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Iwamoto R, Nishikawa T, Musangile FY, Matsuzaki I, Sagan K, Nishikawa M, Mikasa Y, Takahashi Y, Kojima F, Hori Y, Hosoi H, Mori H, Sonoki T, Murata SI. Small sized centroblasts as poor prognostic factor in follicular lymphoma - Based on artificial intelligence analysis. Comput Biol Med 2024; 178:108774. [PMID: 38897149 DOI: 10.1016/j.compbiomed.2024.108774] [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: 02/18/2024] [Revised: 05/26/2024] [Accepted: 06/15/2024] [Indexed: 06/21/2024]
Abstract
Histological assessment of centroblasts is an important evaluation in the diagnosis of follicular lymphoma, but there is substantial observer variation in assessment among hematopathologists. We aimed to perform quantitative morphological analysis of centroblasts in follicular lymphoma using new artificial intelligence technology in relation to the clinical prognosis. Hematoxylin and eosin slides of lesions were prepared from 36 cases of follicular lymphoma before initial chemotherapy. Cases were classified into three groups by clinical course after initial treatment. The 'excellent prognosis' group were without recurrence or progression of follicular lymphoma within 60 months, the 'poor prognosis' group were those that had relapse, exacerbation, or who died due to the follicular lymphoma within 60 months, and the 'indeterminate prognosis' group were those without recurrence or progression but before the passage of 60 months. We created whole slide images and image patches of hematoxylin and eosin sections for all cases. We designed an object detection model specialized for centroblasts by fine-tuning YOLOv5 and segmented all centroblasts in whole slide images. The morphological characteristics of centroblasts in relation to the clinical prognosis of follicular lymphoma were analyzed. Centroblasts in follicular lymphoma of the poor prognosis group were significantly smaller in nuclear size than those in follicular lymphoma of the excellent prognosis group in the following points: median of nuclear area (p = 0.013), long length (p = 0.042), short length (p = 0.007), nuclear area of top 10 % cells (p = 0.024) and short length of top 10 % cells (p = 0.020). Cases with a mean nuclear area of <55 μm2 had poorer event-free survival than those with a mean nuclear area of ≥55 μm2 (p < 0.0123). AI methodology is suggested to be able to surpass pathologist's observation in capturing morphological features. Small-sized centroblasts will likely become a new prognostic factor of follicular lymphoma.
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Affiliation(s)
- Ryuta Iwamoto
- Department of Human Pathology, Wakayama Medical University, Wakayama, Japan
| | - Toui Nishikawa
- Department of Human Pathology, Wakayama Medical University, Wakayama, Japan
| | | | - Ibu Matsuzaki
- Department of Human Pathology, Wakayama Medical University, Wakayama, Japan
| | - Kanako Sagan
- Department of Human Pathology, Wakayama Medical University, Wakayama, Japan
| | - Mizuki Nishikawa
- Department of Human Pathology, Wakayama Medical University, Wakayama, Japan
| | - Yurina Mikasa
- Department of Human Pathology, Wakayama Medical University, Wakayama, Japan
| | - Yuichi Takahashi
- Department of Human Pathology, Wakayama Medical University, Wakayama, Japan
| | - Fumiyoshi Kojima
- Department of Human Pathology, Wakayama Medical University, Wakayama, Japan
| | - Yoshikazu Hori
- Department of Hematology/Oncology, Wakayama Medical University, Wakayama, Japan
| | - Hiroki Hosoi
- Department of Hematology/Oncology, Wakayama Medical University, Wakayama, Japan
| | - Hideo Mori
- Department of Pathology, Osaka Habikino Medical Center, Osaka, Japan
| | - Takashi Sonoki
- Department of Hematology/Oncology, Wakayama Medical University, Wakayama, Japan
| | - Shin-Ichi Murata
- Department of Human Pathology, Wakayama Medical University, Wakayama, Japan.
