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Majumdar A, Lad J, Tumanova K, Serra S, Quereshy F, Khorasani M, Vitkin A. Machine learning based local recurrence prediction in colorectal cancer using polarized light imaging. JOURNAL OF BIOMEDICAL OPTICS 2024; 29:052915. [PMID: 38077502 PMCID: PMC10704263 DOI: 10.1117/1.jbo.29.5.052915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 11/03/2023] [Accepted: 11/09/2023] [Indexed: 12/18/2023]
Abstract
Significance Current treatment for stage III colorectal cancer (CRC) patients involves surgery that may not be sufficient in many cases, requiring additional adjuvant systemic therapy. Identification of this latter cohort that is likely to recur following surgery is key to better personalized therapy selection, but there is a lack of proper quantitative assessment tools for potential clinical adoption. Aim The purpose of this study is to employ Mueller matrix (MM) polarized light microscopy in combination with supervised machine learning (ML) to quantitatively analyze the prognostic value of peri-tumoral collagen in CRC in relation to 5-year local recurrence (LR). Approach A simple MM microscope setup was used to image surgical resection samples acquired from stage III CRC patients. Various potential biomarkers of LR were derived from MM elements via decomposition and transformation operations. These were used as features by different supervised ML models to distinguish samples from patients that locally recurred 5 years later from those that did not. Results Using the top five most prognostic polarimetric biomarkers ranked by their relevant feature importances, the best-performing XGBoost model achieved a patient-level accuracy of 86%. When the patient pool was further stratified, 96% accuracy was achieved within a tumor-stage-III sub-cohort. Conclusions ML-aided polarimetric analysis of collagenous stroma may provide prognostic value toward improving the clinical management of CRC patients.
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Affiliation(s)
- Anamitra Majumdar
- University of Toronto, Department of Medical Biophysics, Toronto, Ontario, Canada
| | - Jigar Lad
- McMaster University, Department of Physics and Astronomy, Hamilton, Ontario, Canada
| | - Kseniia Tumanova
- University of Toronto, Department of Medical Biophysics, Toronto, Ontario, Canada
| | - Stefano Serra
- University of Toronto, Department of Laboratory Medicine and Pathobiology, Toronto, Ontario, Canada
| | - Fayez Quereshy
- University of Toronto, Department of Laboratory Medicine and Pathobiology, Toronto, Ontario, Canada
| | - Mohammadali Khorasani
- University of British Columbia, Department of Surgery, Victoria, British Columbia, Canada
| | - Alex Vitkin
- University of Toronto, Department of Medical Biophysics, Toronto, Ontario, Canada
- University of Toronto, Department of Radiation Oncology, Toronto, Ontario, Canada
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Atallah NM, Wahab N, Toss MS, Makhlouf S, Ibrahim AY, Lashen AG, Ghannam S, Mongan NP, Jahanifar M, Graham S, Bilal M, Bhalerao A, Ahmed Raza SE, Snead D, Minhas F, Rajpoot N, Rakha E. Deciphering the Morphology of Tumor-Stromal Features in Invasive Breast Cancer Using Artificial Intelligence. Mod Pathol 2023; 36:100254. [PMID: 37380057 DOI: 10.1016/j.modpat.2023.100254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 06/02/2023] [Accepted: 06/14/2023] [Indexed: 06/30/2023]
Abstract
Tumor-associated stroma in breast cancer (BC) is complex and exhibits a high degree of heterogeneity. To date, no standardized assessment method has been established. Artificial intelligence (AI) could provide an objective morphologic assessment of tumors and stroma, with the potential to identify new features not discernible by visual microscopy. In this study, we used AI to assess the clinical significance of (1) stroma-to-tumor ratio (S:TR) and (2) the spatial arrangement of stromal cells, tumor cell density, and tumor burden in BC. Whole-slide images of a large cohort (n = 1968) of well-characterized luminal BC cases were examined. Region and cell-level annotation was performed, and supervised deep learning models were applied for automated quantification of tumor and stromal features. S:TR was calculated in terms of surface area and cell count ratio, and the S:TR heterogeneity and spatial distribution were also assessed. Tumor cell density and tumor size were used to estimate tumor burden. Cases were divided into discovery (n = 1027) and test (n = 941) sets for validation of the findings. In the whole cohort, the stroma-to-tumor mean surface area ratio was 0.74, and stromal cell density heterogeneity score was high (0.7/1). BC with high S:TR showed features characteristic of good prognosis and longer patient survival in both the discovery and test sets. Heterogeneous spatial distribution of S:TR areas was predictive of worse outcome. Higher tumor burden was associated with aggressive tumor behavior and shorter survival and was an independent predictor of worse outcome (BC-specific survival; hazard ratio: 1.7, P = .03, 95% CI, 1.04-2.83 and distant metastasis-free survival; hazard ratio: 1.64, P = .04, 95% CI, 1.01-2.62) superior to absolute tumor size. The study concludes that AI provides a tool to assess major and subtle morphologic stromal features in BC with prognostic implications. Tumor burden is more prognostically informative than tumor size.
