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Ohnishi C, Ohnishi T, Ibrahim K, Ntiamoah P, Ross D, Yamaguchi M, Yagi Y. Color Standardization and Stain Intensity Calibration for Whole Slide Image-Based Immunohistochemistry Assessment. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2024; 30:118-132. [PMID: 38156737 PMCID: PMC11090401 DOI: 10.1093/micmic/ozad136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 10/26/2023] [Accepted: 11/24/2023] [Indexed: 01/03/2024]
Abstract
Automated quantification of human epidermal growth factor receptor 2 (HER2) immunohistochemistry (IHC) using whole slide imaging (WSI) is expected to eliminate subjectivity in visual assessment. However, the color intensity in WSI varies depending on the staining process and scanner device. Such variations affect the image analysis results. This paper presents methods to diminish the influence of color variation produced in the staining process using a calibrator slide consisting of peptide-coated microbeads. The calibrator slide is stained along with tissue sample slides, and the 3,3'-diaminobenzidine (DAB) color intensities of the microbeads are used for calibrating the color variation of the sample slides. An off-the-shelf image analysis tool is employed for the automated assessment, in which cells are classified by the thresholds for the membrane staining. We have adopted two methods for calibrating the color variation based on the DAB color intensities obtained from the calibrator slide: (1) thresholds for classifying the DAB membranous intensity are adjusted, and (2) the color intensity of WSI is corrected. In the experiment, the calibrator slides and tissue of breast cancer slides were stained together on different days and used to test our protocol. With the proposed protocol, the discordance in the HER2 evaluation was reduced to one slide out of 120 slides.
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Affiliation(s)
- Chie Ohnishi
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, 1133 York Avenue, New York, NY 10065, USA
- School of Engineering, Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku, Yokohama, Kanagawa 226-8503, Japan
| | - Takashi Ohnishi
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, 1133 York Avenue, New York, NY 10065, USA
| | - Kareem Ibrahim
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, 1133 York Avenue, New York, NY 10065, USA
| | - Peter Ntiamoah
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, 1133 York Avenue, New York, NY 10065, USA
| | - Dara Ross
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, 1133 York Avenue, New York, NY 10065, USA
| | - Masahiro Yamaguchi
- School of Engineering, Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku, Yokohama, Kanagawa 226-8503, Japan
| | - Yukako Yagi
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, 1133 York Avenue, New York, NY 10065, USA
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2
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Xing F, Yang X, Cornish TC, Ghosh D. Learning with limited target data to detect cells in cross-modality images. Med Image Anal 2023; 90:102969. [PMID: 37802010 DOI: 10.1016/j.media.2023.102969] [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: 02/27/2023] [Revised: 08/16/2023] [Accepted: 09/11/2023] [Indexed: 10/08/2023]
Abstract
Deep neural networks have achieved excellent cell or nucleus quantification performance in microscopy images, but they often suffer from performance degradation when applied to cross-modality imaging data. Unsupervised domain adaptation (UDA) based on generative adversarial networks (GANs) has recently improved the performance of cross-modality medical image quantification. However, current GAN-based UDA methods typically require abundant target data for model training, which is often very expensive or even impossible to obtain for real applications. In this paper, we study a more realistic yet challenging UDA situation, where (unlabeled) target training data is limited and previous work seldom delves into cell identification. We first enhance a dual GAN with task-specific modeling, which provides additional supervision signals to assist with generator learning. We explore both single-directional and bidirectional task-augmented GANs for domain adaptation. Then, we further improve the GAN by introducing a differentiable, stochastic data augmentation module to explicitly reduce discriminator overfitting. We examine source-, target-, and dual-domain data augmentation for GAN enhancement, as well as joint task and data augmentation in a unified GAN-based UDA framework. We evaluate the framework for cell detection on multiple public and in-house microscopy image datasets, which are acquired with different imaging modalities, staining protocols and/or tissue preparations. The experiments demonstrate that our method significantly boosts performance when compared with the reference baseline, and it is superior to or on par with fully supervised models that are trained with real target annotations. In addition, our method outperforms recent state-of-the-art UDA approaches by a large margin on different datasets.