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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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Yuenyong S, Boonsakan P, Sripodok S, Thuwajit P, Charngkaew K, Pongpaibul A, Angkathunyakul N, Hnoohom N, Thuwajit C. Detection of centroblast cells in H&E stained whole slide image based on object detection. Front Med (Lausanne) 2024; 11:1303982. [PMID: 38384407 PMCID: PMC10879397 DOI: 10.3389/fmed.2024.1303982] [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: 09/28/2023] [Accepted: 01/18/2024] [Indexed: 02/23/2024] Open
Abstract
Introduction Detection and counting of Centroblast cells (CB) in hematoxylin & eosin (H&E) stained whole slide image (WSI) is an important workflow in grading Lymphoma. Each high power field (HPF) patch of a WSI is inspected for the number of CB cells and compared with the World Health Organization (WHO) guideline that organizes lymphoma into 3 grades. Spotting and counting CBs is time-consuming and labor intensive. Moreover, there is often disagreement between different readers, and even a single reader may not be able to perform consistently due to many factors. Method We propose an artificial intelligence system that can scan patches from a WSI and detect CBs automatically. The AI system works on the principle of object detection, where the CB is the single class of object of interest. We trained the AI model on 1,669 example instances of CBs that originate from WSI of 5 different patients. The data was split 80%/20% for training and validation respectively. Result The best performance was from YOLOv5x6 model that used the preprocessed CB dataset achieved precision of 0.808, recall of 0.776, mAP at 0.5 IoU of 0.800 and overall mAP of 0.647. Discussion The results show that centroblast cells can be detected in WSI with relatively high precision and recall.
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Affiliation(s)
- Sumeth Yuenyong
- Department of Computer Engineering, Faculty of Engineering, Mahidol University, Nakhon Pathom, Thailand
| | - Paisarn Boonsakan
- Department of Pathology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Supasan Sripodok
- Department of Pathology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Peti Thuwajit
- Department of Immunology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Komgrid Charngkaew
- Department of Pathology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Ananya Pongpaibul
- Department of Pathology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Napat Angkathunyakul
- Department of Pathology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Narit Hnoohom
- Image Information and Intelligence Laboratory, Department of Computer Engineering, Faculty of Engineering, Mahidol University, Nakhon Pathom, Thailand
| | - Chanitra Thuwajit
- Department of Immunology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
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4
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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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Xu C, Zhang R, Duan M, Zhou Y, Bao J, Lu H, Wang J, Hu M, Hu Z, Zhou F, Zhu W. A polygenic stacking classifier revealed the complicated platelet transcriptomic landscape of adult immune thrombocytopenia. MOLECULAR THERAPY - NUCLEIC ACIDS 2022; 28:477-487. [PMID: 35505964 PMCID: PMC9046129 DOI: 10.1016/j.omtn.2022.04.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 04/01/2022] [Indexed: 01/19/2023]
Abstract
Immune thrombocytopenia (ITP) is an autoimmune disease with the typical symptom of a low platelet count in blood. ITP demonstrated age and sex biases in both occurrences and prognosis, and adult ITP was mainly induced by the living environments. The current diagnosis guideline lacks the integration of molecular heterogenicity. This study recruited the largest cohort of platelet transcriptome samples. A comprehensive procedure of feature selection, feature engineering, and stacking classification was carried out to detect the ITP biomarkers using RNA sequencing (RNA-seq) transcriptomes. The 40 detected biomarkers were loaded to train the final ITP detection model, with an overall accuracy 0.974. The biomarkers suggested that ITP onset may be associated with various transcribed components, including protein-coding genes, long intergenic non-coding RNA (lincRNA) genes, and pseudogenes with apparent transcriptions. The delivered ITP detection model may also be utilized as a complementary ITP diagnosis tool. The code and the example dataset is freely available on http://www.healthinformaticslab.org/supp/resources.php
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Affiliation(s)
- Chengfeng Xu
- Department of Hematology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, 110 Ganhe Road, Hongkou District, Shanghai 200437, China
| | - Ruochi Zhang
- College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
| | - Meiyu Duan
- College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
| | - Yongming Zhou
- Department of Hematology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, 110 Ganhe Road, Hongkou District, Shanghai 200437, China
| | - Jizhang Bao
- Department of Hematology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, 110 Ganhe Road, Hongkou District, Shanghai 200437, China
| | - Hao Lu
- Department of Hematology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, 110 Ganhe Road, Hongkou District, Shanghai 200437, China
| | - Jie Wang
- Department of Hematology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, 110 Ganhe Road, Hongkou District, Shanghai 200437, China
| | - Minghui Hu
- Department of Hematology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, 110 Ganhe Road, Hongkou District, Shanghai 200437, China
| | - Zhaoyang Hu
- Fun-Med Pharmaceutical Technology (Shanghai) Co., Ltd., RM. A310, 115 Xinjunhuan Road, Minhang District, Shanghai 201100, China
- Corresponding author Zhaoyang Hu, PhD, Fengneng Pharmaceutical Technology (Shanghai) Co., Ltd., RM. A310, 115 Xinjunhuan Road, Minhang District, Shanghai 201100, China.
