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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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Tumanova K, Serra S, Majumdar A, Lad J, Quereshy F, Khorasani M, Vitkin A. Mueller matrix polarization parameters correlate with local recurrence in patients with stage III colorectal cancer. Sci Rep 2023; 13:13424. [PMID: 37591987 PMCID: PMC10435541 DOI: 10.1038/s41598-023-40480-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 08/10/2023] [Indexed: 08/19/2023] Open
Abstract
The peri-tumoural stroma has been explored as a useful source of prognostic information in colorectal cancer. Using Mueller matrix (MM) polarized light microscopy for quantification of unstained histology slides, the current study assesses the prognostic potential of polarimetric characteristics of peri-tumoural collagenous stroma architecture in 38 human stage III colorectal cancer (CRC) patient samples. Specifically, Mueller matrix transformation and polar decomposition parameters were tested for association with 5-year patient local recurrence outcomes. The results show that some of these polarimetric parameters were significantly different (p value < 0.05) for the recurrence versus the no-recurrence patient cohorts (Mann-Whitney U test). MM parameters may thus be prognostically valuable towards improving clinical management/treatment stratification in CRC patients.
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Affiliation(s)
- Kseniia Tumanova
- Department of Medical Biophysics, University of Toronto, Toronto, Canada.
| | - Stefano Serra
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
| | - Anamitra Majumdar
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Jigar Lad
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Fayez Quereshy
- Department of Surgery, University of Toronto, Toronto, Canada
| | | | - Alex Vitkin
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
- Division of Biophysics and Bioimaging, Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
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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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Chen D, Chen H, Chi L, Fu M, Wang G, Wu Z, Xu S, Sun C, Xu X, Lin L, Cheng J, Jiang W, Dong X, Lu J, Zheng J, Chen G, Li G, Zhuo S, Yan J. Association of Tumor-Associated Collagen Signature With Prognosis and Adjuvant Chemotherapy Benefits in Patients With Gastric Cancer. JAMA Netw Open 2021; 4:e2136388. [PMID: 34846524 PMCID: PMC8634059 DOI: 10.1001/jamanetworkopen.2021.36388] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
IMPORTANCE The current TNM staging system provides limited information for prognosis prediction and adjuvant chemotherapy benefits for patients with gastric cancer (GC). OBJECTIVE To develop a tumor-associated collagen signature of GC (TACSGC) in the tumor microenvironment to predict prognosis and adjuvant chemotherapy benefits in patients with GC. DESIGN, SETTING, AND PARTICIPANTS This retrospective cohort study included a training cohort of 294 consecutive patients treated between January 1, 2012, and December 31, 2013, from Nanfang Hospital, Southern Medical University, People's Republic of China, and a validation cohort of 225 consecutive patients treated between October 1, 2010, and December 31, 2012, from Fujian Provincial Cancer Hospital, Fujian Medical University, People's Republic of China. In total, 146 collagen features in the tumor microenvironment were extracted with multiphoton imaging. A TACSGC was then constructed using the least absolute shrinkage and selection operator Cox proportional hazards regression model in the training cohort. Data analysis was conducted from October 1, 2020, to April 30, 2021. MAIN OUTCOMES AND MEASURES The association of TACSGC with disease-free survival (DFS) and overall survival (OS) was assessed. An independent external cohort was included to validate the results. Interactions between TACSGC and adjuvant chemotherapy were calculated. RESULTS This study included 519 patients (median age, 57 years [IQR, 49-65 years]; 360 [69.4%] male). A 9 feature-based TACSGC was built. A higher TACSGC level was significantly associated with worse DFS and OS in both the training (DFS: hazard ratio [HR], 3.57 [95% CI, 2.45-5.20]; OS: HR, 3.54 [95% CI, 2.41-5.20]) and validation (DFS: HR, 3.10 [95% CI, 2.26-4.27]; OS: HR, 3.24 [95% CI, 2.33-4.50]) cohorts (continuous variable, P < .001 for all comparisons). Multivariable analyses found that carbohydrate antigen 19-9, depth of invasion, lymph node metastasis, distant metastasis, and TACSGC were independent prognostic predictors of GC, and 2 integrated nomograms that included the 5 predictors were established for predicting DFS and OS. Compared with clinicopathological models that included only the 4 clinicopathological predictors, the integrated nomograms yielded an improved discrimination for prognosis prediction in a C index comparison (training cohort: DFS, 0.80 [95% CI, 0.73-0.88] vs 0.78 [95% CI, 0.71-0.85], P = .03; OS, 0.81 [95% CI, 0.75-0.88] vs 0.80 [95% CI, 0.73-0.86], P = .03; validation cohort: DFS, 0.78 [95% CI, 0.70-0.87] vs 0.76 [95% CI, 0.67-0.84], P = .006; OS, 0.78 [95% CI, 0.69-0.86] vs 0.75 [95% CI, 0.67-0.84], P = .002). Patients with stage II and III GC and low TACSGC levels rather than high TACSGC levels had a favorable response to adjuvant chemotherapy (DFS: HR, 0.65 [95% CI, 0.43-0.96]; P = .03; OS: HR, 0.55 [95% CI, 0.36-0.82]; P = .004; dichotomized variable, P < .001 for interaction for both comparisons). CONCLUSIONS AND RELEVANCE The findings suggest that TACSGC provides additional prognostic information for patients with GC and may distinguish patients with stage II and III disease who are more likely to derive benefits from adjuvant chemotherapy.