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Affiliation(s)
- Fuyong Xing
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, 13001 E 17th Pl, Aurora, CO 80045, USA.
| | - Xinyi Yang
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, 13001 E 17th Pl, Aurora, CO 80045, USA
| | - Toby C Cornish
- Department of Pathology, University of Colorado Anschutz Medical Campus, 13001 E 17th Pl, Aurora, CO 80045, USA
| | - Debashis Ghosh
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, 13001 E 17th Pl, Aurora, CO 80045, USA
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Hanna MG, Ardon O. Digital pathology systems enabling quality patient care. Genes Chromosomes Cancer 2023; 62:685-697. [PMID: 37458325 PMCID: PMC11265285 DOI: 10.1002/gcc.23192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 06/27/2023] [Accepted: 07/06/2023] [Indexed: 09/20/2023] Open
Abstract
Pathology laboratories are undergoing digital transformations, adopting innovative technologies to enhance patient care. Digital pathology systems impact clinical, education, and research use cases where pathologists use digital technologies to perform tasks in lieu of using glass slides and a microscope. Pathology professional societies have established clinical validation guidelines, and the US Food and Drug Administration have also authorized digital pathology systems for primary diagnosis, including image analysis and machine learning systems. Whole slide images, or digital slides, can be viewed and navigated similar to glass slides on a microscope. These modern tools not only enable pathologists to practice their routine clinical activities, but can potentially enable digital computational discovery. Assimilation of whole slide images in pathology clinical workflow can further empower machine learning systems to support computer assisted diagnostics. The potential enrichment these systems can provide is unprecedented in the field of pathology. With appropriate integration, these clinical decision support systems will allow pathologists to increase the delivery of quality patient care. This review describes the digital pathology transformation process, applicable clinical use cases, incorporation of image analysis and machine learning systems in the clinical workflow, as well as future technologies that may further disrupt pathology modalities to deliver quality patient care.
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Affiliation(s)
- Matthew G Hanna
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Orly Ardon
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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4
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Hanna MG, Brogi E. Future Practices of Breast Pathology Using Digital and Computational Pathology. Adv Anat Pathol 2023; 30:421-433. [PMID: 37737690 DOI: 10.1097/pap.0000000000000414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/23/2023]
Abstract
Pathology clinical practice has evolved by adopting technological advancements initially regarded as potentially disruptive, such as electron microscopy, immunohistochemistry, and genomic sequencing. Breast pathology has a critical role as a medical domain, where the patient's pathology diagnosis has significant implications for prognostication and treatment of diseases. The advent of digital and computational pathology has brought about significant advancements in the field, offering new possibilities for enhancing diagnostic accuracy and improving patient care. Digital slide scanning enables to conversion of glass slides into high-fidelity digital images, supporting the review of cases in a digital workflow. Digitization offers the capability to render specimen diagnoses, digital archival of patient specimens, collaboration, and telepathology. Integration of image analysis and machine learning-based systems layered atop the high-resolution digital images offers novel workflows to assist breast pathologists in their clinical, educational, and research endeavors. Decision support tools may improve the detection and classification of breast lesions and the quantification of immunohistochemical studies. Computational biomarkers may help to contribute to patient management or outcomes. Furthermore, using digital and computational pathology may increase standardization and quality assurance, especially in areas with high interobserver variability. This review explores the current landscape and possible future applications of digital and computational techniques in the field of breast pathology.
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Affiliation(s)
- Matthew G Hanna
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
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5
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Ohnishi C, Ohnishi T, Ntiamoah P, Ross DS, Yamaguchi M, Yagi Y. Standardizing HER2 immunohistochemistry assessment: calibration of color and intensity variation in whole slide imaging caused by staining and scanning. Appl Microsc 2023; 53:8. [PMID: 37704877 PMCID: PMC10499734 DOI: 10.1186/s42649-023-00091-8] [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: 06/26/2023] [Accepted: 08/05/2023] [Indexed: 09/15/2023] Open
Abstract
In the evaluation of human epidermal growth factor receptor 2 (HER2) immunohistochemistry (IHC) - one of the standard biomarkers for breast cancer- visual assessment is laborious and subjective. Image analysis using whole slide image (WSI) could produce more consistent results; however, color variability in WSIs due to the choice of stain and scanning processes may impact image analysis. We therefore developed a calibration protocol to diminish the staining and scanning variations of WSI using two calibrator slides. The IHC calibrator slide (IHC-CS) contains peptide-coated microbeads with different concentrations. The color distribution obtained from the WSI of stained IHC-CS reflects the staining process and scanner characteristics. A color chart slide (CCS) is also useful for calibrating the color variation due to the scanner. The results of the automated HER2 assessment were compared to confirm the effectiveness of two calibration slides. The IHC-CS and HER2 breast cancer cases were stained on different days. All stained slides and CCS were digitized by two different WSI scanners. Results revealed 100% concordance between automated evaluation and the pathologist's assessment with both the scanner and staining calibration. The proposed method may enable consistent evaluation of HER2.
