjing, China
| | - Yong Zhou
- Department of Thoracic Surgery, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | | | - Yiqing Wang
- Department of Biomedical Engineering, College of Engineering and Applied Sciences, State Key Laboratory of Analytical Chemistry for Life Science, Nanjing University, Nanjing, China
| | - Christopher J. Butch
- Department of Biomedical Engineering, College of Engineering and Applied Sciences, State Key Laboratory of Analytical Chemistry for Life Science, Nanjing University, Nanjing, China
| | - Huiming Cai
- Department of Biomedical Engineering, College of Engineering and Applied Sciences, State Key Laboratory of Analytical Chemistry for Life Science, Nanjing University, Nanjing, China
- Nanjing Nuoyuan Medical Devices Co., Ltd., Nanjing, China
| | - Jian Hu
- Department of Thoracic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Clinical Evaluation Technology for Medical Device of Zhejiang Province, Hangzhou, China
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3
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Azadi Moghadam P, Bashashati A, Goldenberg SL. Artificial Intelligence and Pathomics: Prostate Cancer. Urol Clin North Am 2024; 51:15-26. [PMID: 37945099 DOI: 10.1016/j.ucl.2023.06.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
Artificial intelligence (AI) has the potential to transform pathologic diagnosis and cancer patient management as a predictive and prognostic biomarker. AI-based systems can be used to examine digitally scanned histopathology slides and differentiate benign from malignant cells and low from high grade. Deep learning models can analyze patient data from individual or multimodal combinations and identify patterns to be used to predict the response to different therapeutic options, the risk of recurrence or progression, and the prognosis of the newly diagnosed patient. AI-based models will improve treatment planning for patients with prostate cancer and improve the efficiency and cost-effectiveness of the pathology laboratory.
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Affiliation(s)
- Puria Azadi Moghadam
- Department of Electrical and Computer Engineering, University of British Columbia, 2332 Main Mall, Vancouver, British Columbia V6T 1Z4, Canada
| | - Ali Bashashati
- School of Biomedical Engineering, University of British Columbia, 2222 Health Sciences Mall, Vancouver, British Columbia V6T 1Z3, Canada; Department of Pathology and Laboratory Medicine, University of British Columbia, 2211 Wesbrook Mall, Vancouver, BC V6T 1Z7, Canada
| | - S Larry Goldenberg
- Department of Urologic Sciences, University of British Columbia, 2775 Laurel Street, Vancouver British Columbia V5Z 1M9, Canada.
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4
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Kim JH, Zhang C, Sperati CJ, Barman I, Bagnasco SM. Hyperspectral Raman Imaging for Automated Recognition of Human Renal Amyloid. J Histochem Cytochem 2023; 71:643-652. [PMID: 37833851 PMCID: PMC10617441 DOI: 10.1369/00221554231206858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 09/15/2023] [Indexed: 10/15/2023] Open
Abstract
In the clinical setting, routine identification of the main types of tissue amyloid deposits, light-chain amyloid (AL) and serum amyloid A (AA), is based on histochemical staining; rarer types of amyloid require mass spectrometry analysis. Raman spectroscopic imaging is an analytical tool, which can be used to chemically map, and thus characterize, the molecular composition of fluid and solid tissue. In this proof-of-concept study, we tested the feasibility of applying Raman spectroscopy combined with artificial intelligence to detect and characterize amyloid deposits in unstained frozen tissue sections from kidney biopsies with pathologic diagnosis of AL and AA amyloidosis and control biopsies with no amyloidosis (NA). Raman hyperspectral images, mapped in a 2D grid-like fashion over the tissue sections, were obtained. Three machine learning-assisted analysis models of the hyperspectral images could accurately distinguish AL (types λ and κ), AA, and NA 93-100% of the time. Although very preliminary, these findings illustrate the potential of Raman spectroscopy as a technique to identify, and possibly, subtype renal amyloidosis.
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Affiliation(s)
- Jeong Hee Kim
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Chi Zhang
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - C. John Sperati
- Division of Nephrology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Ishan Barman
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Serena M. Bagnasco
- Clinical Renal Biopsy Service, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland
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5
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Gagnon JR, Allen CH, Trudel D, Leblond F, Stys PK, Brideau C, Murugkar S. Spectral focusing-based stimulated Raman scattering microscopy using compact glass blocks for adjustable dispersion. BIOMEDICAL OPTICS EXPRESS 2023; 14:2510-2522. [PMID: 37342685 PMCID: PMC10278629 DOI: 10.1364/boe.486753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 04/09/2023] [Accepted: 04/11/2023] [Indexed: 06/23/2023]
Abstract
Spectral focusing is a well-established technique for increasing spectral resolution in coherent Raman scattering microscopy. However, current methods for tuning optical chirp in setups using spectral focusing, such as glass rods, gratings, and prisms, are very cumbersome, time-consuming to use, and difficult to align, all of which limit more widespread use of the spectral focusing technique. Here, we report a stimulated Raman scattering (SRS) configuration which can rapidly tune optical chirp by utilizing compact adjustable-dispersion TIH53 glass blocks. By varying the height of the blocks, the number of bounces in the blocks and therefore path length of the pulses through the glass can be quickly modulated, allowing for a convenient method of adjusting chirp with almost no necessary realignment. To demonstrate the flexibility of this configuration, we characterize our system's signal-to-noise ratio and spectral resolution at different chirp values and perform imaging in both the carbon-hydrogen stretching region (MCF-7 cells) and fingerprint region (prostate cores). Our findings show that adjustable-dispersion glass blocks allow the user to effortlessly modify their optical system to suit their imaging requirements. These blocks can be used to significantly simplify and miniaturize experimental configurations utilizing spectral focusing.
