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Jardim LL, Schieber TA, Santana MP, Cerqueira MH, Lorenzato CS, Franco VKB, Zuccherato LW, da Silva Santos BA, Chaves DG, Ravetti MG, Rezende SM. Prediction of inhibitor development in previously untreated and minimally treated children with severe and moderately severe hemophilia A using a machine-learning network. J Thromb Haemost 2024:S1538-7836(24)00303-9. [PMID: 38810700 DOI: 10.1016/j.jtha.2024.05.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 05/02/2024] [Accepted: 05/12/2024] [Indexed: 05/31/2024]
Abstract
BACKGROUND Prediction of inhibitor development in patients with hemophilia A (HA) remains a challenge. OBJECTIVES To construct a predictive model for inhibitor development in HA using a network of clinical variables and biomarkers based on the individual similarity network. METHODS Previously untreated and minimally treated children with severe/moderately severe HA, participants of the HEMFIL Cohort Study, were followed up until reaching 75 exposure days (EDs) without inhibitor (INH-) or upon inhibitor development (INH+). Clinical data and biological samples were collected before the start of factor (F)VIII replacement (T0). A predictive model (HemfilNET) was built to compare the networks and potential global topological differences between INH- and INH+ at T0, considering the network robustness. For validation, the "leave-one-out" cross-validation technique was employed. Accuracy, precision, recall, and F1-score were used as evaluation metrics for the machine-learning model. RESULTS We included 95 children with HA (CHA), of whom 31 (33%) developed inhibitors. The algorithm, featuring 37 variables, identified distinct patterns of networks at T0 for INH+ and INH-. The accuracy of the model was 74.2% for CHA INH+ and 98.4% for INH-. By focusing the analysis on CHA with high-risk F8 mutations for inhibitor development, the accuracy in identifying CHA INH+ increased to 82.1%. CONCLUSION Our machine-learning algorithm demonstrated an overall accuracy of 90.5% for predicting inhibitor development in CHA, which further improved when restricting the analysis to CHA with a high-risk F8 genotype. However, our model requires validation in other cohorts. Yet, missing data for some variables hindered more precise predictions.
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Affiliation(s)
- Letícia Lemos Jardim
- Instituto René Rachou (Fiocruz Minas), Belo Horizonte, Minas Gerais, Brazil; Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, the Netherlands
| | - Tiago A Schieber
- Faculdade de Ciências Econômicas, School of Economics, Universidade Federal de Minas Gerais, Brazil
| | | | | | | | | | | | | | | | - Martín Gomez Ravetti
- Departamento de Ciência da Computação, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Suely Meireles Rezende
- Faculty of Medicine, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
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Lee JH, Song G, Lee J, Kang S, Moon KM, Choi Y, Shen J, Noh M, Yang D. Prediction of immunochemotherapy response for diffuse large B-cell lymphoma using artificial intelligence digital pathology. J Pathol Clin Res 2024; 10:e12370. [PMID: 38584594 PMCID: PMC10999948 DOI: 10.1002/2056-4538.12370] [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: 10/27/2023] [Revised: 02/13/2024] [Accepted: 03/04/2024] [Indexed: 04/09/2024]
Abstract
Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous and prevalent subtype of aggressive non-Hodgkin lymphoma that poses diagnostic and prognostic challenges, particularly in predicting drug responsiveness. In this study, we used digital pathology and deep learning to predict responses to immunochemotherapy in patients with DLBCL. We retrospectively collected 251 slide images from 216 DLBCL patients treated with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP), with their immunochemotherapy response labels. The digital pathology images were processed using contrastive learning for feature extraction. A multi-modal prediction model was developed by integrating clinical data and pathology image features. Knowledge distillation was employed to mitigate overfitting on gigapixel histopathology images to create a model that predicts responses based solely on pathology images. Based on the importance derived from the attention mechanism of the model, we extracted histological features that were considered key textures associated with drug responsiveness. The multi-modal prediction model achieved an impressive area under the ROC curve of 0.856, demonstrating significant associations with clinical variables such as Ann Arbor stage, International Prognostic Index, and bulky disease. Survival analyses indicated their effectiveness in predicting relapse-free survival. External validation using TCGA datasets supported the model's ability to predict survival differences. Additionally, pathology-based predictions show promise as independent prognostic indicators. Histopathological analysis identified centroblastic and immunoblastic features to be associated with treatment response, aligning with previous morphological classifications and highlighting the objectivity and reproducibility of artificial intelligence-based diagnosis. This study introduces a novel approach that combines digital pathology and clinical data to predict the response to immunochemotherapy in patients with DLBCL. This model shows great promise as a diagnostic and prognostic tool for clinical management of DLBCL. Further research and genomic data integration hold the potential to enhance its impact on clinical practice, ultimately improving patient outcomes.
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Affiliation(s)
- Jeong Hoon Lee
- Department of RadiologyStanford University School of MedicineStanfordCAUSA
| | - Ga‐Young Song
- Department of Hematology‐OncologyChonnam National University Hwasun HospitalHwasunRepublic of Korea
| | - Jonghyun Lee
- Department of Medical and Digital EngineeringHanyang University College of EngineeringSeoulRepublic of Korea
| | - Sae‐Ryung Kang
- Department of Nuclear MedicineChonnam National University Hwasun Hospital and Medical SchoolHwasun‐gunRepublic of Korea
| | - Kyoung Min Moon
- Division of Pulmonary and Allergy Medicine, Department of Internal MedicineChung‐Ang University Hospital, Chung‐Ang University College of MedicineSeoulRepublic of Korea
- Artificial Intelligence, Ziovision Co., Ltd.ChuncheonRepublic of Korea
| | - Yoo‐Duk Choi
- Department of PathologyChonnam National University Medical SchoolGwangjuRepublic of Korea
| | - Jeanne Shen
- Department of Pathology and Center for Artificial Intelligence in Medicine & ImagingStanford University School of MedicineStanfordCAUSA
| | - Myung‐Giun Noh
- Department of PathologyChonnam National University Medical SchoolGwangjuRepublic of Korea
- Department of PathologySchool of Medicine, Ajou UniversitySuwonRepublic of Korea
| | - Deok‐Hwan Yang
- Department of Hematology‐OncologyChonnam National University Hwasun HospitalHwasunRepublic of Korea
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Yan H, Ju X, Huang A, Yuan J. Advancements in technology for characterizing the tumor immune microenvironment. Int J Biol Sci 2024; 20:2151-2167. [PMID: 38617534 PMCID: PMC11008272 DOI: 10.7150/ijbs.92525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 03/12/2024] [Indexed: 04/16/2024] Open
Abstract
Immunotherapy plays a key role in cancer treatment, however, responses are limited to a small number of patients. The biological basis for the success of immunotherapy is the complex interaction between tumor cells and tumor immune microenvironment (TIME). Historically, research on tumor immune constitution was limited to the analysis of one or two markers, more novel technologies are needed to interpret the complex interactions between tumor cells and TIME. In recent years, major advances have already been made in depicting TIME at a considerably elevated degree of throughput, dimensionality and resolution, allowing dozens of markers to be labeled simultaneously, and analyzing the heterogeneity of tumour-immune infiltrates in detail at the single cell level, depicting the spatial landscape of the entire microenvironment, as well as applying artificial intelligence (AI) to interpret a large amount of complex data from TIME. In this review, we summarized emerging technologies that have made contributions to the field of TIME, and provided prospects for future research.
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Affiliation(s)
- Honglin Yan
- Department of Pathology, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, P.R. China
| | | | | | - Jingping Yuan
- Department of Pathology, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, P.R. China
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Fan BE, Yong BSJ, Li R, Wang SSY, Aw MYN, Chia MF, Chen DTY, Neo YS, Occhipinti B, Ling RR, Ramanathan K, Ong YX, Lim KGE, Wong WYK, Lim SP, Latiff STBA, Shanmugam H, Wong MS, Ponnudurai K, Winkler S. From microscope to micropixels: A rapid review of artificial intelligence for the peripheral blood film. Blood Rev 2024; 64:101144. [PMID: 38016837 DOI: 10.1016/j.blre.2023.101144] [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: 10/06/2023] [Revised: 11/12/2023] [Accepted: 11/14/2023] [Indexed: 11/30/2023]
Abstract
Artificial intelligence (AI) and its application in classification of blood cells in the peripheral blood film is an evolving field in haematology. We performed a rapid review of the literature on AI and peripheral blood films, evaluating the condition studied, image datasets, machine learning models, training set size, testing set size and accuracy. A total of 283 studies were identified, encompassing 6 broad domains: malaria (n = 95), leukemia (n = 81), leukocytes (n = 72), mixed (n = 25), erythrocytes (n = 15) or Myelodysplastic syndrome (MDS) (n = 1). These publications have demonstrated high self-reported mean accuracy rates across various studies (95.5% for malaria, 96.0% for leukemia, 94.4% for leukocytes, 95.2% for mixed studies and 91.2% for erythrocytes), with an overall mean accuracy of 95.1%. Despite the high accuracy, the challenges toward real world translational usage of these AI trained models include the need for well-validated multicentre data, data standardisation, and studies on less common cell types and non-malarial blood-borne parasites.