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Affiliation(s)
- Chie Ohnishi
- School of Engineering, Tokyo Institute of Technology, Kanagawa, 226-8503, Japan.
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, 10065, USA.
| | - Takashi Ohnishi
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, 10065, USA
| | - Peter Ntiamoah
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, 10065, USA
| | - Dara S Ross
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, 10065, USA
| | - Masahiro Yamaguchi
- School of Engineering, Tokyo Institute of Technology, Kanagawa, 226-8503, Japan
| | - Yukako Yagi
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, 10065, USA
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6
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Harms PW, Frankel TL, Moutafi M, Rao A, Rimm DL, Taube JM, Thomas D, Chan MP, Pantanowitz L. Multiplex Immunohistochemistry and Immunofluorescence: A Practical Update for Pathologists. Mod Pathol 2023; 36:100197. [PMID: 37105494 DOI: 10.1016/j.modpat.2023.100197] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 03/07/2023] [Accepted: 04/19/2023] [Indexed: 04/29/2023]
Abstract
Our understanding of the biology and management of human disease has undergone a remarkable evolution in recent decades. Improved understanding of the roles of complex immune populations in the tumor microenvironment has advanced our knowledge of antitumor immunity, and immunotherapy has radically improved outcomes for many advanced cancers. Digital pathology has unlocked new possibilities for the assessment and discovery of the tumor microenvironment, such as quantitative and spatial image analysis. Despite these advances, tissue-based evaluations for diagnosis and prognosis continue to rely on traditional practices, such as hematoxylin and eosin staining, supplemented by the assessment of single biomarkers largely using chromogenic immunohistochemistry (IHC). Such approaches are poorly suited to complex quantitative analyses and the simultaneous evaluation of multiple biomarkers. Thus, multiplex staining techniques have significant potential to improve diagnostic practice and immuno-oncology research. The different approaches to achieve multiplexed IHC and immunofluorescence are described in this study. Alternatives to multiplex immunofluorescence/IHC include epitope-based tissue mass spectrometry and digital spatial profiling (DSP), which require specialized platforms not available to most clinical laboratories. Virtual multiplexing, which involves digitally coregistering singleplex IHC stains performed on serial sections, is another alternative to multiplex staining. Regardless of the approach, analysis of multiplexed stains sequentially or simultaneously will benefit from standardized protocols and digital pathology workflows. Although this is a complex and rapidly advancing field, multiplex staining is now technically feasible for most clinical laboratories and may soon be leveraged for routine diagnostic use. This review provides an update on the current state of the art for tissue multiplexing, including the capabilities and limitations of different techniques, with an emphasis on potential relevance to clinical diagnostic practice.
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Affiliation(s)
- Paul W Harms
- Department of Pathology, Michigan Medicine/University of Michigan, Ann Arbor, Michigan; Department of Dermatology, Michigan Medicine/University of Michigan, Ann Arbor, Michigan; Rogel Cancer Center, Michigan Medicine/University of Michigan, Ann Arbor, Michigan.
| | - Timothy L Frankel
- Rogel Cancer Center, Michigan Medicine/University of Michigan, Ann Arbor, Michigan; Department of Surgery, Michigan Medicine/University of Michigan, Ann Arbor, Michigan
| | - Myrto Moutafi
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut
| | - Arvind Rao
- Department of Computational Medicine and Bioinformatics, Michigan Medicine/University of Michigan, Ann Arbor, Michigan; Department of Radiation Oncology, Michigan Medicine/University of Michigan, Ann Arbor, Michigan; Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan
| | - David L Rimm
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut
| | - Janis M Taube
- Department of Oncology, Johns Hopkins University School of Medicine, Sidney Kimmel Comprehensive Cancer Center, and Johns Hopkins Bloomberg-Kimmel Institute for Cancer Immunotherapy, Baltimore, Maryland; Department of Dermatology, Johns Hopkins University School of Medicine, Sidney Kimmel Comprehensive Cancer Center, and Johns Hopkins Bloomberg-Kimmel Institute for Cancer Immunotherapy, Baltimore, Maryland; Department of Pathology, Johns Hopkins University School of Medicine, Sidney Kimmel Comprehensive Cancer Center, and Johns Hopkins Bloomberg-Kimmel Institute for Cancer Immunotherapy, Baltimore, Maryland
| | - Dafydd Thomas
- Department of Pathology, Michigan Medicine/University of Michigan, Ann Arbor, Michigan; Rogel Cancer Center, Michigan Medicine/University of Michigan, Ann Arbor, Michigan
| | - May P Chan
- Department of Pathology, Michigan Medicine/University of Michigan, Ann Arbor, Michigan; Department of Dermatology, Michigan Medicine/University of Michigan, Ann Arbor, Michigan