| | - Fengfeng Zhou
- College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
- Corresponding author Fengfeng Zhou, PhD, College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China.
| | - Wenwei Zhu
- Department of Hematology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, 110 Ganhe Road, Hongkou District, Shanghai 200437, China
- Corresponding author Wenwei Zhu, PhD, Department of Hematology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, 110 Ganhe Road, Hongkou District, Shanghai 200437, China.
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Kornaropoulos EN, Zacharaki EI, Zerbib P, Lin C, Rahmouni A, Paragios N. Joint Deformable Image Registration and ADC Map Regularization: Application to DWI-based Lymphoma Classification. IEEE J Biomed Health Inform 2022; 26:3151-3162. [PMID: 35239496 DOI: 10.1109/jbhi.2022.3156009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The Apparent Diffusion Coefficient (ADC) is considered an important imaging biomarker contributing to the assessment of tissue microstructure and pathophysiology. It is calculated from Diffusion-Weighted Magnetic Resonance Imaging (DWI) by means of a diffusion model, usually without considering any motion during image acquisition. We propose a method to improve the computation of the ADC by coping jointly with both motion artifacts in whole-body DWI (through group-wise registration) and possible instrumental noise in the diffusion model. The proposed deformable registration method yielded on average the lowest ADC reconstruction error on data with simulated motion and diffusion. Moreover, our approach was applied on whole-body diffusion weighted images obtained with five different b-values from a cohort of 38 patients with histologically confirmed lymphomas of three different types (Hodgkin, diffuse large B-cell lymphoma and follicular lymphoma). Evaluation on the real data showed that ADC-based features, extracted using our joint optimization approach classified lymphomas with an accuracy of approximately 78.6\% (yielding a 11\% increase in respect to the standard features extracted from unregistered diffusion-weighted images). Furthermore, the correlation between diffusion characteristics and histopathological findings was higher than any other previous approach of ADC computation.
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Niazi MKK, Parwani AV, Gurcan MN. Digital pathology and artificial intelligence. Lancet Oncol 2019; 20:e253-e261. [PMID: 31044723 PMCID: PMC8711251 DOI: 10.1016/s1470-2045(19)30154-8] [Citation(s) in RCA: 497] [Impact Index Per Article: 99.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Revised: 02/28/2019] [Accepted: 03/13/2019] [Indexed: 02/06/2023]
Abstract
In modern clinical practice, digital pathology has a crucial role and is increasingly a technological requirement in the scientific laboratory environment. The advent of whole-slide imaging, availability of faster networks, and cheaper storage solutions has made it easier for pathologists to manage digital slide images and share them for clinical use. In parallel, unprecedented advances in machine learning have enabled the synergy of artificial intelligence and digital pathology, which offers image-based diagnosis possibilities that were once limited only to radiology and cardiology. Integration of digital slides into the pathology workflow, advanced algorithms, and computer-aided diagnostic techniques extend the frontiers of the pathologist's view beyond a microscopic slide and enable true utilisation and integration of knowledge that is beyond human limits and boundaries, and we believe there is clear potential for artificial intelligence breakthroughs in the pathology setting. In this Review, we discuss advancements in digital slide-based image diagnosis for cancer along with some challenges and opportunities for artificial intelligence in digital pathology.