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Affiliation(s)
- Bingwen Eugene Fan
- Department of Haematology, Tan Tock Seng Hospital, Singapore; Department of Laboratory Medicine, Khoo Teck Puat Hospital, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Bryan Song Jun Yong
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Ruiqi Li
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | | | | | - Ming Fang Chia
- Department of Haematology, Tan Tock Seng Hospital, Singapore
| | | | - Yuan Shan Neo
- ASUS Intelligent Cloud Services, Singapore, Singapore
| | | | - Ryan Ruiyang Ling
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Kollengode Ramanathan
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Cardiothoracic Intensive Care Unit, National University Heart Centre, National University Hospital, Singapore, Singapore
| | - Yi Xiong Ong
- Department of Laboratory Medicine, Tan Tock Seng Hospital, Singapore
| | | | | | - Shu Ping Lim
- Department of Laboratory Medicine, Tan Tock Seng Hospital, Singapore
| | | | | | - Moh Sim Wong
- Department of Laboratory Medicine, Khoo Teck Puat Hospital, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Kuperan Ponnudurai
- Department of Haematology, Tan Tock Seng Hospital, Singapore; Department of Laboratory Medicine, Khoo Teck Puat Hospital, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Stefan Winkler
- ASUS Intelligent Cloud Services, Singapore, Singapore; School of Computing, National University of Singapore, Singapore
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Jiang H, Xu W, Chen W, He J, Jiang H, Mao Z, Liu M, Li M, Liu D, Pan Y, Qu C, Qu L, Sun Z, Sun D, Wang X, Wang J, Wu W, Xing Y, Zhang S, Zhang C, Zheng L, Guan M. Performance of the digital cell morphology analyzer MC-100i in a multicenter study in tertiary hospitals in China. Clin Chim Acta 2024; 555:117801. [PMID: 38296220 DOI: 10.1016/j.cca.2024.117801] [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: 11/01/2023] [Revised: 01/16/2024] [Accepted: 01/22/2024] [Indexed: 02/04/2024]
Abstract
BACKGROUND This study investigated the performance of the MC-100i, a pre-commercial digital morphology analyzer utilizing a convolutional neural network algorithm, in a multicentric setting involving up to 11 tertiary hospitals in China. METHODS Blood smears were analyzed by MC-100i, verified by morphologists, and manually differentiated. The classification performance on WBCs and RBCs was evaluated by comparing the classification results using different methods. The PLT and PLT clump counting performance was also assessed. The total assay time including hands-on time was evaluated. RESULTS The agreements between pre- and post-classification were high for normal WBCs (κ > 0.96) and lower for overall abnormal WBCs (κ = 0.90). The post-classification results correlated well with manual differentials for both normal and abnormal WBCs (r > 0.93), except for basophils (r = 0.8480) and atypical lymphocytes (r = 0.8211). The clinical sensitivity and specificity of each RBC abnormality after verification were above 90 % using microscopy reviews as the reference. The PLTs counted by the MC-100i before and after verification correlated well with those measured by the PLT-O mode (r = 0.98). Moreover, PLT clumps were successfully classified by the analyzer in EDTA-dependent pseudothrombocytopenia blood samples. CONCLUSIONS The MC-100i is an accurate and reliable digital cell morphology analyzer, offering another intelligent option for hematology laboratories.
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Affiliation(s)
- Hong Jiang
- Department of Laboratory Medicine, West China Hospital of Sichuan University, Chengdu 610044, China
| | - Wei Xu
- Department of Laboratory Medicine, The First Bethune Hospital of Jilin University, Jilin 130061, China
| | - Wei Chen
- Department of Laboratory Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China
| | - Jun He
- Department of Laboratory Medicine, The First Affiliated Hospital of Soochow University, Suzhou 215006, China
| | - Haoqin Jiang
- Department of Laboratory Medicine, Huashan Hospital Fudan University, Shanghai 200040, China
| | - Zhigang Mao
- Department of Laboratory Medicine, West China Hospital of Sichuan University, Chengdu 610044, China
| | - Min Liu
- Department of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510062, China
| | - Mianyang Li
- Department of Laboratory Medicine, Chinese PLA Ceneral Hospital, Beijing 100080, China
| | - Dandan Liu
- Department of Laboratory Medicine, The First Affiliated Hospital of Soochow University, Suzhou 215006, China
| | - Yuling Pan
- Department of Laboratory Medicine, Chinese PLA Ceneral Hospital, Beijing 100080, China
| | - Chenxue Qu
- Department of Laboratory Medicine, Peking University First Hospital, Beijing 100034, China
| | - Linlin Qu
- Department of Laboratory Medicine, The First Bethune Hospital of Jilin University, Jilin 130061, China
| | - Ziyong Sun
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College of Hust, Wuhan 430030, China
| | - Dehua Sun
- Department of Laboratory Medicine, Nanfang Hospital, Guangzhou 516006, China
| | - Xuefeng Wang
- Department of Laboratory Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200025, China
| | - Jianbiao Wang
- Department of Laboratory Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200025, China
| | - Wenjing Wu
- Department of Laboratory Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China
| | - Ying Xing
- Department of Laboratory Medicine, Peking University First Hospital, Beijing 100034, China
| | - Shihong Zhang
- Department of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510062, China
| | - Chi Zhang
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College of Hust, Wuhan 430030, China
| | - Lei Zheng
- Department of Laboratory Medicine, Nanfang Hospital, Guangzhou 516006, China.
| | - Ming Guan
- Department of Laboratory Medicine, Huashan Hospital Fudan University, Shanghai 200040, China.
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6
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Rolfe M, Hayes S, Smith M, Owen M, Spruth M, McCarthy C, Forkan A, Banerjee A, Hocking RK. An AI based smart-phone system for asbestos identification. JOURNAL OF HAZARDOUS MATERIALS 2024; 463:132853. [PMID: 37918071 DOI: 10.1016/j.jhazmat.2023.132853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 10/13/2023] [Accepted: 10/23/2023] [Indexed: 11/04/2023]
Abstract
Asbestos identification is a complex environmental and economic challenge. Typical commercial identification of asbestos involves sending samples to a laboratory where someone learned in the field uses light microscopy and specialized mounting to identify the morphologically distinct signatures of Asbestos. In this work we investigate the use of a portable (30x) microscope which works with a smart phone camera to develop an image recognition system. 7328 images from over 1000 distinct samples of cement sheet from Melbourne, Australia were used to train a phone-based image recognition system for Asbestos identification. Three common CNN's were tested ResNet101, InceptionV3 and VGG_16 with ResNet101 achieving the best result. The distinctiveness of Asbestos was found to be identified correctly 90% of the time using a phone-based system and no specialized mounting. The image recognition system was trained with ResNet101 a convolutional neural network deep learning model which weights layers with a residual function. Resulting in an accuracy of 98.46% and loss of 3.8% ResNet101 was found to produce a more accurate model for this use-case than other deep learning neural networks.
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Affiliation(s)
- Michael Rolfe
- Department of Chemistry and Biotechnology and Department of Computing Technologies, School of Science, Computing and Engineering Technologies, Swinburne University of Technology Melbourne, VIC 3122, Australia
| | - Samantha Hayes
- Agon Environmental Pty, Ltd 63-85 Turner Street, Port Melbourne, VIC 3207, Australia
| | - Meaghan Smith
- Department of Chemistry and Biotechnology and Department of Computing Technologies, School of Science, Computing and Engineering Technologies, Swinburne University of Technology Melbourne, VIC 3122, Australia
| | - Matthew Owen
- Identifibre Pty Ltd., 67 Atherton Road, Oakleigh, VIC 3166, Australia
| | - Michael Spruth
- Agon Environmental Pty, Ltd 63-85 Turner Street, Port Melbourne, VIC 3207, Australia
| | - Chris McCarthy
- Department of Chemistry and Biotechnology and Department of Computing Technologies, School of Science, Computing and Engineering Technologies, Swinburne University of Technology Melbourne, VIC 3122, Australia
| | - Abdur Forkan
- Department of Chemistry and Biotechnology and Department of Computing Technologies, School of Science, Computing and Engineering Technologies, Swinburne University of Technology Melbourne, VIC 3122, Australia
| | - Abhik Banerjee
- Department of Chemistry and Biotechnology and Department of Computing Technologies, School of Science, Computing and Engineering Technologies, Swinburne University of Technology Melbourne, VIC 3122, Australia
| | - Rosalie K Hocking
- Department of Chemistry and Biotechnology and Department of Computing Technologies, School of Science, Computing and Engineering Technologies, Swinburne University of Technology Melbourne, VIC 3122, Australia.
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Yuan J, Zhang Y, Wang X. Application of machine learning in the management of lymphoma: Current practice and future prospects. Digit Health 2024; 10:20552076241247963. [PMID: 38628632 PMCID: PMC11020711 DOI: 10.1177/20552076241247963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 03/28/2024] [Indexed: 04/19/2024] Open
Abstract
In the past decade, digitization of medical records and multiomics data analysis in lymphoma has led to the accessibility of high-dimensional records. The digitization of medical records, the visualization of extensive volume data extracted from medical images, and the integration of multiomics methods into clinical decision-making have produced many datasets. As a promising auxiliary tool, machine learning (ML) intends to extract homologous features in large-scale data sets and encode them into various patterns to complete complicated tasks. At present, artificial intelligence and digital mining have shown promising prospects in the field of lymphoma pathological image analysis. The paradigm shift from qualitative analysis to quantitative analysis makes the pathological diagnosis more intelligent and the results more accurate and objective. ML can promote accurate lymphoma diagnosis and provide patients with prognostic information and more individualized treatment options. Based on the above, this comprehensive review of the general workflow of ML highlights recent advances in ML techniques in the diagnosis, treatment, and prognosis of lymphoma, and clarifies the boundedness and future orientation of the ML technique in the clinical practice of lymphoma.