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Affiliation(s)
- Dexin Chen
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, People’s Republic of China
- School of Science, Jimei University, Xiamen, People’s Republic of China
| | - Hao Chen
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, People’s Republic of China
| | - Liangjie Chi
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, People’s Republic of China
- Department of Gastrointestinal Surgery, Fujian Provincial Hospital, Teaching Hospital of Fujian Medical University, Fuzhou, People’s Republic of China
| | - Meiting Fu
- Department of Gastroenterology, Guangdong Provincial Key Laboratory of Gastroenterology, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, People’s Republic of China
| | - Guangxing Wang
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou, People’s Republic of China
| | - Zhida Wu
- Department of Pathology, Fujian Medical University Cancer Hospital and Fujian Cancer Hospital, Fuzhou, People’s Republic of China
| | - Shuoyu Xu
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, People’s Republic of China
| | - Caihong Sun
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou, People’s Republic of China
| | - Xueqin Xu
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou, People’s Republic of China
| | - Liyan Lin
- Department of Pathology, Fujian Medical University Cancer Hospital and Fujian Cancer Hospital, Fuzhou, People’s Republic of China
| | - Jiaxin Cheng
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, People’s Republic of China
| | - Wei Jiang
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, People’s Republic of China
| | - Xiaoyu Dong
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, People’s Republic of China
| | - Jianping Lu
- Department of Pathology, Fujian Medical University Cancer Hospital and Fujian Cancer Hospital, Fuzhou, People’s Republic of China
| | - Jixiang Zheng
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, People’s Republic of China
| | - Gang Chen
- Department of Pathology, Fujian Medical University Cancer Hospital and Fujian Cancer Hospital, Fuzhou, People’s Republic of China
| | - Guoxin Li
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, People’s Republic of China
| | - Shuangmu Zhuo
- School of Science, Jimei University, Xiamen, People’s Republic of China
| | - Jun Yan
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, People’s Republic of China
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Sprenger J, Murray C, Lad J, Jones B, Thomas G, Nofech-Mozes S, Khorasani M, Vitkin A. Toward a quantitative method for estimating tumour-stroma ratio in breast cancer using polarized light microscopy. BIOMEDICAL OPTICS EXPRESS 2021; 12:3241-3252. [PMID: 34221657 PMCID: PMC8221948 DOI: 10.1364/boe.422452] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 04/28/2021] [Accepted: 05/01/2021] [Indexed: 05/10/2023]
Abstract
The tumour-stroma ratio (TSR) has been explored as a useful source of prognostic information in various cancers, including colorectal, breast, and gastric. Despite research showing potential prognostic utility, its uptake into the clinic has been limited, in part due to challenges associated with subjectivity, reproducibility, and quantification. We have recently proposed a simple, robust, and quantifiable high-contrast method of imaging intra- and peri-tumoural stroma based on polarized light microscopy. Here we report on its use to quantify TSR in human breast cancer using unstained slides from 40 patient samples of invasive ductal carcinoma (IDC). Polarimetric results based on a stromal abundance metric correlated well with pathology designations, showing a statistically significant difference between high- and low-stroma samples as scored by two clinical pathologists. The described polarized light imaging methodology shows promise for use as a quantitative, automatic, and standardizable tool for quantifying TSR, potentially addressing some of the challenges associated with its current estimation.
