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Ferkh A, Tjahjadi C, Stefani L, Geenty P, Byth K, De Silva K, Boyd AC, Richards D, Mollee P, Korczyk D, Taylor MS, Kwok F, Kizana E, Ng ACT, Thomas L. Cardiac "hypertrophy" phenotyping: differentiating aetiologies with increased left ventricular wall thickness on echocardiography. Front Cardiovasc Med 2023; 10:1183485. [PMID: 37465456 PMCID: PMC10351962 DOI: 10.3389/fcvm.2023.1183485] [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: 03/10/2023] [Accepted: 06/15/2023] [Indexed: 07/20/2023] Open
Abstract
Aims Differentiating phenotypes of cardiac "hypertrophy" characterised by increased wall thickness on echocardiography is essential for management and prognostication. Transthoracic echocardiography is the most commonly used screening test for this purpose. We sought to identify echocardiographic markers that distinguish infiltrative and storage disorders that present with increased left ventricular (LV) wall thickness, namely, cardiac amyloidosis (CA) and Anderson-Fabry disease (AFD), from hypertensive heart disease (HHT). Methods Patients were retrospectively recruited from Westmead Hospital, Sydney, and Princess Alexandra Hospital, Brisbane. LV structural, systolic, and diastolic function parameters, as well as global (LVGLS) and segmental longitudinal strains, were assessed. Previously reported echocardiographic parameters including relative apical sparing ratio (RAS), LV ejection fraction-to-strain ratio (EFSR), mass-to-strain ratio (MSR) and amyloidosis index (AMYLI) score (relative wall thickness × E/e') were evaluated. Results A total of 209 patients {120 CA [58 transthyretin amyloidosis (ATTR) and 62 light-chain (AL) amyloidosis], 31 AFD and 58 HHT patients; mean age 64.1 ± 13.7 years, 75% male} comprised the study cohort. Echocardiographic measurements differed across the three groups, The LV mass index was higher in both CA {median 126.6 [interquartile range (IQR) 106.4-157.9 g/m2]} and AFD [median 134 (IQR 108.8-152.2 g/m2)] vs. HHT [median 92.7 (IQR 79.6-102.3 g/m2), p < 0.05]. LVGLS was lowest in CA [median 12.29 (IQR 10.33-15.56%)] followed by AFD [median 16.92 (IQR 14.14-18.78%)] then HHT [median 18.56 (IQR 17.51-19.97%), p < 0.05]. Diastolic function measurements including average e' and E/e' were most impaired in CA and least impaired in AFD. Indexed left atrial volume was highest in CA. EFSR and MSR differentiated secondary (CA + AFD) from HHT [receiver operating curve-area under the curve (ROC-AUC) of 0.80 and 0.91, respectively]. RAS and AMYLI score differentiated CA from AFD (ROC-AUC of 0.79 and 0.80, respectively). A linear discriminant analysis with stepwise variable selection using linear combinations of LV mass index, average e', LVGLS and basal strain correctly classified 79% of all cases. Conclusion Simple echocardiographic parameters differentiate between different "hypertrophic" cardiac phenotypes. These have potential utility as a screening tool to guide further confirmatory testing.
