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Xinsen L, Yang K, Bingzhi C, Xiuhong C, Xinling L, Xinyao X, Jinlin C, Ming T, Pengtao L, Zheng X, Linying C. Vague-Segment Technique: Automatic Computation of Tumor Stroma Ratio for Breast Cancer on Whole Slides. IEEE J Biomed Health Inform 2024; 28:905-916. [PMID: 38079367 DOI: 10.1109/jbhi.2023.3341101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
The calculation of Tumor Stroma Ratio (TSR) is a challenging medical issue that could improve predictions of neoadjuvant chemotherapy benefits and patient prognoses. Although several studies on breast cancer and deep learning methods have achieved promising results, the drawbacks that pixel-level semantic segmentation processes could not extract core tumor regions containing both tumor pixels and stroma pixels make it difficult to accurately calculate TSR. In this paper, we propose a Vague-Segment Technique (VST) consisting of a designed SwinV2UNet module and a modified Suzuki algorithm. Specifically, the SwinV2UNet identifies tumor pixels and generate pixel-level classification results, based on which the modified Suzuki algorithm extracts the contour of core tumor regions in terms of cosine angle. Through this way, VST obtains vaguely segmentation results of core tumor regions containing both tumor pixels and stroma pixels, where the TSR could be calculated by the formula of Intersection over Union (IOU). For the training and evaluation, we utilize the well-known The Cancer Genome Atlas (TCGA) database to create an annotated dataset, while 150 images with TSR annotations from real cases are also collected. The experimental results illustrate that the proposed VST could generate better tumor identification results compared with state-of-the-art methods, where the extracted core tumor regions lead to more consistencies of calculated TSR with senior experts compared to junior pathologists. The experimental results demonstrate the superiority of our proposed pipeline, which has promise for future clinical application.
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Almangush A, Ruuskanen M, Hagström J, Kosma VM, Nieminen P, Mäkitie AA, Leivo I. Prognostic Significance of Tumor-associated Stroma in Nasopharyngeal Carcinoma: A Multicenter Study. Am J Surg Pathol 2024; 48:54-58. [PMID: 37779503 DOI: 10.1097/pas.0000000000002137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/03/2023]
Abstract
Assessment of tumor-associated stroma has shown a reliable prognostic value in recent research. We evaluated the prognostic value of tumor-stroma ratio (TSR) in a large multicenter cohort of nasopharyngeal carcinoma (NPC). We used the conventional hematoxylin and eosin-stained slides of 115 cases of NPC to assess TSR as described in recent guidelines. The amount of tumor-associated stroma was assessed as a percentage and then tumors were classified as stroma-high (>50%) or stroma-low (≤50%). Kaplan-Meier curves, χ 2 test, and Cox regression univariable and multivariable analyses were carried out. A total of 48 (41.7%) tumors were stroma-high and 67 (58.3%) tumors were stroma-low. In the Cox regression multivariable analysis, the tumors categorized as stroma-high were associated with a worse overall survival with a hazard ratio of 2.30 (95% CI: 1.27-4.15, P =0.006) and with poor disease-specific survival (hazard ratio=1.87, 95% CI: 1.07-3.28, P =0.029). The assessment of TSR in NPC is simple and cost-effective, and it has a significant prognostic value. TSR can aid in risk stratification and clinical decision-making in NPC.