| | - Liron Pantanowitz
- Department of Pathology, Michigan Medicine/University of Michigan, Ann Arbor, Michigan
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7
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Chiorean DM, Mitranovici MI, Mureșan MC, Buicu CF, Moraru R, Moraru L, Cotoi TC, Cotoi OS, Apostol A, Turdean SG, Mărginean C, Petre I, Oală IE, Simon-Szabo Z, Ivan V, Roșca AN, Toru HS. The Approach of Artificial Intelligence in Neuroendocrine Carcinomas of the Breast: A Next Step towards Precision Pathology?—A Case Report and Review of the Literature. Medicina (B Aires) 2023; 59:medicina59040672. [PMID: 37109630 PMCID: PMC10141693 DOI: 10.3390/medicina59040672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 03/24/2023] [Accepted: 03/26/2023] [Indexed: 03/31/2023] Open
Abstract
Primary neuroendocrine tumors (NETs) of the breast are considered a rare and undervalued subtype of breast carcinoma that occur mainly in postmenopausal women and are graded as G1 or G2 NETs or an invasive neuroendocrine carcinoma (NEC) (small cell or large cell). To establish a final diagnosis of breast carcinoma with neuroendocrine differentiation, it is essential to perform an immunohistochemical profile of the tumor, using antibodies against synaptophysin or chromogranin, as well as the MIB-1 proliferation index, one of the most controversial markers in breast pathology regarding its methodology in current clinical practice. A standardization error between institutions and pathologists regarding the evaluation of the MIB-1 proliferation index is present. Another challenge refers to the counting process of MIB-1′s expressiveness, which is known as a time-consuming process. The involvement of AI (artificial intelligence) automated systems could be a solution for diagnosing early stages, as well. We present the case of a post-menopausal 79-year-old woman diagnosed with primary neuroendocrine carcinoma of the breast (NECB). The purpose of this paper is to expose the interpretation of MIB-1 expression in our patient’ s case of breast neuroendocrine carcinoma, assisted by artificial intelligence (AI) software (HALO—IndicaLabs), and to analyze the associations between MIB-1 and common histopathological parameters.
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Affiliation(s)
- Diana Maria Chiorean
- Department of Pathology, County Clinical Hospital of Targu Mures, 540072 Targu Mures, Romania
- Correspondence:
| | - Melinda-Ildiko Mitranovici
- Department of Obstetrics and Gynecology, Emergency County Hospital Hunedoara, 14 Victoriei Street, 331057 Hunedoara, Romania
| | - Maria Cezara Mureșan
- Department of Obstetrics and Gynecology, ”Victor Babes” University of Medicine and Pharmacy, 2 Eftimie Murgu Sq., 300041 Timisoara, Romania
| | - Corneliu-Florin Buicu
- Public Health and Management Department, ”George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540139 Targu Mures, Romania
| | - Raluca Moraru
- Faculty of Medicine, “George Emil Palade” University of Medicine, Pharmacy, Sciences and Technology, 540142 Targu Mures, Romania
| | - Liviu Moraru
- Department of Anatomy, ”George Emil Palade” University of Medicine, Pharmacy, Sciences and Technology, 540142 Targu Mures, Romania
| | - Titiana Cornelia Cotoi
- Department of Pharmaceutical Technology, ”George Emil Palade” University of Medicine, Pharmacy, Sciences and Technology, 540142 Targu Mures, Romania
- Close Circuit Pharmacy of County Clinical Hospital of Targu Mures, 540072 Targu Mures, Romania
| | - Ovidiu Simion Cotoi
- Department of Pathology, County Clinical Hospital of Targu Mures, 540072 Targu Mures, Romania
- Department of Pathophysiology, ”George Emil Palade” University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 38 Gheorghe Marinescu Street, 540142 Targu Mures, Romania
| | - Adrian Apostol
- Department of Cardiology, “Victor Babes” University of Medicine and Pharmacy, 2 Eftimie Murgu Sq., 300041 Timisoara, Romania
| | - Sabin Gligore Turdean
- Department of Pathology, County Clinical Hospital of Targu Mures, 540072 Targu Mures, Romania
| | - Claudiu Mărginean
- Department of Obstetrics and Gynecology, “George Emil Palade” University of Medicine, Pharmacy, Sciences and Technology, 540142 Targu Mures, Romania
| | - Ion Petre
- Department of Medical Informatics and Biostatistics, “Victor Babes” University of Medicine and Pharmacy, 2 Eftimie Murgu Sq., 300041 Timisoara, Romania
| | - Ioan Emilian Oală
- Department of Obstetrics and Gynecology, Emergency County Hospital Hunedoara, 14 Victoriei Street, 331057 Hunedoara, Romania
| | - Zsuzsanna Simon-Szabo
- Department of Pathophysiology, ”George Emil Palade” University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 38 Gheorghe Marinescu Street, 540142 Targu Mures, Romania
| | - Viviana Ivan
- Department of Obstetrics and Gynecology, ”Victor Babes” University of Medicine and Pharmacy, 2 Eftimie Murgu Sq., 300041 Timisoara, Romania
- Department of Cardiology, ”Pius Brinzeu” County Hospital, 2 Eftimie Murgu Sq., 300041 Timisoara, Romania
| | - Ancuța Noela Roșca
- Department of Surgery, ”George Emil Palade” University of Medicine, Pharmacy, Sciences and Technology, 540142 Targu Mures, Romania
| | - Havva Serap Toru