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Affiliation(s)
- Justin R. Gagnon
- Department of Physics,
Carleton University, 1125
Colonel By Drive, Ottawa, Ontario, K1S 5B6, Canada
| | - Christian Harry Allen
- Department of Physics,
Carleton University, 1125
Colonel By Drive, Ottawa, Ontario, K1S 5B6, Canada
| | - Dominique Trudel
- Centre de recherche du Centre
hospitalier de l’Université de
Montréal, Montreal, Quebec, Canada
- Institut du cancer de
Montréal, Montreal, Quebec, Canada
- Department of Pathology and cellular
Biology, Université de Montréal
2900, boulevard Édouard-Montpetit, Montreal,
Quebec, Canada
| | - Frederic Leblond
- Centre de recherche du Centre
hospitalier de l’Université de
Montréal, Montreal, Quebec, Canada
- Institut du cancer de
Montréal, Montreal, Quebec, Canada
- Department of Engineering Physics,
Polytechnique Montréal, 2500 chemin
de Polytechnique, Montreal, Quebec, Canada
| | - Peter K. Stys
- Department of Clinical Neurosciences,
University of Calgary, 3330 Hospital Drive
N.W. HRIC 1B37A, Calgary, Alberta, T2N 4N1, Canada
| | - Craig Brideau
- Department of Clinical Neurosciences,
University of Calgary, 3330 Hospital Drive
N.W. HRIC 1B37A, Calgary, Alberta, T2N 4N1, Canada
| | - Sangeeta Murugkar
- Department of Physics,
Carleton University, 1125
Colonel By Drive, Ottawa, Ontario, K1S 5B6, Canada
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Wang W, Shi B, He C, Wu S, Zhu L, Jiang J, Wang L, Lin L, Ye J, Zhang H. Euclidean distance-based Raman spectroscopy (EDRS) for the prognosis analysis of gastric cancer: A solution to tumor heterogeneity. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 288:122163. [PMID: 36462319 DOI: 10.1016/j.saa.2022.122163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 11/20/2022] [Accepted: 11/22/2022] [Indexed: 06/17/2023]
Abstract
The prognosis analysis of gastric cancer is critical for selection of treatments and development of advanced therapeutic methods. A prognosis approach that is accurate, fast, convenient, and of low cost for gastric cancers is in high demand. Raman spectroscopy is a label-free and non-destructive technique to provide molecular fingerprints of biological samples, holding promises for cancer prognosis. However, the major challenge of gastric cancer prognosis lies in the widely existing tumor heterogeneity, which leads to unexpected spectral variations within one type of samples. In this work, we have developed the Euclidean distance (ED)-based Raman spectroscopy (EDRS) method for the prognosis analysis of gastric cancer to eliminate the influence of tumor heterogeneity. Raman spectra were first collected on the slices of paraffin-preserved tumor tissues from gastric cancer patients. A standard spectrum to represent the 'worst prognostic tumor cells' was then established. The similarity between each spectrum of tissues and the standard spectrum was assessed by ED, to provide a direct assessment on the prognosis status. We have successfully classified the patients into poor and favorable prognosis groups, either based on the averaged regional ED values (sensitivity of 75 %, specificity of 96.8 %), or based on the minimal ED values at the patient level (sensitivity of 90 %, specificity of 100 %). EDRS was also investigated for survival analysis (AUC = 0.955), much better than the commonly applied post-neoadjuvant therapy (ypTNM) category (AUC = 0.718). Our work highlights EDRS as a rapid, accurate, low-cost and robust tool for heterogeneous cancer-related prognosis assessment and survival prediction, providing new insights for spectroscopic tumor analysis.
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Affiliation(s)
- Wenfang Wang
- Department of Radiology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, PR China
| | - Bowen Shi
- Department of Radiology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, PR China
| | - Chang He
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, PR China
| | - Siyi Wu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, PR China
| | - Lan Zhu
- Department of Radiology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, PR China
| | - Jiang Jiang
- Department of Radiology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, PR China
| | - Lingyun Wang
- Department of Radiology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, PR China
| | - Li Lin
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, PR China.
| | - Jian Ye
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, PR China; Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai 200240, PR China.
| | - Huan Zhang
- Department of Radiology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, PR China.
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David S, Tran T, Dallaire F, Sheehy G, Azzi F, Trudel D, Tremblay F, Omeroglu A, Leblond F, Meterissian S. In situ Raman spectroscopy and machine learning unveil biomolecular alterations in invasive breast cancer. JOURNAL OF BIOMEDICAL OPTICS 2023; 28:036009. [PMID: 37009577 PMCID: PMC10062385 DOI: 10.1117/1.jbo.28.3.036009] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 02/27/2023] [Indexed: 06/19/2023]
Abstract
SIGNIFICANCE As many as 60% of patients with early stage breast cancer undergo breast-conserving surgery. Of those, 20% to 35% need a second surgery because of incomplete resection of the lesions. A technology allowing in situ detection of cancer could reduce re-excision procedure rates and improve patient survival. AIM Raman spectroscopy was used to measure the spectral fingerprint of normal breast and cancer tissue ex-vivo. The aim was to build a machine learning model and to identify the biomolecular bands that allow one to detect invasive breast cancer. APPROACH The system was used to interrogate specimens from 20 patients undergoing lumpectomy, mastectomy, or breast reduction surgery. This resulted in 238 ex-vivo measurements spatially registered with standard histology classifying tissue as cancer, normal, or fat. A technique based on support vector machines led to the development of predictive models, and their performance was quantified using a receiver-operating-characteristic analysis. RESULTS Raman spectroscopy combined with machine learning detected normal breast from ductal or lobular invasive cancer with a sensitivity of 93% and a specificity of 95%. This was achieved using a model based on only two spectral bands, including the peaks associated with C-C stretching of proteins around 940 cm - 1 and the symmetric ring breathing at 1004 cm - 1 associated with phenylalanine. CONCLUSIONS Detection of cancer on the margins of surgically resected breast specimen is feasible with Raman spectroscopy.