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Affiliation(s)
- Alhadi Almangush
- Department of Pathology, University of Helsinki
- Institute of Biomedicine, Pathology, University of Turku
- Research Program in Systems Oncology, University of Helsinki, Helsinki
- Faculty of Dentistry, Misurata University, Misurata, Libya
| | - Miia Ruuskanen
- Department of Otorhinolaryngology-Head and Neck Surgery, Turku University Hospital and University of Turku
| | - Jaana Hagström
- Department of Pathology, University of Helsinki
- Research Programs Unit, Translational Cancer Medicine, University of Helsinki
- Department of Oral Pathology and Radiology, University of Turku
| | - Veli-Matti Kosma
- School of Medicine, Institute of Clinical Medicine, Pathology and Forensic Medicine
- Cancer Center of Eastern Finland, University of Eastern Finland
- Imaging Center, Clinical Pathology, Kuopio University Hospital, Kuopio
| | - Pentti Nieminen
- Medical Informatics and Data Analysis Research Group, University of Oulu, Oulu, Finland
| | - Antti A Mäkitie
- Department of Otorhinolaryngology-Head and Neck Surgery, University of Helsinki and Helsinki University Hospital
- Research Program in Systems Oncology, University of Helsinki, Helsinki
- Department of Clinical Sciences, Intervention and Technology, Division of Ear, Nose, and Throat Diseases, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Ilmo Leivo
- Institute of Biomedicine, Pathology, University of Turku
- Turku University Central Hospital, Turku
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Sanuki F, Mikami Y, Nishimura H, Fujita Y, Monobe Y, Nomura T, Taira N, Moriya T. Immunohistological analysis of B7-H4, IDO1, and PD-L1 expression and tumor immune microenvironment based on triple-negative breast cancer subtypes. Breast Cancer 2023; 30:1041-1053. [PMID: 37642903 DOI: 10.1007/s12282-023-01498-7] [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: 05/20/2023] [Accepted: 08/16/2023] [Indexed: 08/31/2023]
Abstract
BACKGROUND B7 homolog 4 (B7-H4) and indoleamine 2,3-dioxygenase (IDO1) are factors involved in the inhibition of antitumor activity and are new therapeutic targets for immune checkpoint therapy. Our study aimed to simultaneously investigate the interrelationship among B7-H4, IDO1 and programmed cell death ligand 1 (PD-L1) expression in triple-negative breast cancer (TNBC), including tumor immune microenvironment (TIME) and TNBC subtypes. METHODS Immunostaining for PD-L1, B7-H4, and IDO1 was performed on whole-slide sections of 119 cases of TNBC. The TIME was evaluated based on stromal tumor infiltrating lymphocytes (sTILs; %), pattern classification of TILs, tumor-stroma ratio (TSR), and tertiary lymphoid structure (TLS). TNBC subtypes were also determined by immunohistochemistry analysis of cytokeratin 5/6 and androgen receptor (AR) expression. RESULTS B7-H4 expression was significantly higher in cases with a combined positive score cutoff of 5 for PD-L1 (clone 28-8; p = 0.021), inflamed TIL pattern (p = 0.007), and TLS ≥ 4 (p = 0.006). B7-H4 expression was higher in case of CK5/6 ≥ 10 (p = 0.035). The H-scores of AR and B7-H4 were inversely correlated (ρ = - 0.509, p < 0.001). B7-H4 and IDO1 expression levels were inversely correlated in cases with AR < 10 (ρ = - 0.354, p < 0.001). CONCLUSIONS These results suggest that considering the TIL pattern and TLS and identifying the expression of PD-L1 and the basal-like type are useful for estimating B7-H4 expression. In addition, luminal androgen receptor (LAR)-type is frequently deficient in B7-H4 expression. In non-LAR types, B7-H4 and IDO1 expression are exclusive.
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Affiliation(s)
- Fumiaki Sanuki
- Department of Pathology, Kawasaki Medical School, 577 Matsushima, Kurashiki, 701-0192, Japan
| | - Yuka Mikami
- Department of Pathology, Kawasaki Medical School, 577 Matsushima, Kurashiki, 701-0192, Japan
| | - Hirotake Nishimura
- Department of Pathology, Kawasaki Medical School, 577 Matsushima, Kurashiki, 701-0192, Japan
| | - Yoshinori Fujita
- Department of Pathology, Kawasaki Medical School, 577 Matsushima, Kurashiki, 701-0192, Japan
| | - Yasumasa Monobe
- Department of Pathology, Kawasaki Medical School General Medical Center, Okayama, Japan
| | - Tsunehisa Nomura
- Department of Breast and Thyroid Surgery, Kawasaki Medical School, Kurashiki, Japan
| | - Naruto Taira
- Department of Breast and Thyroid Surgery, Kawasaki Medical School, Kurashiki, Japan
| | - Takuya Moriya
- Department of Pathology, Kawasaki Medical School, 577 Matsushima, Kurashiki, 701-0192, Japan.
