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Saxena P, Aggarwal SK, Sinha A, Saxena S, Singh AK. Review of computer-assisted diagnosis model to classify follicular lymphoma histology. Cell Biochem Funct 2024; 42:e4088. [PMID: 38973163 DOI: 10.1002/cbf.4088] [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: 02/22/2024] [Revised: 04/15/2024] [Accepted: 06/28/2024] [Indexed: 07/09/2024]
Abstract
The field of image processing is experiencing significant advancements to support professionals in analyzing histological images obtained from biopsies. The primary objective is to enhance the process of diagnosis and prognostic evaluations. Various forms of cancer can be diagnosed by employing different segmentation techniques followed by postprocessing approaches that can identify distinct neoplastic areas. Using computer approaches facilitates a more objective and efficient study of experts. The progressive advancement of histological image analysis holds significant importance in modern medicine. This paper provides an overview of the current advances in segmentation and classification approaches for images of follicular lymphoma. This research analyzes the primary image processing techniques utilized in the various stages of preprocessing, segmentation of the region of interest, classification, and postprocessing as described in the existing literature. The study also examines the strengths and weaknesses associated with these approaches. Additionally, this study encompasses an examination of validation procedures and an exploration of prospective future research roads in the segmentation of neoplasias.
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Affiliation(s)
- Pranshu Saxena
- School of Computer Science Engineering & Technology, Bennett University, Greater Noida, Uttar Pradesh, India
| | - Sahil Kumar Aggarwal
- Department of Information Technology, ABES Engineering College, Ghaziabad, India
| | - Amit Sinha
- Department of Information Technology, ABES Engineering College, Ghaziabad, India
| | - Sandeep Saxena
- Greater Noida Institute of Technology, Greater Noida, India
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Saxena P, Goyal A. Computer-assisted grading of follicular lymphoma: a classification based on SVM, machine learning, and transfer learning approaches. THE IMAGING SCIENCE JOURNAL 2023. [DOI: 10.1080/13682199.2022.2162663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Affiliation(s)
- Pranshu Saxena
- I.K. Gujral Punjab Technical University, Jalandhar, India
| | - Anjali Goyal
- Department of Computer Applications, GNIMT, Ludhiana, India
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3
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Wurzel P, Ackermann J, Schäfer H, Scharf S, Hansmann ML, Koch I. Detection of follicular regions in actin-stained whole slide images of the human lymph node by shock filter. Biol Chem 2021; 402:991-999. [PMID: 34261206 DOI: 10.1515/hsz-2020-0178] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 12/02/2020] [Indexed: 12/16/2022]
Abstract
Human lymph nodes play a central part of immune defense against infection agents and tumor cells. Lymphoid follicles are compartments of the lymph node which are spherical, mainly filled with B cells. B cells are cellular components of the adaptive immune systems. In the course of a specific immune response, lymphoid follicles pass different morphological differentiation stages. The morphology and the spatial distribution of lymphoid follicles can be sometimes associated to a particular causative agent and development stage of a disease. We report our new approach for the automatic detection of follicular regions in histological whole slide images of tissue sections immuno-stained with actin. The method is divided in two phases: (1) shock filter-based detection of transition points and (2) segmentation of follicular regions. Follicular regions in 10 whole slide images were manually annotated by visual inspection, and sample surveys were conducted by an expert pathologist. The results of our method were validated by comparing with the manual annotation. On average, we could achieve a Zijbendos similarity index of 0.71, with a standard deviation of 0.07.