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Affiliation(s)
- Junyun Yuan
- Department of Hematology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Ya Zhang
- Department of Hematology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- Department of Hematology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
- Taishan Scholars Program of Shandong Province, Jinan, Shandong, China
| | - Xin Wang
- Department of Hematology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- Department of Hematology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
- Taishan Scholars Program of Shandong Province, Jinan, Shandong, China
- Branch of National Clinical Research Center for Hematologic Diseases, Jinan, Shandong, China
- National Clinical Research Center for Hematologic Diseases, Hospital of Soochow University, Suzhou, China
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Tagami M, Nishio M, Katsuyama-Yoshikawa A, Misawa N, Sakai A, Haruna Y, Azumi A, Honda S. Machine Learning Model with Texture Analysis for Automatic Classification of Histopathological Images of Ocular Adnexal Mucosa-associated Lymphoid Tissue Lymphoma of Two Different Origins. Curr Eye Res 2023; 48:1195-1202. [PMID: 37566457 DOI: 10.1080/02713683.2023.2246696] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 08/03/2023] [Accepted: 08/05/2023] [Indexed: 08/12/2023]
Abstract
PURPOSE The purpose of this study was to develop artificial intelligence algorithms that can distinguish between orbital and conjunctival mucosa-associated lymphoid tissue (MALT) lymphomas in pathological images. METHODS Tissue blocks with residual MALT lymphoma and data from histological and flow cytometric studies and molecular genetic analyses such as gene rearrangement were procured for 129 patients treated between April 2008 and April 2020. We collected pathological hematoxylin and eosin-stained (HE) images of lymphoma from these patients and cropped 10 different image patches at a resolution of 2048 × 2048 from pathological images from each patient. A total of 990 images from 99 patients were used to create and evaluate machine-learning models. Each image patch of three different magnification rates at ×4, ×20, and ×40 underwent texture analysis to extract features, and then seven different machine-learning algorithms were applied to the results to create models. Cross-validation on a patient-by-patient basis was used to create and evaluate models, and then 300 images from the remaining 30 cases were used to evaluate the average accuracy rate. RESULTS Ten-fold cross-validation using the support vector machine with linear kernel algorithm was identified as the best algorithm for discriminating between conjunctival mucosa-associated lymphoid tissue and orbital MALT lymphomas, with an average accuracy rate under cross-validation of 85%. There were ×20 magnification HE images that were more accurate in distinguishing orbital and conjunctival MALT lymphomas among ×4, ×20, and ×40. CONCLUSION Artificial intelligence algorithms can successfully distinguish HE images between orbital and conjunctival MALT lymphomas.
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Affiliation(s)
- Mizuki Tagami
- Department of Ophthalmology and Visual Sciences, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan
- Ophthalmology Department and Eye Center, Kobe Kaisei Hospital, Kobe, Japan
| | - Mizuho Nishio
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan
| | | | - Norihiko Misawa
- Department of Ophthalmology and Visual Sciences, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan
| | - Atsushi Sakai
- Department of Ophthalmology and Visual Sciences, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan
| | - Yusuke Haruna
- Department of Ophthalmology and Visual Sciences, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan
| | - Atsushi Azumi
- Ophthalmology Department and Eye Center, Kobe Kaisei Hospital, Kobe, Japan
| | - Shigeru Honda
- Department of Ophthalmology and Visual Sciences, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan
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Zhang J, Wu J, Zhou XS, Shi F, Shen D. Recent advancements in artificial intelligence for breast cancer: Image augmentation, segmentation, diagnosis, and prognosis approaches. Semin Cancer Biol 2023; 96:11-25. [PMID: 37704183 DOI: 10.1016/j.semcancer.2023.09.001] [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: 05/01/2023] [Revised: 08/03/2023] [Accepted: 09/05/2023] [Indexed: 09/15/2023]
Abstract
Breast cancer is a significant global health burden, with increasing morbidity and mortality worldwide. Early screening and accurate diagnosis are crucial for improving prognosis. Radiographic imaging modalities such as digital mammography (DM), digital breast tomosynthesis (DBT), magnetic resonance imaging (MRI), ultrasound (US), and nuclear medicine techniques, are commonly used for breast cancer assessment. And histopathology (HP) serves as the gold standard for confirming malignancy. Artificial intelligence (AI) technologies show great potential for quantitative representation of medical images to effectively assist in segmentation, diagnosis, and prognosis of breast cancer. In this review, we overview the recent advancements of AI technologies for breast cancer, including 1) improving image quality by data augmentation, 2) fast detection and segmentation of breast lesions and diagnosis of malignancy, 3) biological characterization of the cancer such as staging and subtyping by AI-based classification technologies, 4) prediction of clinical outcomes such as metastasis, treatment response, and survival by integrating multi-omics data. Then, we then summarize large-scale databases available to help train robust, generalizable, and reproducible deep learning models. Furthermore, we conclude the challenges faced by AI in real-world applications, including data curating, model interpretability, and practice regulations. Besides, we expect that clinical implementation of AI will provide important guidance for the patient-tailored management.
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Affiliation(s)
- Jiadong Zhang
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Jiaojiao Wu
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Xiang Sean Zhou
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China; Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China; Shanghai Clinical Research and Trial Center, Shanghai, China.
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Bouchemla F, Akchurin SV, Akchurina IV, Dyulger GP, Latynina ES, Grecheneva AV. Artificial intelligence feasibility in veterinary medicine: A systematic review. Vet World 2023; 16:2143-2149. [PMID: 38023280 PMCID: PMC10668547 DOI: 10.14202/vetworld.2023.2143-2149] [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: 06/13/2023] [Accepted: 09/20/2023] [Indexed: 12/01/2023] Open
Abstract
Background and Aim In recent years, artificial intelligence (AI) has become increasingly necessary in the life sciences, particularly medicine and healthcare. This study aimed to systematically review the literature and critically analyze multiple databases on the use of AI in veterinary medicine to assess its challenges. We aim to foster an understanding of the effects that can be approached and applied for professional awareness. Materials and Methods This study used multiple electronic databases with information on applied AI in veterinary medicine based on the current guidelines outlined in PRISMA and Cochrane for systematic review. The electronic databases PubMed, Embase, Google Scholar, Cochrane Library, and Elsevier were thoroughly screened through March 22, 2023. The study design was carefully chosen to emphasize evidence quality and population heterogeneity. Results A total of 385 of the 883 citations initially obtained were thoroughly reviewed. There were four main areas that AI addressed; the first was diagnostic issues, the second was education, animal production, and epidemiology, the third was animal health and welfare, pathology, and microbiology, and the last was all other categories. The quality assessment of the included studies found that they varied in their relative quality and risk of bias. However, AI aftereffect-linked algorithms have raised criticism of their generated conclusions. Conclusion Quality assessment noted areas of AI outperformance, but there was criticism of its performance as well. It is recommended that the extent of AI in veterinary medicine should be increased, but it should not take over the profession. The concept of ambient clinical intelligence is adaptive, sensitive, and responsive to the digital environment and may be attractive to veterinary professionals as a means of lowering the fear of automating veterinary medicine. Future studies should focus on an AI model with flexible data input, which can be expanded by clinicians/users to maximize their interaction with good algorithms and reduce any errors generated by the process.
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Affiliation(s)
- Fayssal Bouchemla
- Department of Animal Disease, Veterinarian and Sanitarian Expertise, Faculty of Veterinary Medicine, Vavilov Saratov State University of Genetic, Biotechnology and Engineering Saratov, Russia
| | - Sergey Vladimirovich Akchurin
- Department of Veterinary Medicine, Russian State Agrarian University- Moscow Agricultural Academy named after K.A. Timiryazev, 49, str. Timiryazevskaya, Moscow, 127550, Russia
| | - Irina Vladimirovna Akchurina
- Department of Veterinary Medicine, Russian State Agrarian University- Moscow Agricultural Academy named after K.A. Timiryazev, 49, str. Timiryazevskaya, Moscow, 127550, Russia
| | - Georgiy Petrovitch Dyulger
- Department of Veterinary Medicine, Russian State Agrarian University- Moscow Agricultural Academy named after K.A. Timiryazev, 49, str. Timiryazevskaya, Moscow, 127550, Russia
| | - Evgenia Sergeevna Latynina
- Department of Veterinary Medicine, Russian State Agrarian University- Moscow Agricultural Academy named after K.A. Timiryazev, 49, str. Timiryazevskaya, Moscow, 127550, Russia
| | - Anastasia Vladimirovna Grecheneva
- Department of Applied Informatics, Russian State Agrarian University-Moscow Agricultural Academy named after K.A. Timiryazev, 49, str. Timiryazevskaya, Moscow, 127550, Russia
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11
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Hassini H, Dorizzi B, Thellier M, Klossa J, Gottesman Y. Investigating the Joint Amplitude and Phase Imaging of Stained Samples in Automatic Diagnosis. SENSORS (BASEL, SWITZERLAND) 2023; 23:7932. [PMID: 37765989 PMCID: PMC10536387 DOI: 10.3390/s23187932] [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: 08/02/2023] [Revised: 08/29/2023] [Accepted: 09/11/2023] [Indexed: 09/29/2023]
Abstract
The diagnosis of many diseases relies, at least on first intention, on an analysis of blood smears acquired with a microscope. However, image quality is often insufficient for the automation of such processing. A promising improvement concerns the acquisition of enriched information on samples. In particular, Quantitative Phase Imaging (QPI) techniques, which allow the digitization of the phase in complement to the intensity, are attracting growing interest. Such imaging allows the exploration of transparent objects not visible in the intensity image using the phase image only. Another direction proposes using stained images to reveal some characteristics of the cells in the intensity image; in this case, the phase information is not exploited. In this paper, we question the interest of using the bi-modal information brought by intensity and phase in a QPI acquisition when the samples are stained. We consider the problem of detecting parasitized red blood cells for diagnosing malaria from stained blood smears using a Deep Neural Network (DNN). Fourier Ptychographic Microscopy (FPM) is used as the computational microscopy framework to produce QPI images. We show that the bi-modal information enhances the detection performance by 4% compared to the intensity image only when the convolution in the DNN is implemented through a complex-based formalism. This proves that the DNN can benefit from the bi-modal enhanced information. We conjecture that these results should extend to other applications processed through QPI acquisition.