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Affiliation(s)
| | - Anil V Parwani
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | - Metin N Gurcan
- Center for Biomedical Informatics, Wake Forest School of Medicine, Winston-Salem, NC, USA
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Schnotalle P, Koch K, Au-Yeung RKH, Reinke S, Winter K, Loeffler M, Braumann UD, Klapper W. T-Cell Clustering in Neoplastic Follicles of Follicular Lymphoma. CANCER MICROENVIRONMENT 2018; 11:135-140. [PMID: 30203411 PMCID: PMC6250612 DOI: 10.1007/s12307-018-0217-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2018] [Accepted: 09/06/2018] [Indexed: 12/26/2022]
Abstract
The nonneoplastic microenvironment is abundant in follicular lymphoma. Its composition has been reported to be associated with the course of the disease. Lack of animal models hampers studies of interaction between lymphoma and bystander cells. We aimed to identify indicators of cellular interaction exemplified by nonrandom distribution of cell types within neoplastic follicles. Physiological germinal centers and follicles in follicular lymphoma were stained to identify macrophages, all T, follicular T-helper, dendritic and B cells. Density of cell types and cell distribution (spatial point pattern) were analyzed by digital image analysis. The density of all T, follicular T-helper and dendritic cells was higher in the dark zone than in the light zone of physiological germinal centers. Densities of cell types in follicular lymphoma were intermediate between the light and the dark zone. All cell types analyzed showed a completely random spatial distribution pattern within the dark and the light zone, respectively. In follicular lymphoma B cells and macrophages displayed complete spatial randomness. In contrast, all T cells, follicular T-helper cells and dendritic cells showed clustering of each individual cell type within a radius of 6–10 μm in the lymphoma. We conclude that the distribution of nonneoplastic cells within follicles of follicular lymphoma is not random. T cells and dendritic cells form clusters within the follicles, suggestive of sites of interaction between microenvironment and lymphoma cells. These clusters might help to understand the interaction of lymphoma cells with the microenvironment and might provide a structure for therapeutic intervention.
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Affiliation(s)
- Patrick Schnotalle
- Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Medical Faculty, University of Leipzig, Leipzig, Germany
| | - Karoline Koch
- Department of Pathology, Hematopathology Section and Lymph Node Registry, University of Kiel/University Hospital Schleswig-Holstein, Arnold-Heller-Strasse 3, Haus 14, 24105, Kiel, Germany
| | - Rex K H Au-Yeung
- Department of Pathology, Hematopathology Section and Lymph Node Registry, University of Kiel/University Hospital Schleswig-Holstein, Arnold-Heller-Strasse 3, Haus 14, 24105, Kiel, Germany
| | - Sarah Reinke
- Department of Pathology, Hematopathology Section and Lymph Node Registry, University of Kiel/University Hospital Schleswig-Holstein, Arnold-Heller-Strasse 3, Haus 14, 24105, Kiel, Germany
| | - Karsten Winter
- Institute of Anatomy, Medical Faculty, University Leipzig, Leipzig, Germany
| | - Markus Loeffler
- Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Medical Faculty, University of Leipzig, Leipzig, Germany
| | - Ulf-Dietrich Braumann
- Faculty of Electrical Engineering and Information Technology, Leipzig University of Applied Science (HTWK), Leipzig, Germany
- Fraunhofer Institute for Cell Therapy and Immunology (IZI), Leipzig, Germany
| | - Wolfram Klapper
- Department of Pathology, Hematopathology Section and Lymph Node Registry, University of Kiel/University Hospital Schleswig-Holstein, Arnold-Heller-Strasse 3, Haus 14, 24105, Kiel, Germany.