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Affiliation(s)
- Patrick Wurzel
- Goethe-Universität Frankfurt am Main, Molecular Bioinformatics, Institute of Computer Science, Robert-Mayer-Str. 11-15, 60325 Frankfurt am Main, Germany
- Frankfurt Institute for Advanced Studies, Ruth-Moufang-Straße 1, 60438 Frankfurt am Main, Germany
| | - Jörg Ackermann
- Goethe-Universität Frankfurt am Main, Molecular Bioinformatics, Institute of Computer Science, Robert-Mayer-Str. 11-15, 60325 Frankfurt am Main, Germany
| | - Hendrik Schäfer
- Hospital of the Goethe University Frankfurt, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - Sonja Scharf
- Goethe-Universität Frankfurt am Main, Molecular Bioinformatics, Institute of Computer Science, Robert-Mayer-Str. 11-15, 60325 Frankfurt am Main, Germany
- Frankfurt Institute for Advanced Studies, Ruth-Moufang-Straße 1, 60438 Frankfurt am Main, Germany
| | - Martin-Leo Hansmann
- Frankfurt Institute for Advanced Studies, Ruth-Moufang-Straße 1, 60438 Frankfurt am Main, Germany
| | - Ina Koch
- Goethe-Universität Frankfurt am Main, Molecular Bioinformatics, Institute of Computer Science, Robert-Mayer-Str. 11-15, 60325 Frankfurt am Main, Germany
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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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Casiraghi E, Huber V, Frasca M, Cossa M, Tozzi M, Rivoltini L, Leone BE, Villa A, Vergani B. A novel computational method for automatic segmentation, quantification and comparative analysis of immunohistochemically labeled tissue sections. BMC Bioinformatics 2018; 19:357. [PMID: 30367588 PMCID: PMC6191943 DOI: 10.1186/s12859-018-2302-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
Background In the clinical practice, the objective quantification of histological results is essential not only to define objective and well-established protocols for diagnosis, treatment, and assessment, but also to ameliorate disease comprehension. Software The software MIAQuant_Learn presented in this work segments, quantifies and analyzes markers in histochemical and immunohistochemical images obtained by different biological procedures and imaging tools. MIAQuant_Learn employs supervised learning techniques to customize the marker segmentation process with respect to any marker color appearance. Our software expresses the location of the segmented markers with respect to regions of interest by mean-distance histograms, which are numerically compared by measuring their intersection. When contiguous tissue sections stained by different markers are available, MIAQuant_Learn aligns them and overlaps the segmented markers in a unique image enabling a visual comparative analysis of the spatial distribution of each marker (markers’ relative location). Additionally, it computes novel measures of markers’ co-existence in tissue volumes depending on their density. Conclusions Applications of MIAQuant_Learn in clinical research studies have proven its effectiveness as a fast and efficient tool for the automatic extraction, quantification and analysis of histological sections. It is robust with respect to several deficits caused by image acquisition systems and produces objective and reproducible results. Thanks to its flexibility, MIAQuant_Learn represents an important tool to be exploited in basic research where needs are constantly changing.
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Affiliation(s)
- Elena Casiraghi
- Department of Computer Science "Giovanni Degli Antoni", Università degli Studi di Milano, Via Celoria 18, 20135, Milan, Italy.