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Affiliation(s)
- Aaisha Ferkh
- Westmead Clinical School, University of Sydney, Westmead, NSW, Australia
- Cardiology Department, Westmead Hospital, Westmead, NSW, Australia
| | - Catherina Tjahjadi
- Cardiology Department, Princess Alexandra Hospital, Brisbane, QLD, Australia
| | - Luke Stefani
- Westmead Clinical School, University of Sydney, Westmead, NSW, Australia
- Cardiology Department, Westmead Hospital, Westmead, NSW, Australia
| | - Paul Geenty
- Westmead Clinical School, University of Sydney, Westmead, NSW, Australia
- Cardiology Department, Westmead Hospital, Westmead, NSW, Australia
| | - Karen Byth
- WSLHD Research and Education Network, Westmead Hospital, Westmead, NSW, Australia
| | - Kasun De Silva
- Westmead Clinical School, University of Sydney, Westmead, NSW, Australia
- Cardiology Department, Westmead Hospital, Westmead, NSW, Australia
| | - Anita C. Boyd
- Westmead Private Cardiology, Westmead, NSW, Australia
| | | | - Peter Mollee
- Haematology Department, Princess Alexandra Hospital, Brisbane, QLD, Australia
- School of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Dariusz Korczyk
- Cardiology Department, Princess Alexandra Hospital, Brisbane, QLD, Australia
| | - Mark S. Taylor
- Department of Clinical Immunology and Allergy, Westmead Hospital, Westmead, NSW, Australia
| | - Fiona Kwok
- Haematology Department, Westmead Hospital, Westmead, NSW, Australia
| | - Eddy Kizana
- Westmead Clinical School, University of Sydney, Westmead, NSW, Australia
- Cardiology Department, Westmead Hospital, Westmead, NSW, Australia
- Centre for Heart Research, The Westmead Institute for Medical Research, Westmead, NSW, Australia
| | - Arnold C. T. Ng
- Cardiology Department, Princess Alexandra Hospital, Brisbane, QLD, Australia
| | - Liza Thomas
- Westmead Clinical School, University of Sydney, Westmead, NSW, Australia
- Cardiology Department, Westmead Hospital, Westmead, NSW, Australia
- South-West Clinical School, University of New South Wales, Liverpool, NSW, Australia
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Ding Y, Dhawan G, Jones C, Ness T, Nichols E, Krasnogor N, Reynolds NJ. An open source pipeline for quantitative immunohistochemistry image analysis of inflammatory skin disease using artificial intelligence. J Eur Acad Dermatol Venereol 2023; 37:605-614. [PMID: 36367625 PMCID: PMC10947200 DOI: 10.1111/jdv.18726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 08/17/2022] [Indexed: 11/13/2022]
Abstract
BACKGROUND The application of artificial intelligence (AI) to whole slide images has the potential to improve research reliability and ultimately diagnostic efficiency and service capacity. Image annotation plays a key role in AI and digital pathology. However, the work-streams required for tissue-specific (skin) and immunostain-specific annotation has not been extensively studied compared with the development of AI algorithms. OBJECTIVES The objective of this study is to develop a common workflow for annotating whole slide images of biopsies from inflammatory skin disease immunostained with a variety of epidermal and dermal markers prior to the development of the AI-assisted analysis pipeline. METHODS A total of 45 slides containing 3-5 sections each were scanned using Aperio AT2 slide scanner (Leica Biosystems). These slides were annotated by hand using a commonly used image analysis tool which resulted in more than 4000 images blocks. We used deep learning (DL) methodology to first sequentially segment (epidermis and upper dermis), with the exclusion of common artefacts and second to quantify the immunostained signal in those two compartments of skin biopsies and the ratio of positive cells. RESULTS We validated two DL models using 10-fold validation runs and by comparing to ground truth manually annotated data. The models achieved an average (global) accuracy of 95.0% for the segmentation of epidermis and dermis and 86.1% for the segmentation of positive/negative cells. CONCLUSIONS The application of two DL models in sequence facilitates accurate segmentation of epidermal and dermal structures, exclusion of common artefacts and enables the quantitative analysis of the immunostained signal. However, inaccurate annotation of the slides for training the DL model can decrease the accuracy of the output. Our open source code will facilitate further external validation across different immunostaining platforms and slide scanners.