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Affiliation(s)
- Sandryne David
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | - Trang Tran
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | - Frédérick Dallaire
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | - Guillaume Sheehy
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | - Feryel Azzi
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | - Dominique Trudel
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
- Université de Montréal, Department of Pathology and Cellular Biology, Montreal, Quebec, Canada
| | - Francine Tremblay
- McGill University Health Center, Department of Surgery, Montreal, Quebec, Canada
| | - Atilla Omeroglu
- McGill University Health Center, Department of Pathology, Montreal, Quebec, Canada
| | - Frédéric Leblond
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
| | - Sarkis Meterissian
- McGill University Health Center, Department of Surgery, Montreal, Quebec, Canada
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Morato NM, Brown HM, Garcia D, Middlebrooks EH, Jentoft M, Chaichana K, Quiñones-Hinojosa A, Cooks RG. High-throughput analysis of tissue microarrays using automated desorption electrospray ionization mass spectrometry. Sci Rep 2022; 12:18851. [PMID: 36344609 PMCID: PMC9640715 DOI: 10.1038/s41598-022-22924-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 10/20/2022] [Indexed: 11/09/2022] Open
Abstract
Tissue microarrays (TMAs) are commonly used for the rapid analysis of large numbers of tissue samples, often in morphological assessments but increasingly in spectroscopic analysis, where specific molecular markers are targeted via immunostaining. Here we report the use of an automated high-throughput system based on desorption electrospray ionization (DESI) mass spectrometry (MS) for the rapid generation and online analysis of high-density (6144 samples/array) TMAs, at rates better than 1 sample/second. Direct open-air analysis of tissue samples (hundreds of nanograms) not subjected to prior preparation, plus the ability to provide molecular characterization by tandem mass spectrometry (MS/MS), make this experiment versatile and applicable to both targeted and untargeted analysis in a label-free manner. These capabilities are demonstrated in a proof-of-concept study of frozen brain tissue biopsies where we showcase (i) a targeted MS/MS application aimed at identification of isocitrate dehydrogenase mutation in glioma samples and (ii) an untargeted MS tissue type classification using lipid profiles and correlation with tumor cell percentage estimates from histopathology. The small sample sizes and large sample numbers accessible with this methodology make for a powerful analytical system that facilitates the identification of molecular markers for later use in intraoperative applications to guide precision surgeries and ultimately improve patient outcomes.
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Affiliation(s)
- Nicolás M. Morato
- grid.169077.e0000 0004 1937 2197Department of Chemistry, Purdue Center for Cancer Research, and Bindley Bioscience Center, Purdue University, 560 Oval Drive, West Lafayette, IN 47907 USA
| | - Hannah Marie Brown
- grid.169077.e0000 0004 1937 2197Department of Chemistry, Purdue Center for Cancer Research, and Bindley Bioscience Center, Purdue University, 560 Oval Drive, West Lafayette, IN 47907 USA ,grid.4367.60000 0001 2355 7002Present Address: Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO USA
| | - Diogo Garcia
- grid.417467.70000 0004 0443 9942Department of Neurosurgery, Mayo Clinic, Jacksonville, FL USA
| | - Erik H. Middlebrooks
- grid.417467.70000 0004 0443 9942Department of Neurosurgery, Mayo Clinic, Jacksonville, FL USA ,grid.417467.70000 0004 0443 9942Department of Radiology, Mayo Clinic, Jacksonville, FL USA
| | - Mark Jentoft
- grid.417467.70000 0004 0443 9942Department of Laboratory Medicine and Pathology, Mayo Clinic, Jacksonville, FL USA
| | - Kaisorn Chaichana
- grid.417467.70000 0004 0443 9942Department of Neurosurgery, Mayo Clinic, Jacksonville, FL USA
| | | | - R. Graham Cooks
- grid.169077.e0000 0004 1937 2197Department of Chemistry, Purdue Center for Cancer Research, and Bindley Bioscience Center, Purdue University, 560 Oval Drive, West Lafayette, IN 47907 USA
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9
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Picot F, Shams R, Dallaire F, Sheehy G, Trang T, Grajales D, Birlea M, Trudel D, Ménard C, Kadoury S, Leblond F. Image-guided Raman spectroscopy navigation system to improve transperineal prostate cancer detection. Part 1: Raman spectroscopy fiber-optics system and in situ tissue characterization. JOURNAL OF BIOMEDICAL OPTICS 2022; 27:JBO-220045GRR. [PMID: 36045491 PMCID: PMC9433338 DOI: 10.1117/1.jbo.27.9.095003] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 08/16/2022] [Indexed: 05/28/2023]
Abstract
SIGNIFICANCE The diagnosis of prostate cancer (PCa) and focal treatment by brachytherapy are limited by the lack of precise intraoperative information to target tumors during biopsy collection and radiation seed placement. Image-guidance techniques could improve the safety and diagnostic yield of biopsy collection as well as increase the efficacy of radiotherapy. AIM To estimate the accuracy of PCa detection using in situ Raman spectroscopy (RS) in a pilot in-human clinical study and assess biochemical differences between in vivo and ex vivo measurements. APPROACH A new miniature RS fiber-optics system equipped with an electromagnetic (EM) tracker was guided by trans-rectal ultrasound-guided imaging, fused with preoperative magnetic resonance imaging to acquire 49 spectra in situ (in vivo) from 18 PCa patients. In addition, 179 spectra were acquired ex vivo in fresh prostate samples from 14 patients who underwent radical prostatectomy. Two machine-learning models were trained to discriminate cancer from normal prostate tissue from both in situ and ex vivo datasets. RESULTS A support vector machine (SVM) model was trained on the in situ dataset and its performance was evaluated using leave-one-patient-out cross validation from 28 normal prostate measurements and 21 in-tumor measurements. The model performed at 86% sensitivity and 72% specificity. Similarly, an SVM model was trained with the ex vivo dataset from 152 normal prostate measurements and 27 tumor measurements showing reduced cancer detection performance mostly attributable to spatial registration inaccuracies between probe measurements and histology assessment. A qualitative comparison between in situ and ex vivo measurements demonstrated a one-to-one correspondence and similar ratios between the main Raman bands (e.g., amide I-II bands, phenylalanine). CONCLUSIONS PCa detection can be achieved using RS and machine learning models for image-guidance applications using in situ measurements during prostate biopsy procedures.