| | - Veronica Huber
- Unit of Immunotherapy of Human Tumors, Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Marco Frasca
- Department of Computer Science "Giovanni Degli Antoni", Università degli Studi di Milano, Via Celoria 18, 20135, Milan, Italy
| | - Mara Cossa
- Unit of Immunotherapy of Human Tumors, Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Matteo Tozzi
- Department of medicine and surgery, Vascular Surgery, University of Insubria Hospital, Varese, Italy
| | - Licia Rivoltini
- Unit of Immunotherapy of Human Tumors, Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | | | - Antonello Villa
- School of Medicine and Surgery, University of Milano Bicocca, Monza, Italy.,Consorzio MIA - Microscopy and Image Analysis, University of Milano Bicocca, Monza, Italy
| | - Barbara Vergani
- School of Medicine and Surgery, University of Milano Bicocca, Monza, Italy.,Consorzio MIA - Microscopy and Image Analysis, University of Milano Bicocca, Monza, Italy
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6
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Fitness Functions Evaluation for Segmentation of Lymphoma Histological Images Using Genetic Algorithm. ACTA ACUST UNITED AC 2018. [DOI: 10.1007/978-3-319-77538-8_4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023]
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7
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Azevedo Tosta TA, Faria PR, Batista VR, Neves LA, do Nascimento MZ. Using wavelet sub-band and fuzzy 2-partition entropy to segment chronic lymphocytic leukemia images. Appl Soft Comput 2018. [DOI: 10.1016/j.asoc.2017.11.039] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Jordan J, Goldstein JS, Jaye DL, Gurcan M, Flowers CR, Cooper LAD. Informatics Approaches to Address New Challenges in the Classification of Lymphoid Malignancies. JCO Clin Cancer Inform 2018; 2. [PMID: 30637363 DOI: 10.1200/cci.17.00039] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Purpose Lymphoid malignancies are remarkably heterogeneous, with variations in outcomes and clinical, biologic, and histologic presentation complicating classification according to the World Health Organization guidelines. Incorrect classification of lymphoid neoplasms can result in suboptimal therapeutic strategies for individual patients and confound the interpretation of clinical trials involving personalized, class-based treatments. This review discusses the potential role of pathology informatics in improving the classification accuracy and objectivity for lymphoid malignancies. Design We identified peer-reviewed publications examining pathology informatics approaches for the classification of lymphoid malignancies, reviewed developments in the lymphoma classification systems, and summarized computational methods for pathologic assessment that can impact practice. Results Computer-assisted pathology image analysis algorithms in lymphoma most commonly have been applied to follicular lymphoma to address biologic heterogeneity and subjectivity in the process of classification. Conclusion Objective methods are available to assist pathologists in lymphoma classification and grading, and have been demonstrated to provide measurable benefits in specific contexts. Future validation and extension of these approaches will require datasets that link high resolution pathology images available for image analysis algorithms with clinical variables and follow up outcomes.
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Affiliation(s)
- Jacob Jordan
- Emory University School of Medicine, Atlanta, GA; Metin Gurcan, Ohio State University, Columbus, OH
| | - Jordan S Goldstein
- Emory University School of Medicine, Atlanta, GA; Metin Gurcan, Ohio State University, Columbus, OH
| | - David L Jaye
- Emory University School of Medicine, Atlanta, GA; Metin Gurcan, Ohio State University, Columbus, OH
| | - Metin Gurcan
- Emory University School of Medicine, Atlanta, GA; Metin Gurcan, Ohio State University, Columbus, OH
| | - Christopher R Flowers
- Emory University School of Medicine, Atlanta, GA; Metin Gurcan, Ohio State University, Columbus, OH
| | - Lee A D Cooper
- Emory University School of Medicine, Atlanta, GA; Metin Gurcan, Ohio State University, Columbus, OH
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Senaras C, Pennell M, Chen W, Sahiner B, Shana'ah A, Louissaint A, Hasserjian R, Lozanski G, Gurcan MN. FOXP3-stained image analysis for follicular lymphoma: Optimal adaptive thresholding with maximal nucleus coverage. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2017; 10140:101400E. [PMID: 28579665 PMCID: PMC5452684 DOI: 10.1117/12.2255671] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Immunohistochemical detection of FOXP3 antigen is a usable marker for detection of regulatory T lymphocytes (TR) in formalin fixed and paraffin embedded sections of different types of tumor tissue. TR plays a major role in homeostasis of normal immune systems where they prevent auto reactivity of the immune system towards the host. This beneficial effect of TR is frequently "hijacked" by malignant cells where tumor-infiltrating regulatory T cells are recruited by the malignant nuclei to inhibit the beneficial immune response of the host against the tumor cells. In the majority of human solid tumors, an increased number of tumor-infiltrating FOXP3 positive TR is associated with worse outcome. However, in follicular lymphoma (FL) the impact of the number and distribution of TR on the outcome still remains controversial. In this study, we present a novel method to detect and enumerate nuclei from FOXP3 stained images of FL biopsies. The proposed method defines a new adaptive thresholding procedure, namely the optimal adaptive thresholding (OAT) method, which aims to minimize under-segmented and over-segmented nuclei for coarse segmentation. Next, we integrate a parameter free elliptical arc and line segment detector (ELSD) as additional information to refine segmentation results and to split most of the merged nuclei. Finally, we utilize a state-of-the-art super-pixel method, Simple Linear Iterative Clustering (SLIC) to split the rest of the merged nuclei. Our dataset consists of 13 region-of-interest images containing 769 negative and 88 positive nuclei. Three expert pathologists evaluated the method and reported sensitivity values in detecting negative and positive nuclei ranging from 83-100% and 90-95%, and precision values of 98-100% and 99-100%, respectively. The proposed solution can be used to investigate the impact of FOXP3 positive nuclei on the outcome and prognosis in FL.