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Li F, Chen H, Lu X, Wei Y, Zhao Y, Fu J, Xiao X, Bu H. Combining the tumor-stroma ratio with tumor-infiltrating lymphocytes improves the prediction of pathological complete response in breast cancer patients. Breast Cancer Res Treat 2023; 202:173-183. [PMID: 37528265 DOI: 10.1007/s10549-023-07026-7] [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] [Received: 04/15/2023] [Accepted: 06/26/2023] [Indexed: 08/03/2023]
Abstract
PURPOSE The tumor-stroma ratio (TSR) is a common histological parameter that measures stromal abundance and is prognostic in breast cancer (BC). However, more evidence is needed on the predictive value of the TSR for the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC). The purpose of this study was to determine the importance of the TSR in predicting pCR in NAC settings. METHOD We evaluated the TSR on pretreatment biopsies of 912 BC patients from four independent Chinese hospitals and investigated the potential value of the TSR for predicting pCR. Meanwhile, stromal tumor-infiltrating lymphocytes (sTILs) were assessed, and we evaluated the predictive value of the combination of sTILs and TSR (TSRILs). RESULTS Patients with low stroma showed a higher pCR rate than those with high stroma among the four independent hospitals, and in multivariate analysis, the TSR was proven to be an independent predictor for pCR to NAC with an odds ratio of 1.945 (95% CI 1.230-3.075, P = 0.004). Moreover, we found that TSRILs could improve the area under the curve (AUC) for predicting pCR from 0.750 to 0.785 (P = 0.039); especially in HER2-negative BCs, the inclusion of TSRILs increased the AUC from 0.801 to 0.835 in the discovery dataset (P = 0.048) and 0.734 to 0.801 in the validation dataset (P = 0.003). CONCLUSION TSR and sTILs can be easily measured in pathological routines and provide predictive information without additional cost; with more evidence from clinical trials, TSRILs could be a candidate to better stratify patients in NAC settings.
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Affiliation(s)
- Fengling Li
- Department of Pathology, West China Hospital, Sichuan University, Chengdu, China
- Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, China
| | - Hong Chen
- Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, China
- Key Laboratory of Transplant Engineering and Immunology of the National Health Commission, West China Hospital, Sichuan University, Chengdu, China
| | - Xunxi Lu
- Department of Pathology, West China Hospital, Sichuan University, Chengdu, China
- Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, China
| | - Yani Wei
- Department of Pathology, West China Hospital, Sichuan University, Chengdu, China
- Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, China
| | - Yuanyuan Zhao
- Department of Pathology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China
| | - Jing Fu
- Department of Pathology, Sichuan Provincial People's Hospital, Chengdu, China
| | - Xiuli Xiao
- Department of Pathology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Hong Bu
- Department of Pathology, West China Hospital, Sichuan University, Chengdu, China.
- Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, China.