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Affiliation(s)
- Yuchun Ding
- Interdisciplinary Computing and Complex Biosystems Research Group, School of Computing ScienceNewcastle UniversityNewcastle upon TyneUK
| | - Gaurav Dhawan
- Institute of Translational and Clinical MedicineNewcastle University Medical SchoolNewcastle upon TyneUK
- Department of Dermatology, Royal Victoria InfirmaryNewcastle Hospitals NHS Foundation TrustNewcastle upon TyneUK
| | - Claire Jones
- MRC/EPSRC, Molecular Pathology Node, Department of PathologyNewcastle Hospitals NHS Foundation TrustNewcastle upon TyneUK
| | - Thomas Ness
- MRC/EPSRC, Molecular Pathology Node, Department of PathologyNewcastle Hospitals NHS Foundation TrustNewcastle upon TyneUK
| | - Esme Nichols
- Institute of Translational and Clinical MedicineNewcastle University Medical SchoolNewcastle upon TyneUK
- Department of Dermatology, Royal Victoria InfirmaryNewcastle Hospitals NHS Foundation TrustNewcastle upon TyneUK
| | - Natalio Krasnogor
- Interdisciplinary Computing and Complex Biosystems Research Group, School of Computing ScienceNewcastle UniversityNewcastle upon TyneUK
| | - Nick J. Reynolds
- Institute of Translational and Clinical MedicineNewcastle University Medical SchoolNewcastle upon TyneUK
- Department of Dermatology, Royal Victoria InfirmaryNewcastle Hospitals NHS Foundation TrustNewcastle upon TyneUK
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Papageorgiou SG, Thomopoulos TP, Katagas I, Bouchla A, Pappa V. Prognostic molecular biomarkers in diffuse large B-cell lymphoma in the rituximab era and their therapeutic implications. Ther Adv Hematol 2021; 12:20406207211013987. [PMID: 34104369 PMCID: PMC8150462 DOI: 10.1177/20406207211013987] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Accepted: 04/12/2021] [Indexed: 12/17/2022] Open
Abstract
Diffuse large B-cell lymphoma (DLBCL) represents a group of tumors characterized by substantial heterogeneity in terms of their pathological and biological features, a causal factor of their varied clinical outcome. This variation has persisted despite the implementation of rituximab in treatment regimens over the last 20 years. In this context, prognostic biomarkers are of great importance in order to identify high-risk patients that might benefit from treatment intensification or the introduction of novel therapeutic agents. Herein, we review current knowledge on specific immunohistochemical or genetic biomarkers that might be useful in clinical practice. Gene-expression profiling is a tool of special consideration in this effort, as it has enriched our understanding of DLBCL biology and has allowed for the classification of DLBCL by cell-of-origin as well as by more elaborate molecular signatures based on distinct gene-expression profiles. These subgroups might outperform individual biomarkers in terms of prognostication; however, their use in clinical practice is still limited. Moreover, the underappreciated role of the tumor microenvironment in DLBCL prognosis is discussed in terms of prognostic gene-expression signatures, as well as in terms of individual biomarkers of prognostic significance. Finally, the efficacy of novel therapeutic agents for the treatment of DLBCL patients are discussed and an evidence-based therapeutic approach by specific genetic subgroup is suggested.