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Affiliation(s)
- C. Senaras
- Department of Biomedical Informatics, The Ohio State University
| | - M. Pennell
- Division of Biostatistics, College of Public Health, The Ohio State University
| | - W. Chen
- Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration, S
| | - B. Sahiner
- Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration, S
| | - A. Shana'ah
- Department of Pathology, The Ohio State University
| | - A. Louissaint
- Department of Pathology Massachusetts General Hospital
| | | | - G. Lozanski
- Department of Pathology, The Ohio State University
| | - M. N. Gurcan
- Department of Biomedical Informatics, The Ohio State University
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10
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Segmentation methods of H&E-stained histological images of lymphoma: A review. INFORMATICS IN MEDICINE UNLOCKED 2017. [DOI: 10.1016/j.imu.2017.05.009] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
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11
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Niazi MKK, Zynger DL, Clinton SK, Chen J, Koyuturk M, LaFramboise T, Gurcan M. Visually Meaningful Histopathological Features for Automatic Grading of Prostate Cancer. IEEE J Biomed Health Inform 2016; 21:1027-1038. [PMID: 28113734 DOI: 10.1109/jbhi.2016.2565515] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Histopathologic features, particularly Gleason grading system, have contributed significantly to the diagnosis, treatment, and prognosis of prostate cancer for decades. However, prostate cancer demonstrates enormous heterogeneity in biological behavior, thus establishing improved prognostic and predictive markers is particularly important to personalize therapy of men with clinically localized and newly diagnosed malignancy. Many automated grading systems have been developed for Gleason grading but acceptance in the medical community has been lacking due to poor interpretability. To overcome this problem, we developed a set of visually meaningful features to differentiate between low- and high-grade prostate cancer. The visually meaningful feature set consists of luminal and architectural features. For luminal features, we compute: 1) the shortest path from the nuclei to their closest luminal spaces; 2) ratio of the epithelial nuclei to the total number of nuclei. A nucleus is considered an epithelial nucleus if the shortest path between it and the luminal space does not contain any other nucleus; 3) average shortest distance of all nuclei to their closest luminal spaces. For architectural features, we compute directional changes in stroma and nuclei using directional filter banks. These features are utilized to create two subspaces; one for prostate images histopathologically assessed as low grade and the other for high grade. The grade associated with a subspace, which results in the minimum reconstruction error is considered as the prediction for the test image. For training, we utilized 43 regions of interest (ROI) images, which were extracted from 25 prostate whole slide images of The Cancer Genome Atlas (TCGA) database. For testing, we utilized an independent dataset of 88 ROIs extracted from 30 prostate whole slide images. The method resulted in 93.0% and 97.6% training and testing accuracies, respectively, for the spectrum of cases considered. The application of visually meaningful features provided promising levels of accuracy and consistency for grading prostate cancer.