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Valkonen M, Kartasalo K, Liimatainen K, Nykter M, Latonen L, Ruusuvuori P. Metastasis detection from whole slide images using local features and random forests. Cytometry A 2017; 91:555-565. [PMID: 28426134 DOI: 10.1002/cyto.a.23089] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Digital pathology has led to a demand for automated detection of regions of interest, such as cancerous tissue, from scanned whole slide images. With accurate methods using image analysis and machine learning, significant speed-up, and savings in costs through increased throughput in histological assessment could be achieved. This article describes a machine learning approach for detection of cancerous tissue from scanned whole slide images. Our method is based on feature engineering and supervised learning with a random forest model. The features extracted from the whole slide images include several local descriptors related to image texture, spatial structure, and distribution of nuclei. The method was evaluated in breast cancer metastasis detection from lymph node samples. Our results show that the method detects metastatic areas with high accuracy (AUC = 0.97-0.98 for tumor detection within whole image area, AUC = 0.84-0.91 for tumor vs. normal tissue detection) and that the method generalizes well for images from more than one laboratory. Further, the method outputs an interpretable classification model, enabling the linking of individual features to differences between tissue types. © 2017 International Society for Advancement of Cytometry.
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Affiliation(s)
- Mira Valkonen
- BioMediTech and Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland.,BioMediTech Institute and Faculty of Biomedical Science and Engineering, Tampere University of Technology, Tampere, Finland
| | - Kimmo Kartasalo
- BioMediTech and Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland.,BioMediTech Institute and Faculty of Biomedical Science and Engineering, Tampere University of Technology, Tampere, Finland
| | - Kaisa Liimatainen
- BioMediTech and Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland.,BioMediTech Institute and Faculty of Biomedical Science and Engineering, Tampere University of Technology, Tampere, Finland
| | - Matti Nykter
- BioMediTech and Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland.,BioMediTech Institute and Faculty of Biomedical Science and Engineering, Tampere University of Technology, Tampere, Finland
| | - Leena Latonen
- BioMediTech and Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland
| | - Pekka Ruusuvuori
- BioMediTech and Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland.,Faculty of Computing and Electrical Engineering, Tampere University of Technology, Pori, Finland
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10
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Classification of Nuclei in Follicular Lyphoma Tissue Sections Using Different Stains and Bayesian Networks. ACTA ACUST UNITED AC 2016. [DOI: 10.1007/978-3-319-32703-7_47] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
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11
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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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Michail E, Dimitropoulos K, Koletsa T, Kostopoulos I, Grammalidis N. Morphological and textural analysis of centroblasts in low-thickness sliced tissue biopsies of follicular lymphoma. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2014:3374-7. [PMID: 25570714 DOI: 10.1109/embc.2014.6944346] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
This paper presents a new method for discriminating centroblast (CB) from non-centroblast cells in microscopic images acquired from tissue biopsies of follicular lymphoma. In the proposed method tissue sections are sliced at a low thickness level, around 1-1.5 μm, which provides a more detailed depiction of the nuclei and other textural information of cells usually not distinguishable in thicker specimens, such as 4-5 μm, that have been used in the past by other researchers. To identify CBs, a morphological and textural analysis is applied in order to extract various features related to their nuclei, nucleoli and cytoplasm. The generated feature vector is then used as input in a two-class SVM classifier with ε-Support Vector Regression and radial basis kernel function. Experimental results with an annotated dataset consisting of 300 images of centroblasts and non-centroblasts, derived from high-power field images of follicular lymphoma stained with Hematoxylin and Eosin, have shown the great potential of the proposed method with an average detection rate of 97.44%.