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Affiliation(s)
- Houda Hassini
- Samovar, Télécom SudParis, Institut Polytechnique de Paris, 91120 Palaiseau, France; (B.D.); (Y.G.)
- TRIBVN/T-Life, 92800 Puteaux, France;
| | - Bernadette Dorizzi
- Samovar, Télécom SudParis, Institut Polytechnique de Paris, 91120 Palaiseau, France; (B.D.); (Y.G.)
| | - Marc Thellier
- AP-HP, Centre National de Référence du Paludisme, 75013 Paris, France;
- Institut Pierre-Louis d’Épidémiologie et de Santé Publique, Sorbonne Université, INSERM, 75013 Paris, France
| | | | - Yaneck Gottesman
- Samovar, Télécom SudParis, Institut Polytechnique de Paris, 91120 Palaiseau, France; (B.D.); (Y.G.)
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12
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Zarella MD, McClintock DS, Batra H, Gullapalli RR, Valante M, Tan VO, Dayal S, Oh KS, Lara H, Garcia CA, Abels E. Artificial intelligence and digital pathology: clinical promise and deployment considerations. J Med Imaging (Bellingham) 2023; 10:051802. [PMID: 37528811 PMCID: PMC10389766 DOI: 10.1117/1.jmi.10.5.051802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 06/02/2023] [Accepted: 06/29/2023] [Indexed: 08/03/2023] Open
Abstract
Artificial intelligence (AI) presents an opportunity in anatomic pathology to provide quantitative objective support to a traditionally subjective discipline, thereby enhancing clinical workflows and enriching diagnostic capabilities. AI requires access to digitized pathology materials, which, at present, are most commonly generated from the glass slide using whole-slide imaging. Models are developed collaboratively or sourced externally, and best practices suggest validation with internal datasets most closely resembling the data expected in practice. Although an array of AI models that provide operational support for pathology practices or improve diagnostic quality and capabilities has been described, most of them can be categorized into one or more discrete types. However, their function in the pathology workflow can vary, as a single algorithm may be appropriate for screening and triage, diagnostic assistance, virtual second opinion, or other uses depending on how it is implemented and validated. Despite the clinical promise of AI, the barriers to adoption have been numerous, to which inclusion of new stakeholders and expansion of reimbursement opportunities may be among the most impactful solutions.
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Affiliation(s)
- Mark D. Zarella
- Mayo Clinic, Division of Computational Pathology and AI, Department of Laboratory Medicine and Pathology, Rochester, Minnesota, United States
| | - David S. McClintock
- Mayo Clinic, Division of Computational Pathology and AI, Department of Laboratory Medicine and Pathology, Rochester, Minnesota, United States
| | - Harsh Batra
- University of Texas MD Anderson Cancer Center, Department of Translational Molecular Pathology, Houston, Texas, United States
| | - Rama R. Gullapalli
- University of New Mexico, Department of Pathology, Albuquerque, New Mexico, United States
- University of New Mexico, Chemical and Biological Engineering, Albuquerque, New Mexico, United States
| | - Michael Valante
- Dell Technologies, Unstructured Data Solutions, Hopkinton, Massachusetts, United States
| | - Vivian O. Tan
- Leica Biosystems, Medical and Scientific Affairs, Vista, California, United States
| | - Shubham Dayal
- Leica Biosystems, Medical and Scientific Affairs, Danvers, Massachusetts, United States
| | - Kei Shing Oh
- Mount Sinai Medical Center, Miami Beach, Florida, United States
| | - Haydee Lara
- Biomarker Development, Alexion-AstraZeneca Rare Disease Unit, New Haven, Connecticut, United States
| | - Chris A. Garcia
- Mayo Clinic, Division of Computational Pathology and AI, Department of Laboratory Medicine and Pathology, Rochester, Minnesota, United States
| | - Esther Abels
- SolarisRTC LLC, Boston, Massachusetts, United States
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13
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Doeleman T, Hondelink LM, Vermeer MH, van Dijk MR, Schrader AMR. Artificial intelligence in digital pathology of cutaneous lymphomas: a review of the current state and future perspectives. Semin Cancer Biol 2023:S1044-579X(23)00095-0. [PMID: 37331571 DOI: 10.1016/j.semcancer.2023.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 06/01/2023] [Accepted: 06/12/2023] [Indexed: 06/20/2023]
Abstract
Primary cutaneous lymphomas (CLs) represent a heterogeneous group of T-cell lymphomas and B-cell lymphomas that present in the skin without evidence of extracutaneous involvement at time of diagnosis. CLs are largely distinct from their systemic counterparts in clinical presentation, histopathology, and biological behavior and, therefore, require different therapeutic management. Additional diagnostic burden is added by the fact that several benign inflammatory dermatoses mimic CL subtypes, requiring clinicopathological correlation for definitive diagnosis. Due to the heterogeneity and rarity of CL, adjunct diagnostic tools are welcomed, especially by pathologists without expertise in this field or with limited access to a centralized specialist panel. The transition into digital pathology workflows enables artificial intelligence (AI)-based analysis of patients' whole-slide pathology images (WSIs). AI can be used to automate manual processes in histopathology but, more importantly, can be applied to complex diagnostic tasks, especially suitable for rare disease like CL. To date, AI-based applications for CL have been minimally explored in literature. However, in other skin cancers and systemic lymphomas, disciplines that are recognized here as the building blocks for CLs, several studies demonstrated promising results using AI for disease diagnosis and subclassification, cancer detection, specimen triaging, and outcome prediction. Additionally, AI allows discovery of novel biomarkers or may help to quantify established biomarkers. This review summarizes and blends applications of AI in pathology of skin cancer and lymphoma and proposes how these findings can be applied to diagnostics of CL.
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Affiliation(s)
- Thom Doeleman
- Department of Pathology, Leiden University Medical Centre, Leiden, the Netherlands; Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands.
| | - Liesbeth M Hondelink
- Department of Pathology, Leiden University Medical Centre, Leiden, the Netherlands
| | - Maarten H Vermeer
- Department of Dermatology, Leiden University Medical Center, Leiden, the Netherlands
| | - Marijke R van Dijk
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Anne M R Schrader
- Department of Pathology, Leiden University Medical Centre, Leiden, the Netherlands
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14
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Pang J, Xiu W, Ma X. Application of Artificial Intelligence in the Diagnosis, Treatment, and Prognostic Evaluation of Mediastinal Malignant Tumors. J Clin Med 2023; 12:jcm12082818. [PMID: 37109155 PMCID: PMC10144939 DOI: 10.3390/jcm12082818] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 03/01/2023] [Accepted: 04/06/2023] [Indexed: 04/29/2023] Open
Abstract
Artificial intelligence (AI), also known as machine intelligence, is widely utilized in the medical field, promoting medical advances. Malignant tumors are the critical focus of medical research and improvement of clinical diagnosis and treatment. Mediastinal malignancy is an important tumor that attracts increasing attention today due to the difficulties in treatment. Combined with artificial intelligence, challenges from drug discovery to survival improvement are constantly being overcome. This article reviews the progress of the use of AI in the diagnosis, treatment, and prognostic prospects of mediastinal malignant tumors based on current literature findings.
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Affiliation(s)
- Jiyun Pang
- Division of Thoracic Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China
- State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China
- West China School of Medicine, Sichuan University, Chengdu 610041, China
| | - Weigang Xiu
- Division of Thoracic Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China
- State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China
- West China School of Medicine, Sichuan University, Chengdu 610041, China
| | - Xuelei Ma
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China
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15
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van Eekelen L, Litjens G, Hebeda KM. Artificial Intelligence in Bone Marrow Histological Diagnostics: Potential Applications and Challenges. Pathobiology 2023; 91:8-17. [PMID: 36791682 PMCID: PMC10937040 DOI: 10.1159/000529701] [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: 12/30/2022] [Accepted: 02/13/2023] [Indexed: 02/17/2023] Open
Abstract
The expanding digitalization of routine diagnostic histological slides holds a potential to apply artificial intelligence (AI) to pathology, including bone marrow (BM) histology. In this perspective, we describe potential tasks in diagnostics that can be supported, investigations that can be guided, and questions that can be answered by the future application of AI on whole-slide images of BM biopsies. These range from characterization of cell lineages and quantification of cells and stromal structures to disease prediction. First glimpses show an exciting potential to detect subtle phenotypic changes with AI that are due to specific genotypes. The discussion is illustrated by examples of current AI research using BM biopsy slides. In addition, we briefly discuss current challenges for implementation of AI-supported diagnostics.