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12
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Fauzi MFA, Pennell M, Sahiner B, Chen W, Shana'ah A, Hemminger J, Gru A, Kurt H, Losos M, Joehlin-Price A, Kavran C, Smith SM, Nowacki N, Mansor S, Lozanski G, Gurcan MN. Classification of follicular lymphoma: the effect of computer aid on pathologists grading. BMC Med Inform Decis Mak 2015; 15:115. [PMID: 26715518 PMCID: PMC4696238 DOI: 10.1186/s12911-015-0235-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2015] [Accepted: 12/15/2015] [Indexed: 11/28/2022] Open
Abstract
Background Follicular lymphoma (FL) is one of the most common lymphoid malignancies in the western world. FL cases are stratified into three histological grades based on the average centroblast count per high power field (HPF). The centroblast count is performed manually by the pathologist using an optical microscope and hematoxylin and eosin (H&E) stained tissue section. Although this is the current clinical practice, it suffers from high inter- and intra-observer variability and is vulnerable to sampling bias. Methods In this paper, we present a system, called Follicular Lymphoma Grading System (FLAGS), to assist the pathologist in grading FL cases. We also assess the effect of FLAGS on accuracy of expert and inexperienced readers. FLAGS automatically identifies possible HPFs for examination by analyzing H&E and CD20 stains, before classifying them into low or high risk categories. The pathologist is first asked to review the slides according to the current routine clinical practice, before being presented with FLAGS classification via color-coded map. The accuracy of the readers with and without FLAGS assistance is measured. Results FLAGS was used by four experts (board-certified hematopathologists) and seven pathology residents on 20 FL slides. Access to FLAGS improved overall reader accuracy with the biggest improvement seen among residents. An average AUC value of 0.75 was observed which generally indicates “acceptable” diagnostic performance. Conclusions The results of this study show that FLAGS can be useful in increasing the pathologists’ accuracy in grading the tissue. To the best of our knowledge, this study measure, for the first time, the effect of computerized image analysis on pathologists’ grading of follicular lymphoma. When fully developed, such systems have the potential to reduce sampling bias by examining an increased proportion of HPFs within follicle regions, as well as to reduce inter- and intra-reader variability. Electronic supplementary material The online version of this article (doi:10.1186/s12911-015-0235-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
| | - Michael Pennell
- Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH, USA
| | - Berkman Sahiner
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA
| | - Weijie Chen
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA
| | - Arwa Shana'ah
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | - Jessica Hemminger
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | - Alejandro Gru
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | - Habibe Kurt
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | - Michael Losos
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | - Amy Joehlin-Price
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | - Christina Kavran
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | - Stephen M Smith
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | - Nicholas Nowacki
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | - Sharmeen Mansor
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | - Gerard Lozanski
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | - Metin N Gurcan
- Department of Biomedical Informatics, The Ohio State University, 250 Lincoln Tower, 1800 Cannon Drive, Columbus, OH, 43210, USA.