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Affiliation(s)
- Fabien Picot
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | - Roozbeh Shams
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Polytechnique Montréal, Medical Laboratory, Montreal, Quebec, Canada
| | - Frédérick Dallaire
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | - Guillaume Sheehy
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | - Tran Trang
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | - David Grajales
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Polytechnique Montréal, Medical Laboratory, Montreal, Quebec, Canada
| | - Mirela Birlea
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | - Dominique Trudel
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | - Cynthia Ménard
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | - Samuel Kadoury
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Polytechnique Montréal, Medical Laboratory, Montreal, Quebec, Canada
| | - Frédéric Leblond
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
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10
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Grajales D, Picot F, Shams R, Dallaire F, Sheehy G, Alley S, Barkati M, Delouya G, Carrier JF, Birlea M, Trudel D, Leblond F, Ménard C, Kadoury S. Image-guided Raman spectroscopy navigation system to improve transperineal prostate cancer detection. Part 2: in-vivo tumor-targeting using a classification model combining spectral and MRI-radiomics features. JOURNAL OF BIOMEDICAL OPTICS 2022; 27:JBO-220064GR. [PMID: 36085571 PMCID: PMC9459023 DOI: 10.1117/1.jbo.27.9.095004] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 08/12/2022] [Indexed: 06/01/2023]
Abstract
SIGNIFICANCE The diagnosis and treatment of prostate cancer (PCa) are limited by a lack of intraoperative information to accurately target tumors with needles for biopsy and brachytherapy. An innovative image-guidance technique using optical devices could improve the diagnostic yield of biopsy and efficacy of radiotherapy. AIM To evaluate the performance of multimodal PCa detection using biomolecular features from in-situ Raman spectroscopy (RS) combined with image-based (radiomics) features from multiparametric magnetic resonance images (mpMRI). APPROACH In a prospective pilot clinical study, 18 patients were recruited and underwent high-dose-rate brachytherapy. Multimodality image fusion (preoperative mpMRI with intraoperative transrectal ultrasound) combined with electromagnetic tracking was used to navigate an RS needle in the prostate prior to brachytherapy. This resulting dataset consisted of Raman spectra and co-located radiomics features from mpMRI. Feature selection was performed with the constraint that no more than 10 features were retained overall from a combination of inelastic scattering spectra and radiomics. These features were used to train support vector machine classifiers for PCa detection based on leave-one-patient-out cross-validation. RESULTS RS along with biopsy samples were acquired from 47 sites along the insertion trajectory of the fiber-optics needle: 26 were confirmed as benign or grade group = 1, and 21 as grade group >1, according to histopathological reports. The combination of the fingerprint region of the RS and radiomics showed an accuracy of 83% (sensitivity = 81 % and a specificity = 85 % ), outperforming by more than 9% models trained with either spectroscopic or mpMRI data alone. An optimal number of features was identified between 6 and 8 features, which have good potential for discriminating grade group ≥1 / grade group <1 (accuracy = 87 % ) or grade group >1 / grade group ≤1 (accuracy = 91 % ). CONCLUSIONS In-situ Raman spectroscopy combined with mpMRI radiomics features can lead to highly accurate PCa detection for improved in-vivo targeting of biopsy sample collection and radiotherapy seed placement.
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Affiliation(s)
- David Grajales
- Polytechnique Montréal, Montreal, Québec, Canada
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, Québec, Canada
| | - Fabien Picot
- Polytechnique Montréal, Montreal, Québec, Canada
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, Québec, Canada
| | - Roozbeh Shams
- Polytechnique Montréal, Montreal, Québec, Canada
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, Québec, Canada
| | - Frédérick Dallaire
- Polytechnique Montréal, Montreal, Québec, Canada
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, Québec, Canada
| | - Guillaume Sheehy
- Polytechnique Montréal, Montreal, Québec, Canada
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, Québec, Canada
| | - Stephanie Alley
- Polytechnique Montréal, Montreal, Québec, Canada
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, Québec, Canada
| | - Maroie Barkati
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, Québec, Canada
| | - Guila Delouya
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, Québec, Canada
| | - Jean-Francois Carrier
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, Québec, Canada
| | - Mirela Birlea
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, Québec, Canada
| | - Dominique Trudel
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, Québec, Canada
| | - Frédéric Leblond
- Polytechnique Montréal, Montreal, Québec, Canada
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, Québec, Canada
- Institut du Cancer de Montréal, Montreal, Québec, Canada
| | - Cynthia Ménard
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, Québec, Canada
| | - Samuel Kadoury
- Polytechnique Montréal, Montreal, Québec, Canada
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, Québec, Canada
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11
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Li D, Aubertin K, Onidas D, Nizard P, Félidj N, Gazeau F, Mangeney C, Luo Y. Recent advances in non-plasmonic surface-enhanced Raman spectroscopy nanostructures for biomedical applications. WILEY INTERDISCIPLINARY REVIEWS. NANOMEDICINE AND NANOBIOTECHNOLOGY 2022; 14:e1795. [PMID: 35362261 DOI: 10.1002/wnan.1795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 02/18/2022] [Accepted: 02/21/2022] [Indexed: 06/14/2023]
Abstract
Surface-enhanced Raman spectroscopy (SERS) is an emerging powerful vibrational technique offering unprecedented opportunities in biomedical science for the sensitive detection of biomarkers and the imaging and tracking of biological samples. Conventional SERS detection is based on the use of plasmonic substrates (e.g., Au and Ag nanostructures), which exhibit very high enhancement factors (EF = 1010 -1011 ) but suffers from serious limitations, including light-induced local heating effect due to ohmic loss and expensive price. These drawbacks may limit detection accuracy and large-scaled practical applications. In this review, we focus on alternative approaches based on plasmon-free SERS detection on low-cost nanostructures, such as carbons, oxides, chalcogenides, polymers, silicons, and so forth. The mechanism of non-plasmonic SERS detection has been attributed to interfacial charge transfer between the substrate and the adsorbed molecules, with no photothermal side-effects but usually less EF compared with plasmonic nanostructures. The strategies to improve Raman signal detection, through the tailoring of substrate composition, structure, and surface chemistry, is reviewed and discussed. The biomedical applications, for example, SERS cell characterization, biosensing, and bioimaging are also presented, highlighting the importance of substrate surface functionalization to achieve sensitive, accurate analysis, and excellent biocompatibility. This article is categorized under: Diagnostic Tools > Diagnostic Nanodevices Diagnostic Tools > Biosensing Diagnostic Tools > In Vivo Nanodiagnostics and Imaging.