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Affiliation(s)
- Leander van Eekelen
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
- Computational Pathology Group, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Geert Litjens
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
- Computational Pathology Group, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Konnie M. Hebeda
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
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16
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Lewis JE, Shebelut CW, Drumheller BR, Zhang X, Shanmugam N, Attieh M, Horwath MC, Khanna A, Smith GH, Gutman DA, Aljudi A, Cooper LAD, Jaye DL. An Automated Pipeline for Differential Cell Counts on Whole-Slide Bone Marrow Aspirate Smears. Mod Pathol 2023; 36:100003. [PMID: 36853796 PMCID: PMC10310355 DOI: 10.1016/j.modpat.2022.100003] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 08/10/2022] [Accepted: 09/18/2022] [Indexed: 01/11/2023]
Abstract
The pathologic diagnosis of bone marrow disorders relies in part on the microscopic analysis of bone marrow aspirate (BMA) smears and the manual counting of marrow nucleated cells to obtain a differential cell count (DCC). This manual process has significant limitations, including the analysis of only a small subset of optimal slide areas and nucleated cells, as well as interobserver variability due to differences in cell selection and classification. To address these shortcomings, we developed an automated machine learning-based pipeline for obtaining 11-component DCCs on whole-slide BMAs. This pipeline uses a sequential process of identifying optimal BMA regions with high proportions of marrow nucleated cells, detecting individual cells within these optimal areas, and classifying these cells into 1 of 11 DCC components. Convolutional neural network models were trained on 396,048 BMA region, 28,914 cell boundary, and 1,510,976 cell class images from manual annotations. The resulting automated pipeline produced 11-component DCCs that demonstrated a high statistical and diagnostic concordance with manual DCCs among a heterogeneous group of testing BMA slides with varying pathologies and cellularities. Additionally, we demonstrated that an automated analysis can reduce the intraslide variance in DCCs by analyzing the whole slide and marrow nucleated cells within all optimal regions. Finally, the pipeline outputs of region classification, cell detection, and cell classification can be visualized using whole-slide image analysis software. This study demonstrates the feasibility of a fully automated pipeline for generating DCCs on scanned whole-slide BMA images, with the potential for improving the current standard of practice for utilizing BMA smears in the laboratory analysis of hematologic disorders.
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Affiliation(s)
- Joshua E Lewis
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, Georgia
| | - Conrad W Shebelut
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, Georgia
| | - Bradley R Drumheller
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, Georgia
| | - Xuebao Zhang
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, Georgia
| | - Nithya Shanmugam
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, Georgia
| | - Michel Attieh
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, Georgia
| | - Michael C Horwath
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, Georgia
| | - Anurag Khanna
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, Georgia
| | - Geoffrey H Smith
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, Georgia
| | - David A Gutman
- Department of Biomedical Informatics, Emory University, Atlanta, Georgia
| | - Ahmed Aljudi
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, Georgia
| | - Lee A D Cooper
- Department of Pathology, Northwestern University, Chicago, Illinois.
| | - David L Jaye
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, Georgia; Winship Cancer Institute, Emory University, Atlanta, Georgia.
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17
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Barrera K, Merino A, Molina A, Rodellar J. Automatic generation of artificial images of leukocytes and leukemic cells using generative adversarial networks (syntheticcellgan). COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 229:107314. [PMID: 36565666 DOI: 10.1016/j.cmpb.2022.107314] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 11/29/2022] [Accepted: 12/14/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND OBJECTIVES Visual analysis of cell morphology has an important role in the diagnosis of hematological diseases. Morphological cell recognition is a challenge that requires experience and in-depth review by clinical pathologists. Within the new trend of introducing computer-aided diagnostic tools in laboratory medicine, models based on deep learning are being developed for the automatic identification of different types of cells in peripheral blood. In general, well-annotated large image sets are needed to train the models to reach a desired classification performance. This is especially relevant when it comes to discerning between cell images in which morphological differences are subtle and when it comes to low prevalent diseases with the consequent difficulty in collecting cell images. The objective of this work is to develop, train and validate SyntheticCellGAN (SCG), a new system for the automatic generation of artificial images of white blood cells, maintaining morphological characteristics very close to real cells found in practice in clinical laboratories. METHODS SCG is designed with two sequential generative adversarial networks. First, a Wasserstein structure is used to transform random noise vectors into low resolution images of basic mononuclear cells. Second, the concept of image-to-image translation is used to build specific models that transform the basic images into high-resolution final images with the realistic morphology of each cell type target: 1) the five groups of normal leukocytes (lymphocytes, monocytes, eosinophils, neutrophils and basophils); 2) atypical promyelocytes and hairy cells, which are two relevant cell types of complex morphology with low abundance in blood smears. RESULTS The images of the SCG system are evaluated with four experimental tests. In the first test we evaluated the generated images with quantitative metrics for GANs. In the second test, morphological verification of the artificial images is performed by expert clinical pathologists with 100% accuracy. In the third test, two classifiers based on convolutional neural networks (CNN) previously trained with images of real cells are used. Two sets of artificial images of the SCG system are classified with an accuracy of 95.36% and 94%, respectively. In the fourth test, three CNN classifiers are trained with artificial images of the SCG system. Real cells are identified with an accuracy ranging from 87.7% to 100%. CONCLUSIONS The SCG system has proven effective in creating images of all normal leukocytes and two low-prevalence cell classes associated with diseases such as acute promyelocyte leukemia and hairy cell leukemia. Once trained, the system requires low computational cost and can help augment high-quality image datasets to improve automatic recognition model training for clinical laboratory practice.
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Affiliation(s)
- Kevin Barrera
- Technical University of Catalonia, Barcelona East Engineering School, Department of Mathematics, Barcelona, Spain.
| | - Anna Merino
- Hospital Clínic of Barcelona-IDIBAPS, Biochemistry and Molecular Genetics Department, CORE Laboratory, Biomedical Diagnostic, Barcelona, Spain.
| | - Angel Molina
- Hospital Clínic of Barcelona-IDIBAPS, Biochemistry and Molecular Genetics Department, CORE Laboratory, Biomedical Diagnostic, Barcelona, Spain.
| | - José Rodellar
- Technical University of Catalonia, Barcelona East Engineering School, Department of Mathematics, Barcelona, Spain.
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18
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Weber L, Lee BS, Imboden S, Hsieh CJ, Lin NY. Phenotyping senescent mesenchymal stromal cells using AI image translation. CURRENT RESEARCH IN BIOTECHNOLOGY 2023; 5:100120. [PMID: 38045568 PMCID: PMC10691861 DOI: 10.1016/j.crbiot.2023.100120] [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] [Indexed: 02/04/2023] Open
Abstract
Mesenchymal stromal cells (MSCs) offer promising potential in biomedical research, clinical therapeutics, and immunomodulatory therapies due to their ease of isolation and multipotent, immunoprivileged, and immunosuppersive properties. Extensive efforts have focused on optimizing the cell isolation and culture methods to generate scalable, therapeutically-relevant MSCs for clinical applications. However, MSC-based therapies are often hindered by cell heterogeneity and inconsistency of therapeutic function caused, in part, by MSC senescence. As such, noninvasive and molecular-based MSC characterizations play an essential role in assuring the consistency of MSC functions. Here, we demonstrated that AI image translation algorithms can effectively predict immunofluorescence images of MSC senescence markers from phase contrast images. We showed that the expression level of senescence markers including senescence-associated beta-galactosidase (SABG), p16, p21, and p38 are accurately predicted by deep-learning models for Doxorubicin-induced MSC senescence, irradiation-induced MSC senescence, and replicative MSC senescence. Our AI model distinguished the non-senescent and senescent MSC populations and simultaneously captured the cell-to-cell variability within a population. Our microscopy-based phenotyping platform can be integrated with cell culture routines making it an easily accessible tool for MSC engineering and manufacturing.
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Affiliation(s)
- Leya Weber
- Department of Mechanical and Aerospace Engineering, University of California, Los Angeles 90095, CA, United States
| | - Brandon S. Lee
- Department of Bioengineering, University of California, Los Angeles 90095, CA, United States
| | - Sara Imboden
- Department of Mechanical and Aerospace Engineering, University of California, Los Angeles 90095, CA, United States
| | - Cho-Jui Hsieh
- Department of Computer Science, University of California, Los Angeles 90095, CA, United States
| | - Neil Y.C. Lin
- Department of Mechanical and Aerospace Engineering, University of California, Los Angeles 90095, CA, United States
- Department of Bioengineering, University of California, Los Angeles 90095, CA, United States
- California NanoSystems Institute, University of California, Los Angeles 90095, CA, United States
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles 90095, CA, United States
- Institute for Quantitative and Computational Biosciences, University of California, Los Angeles 90095, CA, United States
- Broad Stem Cell Center, University of California, Los Angeles 90095, CA, United States
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19
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Santisteban-Espejo A, Bernal-Florindo I, Perez-Requena J, Atienza-Cuevas L, Catalina-Fernandez I, Fernandez-Valle MDC, Romero-Garcia R, Garcia-Rojo M. Identification of prognostic factors in classic Hodgkin lymphoma by integrating whole slide imaging and next generation sequencing. Mol Omics 2022; 18:1015-1028. [PMID: 36382626 DOI: 10.1039/d2mo00195k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Digital pathology and genomics are increasingly used to improve our understanding of lymphoid neoplasms. Algorithms for quantifying cell populations in the lymph node and genetics can be integrated to identify new biomarkers with prognostic impact in classic Hodgkin lymphoma (cHL). In 16 cHL patients, we have performed whole slide imaging (WSI) analysis and quantification of CD30+, CD20+, CD3+ and MUM1+ cells in whole tissue slides, and Next Generation Sequencing (NGS) in formalin fixed paraffin-embedded (FFPE) tissue, using a widely used NSG panel (Oncomine® Focus Assay) to define genetic variants underlying tumor development. The different cell populations could be successfully identified in scanned slides of cHL, supporting the inclusion of WSI in the histopathological evaluation of cHL as an adequate method for the quantification of different cell populations. We also performed genetic profiling in FFPE samples of cHL leading to the identification of copy number variations in the Neurofibromin 1 gene (17q11.2) and the Androgen Receptor gene (Xq12) accompanied by chromosomal gains and losses in CDK4, KRAS and FGFR2 genes. Progression-free survival (PFS) was statistically significantly higher in cHL patients with amplification in the NF1 gene combined with CD3+ cells above 28.6% (p = 0.006) and MUM1+ cells above 21.8% (p < 0.001). Moreover, patients with MUM1+ cells above 21.8% showed a statistically significantly higher PFS when combined with amplification of the AR gene (p < 0.001) and wild-type KRAS (p < 0.001). The integration of WSI analysis and DNA sequencing could be useful to improve our understanding of the biology of cHL and define risk subgroups.