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Niazi MKK, Beamer G, Gurcan MN. Detecting and characterizing cellular responses toMycobacterium tuberculosisfrom histology slides. Cytometry A 2013; 85:151-61. [DOI: 10.1002/cyto.a.22424] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2013] [Revised: 11/07/2013] [Accepted: 11/16/2013] [Indexed: 01/17/2023]
Affiliation(s)
| | - Gillian Beamer
- Department of Infectious Disease and Global Health; Tufts University; Grafton Massachusetts
| | - Metin N. Gurcan
- Department of Biomedical Informatics; The Ohio State University; Columbus Ohio
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Lozanski G, Pennell M, Shana'ah A, Zhao W, Gewirtz A, Racke F, Hsi E, Simpson S, Mosse C, Alam S, Swierczynski S, Hasserjian RP, Gurcan MN. Inter-reader variability in follicular lymphoma grading: Conventional and digital reading. J Pathol Inform 2013; 4:30. [PMID: 24392244 PMCID: PMC3869955 DOI: 10.4103/2153-3539.120747] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2013] [Accepted: 09/03/2013] [Indexed: 11/04/2022] Open
Abstract
CONTEXT Pathologists grade follicular lymphoma (FL) cases by selecting 10, random high power fields (HPFs), counting the number of centroblasts (CBs) in these HPFs under the microscope and then calculating the average CB count for the whole slide. Previous studies have demonstrated that there is high inter-reader variability among pathologists using this methodology in grading. AIMS The objective of this study was to explore if newly available digital reading technologies can reduce inter-reader variability. SETTINGS AND DESIGN IN THIS STUDY, WE CONSIDERED THREE DIFFERENT READING CONDITIONS (RCS) IN GRADING FL: (1) Conventional (glass-slide based) to establish the baseline, (2) digital whole slide viewing, (3) digital whole slide viewing with selected HPFs. Six board-certified pathologists from five different institutions read 17 FL slides in these three different RCs. RESULTS Although there was relative poor consensus in conventional reading, with lack of consensus in 41.2% of cases, which was similar to previously reported studies; we found that digital reading with pre-selected fields improved the inter-reader agreement, with only 5.9% lacking consensus among pathologists. CONCLUSIONS Digital whole slide RC resulted in the worst concordance among pathologists while digital whole slide reading selected HPFs improved the concordance. Further studies are underway to determine if this performance can be sustained with a larger dataset and our automated HPF and CB detection algorithms can be employed to further improve the concordance.
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Affiliation(s)
- Gerard Lozanski
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | - Michael Pennell
- Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH, USA
| | - Arwa Shana'ah
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | - Weiqiang Zhao
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | - Amy Gewirtz
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | - Frederick Racke
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | - Eric Hsi
- Cleveland Clinic, Cleveland, OH, USA
| | - Sabrina Simpson
- Department of Pathology, Central Ohio Pathology Associates, Westerville, OH, USA
| | | | - Shadia Alam
- Department of Pathology, Battle Creek, MI, USA
| | | | | | - Metin N Gurcan
- Department of Biomedical Informatics, Ohio State University, Columbus, OH, USA
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Samols MA, Smith NE, Gerber JM, Vuica-Ross M, Gocke CD, Burns KH, Borowitz MJ, Cornish TC, Duffield AS. Software-automated counting of Ki-67 proliferation index correlates with pathologic grade and disease progression of follicular lymphomas. Am J Clin Pathol 2013; 140:579-87. [PMID: 24045557 DOI: 10.1309/ajcptma1f6lwytqv] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022] Open
Abstract
OBJECTIVES To examine the accuracy of software-assisted measurement of the Ki-67 proliferation index (PI) and its correlation with the grade and clinical progression of follicular lymphoma (FL). METHODS High-power field equivalents were extracted from H&E- and Ki-67-immunostained slides of FL, and a nuclear quantitation algorithm was used to calculate a PI. Representative fields were manually counted for validation with close agreement. RESULTS The PI was significantly higher in World Health Organization grade 3 FL than grade 1 to 2 FL. Disease progression, as defined by subsequent treatment with radiation or cytotoxic chemotherapy, was also significantly associated with elevated PI but not pathologic grade. CONCLUSIONS These data show that software-automated quantitation of Ki-67 can provide both a useful adjunct to pathologic grade in FL and improved prognostic information for patients.