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Affiliation(s)
- Da Li
- LCBPT, CNRS UMR 8601, Université Paris Cité, 45, rue des Saints-Pères, Paris, France
| | - Kelly Aubertin
- MSC, CNRS UMR 7057, Université Paris Cité, 45, rue des Saints-Pères, Paris, France
| | - Delphine Onidas
- LCBPT, CNRS UMR 8601, Université Paris Cité, 45, rue des Saints-Pères, Paris, France
| | - Philippe Nizard
- LCBPT, CNRS UMR 8601, Université Paris Cité, 45, rue des Saints-Pères, Paris, France
| | - Nordin Félidj
- ITODYS, CNRS UMR 7086, Université Paris Cité, 15, rue Jean Antoine de Baïf, Paris, France
| | - Florence Gazeau
- MSC, CNRS UMR 7057, Université Paris Cité, 45, rue des Saints-Pères, Paris, France
| | - Claire Mangeney
- LCBPT, CNRS UMR 8601, Université Paris Cité, 45, rue des Saints-Pères, Paris, France
| | - Yun Luo
- LCBPT, CNRS UMR 8601, Université Paris Cité, 45, rue des Saints-Pères, Paris, France
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12
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Pantazopoulos H, Diop MK, Grosset AA, Rouleau-Gagné F, Al-Saleh A, Boblea T, Trudel D. Intraductal Carcinoma of the Prostate as a Cause of Prostate Cancer Metastasis: A Molecular Portrait. Cancers (Basel) 2022; 14:820. [PMID: 35159086 PMCID: PMC8834356 DOI: 10.3390/cancers14030820] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 02/01/2022] [Accepted: 02/02/2022] [Indexed: 02/01/2023] Open
Abstract
Intraductal carcinoma of the prostate (IDC-P) is one of the most aggressive types of prostate cancer (PCa). IDC-P is identified in approximately 20% of PCa patients and is associated with recurrence, metastasis, and PCa-specific death. The main feature of this histological variant is the colonization of benign glands by PCa cells. Although IDC-P is a well-recognized independent parameter for metastasis, mechanisms by which IDC-P cells can spread and colonize other tissues are not fully known. In this review, we discuss the molecular portraits of IDC-P determined by immunohistochemistry and genomic approaches and highlight the areas in which more research is needed.
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Affiliation(s)
- Helen Pantazopoulos
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM), 900 Saint-Denis, Montreal, QC H2X 0A9, Canada; (H.P.); (M.-K.D.); (A.-A.G.); (F.R.-G.); (A.A.-S.); (T.B.)
- Institut du Cancer de Montréal, 900 Saint-Denis, Montreal, QC H2X 0A9, Canada
- Department of Pathology and Cellular Biology, Université de Montréal, 2900 Boulevard Édouard-Montpetit, Montreal, QC H3T 1J4, Canada
| | - Mame-Kany Diop
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM), 900 Saint-Denis, Montreal, QC H2X 0A9, Canada; (H.P.); (M.-K.D.); (A.-A.G.); (F.R.-G.); (A.A.-S.); (T.B.)
- Institut du Cancer de Montréal, 900 Saint-Denis, Montreal, QC H2X 0A9, Canada
- Department of Pathology and Cellular Biology, Université de Montréal, 2900 Boulevard Édouard-Montpetit, Montreal, QC H3T 1J4, Canada
| | - Andrée-Anne Grosset
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM), 900 Saint-Denis, Montreal, QC H2X 0A9, Canada; (H.P.); (M.-K.D.); (A.-A.G.); (F.R.-G.); (A.A.-S.); (T.B.)
- Institut du Cancer de Montréal, 900 Saint-Denis, Montreal, QC H2X 0A9, Canada
- Department of Pathology and Cellular Biology, Université de Montréal, 2900 Boulevard Édouard-Montpetit, Montreal, QC H3T 1J4, Canada
| | - Frédérique Rouleau-Gagné
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM), 900 Saint-Denis, Montreal, QC H2X 0A9, Canada; (H.P.); (M.-K.D.); (A.-A.G.); (F.R.-G.); (A.A.-S.); (T.B.)
- Institut du Cancer de Montréal, 900 Saint-Denis, Montreal, QC H2X 0A9, Canada
- Department of Pathology and Cellular Biology, Université de Montréal, 2900 Boulevard Édouard-Montpetit, Montreal, QC H3T 1J4, Canada
| | - Afnan Al-Saleh
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM), 900 Saint-Denis, Montreal, QC H2X 0A9, Canada; (H.P.); (M.-K.D.); (A.-A.G.); (F.R.-G.); (A.A.-S.); (T.B.)
- Institut du Cancer de Montréal, 900 Saint-Denis, Montreal, QC H2X 0A9, Canada
- Department of Pathology and Cellular Biology, Université de Montréal, 2900 Boulevard Édouard-Montpetit, Montreal, QC H3T 1J4, Canada
| | - Teodora Boblea
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM), 900 Saint-Denis, Montreal, QC H2X 0A9, Canada; (H.P.); (M.-K.D.); (A.-A.G.); (F.R.-G.); (A.A.-S.); (T.B.)
- Institut du Cancer de Montréal, 900 Saint-Denis, Montreal, QC H2X 0A9, Canada
| | - Dominique Trudel
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM), 900 Saint-Denis, Montreal, QC H2X 0A9, Canada; (H.P.); (M.-K.D.); (A.-A.G.); (F.R.-G.); (A.A.-S.); (T.B.)