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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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Kumarguru BN, Ramaswamy AS, Arathi CA, Swathi D. Utility of Indigenously Developed Square Grid Method for Evaluation of Tumor-Stroma Ratio and Stromal Tumor-Infiltrating Lymphocytes in Invasive Breast Carcinoma: A Pilot Study. IRANIAN JOURNAL OF PATHOLOGY 2023; 18:335-346. [PMID: 37942205 PMCID: PMC10628375 DOI: 10.30699/ijp.2023.1989528.3063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 05/01/2023] [Indexed: 11/10/2023]
Abstract
Background & Objective Invasive breast carcinoma (IBC) is the most commonly diagnosed cancer among women in India. The conventional visual method of evaluation of Tumor-Stroma Ratio (TSR) and Stromal Tumor-Infiltrating Lymphocytes (sTIL) appears to be subjective. The present study aims to evaluate the utility of the indigenously designed square grid method for the evaluation of tumor-stroma ratio and stromal tumor-infiltrating lymphocytes in invasive breast carcinoma by assessing the inter-observer variability. Methods This was a retrospective study conducted at a rural tertiary care referral institute from July 2018 to June 2020. In each case, microphotographs were taken from 10 representative fields in H&E-stained sections for evaluating TSR in low-power and sTIL in high-power. Both the parameters were evaluated employing an indigenously designed square grid applied onto microphotographs in the power-point slides by making use of principles of the Pythagorean theorem. Both parameters were separately evaluated by two pathologists. Cohen kappa statistics was the statistical tool used to analyze inter-observer variability. Results Thirty cases were analyzed. Invasive breast carcinoma of no special type (IBC-NST) was the most common histopathological type (26 cases (86.67%)). For TRS evaluation, a Kappa value of 0.78 suggested substantial agreement with an agreement of 91.67%. For sTIL evaluation, a Kappa value of 0.51 suggested moderate agreement with an agreement of 88.33%. The P-values were statistically highly significant (P<0.001). Conclusion Square grid method is a novel technique for evaluating TSR and sTIL in invasive breast carcinoma. It can be considered an example of the application of Pythagoras' theorem in Pathology.
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Affiliation(s)
- B N Kumarguru
- Department of Pathology, PES Institute of Medical Sciences and Research, Kuppam, Chittoor, Andhra Pradesh, India
| | - A S Ramaswamy
- Department of Pathology, PES Institute of Medical Sciences and Research, Kuppam, Chittoor, Andhra Pradesh, India
| | - C A Arathi
- Department of Pathology, PES Institute of Medical Sciences and Research, Kuppam, Chittoor, Andhra Pradesh, India
| | - D Swathi
- Department of Pathology, PES Institute of Medical Sciences and Research, Kuppam, Chittoor, Andhra Pradesh, India
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Karancsi Z, Hagenaars SC, Németh K, Mesker WE, Tőkés AM, Kulka J. Tumour-stroma ratio (TSR) in breast cancer: comparison of scoring core biopsies versus resection specimens. Virchows Arch 2023:10.1007/s00428-023-03555-0. [PMID: 37198327 DOI: 10.1007/s00428-023-03555-0] [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: 12/14/2022] [Revised: 03/29/2023] [Accepted: 04/27/2023] [Indexed: 05/19/2023]
Abstract
PURPOSE Tumour-stroma ratio (TSR) is an important prognostic and predictive factor in several tumour types. The aim of this study is to determine whether TSR evaluated in breast cancer core biopsies is representative of the whole tumour. METHOD Different TSR scoring methods, their reproducibility, and the association of TSR with clinicopathological characteristics were investigated in 178 breast carcinoma core biopsies and corresponding resection specimens. TSR was assessed by two trained scientists on the most representative H&E-stained digitised slides. Patients were treated primarily with surgery between 2010 and 2021 at Semmelweis University, Budapest. RESULTS Ninety-one percent of the tumours were hormone receptor (HR)-positive (luminal-like). Interobserver agreement was highest using 100 × magnification (κcore = 0.906, κresection specimen = 0.882). The agreement between TSR of core biopsies and resection specimens of the same patients was moderate (κ = 0.514). Differences between the two types of samples were most frequent in cases with TSR scores close to the 50% cut-off point. TSR was strongly correlated with age at diagnosis, pT category, histological type, histological grade, and surrogate molecular subtype. A tendency was identified for more recurrences among stroma-high (SH) tumours (p = 0.07). Significant correlation was detected between the TSR and tumour recurrence in grade 1 HR-positive breast cancer cases (p = 0.03). CONCLUSIONS TSR is easy to determine and reproducible on both core biopsies and in resection specimens and is associated with several clinicopathological characteristics of breast cancer. TSR scored on core biopsies is moderately representative for the whole tumour.