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Affiliation(s)
- Mark A. Samols
- Department of Pathology, The Johns Hopkins University School of Medicine, Baltimore, MD
| | - Nathan E. Smith
- Department of Pathology, The Johns Hopkins University School of Medicine, Baltimore, MD
| | - Jonathan M. Gerber
- Division of Hematology, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD
| | - Milena Vuica-Ross
- Department of Pathology, The Johns Hopkins University School of Medicine, Baltimore, MD
| | - Christopher D. Gocke
- Department of Pathology, The Johns Hopkins University School of Medicine, Baltimore, MD
| | - Kathleen H. Burns
- Department of Pathology, The Johns Hopkins University School of Medicine, Baltimore, MD
| | - Michael J. Borowitz
- Department of Pathology, The Johns Hopkins University School of Medicine, Baltimore, MD
| | - Toby C. Cornish
- Department of Pathology, The Johns Hopkins University School of Medicine, Baltimore, MD
| | - Amy S. Duffield
- Department of Pathology, The Johns Hopkins University School of Medicine, Baltimore, MD
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Bueno G, Fernández-Carrobles MM, Déniz O, Salido J, Vállez N, García-Rojo M. An entropy-based automated approach to prostate biopsy ROI segmentation. Diagn Pathol 2013. [PMCID: PMC3849514 DOI: 10.1186/1746-1596-8-s1-s24] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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17
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Bueno G, Déniz O, Salido J, Milagro Fernández M, Vállez N, García-Rojo M. Colour Model Analysis for Histopathology Image Processing. ACTA ACUST UNITED AC 2013. [DOI: 10.1007/978-94-007-5389-1_9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/20/2023]
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Bueno G, González R, Déniz O, García-Rojo M, González-García J, Fernández-Carrobles MM, Vállez N, Salido J. A parallel solution for high resolution histological image analysis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2012; 108:388-401. [PMID: 22522064 DOI: 10.1016/j.cmpb.2012.03.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2011] [Revised: 02/25/2012] [Accepted: 03/17/2012] [Indexed: 05/31/2023]
Abstract
This paper describes a general methodology for developing parallel image processing algorithms based on message passing for high resolution images (on the order of several Gigabytes). These algorithms have been applied to histological images and must be executed on massively parallel processing architectures. Advances in new technologies for complete slide digitalization in pathology have been combined with developments in biomedical informatics. However, the efficient use of these digital slide systems is still a challenge. The image processing that these slides are subject to is still limited both in terms of data processed and processing methods. The work presented here focuses on the need to design and develop parallel image processing tools capable of obtaining and analyzing the entire gamut of information included in digital slides. Tools have been developed to assist pathologists in image analysis and diagnosis, and they cover low and high-level image processing methods applied to histological images. Code portability, reusability and scalability have been tested by using the following parallel computing architectures: distributed memory with massive parallel processors and two networks, INFINIBAND and Myrinet, composed of 17 and 1024 nodes respectively. The parallel framework proposed is flexible, high performance solution and it shows that the efficient processing of digital microscopic images is possible and may offer important benefits to pathology laboratories.
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Affiliation(s)
- G Bueno
- VISILAB, E.T.S. Ingenieros Industriales, Universidad de Castilla-La Mancha, Spain.
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Akakin HC, Gurcan MN. Content-based microscopic image retrieval system for multi-image queries. ACTA ACUST UNITED AC 2012; 16:758-69. [PMID: 22311866 DOI: 10.1109/titb.2012.2185829] [Citation(s) in RCA: 77] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
In this paper, we describe the design and development of a multitiered content-based image retrieval (CBIR) system for microscopic images utilizing a reference database that contains images of more than one disease. The proposed CBIR system uses a multitiered approach to classify and retrieve microscopic images involving their specific subtypes, which are mostly difficult to discriminate and classify. This system enables both multi-image query and slide-level image retrieval in order to protect the semantic consistency among the retrieved images. New weighting terms, inspired from information retrieval theory, are defined for multiple-image query and retrieval. The performance of the system was tested on a dataset including 1666 imaged high power fields extracted from 57 follicular lymphoma (FL) tissue slides with three subtypes and 44 neuroblastoma (NB) tissue slides with four subtypes. Each slide is semantically annotated according to their subtypes by expert pathologists. By using leave-one-slide out testing scheme, the multi-image query algorithm with the proposed weighting strategy achieves about 93% and 86% of average classification accuracy at the first rank retrieval, outperforming the image-level retrieval accuracy by about 38 and 26 percentage points, for FL and NB diseases, respectively.
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Affiliation(s)
- Hatice Cinar Akakin
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA.
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