- Institut du Cancer de Montréal, 900 Saint-Denis, Montreal, QC H2X 0A9, Canada
- Department of Pathology and Cellular Biology, Université de Montréal, 2900 Boulevard Édouard-Montpetit, Montreal, QC H3T 1J4, Canada
- Department of Pathology, Centre Hospitalier de l’Université de Montréal (CHUM), 1051 Sanguinet, Montreal, QC H2X 0C1, Canada
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13
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Ember K, Daoust F, Mahfoud M, Dallaire F, Ahmad EZ, Tran T, Plante A, Diop MK, Nguyen T, St-Georges-Robillard A, Ksantini N, Lanthier J, Filiatrault A, Sheehy G, Beaudoin G, Quach C, Trudel D, Leblond F. Saliva-based detection of COVID-19 infection in a real-world setting using reagent-free Raman spectroscopy and machine learning. JOURNAL OF BIOMEDICAL OPTICS 2022; 27:JBO-210270RR. [PMID: 35142113 PMCID: PMC8825664 DOI: 10.1117/1.jbo.27.2.025002] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 01/20/2022] [Indexed: 05/31/2023]
Abstract
SIGNIFICANCE The primary method of COVID-19 detection is reverse transcription polymerase chain reaction (RT-PCR) testing. PCR test sensitivity may decrease as more variants of concern arise and reagents may become less specific to the virus. AIM We aimed to develop a reagent-free way to detect COVID-19 in a real-world setting with minimal constraints on sample acquisition. The machine learning (ML) models involved could be frequently updated to include spectral information about variants without needing to develop new reagents. APPROACH We present a workflow for collecting, preparing, and imaging dried saliva supernatant droplets using a non-invasive, label-free technique-Raman spectroscopy-to detect changes in the molecular profile of saliva associated with COVID-19 infection. RESULTS We used an innovative multiple instance learning-based ML approach and droplet segmentation to analyze droplets. Amongst all confounding factors, we discriminated between COVID-positive and COVID-negative individuals yielding receiver operating coefficient curves with an area under curve (AUC) of 0.8 in both males (79% sensitivity and 75% specificity) and females (84% sensitivity and 64% specificity). Taking the sex of the saliva donor into account increased the AUC by 5%. CONCLUSION These findings may pave the way for new rapid Raman spectroscopic screening tools for COVID-19 and other infectious diseases.
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Affiliation(s)
- Katherine Ember
- Polytechnique Montréal, Montreal, Canada
- Center de recherche du Center hospitalier de l’Université de Montréal, Montreal, Canada
| | - François Daoust
- Polytechnique Montréal, Montreal, Canada
- Center de recherche du Center hospitalier de l’Université de Montréal, Montreal, Canada
| | - Myriam Mahfoud
- Polytechnique Montréal, Montreal, Canada
- Center de recherche du Center hospitalier de l’Université de Montréal, Montreal, Canada
| | - Frédérick Dallaire
- Polytechnique Montréal, Montreal, Canada
- Center de recherche du Center hospitalier de l’Université de Montréal, Montreal, Canada
| | - Esmat Zamani Ahmad
- Polytechnique Montréal, Montreal, Canada
- Center de recherche du Center hospitalier de l’Université de Montréal, Montreal, Canada
- Institut du cancer de Montréal, Montreal, Canada
| | - Trang Tran
- Polytechnique Montréal, Montreal, Canada
- Center de recherche du Center hospitalier de l’Université de Montréal, Montreal, Canada
| | - Arthur Plante
- Polytechnique Montréal, Montreal, Canada
- Center de recherche du Center hospitalier de l’Université de Montréal, Montreal, Canada
| | - Mame-Kany Diop
- Center de recherche du Center hospitalier de l’Université de Montréal, Montreal, Canada
- Institut du cancer de Montréal, Montreal, Canada
| | - Tien Nguyen
- Polytechnique Montréal, Montreal, Canada
- Center de recherche du Center hospitalier de l’Université de Montréal, Montreal, Canada
- Institut du cancer de Montréal, Montreal, Canada
| | - Amélie St-Georges-Robillard
- Polytechnique Montréal, Montreal, Canada
- Center de recherche du Center hospitalier de l’Université de Montréal, Montreal, Canada
| | - Nassim Ksantini
- Polytechnique Montréal, Montreal, Canada
- Center de recherche du Center hospitalier de l’Université de Montréal, Montreal, Canada
| | - Julie Lanthier
- Polytechnique Montréal, Montreal, Canada
- Center de recherche du Center hospitalier de l’Université de Montréal, Montreal, Canada
| | - Antoine Filiatrault
- Polytechnique Montréal, Montreal, Canada
- Center de recherche du Center hospitalier de l’Université de Montréal, Montreal, Canada
| | - Guillaume Sheehy
- Polytechnique Montréal, Montreal, Canada
- Center de recherche du Center hospitalier de l’Université de Montréal, Montreal, Canada
| | - Gabriel Beaudoin
- Polytechnique Montréal, Montreal, Canada
- Center de recherche du Center hospitalier de l’Université de Montréal, Montreal, Canada
| | - Caroline Quach
- Research Center, CHU Sainte-Justine, Montreal, Canada
- University of Montreal, Faculty of Medicine, Montreal, Quebec, Canada
| | - Dominique Trudel
- Center de recherche du Center hospitalier de l’Université de Montréal, Montreal, Canada
- Institut du cancer de Montréal, Montreal, Canada
- Université de Montréal, Department of Pathology and Cellular Biology, Montreal, Quebec, Canada
- Center Hospitalier de l’Université de Montréal, Department of Pathology, Montreal, Quebec, Canada
| | - Frédéric Leblond
- Polytechnique Montréal, Montreal, Canada
- Center de recherche du Center hospitalier de l’Université de Montréal, Montreal, Canada
- Institut du cancer de Montréal, Montreal, Canada
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14
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Hidden clues in prostate cancer - Lessons learned from clinical and pre-clinical approaches on diagnosis and risk stratification. Cancer Lett 2022; 524:182-192. [PMID: 34687792 DOI: 10.1016/j.canlet.2021.10.020] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2021] [Revised: 09/17/2021] [Accepted: 10/13/2021] [Indexed: 12/18/2022]
Abstract
The heterogeneity of prostate cancer is evident at clinical, morphological and molecular levels. To aid clinical decision making, a three-tiered system for risk stratification is used to designate low-, intermediate-, and high-risk of disease progression. Intermediate-risk prostate cancers are the most frequently diagnosed, and even with common diagnostic features, can exhibit vastly different clinical progression. Thus, improved risk stratification methods are needed to better predict patient outcomes. Here, we provide an overview of the improvements in diagnosis/prognosis arising from advances in pathology reporting of prostate cancer, which can improve risk stratification, especially for patients with intermediate-risk disease. This review discusses updates to pathology reporting of morphological growth patterns, and proposes the utility of integrating prognostic biomarkers or innovative imaging techniques to enhance clinical decision-making. To complement clinical studies, experimental approaches using patient-derived tumors have highlighted important cellular and morphological features associated with aggressive disease that may impact treatment response. The intersection of urology, pathology and scientific disciplines is required to work towards a common goal of understanding disease pathogenesis, improving the stratification of patients with intermediate-risk disease and subsequently defining optimal treatment strategies using precision-based approaches.