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Affiliation(s)
- Zsófia Karancsi
- Department of Pathology, Forensic and Insurance Medicine, Semmelweis University, Üllői út 93, 1091, Budapest, Hungary.
| | - Sophie C Hagenaars
- Department of Surgery, Leiden University Medical Centre, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands
| | - Kristóf Németh
- Department of Pathology, Forensic and Insurance Medicine, Semmelweis University, Üllői út 93, 1091, Budapest, Hungary
| | - Wilma E Mesker
- Department of Surgery, Leiden University Medical Centre, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands
| | - Anna Mária Tőkés
- Department of Pathology, Forensic and Insurance Medicine, Semmelweis University, Üllői út 93, 1091, Budapest, Hungary
| | - Janina Kulka
- Department of Pathology, Forensic and Insurance Medicine, Semmelweis University, Üllői út 93, 1091, Budapest, Hungary
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Firmbach D, Benz M, Kuritcyn P, Bruns V, Lang-Schwarz C, Stuebs FA, Merkel S, Leikauf LS, Braunschweig AL, Oldenburger A, Gloßner L, Abele N, Eck C, Matek C, Hartmann A, Geppert CI. Tumor-Stroma Ratio in Colorectal Cancer-Comparison between Human Estimation and Automated Assessment. Cancers (Basel) 2023; 15:2675. [PMID: 37345012 DOI: 10.3390/cancers15102675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 04/27/2023] [Accepted: 05/02/2023] [Indexed: 06/23/2023] Open
Abstract
The tumor-stroma ratio (TSR) has been repeatedly shown to be a prognostic factor for survival prediction of different cancer types. However, an objective and reliable determination of the tumor-stroma ratio remains challenging. We present an easily adaptable deep learning model for accurately segmenting tumor regions in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of colon cancer patients into five distinct classes (tumor, stroma, necrosis, mucus, and background). The tumor-stroma ratio can be determined in the presence of necrotic or mucinous areas. We employ a few-shot model, eventually aiming for the easy adaptability of our approach to related segmentation tasks or other primaries, and compare the results to a well-established state-of-the art approach (U-Net). Both models achieve similar results with an overall accuracy of 86.5% and 86.7%, respectively, indicating that the adaptability does not lead to a significant decrease in accuracy. Moreover, we comprehensively compare with TSR estimates of human observers and examine in detail discrepancies and inter-rater reliability. Adding a second survey for segmentation quality on top of a first survey for TSR estimation, we found that TSR estimations of human observers are not as reliable a ground truth as previously thought.
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Affiliation(s)
- Daniel Firmbach
- Digital Health Systems Department, Fraunhofer-Institute for Integrated Circuits IIS, Am Wolfsmantel 33, 91058 Erlangen, Germany
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
| | - Michaela Benz
- Digital Health Systems Department, Fraunhofer-Institute for Integrated Circuits IIS, Am Wolfsmantel 33, 91058 Erlangen, Germany
| | - Petr Kuritcyn
- Digital Health Systems Department, Fraunhofer-Institute for Integrated Circuits IIS, Am Wolfsmantel 33, 91058 Erlangen, Germany
| | - Volker Bruns
- Digital Health Systems Department, Fraunhofer-Institute for Integrated Circuits IIS, Am Wolfsmantel 33, 91058 Erlangen, Germany
| | - Corinna Lang-Schwarz
- Institute of Pathology, Hospital Bayreuth, Preuschwitzer Str. 101, 95445 Bayreuth, Germany
| | - Frederik A Stuebs
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
- Department of Obstetrics and Gynaecology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Universitätsstraße 21-23, 91054 Erlangen, Germany
| | - Susanne Merkel
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
- Department of Surgery, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 12, 91054 Erlangen, Germany
| | - Leah-Sophie Leikauf
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
| | - Anna-Lea Braunschweig
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
| | - Angelika Oldenburger
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
| | - Laura Gloßner
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
| | - Niklas Abele
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