- Department of Pathology, Akdeniz University School of Medicine, Antalya Pınarbaşı, Konyaaltı, 07070 Antalya, Turkey
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8
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Kamran DES, Hussain M, Mirza T. Investigating In Situ Expression of c-MYC and Candidate Ubiquitin-Specific Proteases in DLBCL and Assessment for Peptidyl Disruptor Molecule against c-MYC-USP37 Complex. Molecules 2023; 28:molecules28062441. [PMID: 36985413 PMCID: PMC10058055 DOI: 10.3390/molecules28062441] [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: 01/30/2023] [Revised: 03/02/2023] [Accepted: 03/05/2023] [Indexed: 03/30/2023] Open
Abstract
Diffuse Large B-Cell Lymphoma (DLBCL) is the most common form of non-Hodgkin's lymphoma (NHL). Elevated expression of c-MYC in DLBCL is associated with poor prognosis of the disease. In different cancers, c-MYC has been found regulated by different ubiquitin-specific proteases (USPs), but to date, the role of USPs in c-MYC regulation has not been investigated in DLBCL. In this study, in situ co expression of c-MYC and three candidates USPs, USP28, USP36 and USP37, have been investigated in both the ABC and GCB subtypes of DLBCL. This shows that USP37 expression is positively correlated with the c-MYC expression in the ABC subtype of DLBCL. Structurally, both c-MYC and USP37 has shown large proportion of intrinsically disordered regions, minimizing their chances for full structure crystallization. Peptide array and docking simulations has shown that N-terminal region of c-MYC interacts directly with residues within and in proximity of catalytically active C19 domain of the USP37. Given the structural properties of the interaction sites in the c-MYC-USP37 complex, a peptidyl inhibitor has been designed. Molecular docking has shown that the peptide fits well in the targeted site of c-MYC, masking most of its residues involved in the binding with USP37. The findings could further be exploited to develop therapeutic interventions against the ABC subtype of DLBCL.
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Affiliation(s)
- Durr E Sameen Kamran
- Department of Pathology, Dow Ishrat-ul-Ebad Khan Institute of Oral Health Sciences, Dow University of Health Sciences, Karachi 75330, Pakistan
- Bioinformatics and Molecular Medicine Research Group, Dow Research Institute of Biotechnology and Biomedical Sciences, Dow College of Biotechnology, Dow University of Health Sciences, Karachi 75330, Pakistan
| | - Mushtaq Hussain
- Bioinformatics and Molecular Medicine Research Group, Dow Research Institute of Biotechnology and Biomedical Sciences, Dow College of Biotechnology, Dow University of Health Sciences, Karachi 75330, Pakistan
| | - Talat Mirza
- Department of Research, Ziauddin University, Karachi 75000, Pakistan
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9
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Finkelman BS, Zhang H, Hicks DG, Turner BM. The Evolution of Ki-67 and Breast Carcinoma: Past Observations, Present Directions, and Future Considerations. Cancers (Basel) 2023; 15:cancers15030808. [PMID: 36765765 PMCID: PMC9913317 DOI: 10.3390/cancers15030808] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 01/19/2023] [Accepted: 01/24/2023] [Indexed: 01/31/2023] Open
Abstract
The 1983 discovery of a mouse monoclonal antibody-the Ki-67 antibody-that recognized a nuclear antigen present only in proliferating cells represented a seminal discovery for the pathologic assessment of cellular proliferation in breast cancer and other solid tumors. Cellular proliferation is a central determinant of prognosis and response to cytotoxic chemotherapy in patients with breast cancer, and since the discovery of the Ki-67 antibody, Ki-67 has evolved as an important biomarker with both prognostic and predictive potential in breast cancer. Although there is universal recognition among the international guideline recommendations of the value of Ki-67 in breast cancer, recommendations for the actual use of Ki-67 assays in the prognostic and predictive evaluation of breast cancer remain mixed, primarily due to the lack of assay standardization and inconsistent inter-observer and inter-laboratory reproducibility. The treatment of high-risk ER-positive/human epidermal growth factor receptor-2 (HER2) negative breast cancer with the recently FDA-approved drug abemaciclib relies on a quantitative assessment of Ki-67 expression in the treatment decision algorithm. This further reinforces the urgent need for standardization of Ki-67 antibody selection and staining interpretation, which will hopefully lead to multidisciplinary consensus on the use of Ki-67 as a prognostic and predictive marker in breast cancer. The goals of this review are to highlight the historical evolution of Ki-67 in breast cancer, summarize the present literature on Ki-67 in breast cancer, and discuss the evolving literature on the use of Ki-67 as a companion diagnostic biomarker in breast cancer, with consideration for the necessary changes required across pathology practices to help increase the reliability and widespread adoption of Ki-67 as a prognostic and predictive marker for breast cancer in clinical practice.