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15
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Plante A, Dallaire F, Grosset AA, Nguyen T, Birlea M, Wong J, Daoust F, Roy N, Kougioumoutzakis A, Azzi F, Aubertin K, Kadoury S, Latour M, Albadine R, Prendeville S, Boutros P, Fraser M, Bristow RG, van der Kwast T, Orain M, Brisson H, Benzerdjeb N, Hovington H, Bergeron A, Fradet Y, Têtu B, Saad F, Trudel D, Leblond F. Dimensional reduction based on peak fitting of Raman micro spectroscopy data improves detection of prostate cancer in tissue specimens. JOURNAL OF BIOMEDICAL OPTICS 2021; 26:JBO-210212R. [PMID: 34743445 PMCID: PMC8571651 DOI: 10.1117/1.jbo.26.11.116501] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 10/04/2021] [Indexed: 06/13/2023]
Abstract
SIGNIFICANCE 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. AIM To improve the classification performance of machine learning models identifying different types of prostate cancer tissue using a new dimensional reduction technique. APPROACH 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. RESULTS 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. CONCLUSIONS 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.
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Affiliation(s)
- Arthur Plante
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
| | - Frédérick Dallaire
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
| | - Andrée-Anne Grosset
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
- Université de Montréal, Department of Pathology and Cellular Biology, Montreal, Quebec, Canada
| | - Tien Nguyen
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
| | - Mirela Birlea
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
| | - Jahg Wong
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
| | - François Daoust
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
| | - Noémi Roy
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
| | - André Kougioumoutzakis
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
| | - Feryel Azzi
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
| | - Kelly Aubertin
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
| | - Samuel Kadoury
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
- Polytechnique Montréal, Department of Computer Engineering and Software Engineering, Montreal, Quebec, Canada
| | - Mathieu Latour
- Université de Montréal, Department of Pathology and Cellular Biology, Montreal, Quebec, Canada
- Centre hospitalier de l’Université de Montréal, Department of Pathology, Montreal, Quebec, Canada
| | - Roula Albadine
- Université de Montréal, Department of Pathology and Cellular Biology, Montreal, Quebec, Canada
- Centre hospitalier de l’Université de Montréal, Department of Pathology, Montreal, Quebec, Canada
| | - Susan Prendeville
- University Health Network, Laboratory Medicine Program, Toronto, Ontario, Canada
| | - Paul Boutros
- Informatics & Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
- University of California, Los Angeles, Department of Human Genetics, Los Angeles, California, United States
- University of California, Los Angeles, Department of Urology, Los Angeles, California, United States
- University of California, Los Angeles, Institute for Precision Health, Los Angeles, California, United States
- University of California, Los Angeles, Jonsson Comprehensive Cancer Center, Los Angeles, California, United States
- University Health Network, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Michael Fraser
- Informatics & Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
- University Health Network, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Rob G. Bristow
- University Health Network, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | | | - Michèle Orain
- Centre de recherche du Centre hospitalier universitaire de Québec-Université Laval, Oncology Division, Quebec City, Quebec, Canada
- Université Laval, Centre de recherche sur le cancer, Quebec City, Quebec, Canada
| | - Hervé Brisson
- Centre de recherche du Centre hospitalier universitaire de Québec-Université Laval, Oncology Division, Quebec City, Quebec, Canada
- Université Laval, Centre de recherche sur le cancer, Quebec City, Quebec, Canada
| | - Nazim Benzerdjeb
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
- Centre de recherche du Centre hospitalier universitaire de Québec-Université Laval, Oncology Division, Quebec City, Quebec, Canada
- Université Laval, Centre de recherche sur le cancer, Quebec City, Quebec, Canada
| | - Hélène Hovington
- Centre de recherche du Centre hospitalier universitaire de Québec-Université Laval, Oncology Division, Quebec City, Quebec, Canada
- Université Laval, Centre de recherche sur le cancer, Quebec City, Quebec, Canada
| | - Alain Bergeron
- Centre de recherche du Centre hospitalier universitaire de Québec-Université Laval, Oncology Division, Quebec City, Quebec, Canada
- Université Laval, Centre de recherche sur le cancer, Quebec City, Quebec, Canada
- Université Laval, Department of Surgery, Quebec City, Quebec, Canada
| | - Yves Fradet
- Centre de recherche du Centre hospitalier universitaire de Québec-Université Laval, Oncology Division, Quebec City, Quebec, Canada
- Université Laval, Centre de recherche sur le cancer, Quebec City, Quebec, Canada
- Université Laval, Department of Surgery, Quebec City, Quebec, Canada
| | - Bernard Têtu
- Centre de recherche du Centre hospitalier universitaire de Québec-Université Laval, Oncology Division, Quebec City, Quebec, Canada
- Université Laval, Centre de recherche sur le cancer, Quebec City, Quebec, Canada
| | - Fred Saad
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
| | - Dominique Trudel
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
| | - Frédéric Leblond
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
- Université de Montréal, Department of Pathology and Cellular Biology, Montreal, Quebec, Canada
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16
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Oszwald A, Wasinger G, Pradere B, Shariat SF, Compérat EM. Artificial intelligence in prostate histopathology: where are we in 2021? Curr Opin Urol 2021; 31:430-435. [PMID: 33965977 DOI: 10.1097/mou.0000000000000883] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
PURPOSE OF REVIEW Artificial intelligence has made an entrance into mainstream applications of daily life but the clinical deployment of artificial intelligence-supported histological analysis is still at infancy. Recent years have seen a surge in technological advance regarding the use of artificial intelligence in pathology, in particular in the diagnosis of prostate cancer. RECENT FINDINGS We review first impressions of how artificial intelligence impacts the clinical performance of pathologists in the analysis of prostate tissue. Several challenges in the deployment of artificial intelligence remain to be overcome. Finally, we discuss how artificial intelligence can help in generating new knowledge that is interpretable by humans. SUMMARY It is evident that artificial intelligence has the potential to outperform most pathologists in detecting prostate cancer, and does not suffer from inherent interobserver variability. Nonetheless, large clinical validation studies that unequivocally prove the benefit of artificial intelligence support in pathology are necessary. Regardless, artificial intelligence may soon automate and standardize many facets of routine work, including qualitative (i.e. Gleason Grading) and quantitative measures (i.e. portion of Gleason Grades and tumor volume). For the near future, a model where pathologists are enhanced by second-review or real-time artificial intelligence systems appears to be the most promising approach.