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13
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Niazi MKK, Dhulekar N, Schmidt D, Major S, Cooper R, Abeijon C, Gatti DM, Kramnik I, Yener B, Gurcan M, Beamer G. Lung necrosis and neutrophils reflect common pathways of susceptibility to Mycobacterium tuberculosis in genetically diverse, immune-competent mice. Dis Model Mech 2015. [PMID: 26204894 PMCID: PMC4582107 DOI: 10.1242/dmm.020867] [Citation(s) in RCA: 68] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Pulmonary tuberculosis (TB) is caused by Mycobacterium tuberculosis in susceptible humans. Here, we infected Diversity Outbred (DO) mice with ∼100 bacilli by aerosol to model responses in a highly heterogeneous population. Following infection, ‘supersusceptible’, ‘susceptible’ and ‘resistant’ phenotypes emerged. TB disease (reduced survival, weight loss, high bacterial load) correlated strongly with neutrophils, neutrophil chemokines, tumor necrosis factor (TNF) and cell death. By contrast, immune cytokines were weak correlates of disease. We next applied statistical and machine learning approaches to our dataset of cytokines and chemokines from lungs and blood. Six molecules from the lung: TNF, CXCL1, CXCL2, CXCL5, interferon-γ (IFN-γ), interleukin 12 (IL-12); and two molecules from blood – IL-2 and TNF – were identified as being important by applying both statistical and machine learning methods. Using molecular features to generate tree classifiers, CXCL1, CXCL2 and CXCL5 distinguished four classes (supersusceptible, susceptible, resistant and non-infected) from each other with approximately 77% accuracy using completely independent experimental data. By contrast, models based on other molecules were less accurate. Low to no IFN-γ, IL-12, IL-2 and IL-10 successfully discriminated non-infected mice from infected mice but failed to discriminate disease status amongst supersusceptible, susceptible and resistant M.-tuberculosis-infected DO mice. Additional analyses identified CXCL1 as a promising peripheral biomarker of disease and of CXCL1 production in the lungs. From these results, we conclude that: (1) DO mice respond variably to M. tuberculosis infection and will be useful to identify pathways involving necrosis and neutrophils; (2) data from DO mice is suited for machine learning methods to build, validate and test models with independent data based solely on molecular biomarkers; (3) low levels of immunological cytokines best indicate a lack of exposure to M. tuberculosis but cannot distinguish infection from disease. Summary: Molecular biomarkers of tuberculosis are identified and used to classify disease status of Diversity Outbred mice that have been infected with Mycobacterium tuberculosis.
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Affiliation(s)
- Muhammad K K Niazi
- Department of Biomedical Informatics, The Ohio State University, Columbus, 43210 OH, USA
| | - Nimit Dhulekar
- Department of Computer Science and Department of Electrical, Computer and Systems Engineering, Rensselaer Polytechnic Institute, Troy, 12810 NY, USA
| | - Diane Schmidt
- Department of Infectious Disease and Global Health, Cummings School of Veterinary Medicine, Tufts University, Grafton, 01536 MA, USA
| | - Samuel Major
- Department of Infectious Disease and Global Health, Cummings School of Veterinary Medicine, Tufts University, Grafton, 01536 MA, USA
| | - Rachel Cooper
- Department of Infectious Disease and Global Health, Cummings School of Veterinary Medicine, Tufts University, Grafton, 01536 MA, USA
| | - Claudia Abeijon
- Department of Infectious Disease and Global Health, Cummings School of Veterinary Medicine, Tufts University, Grafton, 01536 MA, USA
| | | | - Igor Kramnik
- Department of Medicine, Boston University School of Medicine, Boston, 02215 MA, USA
| | - Bulent Yener
- Department of Computer Science and Department of Electrical, Computer and Systems Engineering, Rensselaer Polytechnic Institute, Troy, 12810 NY, USA
| | - Metin Gurcan
- Department of Biomedical Informatics, The Ohio State University, Columbus, 43210 OH, USA
| | - Gillian Beamer
- Department of Infectious Disease and Global Health, Cummings School of Veterinary Medicine, Tufts University, Grafton, 01536 MA, USA
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