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10
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Casagrande N, Borghese C, Aldinucci D. Current and Emerging Approaches to Study Microenvironmental Interactions and Drug Activity in Classical Hodgkin Lymphoma. Cancers (Basel) 2022; 14:cancers14102427. [PMID: 35626032 PMCID: PMC9139207 DOI: 10.3390/cancers14102427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 05/06/2022] [Accepted: 05/11/2022] [Indexed: 11/24/2022] Open
Abstract
Simple Summary In classical Hodgkin Lymphoma (cHL), the tumor microenvironment (TME) plays an important role in tumor progression and treatment response, making its evaluation critical for determining prognosis, treatment strategies and predicting an increase in drug toxicity. Therefore, there is a need to utilize more complex systems to study the cHL-TME and its interplay with tumor cells. To evaluate new anticancer drugs and to find the mechanisms of drug resistance, this review summarizes emerging approaches for the analysis of the TME composition and to identify the state of the disease; the in vitro techniques used to determine the mechanisms involved in the building of an immunosuppressive and protective TME; new 3-dimensional (3D) models, the heterospheroids (HS), developed to mimic TME interactions. Here, we describe the present and likely future clinical applications indicated by the results of these studies and propose a classification for the in vitro culture methods used to study TME interactions in cHL. Abstract Classic Hodgkin lymphoma is characterized by a few tumor cells surrounded by a protective and immunosuppressive tumor microenvironment (TME) composed by a wide variety of noncancerous cells that are an active part of the disease. Therefore, new techniques to study the cHL-TME and new therapeutic strategies targeting specifically tumor cells, reactivating the antitumor immunity, counteracting the protective effects of the TME, were developed. Here, we describe new methods used to study the cell composition, the phenotype, and the spatial distribution of Hodgkin and Reed–Sternberg (HRS) cells and of noncancerous cells in tumor tissues. Moreover, we propose a classification, with increasing complexity, of the in vitro functional studies used to clarify the interactions leading not only to HRS cell survival, growth and drug resistance, but also to the immunosuppressive tumor education of monocytes, T lymphocytes and fibroblasts. This classification also includes new 3-dimensional (3D) models, obtained by cultivating HRS cells in extracellular matrix scaffolds or in sponge scaffolds, under non-adherent conditions with noncancerous cells to form heterospheroids (HS), implanted in developing chick eggs (ovo model). We report results obtained with these approaches and their applications in clinical setting.