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Affiliation(s)
| | | | - Benjamin Pradere
- Department of Urology, Medical University of Vienna, Vienna, Austria
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17
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Doherty T, McKeever S, Al-Attar N, Murphy T, Aura C, Rahman A, O'Neill A, Finn SP, Kay E, Gallagher WM, Watson RWG, Gowen A, Jackman P. Feature fusion of Raman chemical imaging and digital histopathology using machine learning for prostate cancer detection. Analyst 2021; 146:4195-4211. [PMID: 34060548 DOI: 10.1039/d1an00075f] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The diagnosis of prostate cancer is challenging due to the heterogeneity of its presentations, leading to the over diagnosis and treatment of non-clinically important disease. Accurate diagnosis can directly benefit a patient's quality of life and prognosis. Towards addressing this issue, we present a learning model for the automatic identification of prostate cancer. While many prostate cancer studies have adopted Raman spectroscopy approaches, none have utilised the combination of Raman Chemical Imaging (RCI) and other imaging modalities. This study uses multimodal images formed from stained Digital Histopathology (DP) and unstained RCI. The approach was developed and tested on a set of 178 clinical samples from 32 patients, containing a range of non-cancerous, Gleason grade 3 (G3) and grade 4 (G4) tissue microarray samples. For each histological sample, there is a pathologist labelled DP-RCI image pair. The hypothesis tested was whether multimodal image models can outperform single modality baseline models in terms of diagnostic accuracy. Binary non-cancer/cancer models and the more challenging G3/G4 differentiation were investigated. Regarding G3/G4 classification, the multimodal approach achieved a sensitivity of 73.8% and specificity of 88.1% while the baseline DP model showed a sensitivity and specificity of 54.1% and 84.7% respectively. The multimodal approach demonstrated a statistically significant 12.7% AUC advantage over the baseline with a value of 85.8% compared to 73.1%, also outperforming models based solely on RCI and mean and median Raman spectra. Feature fusion of DP and RCI does not improve the more trivial task of tumour identification but does deliver an observed advantage in G3/G4 discrimination. Building on these promising findings, future work could include the acquisition of larger datasets for enhanced model generalization.
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Affiliation(s)
- Trevor Doherty
- Technological University Dublin, School of Computer Science, City Campus, Grangegorman Lower, Dublin 7, Ireland.
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18
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Shabestri B, Anastasio MA, Fei B, Leblond F. Special Series Guest Editorial: Artificial Intelligence and Machine Learning in Biomedical Optics. JOURNAL OF BIOMEDICAL OPTICS 2021; 26:JBO-21-0414. [PMID: 33973425 PMCID: PMC8109026 DOI: 10.1117/1.jbo.26.5.052901] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/01/2021] [Accepted: 01/01/2021] [Indexed: 06/12/2023]
Abstract
Guest editors Behrouz Shabestri, Mark Anastasio, Baowei Fei, and Frédéric Leblond provide an overview of the JBO Special Series on Artificial Intelligence Machine Learning in Biomedical Optics.
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Affiliation(s)
- Behrouz Shabestri
- National Institute of Biomedical Imaging and Bioengineering, Maryland, United States
| | | | - Baowei Fei
- University of Texas at Dallas, Texas, United States
- UT Southwestern Medical Center, Texas United States
| | - Frédéric Leblond
- Department of Engineering Physics, Polytechnique Montréal, Montreal, Quebec, Canada
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
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19
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Picot F, Daoust F, Sheehy G, Dallaire F, Chaikho L, Bégin T, Kadoury S, Leblond F. Data consistency and classification model transferability across biomedical Raman spectroscopy systems. TRANSLATIONAL BIOPHOTONICS 2020. [DOI: 10.1002/tbio.202000019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Affiliation(s)
- Fabien Picot
- Department of Engineering Physics Polytechnique Montréal, 2500 chemin de Polytechnique Montreal Quebec Canada
- Centre de recherche du Centre Hospitalier de l'Université de Montréal Montreal Quebec Canada
| | - François Daoust
- Department of Engineering Physics Polytechnique Montréal, 2500 chemin de Polytechnique Montreal Quebec Canada
- Centre de recherche du Centre Hospitalier de l'Université de Montréal Montreal Quebec Canada
| | - Guillaume Sheehy
- Department of Engineering Physics Polytechnique Montréal, 2500 chemin de Polytechnique Montreal Quebec Canada
- Centre de recherche du Centre Hospitalier de l'Université de Montréal Montreal Quebec Canada
| | - Frédérick Dallaire
- Centre de recherche du Centre Hospitalier de l'Université de Montréal Montreal Quebec Canada
| | - Layal Chaikho
- Department of Engineering Physics Polytechnique Montréal, 2500 chemin de Polytechnique Montreal Quebec Canada
| | - Théophile Bégin
- Department of Engineering Physics Polytechnique Montréal, 2500 chemin de Polytechnique Montreal Quebec Canada
| | - Samuel Kadoury
- Department of Engineering Physics Polytechnique Montréal, 2500 chemin de Polytechnique Montreal Quebec Canada
- Centre de recherche du Centre Hospitalier de l'Université de Montréal Montreal Quebec Canada
| | - Frédéric Leblond
- Department of Engineering Physics Polytechnique Montréal, 2500 chemin de Polytechnique Montreal Quebec Canada
- Centre de recherche du Centre Hospitalier de l'Université de Montréal Montreal Quebec Canada
- Institut du Cancer de Montréal Montreal Quebec Canada
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