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Affiliation(s)
- Antonio Santisteban-Espejo
- Pathology Department, Puerta del Mar University Hospital, Av. Ana de Viya, 21. 11009, Cadiz, Spain. .,Institute of Research and Innovation in Biomedical Sciences of the Province of Cadiz (INiBICA), Cadiz, Spain.,Department of Medicine, University of Cadiz, Cadiz, Spain
| | - Irene Bernal-Florindo
- Institute of Research and Innovation in Biomedical Sciences of the Province of Cadiz (INiBICA), Cadiz, Spain.,Pathology Department, Jerez de la Frontera University Hospital, Cadiz, Spain
| | - Jose Perez-Requena
- Pathology Department, Puerta del Mar University Hospital, Av. Ana de Viya, 21. 11009, Cadiz, Spain.
| | - Lidia Atienza-Cuevas
- Pathology Department, Puerta del Mar University Hospital, Av. Ana de Viya, 21. 11009, Cadiz, Spain.
| | | | | | - Raquel Romero-Garcia
- Institute of Research and Innovation in Biomedical Sciences of the Province of Cadiz (INiBICA), Cadiz, Spain
| | - Marcial Garcia-Rojo
- Institute of Research and Innovation in Biomedical Sciences of the Province of Cadiz (INiBICA), Cadiz, Spain.,Pathology Department, Jerez de la Frontera University Hospital, Cadiz, Spain
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20
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Dileep G, Gianchandani Gyani SG. Artificial Intelligence in Breast Cancer Screening and Diagnosis. Cureus 2022; 14:e30318. [PMID: 36381716 PMCID: PMC9650950 DOI: 10.7759/cureus.30318] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 10/15/2022] [Indexed: 11/05/2022] Open
Abstract
Cancer is a disease that continues to plague our modern society. Among all types of cancer, breast cancer is now the most common type of cancer occurring in women worldwide. Various factors, including genetics, lifestyle, and the environment, have contributed to the rise in the prevalence of breast cancer among women of all socioeconomic strata. Therefore, proper screening for early diagnosis and treatment becomes a major factor when fighting the disease. Artificial intelligence (AI) continues to revolutionize various spheres of our lives with its numerous applications. Using AI in the existing screening process makes obtaining results even easier and more convenient. Faster, more accurate results are some of the benefits of AI methods in breast cancer screening. Nonetheless, there are many challenges in the process of the integration of AI that needs to be addressed systematically. The following is a review of the application of AI in breast cancer screening.
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21
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Chen H, Liu J, Hua C, Feng J, Pang B, Cao D, Li C. Accurate classification of white blood cells by coupling pre-trained ResNet and DenseNet with SCAM mechanism. BMC Bioinformatics 2022; 23:282. [PMID: 35840897 PMCID: PMC9287918 DOI: 10.1186/s12859-022-04824-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 07/07/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Via counting the different kinds of white blood cells (WBCs), a good quantitative description of a person's health status is obtained, thus forming the critical aspects for the early treatment of several diseases. Thereby, correct classification of WBCs is crucial. Unfortunately, the manual microscopic evaluation is complicated, time-consuming, and subjective, so its statistical reliability becomes limited. Hence, the automatic and accurate identification of WBCs is of great benefit. However, the similarity between WBC samples and the imbalance and insufficiency of samples in the field of medical computer vision bring challenges to intelligent and accurate classification of WBCs. To tackle these challenges, this study proposes a deep learning framework by coupling the pre-trained ResNet and DenseNet with SCAM (spatial and channel attention module) for accurately classifying WBCs. RESULTS In the proposed network, ResNet and DenseNet enables information reusage and new information exploration, respectively, which are both important and compatible for learning good representations. Meanwhile, the SCAM module sequentially infers attention maps from two separate dimensions of space and channel to emphasize important information or suppress unnecessary information, further enhancing the representation power of our model for WBCs to overcome the limitation of sample similarity. Moreover, the data augmentation and transfer learning techniques are used to handle the data of imbalance and insufficiency. In addition, the mixup approach is adopted for modeling the vicinity relation across training samples of different categories to increase the generalizability of the model. By comparing with five representative networks on our developed LDWBC dataset and the publicly available LISC, BCCD, and Raabin WBC datasets, our model achieves the best overall performance. We also implement the occlusion testing by the gradient-weighted class activation mapping (Grad-CAM) algorithm to improve the interpretability of our model. CONCLUSION The proposed method has great potential for application in intelligent and accurate classification of WBCs.
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Affiliation(s)
- Hua Chen
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, 430072, China
| | - Juan Liu
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, 430072, China.
| | - Chunbing Hua
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, 430072, China
| | - Jing Feng
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, 430072, China
| | - Baochuan Pang
- Landing Artificial Intelligence Center for Pathological Diagnosis, Wuhan, 430072, China
| | - Dehua Cao
- Landing Artificial Intelligence Center for Pathological Diagnosis, Wuhan, 430072, China
| | - Cheng Li
- Landing Artificial Intelligence Center for Pathological Diagnosis, Wuhan, 430072, China
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22
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El Alaoui Y, Elomri A, Qaraqe M, Padmanabhan R, Yasin Taha R, El Omri H, El Omri A, Aboumarzouk O. A Review of Artificial Intelligence Applications in Hematology Management: Current Practices and Future Prospects. J Med Internet Res 2022; 24:e36490. [PMID: 35819826 PMCID: PMC9328784 DOI: 10.2196/36490] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Revised: 05/14/2022] [Accepted: 05/29/2022] [Indexed: 12/23/2022] Open
Abstract
Background Machine learning (ML) and deep learning (DL) methods have recently garnered a great deal of attention in the field of cancer research by making a noticeable contribution to the growth of predictive medicine and modern oncological practices. Considerable focus has been particularly directed toward hematologic malignancies because of the complexity in detecting early symptoms. Many patients with blood cancer do not get properly diagnosed until their cancer has reached an advanced stage with limited treatment prospects. Hence, the state-of-the-art revolves around the latest artificial intelligence (AI) applications in hematology management. Objective This comprehensive review provides an in-depth analysis of the current AI practices in the field of hematology. Our objective is to explore the ML and DL applications in blood cancer research, with a special focus on the type of hematologic malignancies and the patient’s cancer stage to determine future research directions in blood cancer. Methods We searched a set of recognized databases (Scopus, Springer, and Web of Science) using a selected number of keywords. We included studies written in English and published between 2015 and 2021. For each study, we identified the ML and DL techniques used and highlighted the performance of each model. Results Using the aforementioned inclusion criteria, the search resulted in 567 papers, of which 144 were selected for review. Conclusions The current literature suggests that the application of AI in the field of hematology has generated impressive results in the screening, diagnosis, and treatment stages. Nevertheless, optimizing the patient’s pathway to treatment requires a prior prediction of the malignancy based on the patient’s symptoms or blood records, which is an area that has still not been properly investigated.
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Affiliation(s)
- Yousra El Alaoui
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Adel Elomri
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Marwa Qaraqe
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Regina Padmanabhan
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Ruba Yasin Taha
- National Center for Cancer Care and Research, Hamad Medical Corporation, Doha, Qatar
| | - Halima El Omri
- National Center for Cancer Care and Research, Hamad Medical Corporation, Doha, Qatar
| | - Abdelfatteh El Omri
- Surgical Research Section, Department of Surgery, Hamad Medical Corporation, Doha, Qatar
| | - Omar Aboumarzouk
- Surgical Research Section, Department of Surgery, Hamad Medical Corporation, Doha, Qatar.,College of Medicine, Qatar University, Doha, Qatar.,College of Medicine, University of Glasgow, Glasgow, United Kingdom
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23
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Artificial intelligence and its applications in digital hematopathology. BLOOD SCIENCE 2022; 4:136-142. [DOI: 10.1097/bs9.0000000000000130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 06/16/2022] [Indexed: 11/26/2022] Open
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24
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Szittner Z, Péter B, Kurunczi S, Székács I, Horváth R. Functional blood cell analysis by label-free biosensors and single-cell technologies. Adv Colloid Interface Sci 2022; 308:102727. [DOI: 10.1016/j.cis.2022.102727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 05/25/2022] [Accepted: 06/27/2022] [Indexed: 11/01/2022]
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25
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Rodellar J, Barrera K, Alférez S, Boldú L, Laguna J, Molina A, Merino A. A Deep Learning Approach for the Morphological Recognition of Reactive Lymphocytes in Patients with COVID-19 Infection. Bioengineering (Basel) 2022; 9:bioengineering9050229. [PMID: 35621507 PMCID: PMC9137554 DOI: 10.3390/bioengineering9050229] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Revised: 05/11/2022] [Accepted: 05/17/2022] [Indexed: 11/16/2022] Open
Abstract
Laboratory medicine plays a fundamental role in the detection, diagnosis and management of COVID-19 infection. Recent observations of the morphology of cells circulating in blood found the presence of particular reactive lymphocytes (COVID-19 RL) in some of the infected patients and demonstrated that it was an indicator of a better prognosis of the disease. Visual morphological analysis is time consuming, requires smear review by expert clinical pathologists, and is prone to subjectivity. This paper presents a convolutional neural network system designed for automatic recognition of COVID-19 RL. It is based on the Xception71 structure and is trained using images of blood cells from real infected patients. An experimental study is carried out with a group of 92 individuals. The input for the system is a set of images selected by the clinical pathologist from the blood smear of a patient. The output is the prediction whether the patient belongs to the group associated with better prognosis of the disease. A threshold is obtained for the classification system to predict that the smear belongs to this group. With this threshold, the experimental test shows excellent performance metrics: 98.3% sensitivity and precision, 97.1% specificity, and 97.8% accuracy. The system does not require costly calculations and can potentially be integrated into clinical practice to assist clinical pathologists in a more objective smear review for early prognosis.