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11
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Zarella MD, Rivera Alvarez K. High throughput whole-slide scanning to enable large-scale data repository building. J Pathol 2022; 257:383-390. [PMID: 35511469 PMCID: PMC9327504 DOI: 10.1002/path.5923] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 04/19/2022] [Accepted: 05/02/2022] [Indexed: 11/07/2022]
Abstract
Digital pathology and artificial intelligence (AI) rely on digitization of patient material as a necessary first step. AI development benefits from large sample sizes and diverse cohorts, and therefore efforts to digitize glass slides must meet these needs in an efficient and cost-effective manner. Technical innovation in whole-slide imaging has enabled high throughput slide scanning through the coordinated increase in scanner capacity, speed, and automation. Combining these hardware innovations with automated informatics approaches has enabled more efficient workflows and the opportunity to provide higher quality imaging data using fewer personnel. Here we review several practical considerations for deploying high throughput scanning and we present strategies to increase efficiency with a focus on quality. Finally, we review remaining challenges and issue a call to vendors to innovate in the areas of automation and quality control in order to make high throughput scanning realizable to laboratories with limited resources. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Mark D Zarella
- Department of Pathology, Johns Hopkins University, Baltimore, MD, USA.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
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Rakha EA, Vougas K, Tan PH. Digital Technology in Diagnostic Breast Pathology and Immunohistochemistry. Pathobiology 2021; 89:334-342. [DOI: 10.1159/000521149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 11/11/2021] [Indexed: 11/19/2022] Open
Abstract
Digital technology has been used in the field of diagnostic breast pathology and immunohistochemistry (IHC) for decades. Examples include automated tissue processing and staining, digital data processing, storing and management, voice recognition systems, and digital technology-based production of antibodies and other IHC reagents. However, the recent application of whole slide imaging technology and artificial intelligence (AI)-based tools has attracted a lot of attention. The use of AI tools in breast pathology is discussed briefly as it is covered in other reviews. Here, we present the main application of digital technology in IHC. This includes automation of IHC staining, using image analysis systems and computer vision technology to interpret IHC staining, and the use of AI-based tools to predict marker expression from haematoxylin and eosin-stained digitalized images.
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Cummins DM, Chaudhry IH, Harries M. Scarring Alopecias: Pathology and an Update on Digital Developments. Biomedicines 2021; 9:biomedicines9121755. [PMID: 34944572 PMCID: PMC8698437 DOI: 10.3390/biomedicines9121755] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Revised: 11/15/2021] [Accepted: 11/20/2021] [Indexed: 01/06/2023] Open
Abstract
Primary cicatricial alopecias (PCA) represent a challenging group of disorders that result in irreversible hair loss from the destruction and fibrosis of hair follicles. Scalp skin biopsies are considered essential in investigating these conditions. Unfortunately, the recognised complexity of histopathologic interpretation is compounded by inadequate sampling and inappropriate laboratory processing. By sharing our successes in developing the communication pathway between the clinician, laboratory and histopathologist, we hope to mitigate some of the difficulties that can arise in managing these conditions. We provide insight from clinical and pathology practice into how diagnoses are derived and the key histological features observed across the most common PCAs seen in practice. Additionally, we highlight the opportunities that have emerged with advances in digital pathology and how these technologies may be used to develop clinicopathological relationships, improve working practices, enhance remote learning, reduce inefficiencies, optimise diagnostic yield, and harness the potential of artificial intelligence (AI).
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Affiliation(s)
- Donna M. Cummins
- The Dermatology Centre, Salford Royal Hospital, Northern Care Alliance NHS Foundation Trust, Manchester M6 8HD, UK;
| | - Iskander H. Chaudhry
- Department of Pathology, Royal Liverpool University Hospital, Liverpool L7 8XP, UK;
| | - Matthew Harries
- The Dermatology Centre, Salford Royal Hospital, Northern Care Alliance NHS Foundation Trust, Manchester M6 8HD, UK;
- Centre for Dermatology Research, University of Manchester, MAHSC and NIHR Manchester, Biomedical Research Centre, Manchester M13 9WU, UK
- Correspondence:
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Xing F, Cornish TC, Bennett TD, Ghosh D. Bidirectional Mapping-Based Domain Adaptation for Nucleus Detection in Cross-Modality Microscopy Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:2880-2896. [PMID: 33284750 PMCID: PMC8543886 DOI: 10.1109/tmi.2020.3042789] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Cell or nucleus detection is a fundamental task in microscopy image analysis and has recently achieved state-of-the-art performance by using deep neural networks. However, training supervised deep models such as convolutional neural networks (CNNs) usually requires sufficient annotated image data, which is prohibitively expensive or unavailable in some applications. Additionally, when applying a CNN to new datasets, it is common to annotate individual cells/nuclei in those target datasets for model re-learning, leading to inefficient and low-throughput image analysis. To tackle these problems, we present a bidirectional, adversarial domain adaptation method for nucleus detection on cross-modality microscopy image data. Specifically, the method learns a deep regression model for individual nucleus detection with both source-to-target and target-to-source image translation. In addition, we explicitly extend this unsupervised domain adaptation method to a semi-supervised learning situation and further boost the nucleus detection performance. We evaluate the proposed method on three cross-modality microscopy image datasets, which cover a wide variety of microscopy imaging protocols or modalities, and obtain a significant improvement in nucleus detection compared to reference baseline approaches. In addition, our semi-supervised method is very competitive with recent fully supervised learning models trained with all real target training labels.