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Affiliation(s)
- Sotirios G. Papageorgiou
- Second Department of Internal Medicine and Research Unit, University General Hospital ‘Attikon’, 1 Rimini Street, Haidari, Athens 12462, Greece
| | - Thomas P. Thomopoulos
- Second Department of Internal Medicine and Research Unit, Hematology Unit, University General Hospital, ‘Attikon’, Haidari, Athens, Greece
| | - Ioannis Katagas
- Second Department of Internal Medicine and Research Unit, Hematology Unit, University General Hospital, ‘Attikon’, Haidari, Athens, Greece
| | - Anthi Bouchla
- Second Department of Internal Medicine and Research Unit, Hematology Unit, University General Hospital, ‘Attikon’, Haidari, Athens, Greece
| | - Vassiliki Pappa
- Second Department of Internal Medicine and Research Unit, Hematology Unit, University General Hospital, ‘Attikon’, Haidari, Athens, Greece
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Tsagarakis NJ, Papadhimitriou SI, Pavlidis D, Liapis K, Gortzolidis G, Kostopoulos IV, Marinakis T, Paterakis G. Contribution of immunophenotype to the investigation and differential diagnosis of Burkitt lymphoma, double‐hit high‐grade B‐cell lymphoma, and single‐hit
MYC
‐rearranged diffuse large B‐cell lymphoma. CYTOMETRY PART B-CLINICAL CYTOMETRY 2020; 98:412-420. [DOI: 10.1002/cyto.b.21887] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 04/22/2020] [Accepted: 04/23/2020] [Indexed: 11/08/2022]
Affiliation(s)
- Nikolaos J. Tsagarakis
- Department of Laboratory Hematology Athens Regional General Hospital Georgios Gennimatas Athens Greece
| | | | - Dimitris Pavlidis
- Department of Laboratory Hematology Athens Regional General Hospital Georgios Gennimatas Athens Greece
| | - Konstantinos Liapis
- Department of Clinical Hematology Athens Regional General Hospital Georgios Gennimatas Athens Greece
| | - Georgios Gortzolidis
- Department of Clinical Hematology Athens Regional General Hospital Georgios Gennimatas Athens Greece
| | - Ioannis V. Kostopoulos
- Department of Laboratory Hematology Athens Regional General Hospital Georgios Gennimatas Athens Greece
| | - Theodoros Marinakis
- Department of Clinical Hematology Athens Regional General Hospital Georgios Gennimatas Athens Greece
| | - Georgios Paterakis
- Flow Cytometry Laboratory, Department of Immunology Athens Regional General Hospital Georgios Gennimatas Athens Greece
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El Achi H, Khoury JD. Artificial Intelligence and Digital Microscopy Applications in Diagnostic Hematopathology. Cancers (Basel) 2020; 12:cancers12040797. [PMID: 32224980 PMCID: PMC7226574 DOI: 10.3390/cancers12040797] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 03/20/2020] [Accepted: 03/24/2020] [Indexed: 12/15/2022] Open
Abstract
Digital Pathology is the process of converting histology glass slides to digital images using sophisticated computerized technology to facilitate acquisition, evaluation, storage, and portability of histologic information. By its nature, digitization of analog histology data renders it amenable to analysis using deep learning/artificial intelligence (DL/AI) techniques. The application of DL/AI to digital pathology data holds promise, even if the scope of use cases and regulatory framework for deploying such applications in the clinical environment remains in the early stages. Recent studies using whole-slide images and DL/AI to detect histologic abnormalities in general and cancer in particular have shown encouraging results. In this review, we focus on these emerging technologies intended for use in diagnostic hematology and the evaluation of lymphoproliferative diseases.
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Affiliation(s)
- Hanadi El Achi
- Department of Pathology and Laboratory Medicine, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA;
| | - Joseph D. Khoury
- Department of Hematopathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Correspondence:
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Molecular Complexity of Diffuse Large B-Cell Lymphoma: Can It Be a Roadmap for Precision Medicine? Cancers (Basel) 2020; 12:cancers12010185. [PMID: 31940809 PMCID: PMC7017344 DOI: 10.3390/cancers12010185] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 01/07/2020] [Accepted: 01/09/2020] [Indexed: 12/11/2022] Open
Abstract
Diffuse large B-cell lymphoma (DLBCL) is the most common non-Hodgkin lymphoma; it features extreme molecular heterogeneity regardless of the classical cell-of-origin (COO) classification. Despite this, the standard therapeutic approach is still immunochemotherapy (rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone-R-CHOP), which allows a 60% overall survival (OS) rate, but up to 40% of patients experience relapse or refractory (R/R) disease. With the purpose of searching for new clinical parameters and biomarkers helping to make a better DLBCL patient characterization and stratification, in the last years a series of large discovery genomic and transcriptomic studies has been conducted, generating a wealth of information that needs to be put in order. We reviewed these researches, trying ultimately to understand if there are bases offering a roadmap toward personalized and precision medicine also for DLBCL.
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