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Affiliation(s)
- Nehal M Atallah
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK; Department of Pathology, Faculty of Medicine, Menoufia University, Egypt
| | - Noorul Wahab
- Tissue Image Analytics Centre, University of Warwick, Conventry, UK
| | - Michael S Toss
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK; Histopathology Department, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Shorouk Makhlouf
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK; Department of Pathology, Faculty of Medicine, Assiut University, Egypt
| | - Asmaa Y Ibrahim
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK; Department of Pathology, Faculty of Medicine, Suez Canal University, Egypt
| | - Ayat G Lashen
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK; Department of Pathology, Faculty of Medicine, Menoufia University, Egypt
| | - Suzan Ghannam
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK; Department of Histology and Cell Biology, Faculty of Medicine, Suez Canal University, Egypt
| | - Nigel P Mongan
- Biodiscovery Institute, School of Veterinary Medicine and Sciences, University of Nottingham, Sutton Bonington, UK; Department of Pharmacology, Weill Cornell Medicine, New York
| | | | - Simon Graham
- Tissue Image Analytics Centre, University of Warwick, Conventry, UK
| | - Mohsin Bilal
- Tissue Image Analytics Centre, University of Warwick, Conventry, UK
| | - Abhir Bhalerao
- Tissue Image Analytics Centre, University of Warwick, Conventry, UK
| | | | - David Snead
- Cellular Pathology, University Hospitals Coventry and Warwickshire NHS Trust, UK
| | - Fayyaz Minhas
- Tissue Image Analytics Centre, University of Warwick, Conventry, UK
| | - Nasir Rajpoot
- Tissue Image Analytics Centre, University of Warwick, Conventry, UK.
| | - Emad Rakha
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK; Department of Pathology, Faculty of Medicine, Menoufia University, Egypt; Pathology Department, Hamad Medical Corporation, Doha, Qatar.
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Lee HR, Lotz C, Kai Groeber Becker F, Dembski S, Novikova T. Digital histology of tissue with Mueller microscopy and FastDBSCAN. APPLIED OPTICS 2022; 61:9616-9624. [PMID: 36606902 DOI: 10.1364/ao.473095] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 10/08/2022] [Indexed: 06/17/2023]
Abstract
We present the results of the automated post-processing of Mueller microscopy images of skin tissue models with a new fast version of the algorithm of density-based spatial clustering of applications with noise (FastDBSCAN) and discuss the advantages of its implementation for digital histology of tissue. We demonstrate that using the FastDBSCAN algorithm, one can produce the diagnostic segmentation of high resolution images of tissue by several orders of magnitude faster and with high accuracy (>97%) compared to the original version of the algorithm.
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Polarimetric biomarkers of peri-tumoral stroma can correlate with 5-year survival in patients with left-sided colorectal cancer. Sci Rep 2022; 12:12652. [PMID: 35879367 PMCID: PMC9314438 DOI: 10.1038/s41598-022-16178-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 07/06/2022] [Indexed: 12/24/2022] Open
Abstract
Using a novel variant of polarized light microscopy for high-contrast imaging and quantification of unstained histology slides, the current study assesses the prognostic potential of peri-tumoral collagenous stroma architecture in 32 human stage III colorectal cancer (CRC) patient samples. We analyze three distinct polarimetrically-derived images and their associated texture features, explore different unsupervised clustering algorithm models to group the data, and compare the resultant groupings with patient survival. The results demonstrate an appreciable total accuracy of ~ 78% with significant separation (p < 0.05) across all approaches for the binary classification of 5-year patient survival outcomes. Surviving patients preferentially belonged to Cluster 1 irrespective of model approach, suggesting similar stromal microstructural characteristics in this sub-population. The results suggest that polarimetrically-derived stromal biomarkers may possess prognostic value that could improve clinical management/treatment stratification in CRC patients.
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Bagha T, Kamal AM, Pal UM, Mohan Rao PS, Pandya HJ. Toward the development of a polarimetric tool to diagnose the fibrotic human ventricular myocardium. JOURNAL OF BIOMEDICAL OPTICS 2022; 27:055001. [PMID: 35562842 PMCID: PMC9106211 DOI: 10.1117/1.jbo.27.5.055001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 04/12/2022] [Indexed: 06/15/2023]
Abstract
SIGNIFICANCE Optical polarimetry is an emerging modality that effectively quantifies the bulk optical properties that correlate with the anisotropic structural properties of cardiac tissues. We demonstrate the application of a polarimetric tool for characterizing healthy and fibrotic human myocardial tissues efficiently with a high degree of accuracy. AIM The study was aimed to characterize the myocardial tissues from the left ventricle and right ventricle of N = 7 control and N = 10 diseased subjects. The diseased subjects were composed of two groups: N = 7 with rheumatic heart disease (RHD) and N = 3 with myxomatous valve (MV) disease. APPROACH A portable, affordable, and accurate linear polarization-based diagnostic tool is developed to measure the degree of linear polarization (DOLP) of the myocardial tissues while working at a wavelength of 850 nm. RESULTS The sensitivity, specificity, and accuracy of the polarimetric tool in distinguishing the control group from the RHD group were found to be 73.33%, 76.92%, and 75%, respectively, and from the MV group were 91.6%, 62.5%, and 80%, respectively, which demonstrates the efficacy of the polarimetric tool to distinguish the healthy myocardial tissues from diseased tissues. CONCLUSIONS We have successfully developed a polarimetric tool that can aid cardiologists in characterizing the myocardial tissues in conjunction with endomyocardial biopsy. This work should be followed up with experiments on a large cohort of control and diseased subjects. We intend to create and develop a probe to quantify the DOLP of in vivo heart tissue during surgery.