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Affiliation(s)
- José Rodellar
- Department of Mathematics, Barcelona Est Engineering School, Universitat Politècnica de Catalunya, 08019 Barcelona, Spain;
- Correspondence:
| | - Kevin Barrera
- Department of Mathematics, Barcelona Est Engineering School, Universitat Politècnica de Catalunya, 08019 Barcelona, Spain;
| | - Santiago Alférez
- School of Engineering, Science and Technology, Universidad del Rosario, Bogotá 111711, Colombia;
| | - Laura Boldú
- Biomedical Diagnostic Center, Core Laboratory, Department of Biochemistry and Molecular Genetics, Hospital Clinic de Barcelona, 08036 Barcelona, Spain; (L.B.); (J.L.); (A.M.); (A.M.)
| | - Javier Laguna
- Biomedical Diagnostic Center, Core Laboratory, Department of Biochemistry and Molecular Genetics, Hospital Clinic de Barcelona, 08036 Barcelona, Spain; (L.B.); (J.L.); (A.M.); (A.M.)
| | - Angel Molina
- Biomedical Diagnostic Center, Core Laboratory, Department of Biochemistry and Molecular Genetics, Hospital Clinic de Barcelona, 08036 Barcelona, Spain; (L.B.); (J.L.); (A.M.); (A.M.)
| | - Anna Merino
- Biomedical Diagnostic Center, Core Laboratory, Department of Biochemistry and Molecular Genetics, Hospital Clinic de Barcelona, 08036 Barcelona, Spain; (L.B.); (J.L.); (A.M.); (A.M.)
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26
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Chronic Lymphocytic Leukemia Progression Diagnosis with Intrinsic Cellular Patterns via Unsupervised Clustering. Cancers (Basel) 2022; 14:cancers14102398. [PMID: 35626003 PMCID: PMC9139505 DOI: 10.3390/cancers14102398] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 04/21/2022] [Accepted: 04/25/2022] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Distinguishing between chronic lymphocytic leukemia (CLL), accelerated CLL (aCLL), and full-blown transformation to diffuse large B-cell lymphoma (Richter transformation; RT) has significant clinical implications. Identifying cellular phenotypes via unsupervised clustering provides the most robust analytic performance in analyzing digitized pathology slides. This study serves as a proof of concept that using an unsupervised machine learning scheme can enhance diagnostic accuracy. Abstract Identifying the progression of chronic lymphocytic leukemia (CLL) to accelerated CLL (aCLL) or transformation to diffuse large B-cell lymphoma (Richter transformation; RT) has significant clinical implications as it prompts a major change in patient management. However, the differentiation between these disease phases may be challenging in routine practice. Unsupervised learning has gained increased attention because of its substantial potential in data intrinsic pattern discovery. Here, we demonstrate that cellular feature engineering, identifying cellular phenotypes via unsupervised clustering, provides the most robust analytic performance in analyzing digitized pathology slides (accuracy = 0.925, AUC = 0.978) when compared to alternative approaches, such as mixed features, supervised features, unsupervised/mixed/supervised feature fusion and selection, as well as patch-based convolutional neural network (CNN) feature extraction. We further validate the reproducibility and robustness of unsupervised feature extraction via stability and repeated splitting analysis, supporting its utility as a diagnostic aid in identifying CLL patients with histologic evidence of disease progression. The outcome of this study serves as proof of principle using an unsupervised machine learning scheme to enhance the diagnostic accuracy of the heterogeneous histology patterns that pathologists might not easily see.
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27
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Manimaran M, Arora A, Lovejoy CA, Gao W, Maruthappu M. Role of artificial intelligence and machine learning in haematology. J Clin Pathol 2022; 75:585-587. [PMID: 35470252 DOI: 10.1136/jclinpath-2021-208127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 04/14/2022] [Indexed: 11/03/2022]
Affiliation(s)
| | | | - Christopher A Lovejoy
- University College London Hospitals NHS Foundation Trust, London, UK.,University College London, London, UK
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28
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Stålhammar G, Yeung A, Mendoza P, Dubovy SR, William Harbour J, Grossniklaus HE. Gain of Chromosome 6p Correlates with Severe Anaplasia, Cellular Hyperchromasia, and Extraocular Spread of Retinoblastoma. OPHTHALMOLOGY SCIENCE 2022; 2:100089. [PMID: 36246172 PMCID: PMC9560556 DOI: 10.1016/j.xops.2021.100089] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 11/03/2021] [Accepted: 12/03/2021] [Indexed: 06/16/2023]
Abstract
PURPOSE Gain of chromosome 6p has been associated with poor ocular survival in retinoblastoma and histopathologic grading of anaplasia with increased risk of metastatic spread and death. This study examined the correlation between these factors and other chromosomal abnormalities as well as results of whole genome sequencing, digital morphometry, and progression-free survival. DESIGN Retrospective cohort study from 2 United States tertiary referral centers. PARTICIPANTS Forty-two children who had undergone enucleation for retinoblastoma from January 2000 through December 2017. METHODS Status of chromosomes 6p, 1q, 9q, and 16q was evaluated with fluorescence in situ hybridization, the degree of anaplasia and presence of histologic high-risk features were assessed by ocular pathologists, digital morphometry was performed on scanned tumor slides, and whole genome sequencing was performed on a subset of tumors. Progression-free survival was defined as absence of distant or local metastases or tumor growth beyond the cut end of the optic nerve. MAIN OUTCOME MEASURES Correlation between each of chromosomal abnormalities, anaplasia, morphometry and sequencing results, and survival. RESULTS Forty-one of 42 included patients underwent primary enucleation and 1 was treated first with intra-arterial chemotherapy. Seven tumors showed mild anaplasia, 19 showed moderate anaplasia, and 16 showed severe anaplasia. All tumors had gain of 1q, 18 tumors had gain of 6p, 6 tumors had gain of 9q, and 36 tumors had loss of 16q. Tumors with severe anaplasia were significantly more likely to harbor 6p gains than tumors with nonsevere anaplasia (P < 0.001). Further, the hematoxylin staining intensity was significantly greater and that of eosin staining significantly lower in tumors with severe anaplasia (P < 0.05). Neither severe anaplasia (P = 0.10) nor gain of 6p (P = 0.21) correlated with histologic high-risk features, and severe anaplasia did not correlate to RB1, CREBBP, NSD1, or BCOR mutations in a subset of 14 tumors (P > 0.5). Patients with gain of 6p showed significantly shorter progression-free survival (P = 0.03, Wilcoxon test). CONCLUSIONS Gain of chromosome 6p emerges as a strong prognostic biomarker in retinoblastoma because it correlates with severe anaplasia, quantifiable changes in tumor cell staining characteristics, and extraocular spread.
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Affiliation(s)
- Gustav Stålhammar
- Ocular Pathology Service, St. Erik Eye Hospital, Stockholm, Sweden
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Aaron Yeung
- Royal Victorian Eye and Ear Hospital, Melbourne, Australia
- Departments of Ophthalmology and Pathology, Emory University School of Medicine, Atlanta, Georgia
| | - Pia Mendoza
- Departments of Ophthalmology and Pathology, Emory University School of Medicine, Atlanta, Georgia
| | - Sander R. Dubovy
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida
| | - J. William Harbour
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, Florida
- Interdisciplinary Stem Cell Institute, University of Miami Miller School of Medicine, Miami, Florida
| | - Hans E. Grossniklaus
- Departments of Ophthalmology and Pathology, Emory University School of Medicine, Atlanta, Georgia
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29
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Merino A, Vlagea A, Molina A, Egri N, Laguna J, Barrera K, Boldú L, Acevedo A, Díaz-Pavón M, Sibina F, Bascón F, Sibila O, Juan M, Rodellar J. Atypical lymphoid cells circulating in blood in COVID-19 infection: morphology, immunophenotype and prognosis value. J Clin Pathol 2022; 75:104-111. [PMID: 33310786 PMCID: PMC7735067 DOI: 10.1136/jclinpath-2020-207087] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 10/15/2020] [Accepted: 11/24/2020] [Indexed: 01/05/2023]
Abstract
AIMS Atypical lymphocytes circulating in blood have been reported in COVID-19 patients. This study aims to (1) analyse if patients with reactive lymphocytes (COVID-19 RL) show clinical or biological characteristics related to outcome; (2) develop an automatic system to recognise them in an objective way and (3) study their immunophenotype. METHODS Clinical and laboratory findings in 36 COVID-19 patients were compared between those showing COVID-19 RL in blood (18) and those without (18). Blood samples were analysed in Advia2120i and stained with May Grünwald-Giemsa. Digital images were acquired in CellaVisionDM96. Convolutional neural networks (CNNs) were used to accurately recognise COVID-19 RL. Immunophenotypic study was performed throughflow cytometry. RESULTS Neutrophils, D-dimer, procalcitonin, glomerular filtration rate and total protein values were higher in patients without COVID-19 RL (p<0.05) and four of these patients died. Haemoglobin and lymphocyte counts were higher (p<0.02) and no patients died in the group showing COVID-19 RL. COVID-19 RL showed a distinct deep blue cytoplasm with nucleus mostly in eccentric position. Through two sequential CNNs, they were automatically distinguished from normal lymphocytes and classical RL with sensitivity, specificity and overall accuracy values of 90.5%, 99.4% and 98.7%, respectively. Immunophenotypic analysis revealed COVID-19 RL are mostly activated effector memory CD4 and CD8 T cells. CONCLUSION We found that COVID-19 RL are related to a better evolution and prognosis. They can be detected by morphology in the smear review, being the computerised approach proposed useful to enhance a more objective recognition. Their presence suggests an abundant production of virus-specific T cells, thus explaining the better outcome of patients showing these cells circulating in blood.