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Cornish TC. Artificial intelligence for automating the measurement of histologic image biomarkers. J Clin Invest 2021; 131:147966. [PMID: 33855974 DOI: 10.1172/jci147966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Artificial intelligence has been applied to histopathology for decades, but the recent increase in interest is attributable to well-publicized successes in the application of deep-learning techniques, such as convolutional neural networks, for image analysis. Recently, generative adversarial networks (GANs) have provided a method for performing image-to-image translation tasks on histopathology images, including image segmentation. In this issue of the JCI, Koyuncu et al. applied GANs to whole-slide images of p16-positive oropharyngeal squamous cell carcinoma (OPSCC) to automate the calculation of a multinucleation index (MuNI) for prognostication in p16-positive OPSCC. Multivariable analysis showed that the MuNI was prognostic for disease-free survival, overall survival, and metastasis-free survival. These results are promising, as they present a prognostic method for p16-positive OPSCC and highlight methods for using deep learning to measure image biomarkers from histopathologic samples in an inherently explainable manner.
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Fu H, Mi W, Pan B, Guo Y, Li J, Xu R, Zheng J, Zou C, Zhang T, Liang Z, Zou J, Zou H. Automatic Pancreatic Ductal Adenocarcinoma Detection in Whole Slide Images Using Deep Convolutional Neural Networks. Front Oncol 2021; 11:665929. [PMID: 34249702 PMCID: PMC8267174 DOI: 10.3389/fonc.2021.665929] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 06/10/2021] [Indexed: 01/11/2023] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancer types worldwide, with the lowest 5-year survival rate among all kinds of cancers. Histopathology image analysis is considered a gold standard for PDAC detection and diagnosis. However, the manual diagnosis used in current clinical practice is a tedious and time-consuming task and diagnosis concordance can be low. With the development of digital imaging and machine learning, several scholars have proposed PDAC analysis approaches based on feature extraction methods that rely on field knowledge. However, feature-based classification methods are applicable only to a specific problem and lack versatility, so that the deep-learning method is becoming a vital alternative to feature extraction. This paper proposes the first deep convolutional neural network architecture for classifying and segmenting pancreatic histopathological images on a relatively large WSI dataset. Our automatic patch-level approach achieved 95.3% classification accuracy and the WSI-level approach achieved 100%. Additionally, we visualized the classification and segmentation outcomes of histopathological images to determine which areas of an image are more important for PDAC identification. Experimental results demonstrate that our proposed model can effectively diagnose PDAC using histopathological images, which illustrates the potential of this practical application.
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Affiliation(s)
- Hao Fu
- Department of Automation, School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China
| | - Weiming Mi
- Department of Automation, School of Information Science and Technology, Tsinghua University, Beijing, China
| | - Boju Pan
- Molecular Pathology Research Center, Department of Pathology, Peking Union Medical College Hospital (PUMCH), Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Yucheng Guo
- Yihai Center, Tsimage Medical Technology, Shenzhen, China
- Center for Intelligent Medical Imaging & Health, Research Institute of Tsinghua University in Shenzhen, Shenzhen, China
| | - Junjie Li
- Molecular Pathology Research Center, Department of Pathology, Peking Union Medical College Hospital (PUMCH), Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Rongyan Xu
- Shanghai Chenshan Plant Science Research Center, Chinese Academy of Sciences, Shanghai, China
| | - Jie Zheng
- Yihai Center, Tsimage Medical Technology, Shenzhen, China
- Center for Intelligent Medical Imaging & Health, Research Institute of Tsinghua University in Shenzhen, Shenzhen, China
| | - Chunli Zou
- Yihai Center, Tsimage Medical Technology, Shenzhen, China
- Center for Intelligent Medical Imaging & Health, Research Institute of Tsinghua University in Shenzhen, Shenzhen, China
| | - Tao Zhang
- Department of Automation, School of Information Science and Technology, Tsinghua University, Beijing, China
| | - Zhiyong Liang
- Molecular Pathology Research Center, Department of Pathology, Peking Union Medical College Hospital (PUMCH), Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
- *Correspondence: Zhiyong Liang, ; Hao Zou, ; Junzhong Zou,
| | - Junzhong Zou
- Department of Automation, School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China
- *Correspondence: Zhiyong Liang, ; Hao Zou, ; Junzhong Zou,
| | - Hao Zou
- Yihai Center, Tsimage Medical Technology, Shenzhen, China
- Center for Intelligent Medical Imaging & Health, Research Institute of Tsinghua University in Shenzhen, Shenzhen, China
- *Correspondence: Zhiyong Liang, ; Hao Zou, ; Junzhong Zou,
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