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Affiliation(s)
- Twinkle Bagha
- Indian Institute of Science, Department of Electronic Systems Engineering, Bangalore, Karnataka, India
| | - Arif Mohd. Kamal
- Indian Institute of Science, Department of Electronic Systems Engineering, Bangalore, Karnataka, India
| | - Uttam M. Pal
- Indian Institute of Science, Department of Electronic Systems Engineering, Bangalore, Karnataka, India
- Indian Institute of Information Technology Design and Manufacturing, Kancheepuram, Tamil Nadu, India
| | | | - Hardik J. Pandya
- Indian Institute of Science, Department of Electronic Systems Engineering, Bangalore, Karnataka, India
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Kamal AM, Pal UM, Kumar A, Das GR, Pandya HJ. Toward the development of portable light emitting diode-based polarization spectroscopy tools for breast cancer diagnosis. JOURNAL OF BIOPHOTONICS 2022; 15:e202100282. [PMID: 34846777 DOI: 10.1002/jbio.202100282] [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: 09/08/2021] [Revised: 11/24/2021] [Accepted: 11/26/2021] [Indexed: 06/13/2023]
Abstract
A robust, affordable and portable light emitting diode-based diagnostic tools (POLS-NIRDx) using a polarization-sensitive (linear as well as circular polarization) technique were designed and developed to quantify the degree of linear polarization (DOLP), degree of circular polarization (DOCP). The study was performed on malignant (invasive ductal carcinoma) and adjacent normal ex-vivo biopsy tissues excised from N = 10 patients at the operating wavelengths of 850 and 940 nm. The average DOLP and DOCP values were lower for malignant than adjacent normal while operating at 850 and 940 nm. The highest accuracy was observed for DOLP (100%) and DOCP (80%) while operating at 850 nm, which reduced (80% for DOLP and 65% for DOCP) at 940 nm. This pilot study can be utilized as a differentiating factor to delineate malignant tissues from adjacent normal tissues.
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Affiliation(s)
- Arif Mohd Kamal
- Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, India
| | - Uttam M Pal
- Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, India
| | - Adithya Kumar
- Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, India
| | - Gunabhi Ram Das
- Department of Surgery, Assam Medical College, Dibrugarh, India
| | - Hardik J Pandya
- Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, India
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Meglinski I, Novikova T, Dholakia K. Polarization and Orbital Angular Momentum of Light in Biomedical Applications: feature issue introduction. BIOMEDICAL OPTICS EXPRESS 2021; 12:6255-6258. [PMID: 34745733 PMCID: PMC8548002 DOI: 10.1364/boe.442828] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Indexed: 05/25/2023]
Abstract
In the last decade, consistent and successful innovations have been achieved in the field of lasers and optics, collectively known as 'photonics', founding new applications in biomedicine, including clinical biopsy. Non-invasive photonics-based diagnostic modalities are rapidly expanding, and with their exponential improvement, there is a great potential to develop practical instrumentation for automatic detection and identification of different types and/or sub-types of diseases at a very early stage. While using conventional light for the studies of different properties of objects in materials science, astrophysics and biomedicine already has a long history, the interaction of polarized light and optical angular momentum with turbid tissue-like scattering media has not yet been ultimately explored. Since recently this research area became a hot topic. This feature issue is a first attempt to summarize the recognitions achieved in this emerging research field of polarized light and optical angular momentum for practical biomedical applications during the last years.
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Affiliation(s)
- Igor Meglinski
- College of Engineering and Physical Science, Aston University, Birmingham, B4 7ET, United Kingdom
- Institute of Clinical Medicine N.V. Sklifosovsky, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
- Optoelectronics and Measurement Techniques, ITEE, University of Oulu, Oulu, Finland
| | - Tatiana Novikova
- LPICM, CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau, France
- Department of Biomedical Engineering, College of Engineering and Computing, Florida International University, Miami, FL 33174, USA
| | - Kishan Dholakia
- SUPA, School of Physics & Astronomy, University of St. Andrews, St. Andrews, KY16 9SS, United Kingdom
- Department of Physics, College of Science, Yonsei University, Seoul 03722, Republic of Korea
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