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Affiliation(s)
- Jillian Sprenger
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Ciara Murray
- Laboratory Medicine Program, University Health Network, Ontario, Canada
| | - Jigar Lad
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Blake Jones
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Georgia Thomas
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Sharon Nofech-Mozes
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
| | - Mohammadali Khorasani
- Department of Surgery, University of British Columbia, Victoria, Canada
- Co-senior authors
| | - Alex Vitkin
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
- Division of Biophysics and Bioimaging, Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Co-senior authors
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Fu M, Chen D, Luo F, Wang G, Xu S, Wang Y, Sun C, Xu X, Li A, Zhuo S, Liu S, Yan J. Development and validation of a collagen signature-based nomogram for preoperatively predicting lymph node metastasis and prognosis in colorectal cancer. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:651. [PMID: 33987349 PMCID: PMC8106085 DOI: 10.21037/atm-20-7565] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Background Current preoperative evaluation approaches cannot provide adequate information for the prediction of lymph node (LN) metastasis in colorectal cancer (CRC). Collagen alterations in the tumor microenvironment affect the progression of tumor cells. To more accurately assess the LN status of CRC preoperatively, we developed and validated a collagen signature-based nomogram for predicting LN metastasis in CRC. Methods In total, 342 consecutive CRC patients were assigned to the training and validation cohorts. A total of 148 fully quantitative collagen features were extracted based on preoperative biopsies using multiphoton imaging, and the least absolute shrinkage and selection operator method was utilized to construct the collagen signature. A collagen signature-based nomogram was developed by multivariable logistic regression in the training cohort. Nomogram performance was evaluated for its discrimination, calibration, and clinical usefulness and then validated in the validation cohort. The prognostic values of the nomogram were also evaluated. Results A seven-feature-based collagen signature was built. We found that the collagen signature showed a significant association with LN metastasis in CRC. Additionally, a nomogram incorporating preoperative tumor differentiation, computed tomography-reported T stage and LN status, carcinoembryonic antigen level, carbohydrate antigen 19-9 level and collagen signature was developed. This nomogram had good discrimination and calibration, with AUROCs of 0.826 and 0.846 in the training and validation cohorts, respectively, and had a sensitivity of 86.5%, a specificity of 68.2%, an accuracy of 76.9%, a negative predictive value of 84.9%, and a positive predictive value of 71.2% for all patients. Compared to the clinicopathological model, which consisted of the clinicopathological risk factors for LN metastasis, the collagen signature-based nomogram demonstrated a significantly improved ability to discriminate LN status. Moreover, a nomogram-predicted high-risk subgroup had remarkably reduced survival compared with that of the low-risk subgroup. Conclusions The collagen signature in the tumor microenvironment of preoperative biopsies is an independent predictor for LN metastasis in CRC, and the collagen signature-based nomogram is helpful for tailored treatment and prognostic predictions in CRC preoperatively.
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Affiliation(s)
- Meiting Fu
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Dexin Chen
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Fuzheng Luo
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Guangxing Wang
- School of Science, Jimei University, Xiamen, China.,Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou, China
| | - Shuoyu Xu
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yadong Wang
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Caihong Sun
- School of Science, Jimei University, Xiamen, China.,Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou, China
| | - Xueqin Xu
- School of Science, Jimei University, Xiamen, China.,Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou, China
| | - Aimin Li
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Shuangmu Zhuo
- School of Science, Jimei University, Xiamen, China.,Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou, China
| | - Side Liu
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Jun Yan
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China
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Jones B, Thomas G, Sprenger J, Nofech-Mozes S, Khorasani M, Vitkin A. Peri-tumoural stroma collagen organization of invasive ductal carcinoma assessed by polarized light microscopy differs between OncotypeDX risk group. JOURNAL OF BIOPHOTONICS 2020; 13:e202000188. [PMID: 32710711 DOI: 10.1002/jbio.202000188] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 07/01/2020] [Accepted: 07/19/2020] [Indexed: 05/02/2023]
Abstract
A commercially available genomic test, OncotypeDX has emerged as a useful postsurgical treatment guide for early stage breast cancer. Despite widespread clinical adoption, there remain logistical issues with its implementation. Collagenous stromal architecture has been shown to hold prognostic value that may complement OncotypeDX. Polarimetric analysis of breast cancer surgical samples allows for the quantification of collagenous stroma abundance and organization. We examine intratumoural collagen abundance and alignment along the tumor-host interface for 45 human samples of invasive ductal carcinoma categorized as low or higher risk by OncotypeDX. Furthermore, we probe the separatory power of collagen alignment patterns to classify unlabeled samples as low or higher OncotypeDX risk group using a linear discriminant (LD) model. No significant difference in mean collagen abundance was found between the two risk groups. However, collagen alignment along the tumor boundary was found to be significantly lower in higher risk samples. The LD model achieved a 71% total accuracy and 81% sensitivity to higher risk samples. Prognostic information extracted from the stromal morphology has potential to complement OncotypeDX as an easy-to-implement prescreening methodology.