| | - Christine Eck
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
| | - Christian Matek
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
| | - Arndt Hartmann
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
| | - Carol I Geppert
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
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Almangush A, Jouhi L, Haglund C, Hagström J, Mäkitie AA, Leivo I. Tumor-Stroma Ratio is a Promising Prognostic Classifier in Oropharyngeal Cancer. Hum Pathol 2023; 136:16-24. [PMID: 37001738 DOI: 10.1016/j.humpath.2023.03.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 03/14/2023] [Accepted: 03/24/2023] [Indexed: 04/03/2023]
Abstract
Tumor-stroma ratio (TSR) has been analyzed in many tumor types. To date, the clinical significance of TSR has not been investigated in oropharyngeal squamous cell carcinoma (OPSCC). We used a recently introduced recommendation for the assessment of TSR in a large cohort of 182 patients with OPSCC treated at the Helsinki University Hospital. The percentage of tumor-associated stroma was estimated in hematoxylin and eosin (HE)-stained sections and categorized into 2 groups: "stroma-high" (>50%) and "stroma-low" (≤50%). In multivariable analysis, TSR had a significant association with patient survival as stroma-high tumors showed worse disease-free survival (hazard ratio [HR] = 3.22, 95% confidence interval [CI] = 1.43-7.26, P = .005), disease-specific survival (HR = 2.48, 95% CI = 1.29-4.74, P = .006), and overall survival (HR = 2.23, 95% CI = 1.29-3.85, P = .004). The prognostic value of TSR was superior to the Tumor-Node-Metastasis classification. In addition, the significant prognostic value of TSR was demonstrated when analyzing human papillomavirus (HPV)-positive and HPV-negative cases separately (P < .05). In conclusion, TSR is a powerful prognostic indicator in OPSCC. It can be assessed quickly without additional costs using standard HE slides. Owing to its simplicity and reproducibility, TSR can be implemented in routine pathology diagnostics and reporting. Patients with stroma-rich tumors have an increased risk of recurrence and cancer-related mortality and may benefit from appropriate intensive treatment strategies with close follow-up.
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The Relationship between Histological Composition and Metabolic Profile in Breast Tumors and Peritumoral Tissue Determined with 1H HR-MAS NMR Spectroscopy. Cancers (Basel) 2023; 15:cancers15041283. [PMID: 36831625 PMCID: PMC9954108 DOI: 10.3390/cancers15041283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 02/13/2023] [Accepted: 02/14/2023] [Indexed: 02/22/2023] Open
Abstract
Breast tumors constitute the complex entities composed of cancer cells and stromal components. The compositional heterogeneity should be taken into account in bulk tissue metabolomics studies. The aim of this work was to find the relation between the histological content and 1H HR-MAS (high-resolution magic angle spinning nuclear magnetic resonance) metabolic profiles of the tissue samples excised from the breast tumors and the peritumoral areas in 39 patients diagnosed with invasive breast carcinoma. The total number of the histologically verified specimens was 140. The classification accuracy of the OPLS-DA (Orthogonal Partial Least Squares Discriminant Analysis) model differentiating the cancerous from non-involved samples was 87% (sensitivity of 72.2%, specificity of 92.3%). The metabolic contents of the epithelial and stromal compartments were determined from a linear regression analysis of the levels of the evaluated compounds against the cancer cell fraction in 39 samples composed mainly of cancer cells and intratumoral fibrosis. The correlation coefficients between the levels of several metabolites and a tumor purity were found to be dependent on the tumor grade (I vs II/III). The comparison of the levels of the metabolites in the intratumoral fibrosis (obtained from the extrapolation of the regression lines to 0% cancer content) to those levels in the fibrous connective tissue beyond the tumors revealed a profound metabolic reprogramming in the former tissue. The joint analysis of the metabolic profiles of the stromal and epithelial compartments in the breast tumors contributes to the increased understanding of breast cancer biology.