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Affiliation(s)
- Blake Jones
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Georgia Thomas
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Jillian Sprenger
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Sharon Nofech-Mozes
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
| | | | - Alex Vitkin
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
- Division of Biophysics and Bioimaging, Princess Margaret Cancer Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
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Jones B, Thomas G, Westreich J, Nofech-Mozes S, Vitkin A, Khorasani M. Novel quantitative signature of tumor stromal architecture: polarized light imaging differentiates between myxoid and sclerotic human breast cancer stroma. BIOMEDICAL OPTICS EXPRESS 2020; 11:3246-3262. [PMID: 32637252 PMCID: PMC7316019 DOI: 10.1364/boe.392722] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 05/12/2020] [Accepted: 05/14/2020] [Indexed: 05/02/2023]
Abstract
As a leading cause of death in women, breast cancer is a global health concern for which personalized therapy remains largely unrealized, resulting in over- or under-treatment. Recently, tumor stroma has been shown to carry important prognostic information, both in its relative abundance and morphology, but its current assessment methods are few and suboptimal. Herein, we present a novel stromal architecture signature (SAS) methodology based on polarized light imaging that quantifies patterns of tumor connective tissue. We demonstrate its ability to differentiate between myxoid and sclerotic stroma, two pathology-derived categories associated with significantly different patient outcomes. The results demonstrate a 97% sensitivity and 88% specificity for myxoid stroma identification in a pilot study of 102 regions of interest from human invasive ductal carcinoma breast cancer surgical specimens (20 patients). Additionally, the SAS numerical score is indicative of the wide range of stromal characteristics within these binary classes and highlights ambiguous mixed-morphology regions prone to misclassification. The enabling polarized light microscopy technique is inexpensive, fast, fully automatable, applicable to fresh or embedded tissue without the need for staining and thus potentially translatable into research and/or clinical settings. The SAS metric yields quantifiable and objective stromal characterization with promise for prognosis in many types of cancers beyond breast carcinoma, enabling researchers and clinicians to further investigate the emerging and important role of stromal architectural patterns in solid tumors.
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Affiliation(s)
- Blake Jones
- Department of Medical Biophysics, University of Toronto, 101 College St, Toronto, ON M5G 1L7, Canada
- Authors contributed equally
| | - Georgia Thomas
- Department of Medical Biophysics, University of Toronto, 101 College St, Toronto, ON M5G 1L7, Canada
- Authors contributed equally
| | - Jared Westreich
- Department of Medical Biophysics, University of Toronto, 101 College St, Toronto, ON M5G 1L7, Canada
| | - Sharon Nofech-Mozes
- Department of Laboratory Medicine and Pathobiology, University of Toronto, 1 King's College Cir, Toronto, ON M5S 1A8, Canada
| | - Alex Vitkin
- Department of Medical Biophysics, University of Toronto, 101 College St, Toronto, ON M5G 1L7, Canada
- Division of Biophysics and Bioimaging, Princess Margaret Cancer Center, University Health Network, 610 University Ave, Toronto, ON M5G 2C1, Canada
- Department of Radiation Oncology, University of Toronto, Stewart building, 149 College St Suite 504, Toronto, ON M5 T 1P5, Canada
- Co-senior authors
| | - Mohammadali Khorasani
- Department of Surgical Oncology, University of Toronto, Princess Margaret Cancer Center, OPG Wing, 6th floor, 610 University Avenue Toronto, ON M5G 2M9, Canada
- Co-senior authors
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