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Ritter A, Kreis NN, Roth S, Friemel A, Safdar BK, Hoock SC, Wildner JM, Allert R, Louwen F, Solbach C, Yuan J. Cancer-educated mammary adipose tissue-derived stromal/stem cells in obesity and breast cancer: spatial regulation and function. JOURNAL OF EXPERIMENTAL & CLINICAL CANCER RESEARCH : CR 2023; 42:35. [PMID: 36710348 PMCID: PMC9885659 DOI: 10.1186/s13046-022-02592-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 12/29/2022] [Indexed: 01/31/2023]
Abstract
BACKGROUND Breast cancer is the most frequently diagnosed cancer and a common cause of cancer-related death in women. It is well recognized that obesity is associated with an enhanced risk of more aggressive breast cancer as well as reduced patient survival. Breast adipose tissue-derived mesenchymal stromal/stem cells (bASCs) are crucial components of the tumor microenvironment. A key step initially involved in this process might be the de-differentiation of bASCs into tumor supporting phenotypes. METHODS In the present work, we isolated bASCs from adipose tissues adjacent to the tumor (aT bASCs) from lean- (ln-aT bASCs, BMI ≤ 25) and breast cancer patients with obesity (ob-aT bASCs, BMI ≥ 35), and analyzed their phenotypes with functional assays and RNA sequencing, compared to their counterparts isolated from adipose tissues distant from the tumor (dT bASCs). RESULTS We show that ln-aT bASCs are susceptible to be transformed into an inflammatory cancer-associated phenotype, whereas ob-aT bASCs are prone to be cancer-educated into a myofibroblastic phenotype. Both ln-aT- and ob-aT bASCs compromise their physiological differentiation capacity, and upregulate metastasis-promoting factors. While ln-aT bASCs stimulate proliferation, motility and chemoresistance by inducing epithelial-mesenchymal transition of low malignant breast cancer cells, ob-aT bASCs trigger more efficiently a cancer stem cell phenotype in highly malignant breast cancer cells. CONCLUSION Breast cancer-associated bASCs are able to foster malignancy of breast cancer cells by multiple mechanisms, especially, induction of epithelial-mesenchymal transition and activation of stemness-associated genes in breast cancer cells. Blocking the de-differentiation of bASCs in the tumor microenvironment could be a novel strategy to develop an effective intervention for breast cancer patients. SIGNIFICANCE This study provides mechanistic insights into how obesity affects the phenotype of bASCs in the TME. Moreover, it highlights the molecular changes inside breast cancer cells upon cell-cell interaction with cancer-educated bASCs.
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Affiliation(s)
- Andreas Ritter
- Obstetrics and Prenatal Medicine, Gynecology and Obstetrics, University Hospital Frankfurt, J. W. Goethe-University, Theodor-Stern-Kai 7, D-60590 Frankfurt, Germany
| | - Nina-Naomi Kreis
- Obstetrics and Prenatal Medicine, Gynecology and Obstetrics, University Hospital Frankfurt, J. W. Goethe-University, Theodor-Stern-Kai 7, D-60590 Frankfurt, Germany
| | - Susanne Roth
- Obstetrics and Prenatal Medicine, Gynecology and Obstetrics, University Hospital Frankfurt, J. W. Goethe-University, Theodor-Stern-Kai 7, D-60590 Frankfurt, Germany
| | - Alexandra Friemel
- Obstetrics and Prenatal Medicine, Gynecology and Obstetrics, University Hospital Frankfurt, J. W. Goethe-University, Theodor-Stern-Kai 7, D-60590 Frankfurt, Germany
| | - Babek Kahn Safdar
- Obstetrics and Prenatal Medicine, Gynecology and Obstetrics, University Hospital Frankfurt, J. W. Goethe-University, Theodor-Stern-Kai 7, D-60590 Frankfurt, Germany
| | - Samira Catharina Hoock
- Obstetrics and Prenatal Medicine, Gynecology and Obstetrics, University Hospital Frankfurt, J. W. Goethe-University, Theodor-Stern-Kai 7, D-60590 Frankfurt, Germany
| | - Julia Maria Wildner
- Obstetrics and Prenatal Medicine, Gynecology and Obstetrics, University Hospital Frankfurt, J. W. Goethe-University, Theodor-Stern-Kai 7, D-60590 Frankfurt, Germany
| | - Roman Allert
- Obstetrics and Prenatal Medicine, Gynecology and Obstetrics, University Hospital Frankfurt, J. W. Goethe-University, Theodor-Stern-Kai 7, D-60590 Frankfurt, Germany
| | - Frank Louwen
- Obstetrics and Prenatal Medicine, Gynecology and Obstetrics, University Hospital Frankfurt, J. W. Goethe-University, Theodor-Stern-Kai 7, D-60590 Frankfurt, Germany
| | - Christine Solbach
- Obstetrics and Prenatal Medicine, Gynecology and Obstetrics, University Hospital Frankfurt, J. W. Goethe-University, Theodor-Stern-Kai 7, D-60590 Frankfurt, Germany
| | - Juping Yuan
- Obstetrics and Prenatal Medicine, Gynecology and Obstetrics, University Hospital Frankfurt, J. W. Goethe-University, Theodor-Stern-Kai 7, D-60590 Frankfurt, Germany
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Le MK, Odate T, Kawai M, Oishi N, Kondo T. Investigating the role of core needle biopsy in evaluating tumor-stroma ratio (TSR) of invasive breast cancer: a retrospective study. Breast Cancer Res Treat 2023; 197:113-121. [PMID: 36335529 DOI: 10.1007/s10549-022-06768-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 10/06/2022] [Indexed: 11/07/2022]
Abstract
PURPOSE Tumor-stroma ratio (TSR) of invasive breast carcinoma has gained attention in recent years due to its prognostic significance. Previous studies showed TSR is a potential biomarker for indicating the tumor response to neoadjuvant chemotherapy. However, it is not clear how well TSR evaluation in biopsy specimens might reflect the TSR in resection specimens. We conducted a study to investigate whether biopsy evaluation of TSR can be an alternative method. METHOD We collected cases with invasive breast carcinoma of no special type (IBC-NST) from University of Yamanashi hospital between 2011 and 2017 whose biopsy and resection specimens both had a pathologically diagnosis of IBC-NST (n = 146). We conceptualized a method for evaluating TSR in biopsy specimens within a preliminary cohort (n = 50). Within the studied cohort (n = 96), biopsy-based TSR (b-TSR) and resection-based TSR (r-TSR) were scored by two pathologists. We then evaluated our method's validity and performance by measuring interobserver variability between the two pathologists, Spearman's correlation between b-TSR and r-TSR, and the receiver operating characteristics (ROC) analysis for defining stroma-rich and stroma-poor tumors. RESULTS Intra-class coefficient between the two pathologists was 0.59. The correlation coefficients between b-TSR and r-TSR in the two pathologists were 0.45 and 0.37. The ROC areas under the curve were 0.7 and 0.67. By considering an r-TSR of < 50% as stroma-rich, the sensitivity and specificity of detecting stroma-rich tumors were 64.1% and 66.7%, respectively, when b-TSR was < 40%. CONCLUSION Our current b-TSR evaluation method can provide information about r-TSR and facilitate pre-treatment therapy follow-up.
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Affiliation(s)
- Minh-Khang Le
- Department of Pathology, University of Yamanashi, Yamanashi, 409-3898, Japan
| | - Toru Odate
- Department of Pathology, University of Yamanashi, Yamanashi, 409-3898, Japan
| | - Masataka Kawai
- Department of Pathology, University of Yamanashi, Yamanashi, 409-3898, Japan
| | - Naoki Oishi
- Department of Pathology, University of Yamanashi, Yamanashi, 409-3898, Japan
| | - Tetsuo Kondo
- Department of Pathology, University of Yamanashi, Yamanashi, 409-3898, Japan.
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