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Affiliation(s)
- Anna Merino
- Core Laboratory, Biomedical Diagnostic Center, Hospital Clinic de Barcelona, Barcelona, Spain
- Biochemistry and Molecular Genetics, Biomedical Diagnostic Center, Hospital Clinic de Barcelona, Barcelona, Spain
| | - Alexandru Vlagea
- Department of Immunology, Biomedical Diagnostic Center, Hospital Clinic de Barcelona, Barcelona, Spain
| | - Angel Molina
- Core Laboratory, Biomedical Diagnostic Center, Hospital Clinic de Barcelona, Barcelona, Spain
- Biochemistry and Molecular Genetics, Biomedical Diagnostic Center, Hospital Clinic de Barcelona, Barcelona, Spain
| | - Natalia Egri
- Department of Immunology, Biomedical Diagnostic Center, Hospital Clinic de Barcelona, Barcelona, Spain
| | - Javier Laguna
- Core Laboratory, Biomedical Diagnostic Center, Hospital Clinic de Barcelona, Barcelona, Spain
- Biochemistry and Molecular Genetics, Biomedical Diagnostic Center, Hospital Clinic de Barcelona, Barcelona, Spain
| | - Kevin Barrera
- Department of Mathematics, Universitat Politecnica de Catalunya, Barcelona, Spain
| | - Laura Boldú
- Core Laboratory, Biomedical Diagnostic Center, Hospital Clinic de Barcelona, Barcelona, Spain
- Biochemistry and Molecular Genetics, Biomedical Diagnostic Center, Hospital Clinic de Barcelona, Barcelona, Spain
| | - Andrea Acevedo
- Department of Mathematics, Universitat Politecnica de Catalunya, Barcelona, Spain
| | - Mar Díaz-Pavón
- Department of Immunology, Biomedical Diagnostic Center, Hospital Clinic de Barcelona, Barcelona, Spain
| | - Francesc Sibina
- Department of Immunology, Biomedical Diagnostic Center, Hospital Clinic de Barcelona, Barcelona, Spain
| | - Francisca Bascón
- Core Laboratory, Biomedical Diagnostic Center, Hospital Clinic de Barcelona, Barcelona, Spain
| | - Oriol Sibila
- Institut Clínic del tórax, Hospital Clinic de Barcelona, Barcelona, Spain
| | - Manel Juan
- Department of Immunology, Biomedical Diagnostic Center, Hospital Clinic de Barcelona, Barcelona, Spain
| | - José Rodellar
- Department of Mathematics, Universitat Politecnica de Catalunya, Barcelona, Spain
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30
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Tran KA, Kondrashova O, Bradley A, Williams ED, Pearson JV, Waddell N. Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Med 2021; 13:152. [PMID: 34579788 PMCID: PMC8477474 DOI: 10.1186/s13073-021-00968-x] [Citation(s) in RCA: 221] [Impact Index Per Article: 73.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Accepted: 09/12/2021] [Indexed: 12/13/2022] Open
Abstract
Deep learning is a subdiscipline of artificial intelligence that uses a machine learning technique called artificial neural networks to extract patterns and make predictions from large data sets. The increasing adoption of deep learning across healthcare domains together with the availability of highly characterised cancer datasets has accelerated research into the utility of deep learning in the analysis of the complex biology of cancer. While early results are promising, this is a rapidly evolving field with new knowledge emerging in both cancer biology and deep learning. In this review, we provide an overview of emerging deep learning techniques and how they are being applied to oncology. We focus on the deep learning applications for omics data types, including genomic, methylation and transcriptomic data, as well as histopathology-based genomic inference, and provide perspectives on how the different data types can be integrated to develop decision support tools. We provide specific examples of how deep learning may be applied in cancer diagnosis, prognosis and treatment management. We also assess the current limitations and challenges for the application of deep learning in precision oncology, including the lack of phenotypically rich data and the need for more explainable deep learning models. Finally, we conclude with a discussion of how current obstacles can be overcome to enable future clinical utilisation of deep learning.
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Affiliation(s)
- Khoa A. Tran
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, 4006 Australia
- School of Biomedical Sciences, Faculty of Health, Queensland University of Technology (QUT), Brisbane, 4059 Australia
| | - Olga Kondrashova
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, 4006 Australia
| | - Andrew Bradley
- Faculty of Engineering, Queensland University of Technology (QUT), Brisbane, 4000 Australia
| | - Elizabeth D. Williams
- School of Biomedical Sciences, Faculty of Health, Queensland University of Technology (QUT), Brisbane, 4059 Australia
- Australian Prostate Cancer Research Centre - Queensland (APCRC-Q) and Queensland Bladder Cancer Initiative (QBCI), Brisbane, 4102 Australia
| | - John V. Pearson
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, 4006 Australia
| | - Nicola Waddell
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, 4006 Australia
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31
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El Hussein S, Chen P, Medeiros LJ, Wistuba II, Jaffray D, Wu J, Khoury JD. Artificial intelligence strategy integrating morphologic and architectural biomarkers provides robust diagnostic accuracy for disease progression in chronic lymphocytic leukemia. J Pathol 2021; 256:4-14. [PMID: 34505705 DOI: 10.1002/path.5795] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 08/04/2021] [Accepted: 09/03/2021] [Indexed: 12/17/2022]
Abstract
Artificial intelligence-based tools designed to assist in the diagnosis of lymphoid neoplasms remain limited. The development of such tools can add value as a diagnostic aid in the evaluation of tissue samples involved by lymphoma. A common diagnostic question is the determination of chronic lymphocytic leukemia (CLL) progression to accelerated CLL (aCLL) or transformation to diffuse large B-cell lymphoma (Richter transformation; RT) in patients who develop progressive disease. The morphologic assessment of CLL, aCLL, and RT can be diagnostically challenging. Using established diagnostic criteria of CLL progression/transformation, we designed four artificial intelligence-constructed biomarkers based on cytologic (nuclear size and nuclear intensity) and architectural (cellular density and cell to nearest-neighbor distance) features. We analyzed the predictive value of implementing these biomarkers individually and then in an iterative sequential manner to distinguish tissue samples with CLL, aCLL, and RT. Our model, based on these four morphologic biomarker attributes, achieved a robust analytic accuracy. This study suggests that biomarkers identified using artificial intelligence-based tools can be used to assist in the diagnostic evaluation of tissue samples from patients with CLL who develop aggressive disease features. © 2021 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
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Affiliation(s)
- Siba El Hussein
- Department of Pathology, The University of Rochester Medical Center, Rochester, NY, USA.,Department of Hematopathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Pingjun Chen
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - L Jeffrey Medeiros
- Department of Hematopathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ignacio I Wistuba
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - David Jaffray
- Department of Technology and Digital Office, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jia Wu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Joseph D Khoury
- Department of Hematopathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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32
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Walter W, Haferlach C, Nadarajah N, Schmidts I, Kühn C, Kern W, Haferlach T. How artificial intelligence might disrupt diagnostics in hematology in the near future. Oncogene 2021; 40:4271-4280. [PMID: 34103684 PMCID: PMC8225509 DOI: 10.1038/s41388-021-01861-y] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 05/11/2021] [Accepted: 05/24/2021] [Indexed: 02/07/2023]
Abstract
Artificial intelligence (AI) is about to make itself indispensable in the health care sector. Examples of successful applications or promising approaches range from the application of pattern recognition software to pre-process and analyze digital medical images, to deep learning algorithms for subtype or disease classification, and digital twin technology and in silico clinical trials. Moreover, machine-learning techniques are used to identify patterns and anomalies in electronic health records and to perform ad-hoc evaluations of gathered data from wearable health tracking devices for deep longitudinal phenotyping. In the last years, substantial progress has been made in automated image classification, reaching even superhuman level in some instances. Despite the increasing awareness of the importance of the genetic context, the diagnosis in hematology is still mainly based on the evaluation of the phenotype. Either by the analysis of microscopic images of cells in cytomorphology or by the analysis of cell populations in bidimensional plots obtained by flow cytometry. Here, AI algorithms not only spot details that might escape the human eye, but might also identify entirely new ways of interpreting these images. With the introduction of high-throughput next-generation sequencing in molecular genetics, the amount of available information is increasing exponentially, priming the field for the application of machine learning approaches. The goal of all the approaches is to allow personalized and informed interventions, to enhance treatment success, to improve the timeliness and accuracy of diagnoses, and to minimize technically induced misclassifications. The potential of AI-based applications is virtually endless but where do we stand in hematology and how far can we go?
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33
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Moran-Sanchez J, Santisteban-Espejo A, Martin-Piedra MA, Perez-Requena J, Garcia-Rojo M. Translational Applications of Artificial Intelligence and Machine Learning for Diagnostic Pathology in Lymphoid Neoplasms: A Comprehensive and Evolutive Analysis. Biomolecules 2021; 11:793. [PMID: 34070632 PMCID: PMC8227233 DOI: 10.3390/biom11060793] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 05/13/2021] [Accepted: 05/24/2021] [Indexed: 12/12/2022] Open
Abstract
Genomic analysis and digitalization of medical records have led to a big data scenario within hematopathology. Artificial intelligence and machine learning tools are increasingly used to integrate clinical, histopathological, and genomic data in lymphoid neoplasms. In this study, we identified global trends, cognitive, and social framework of this field from 1990 to 2020. Metadata were obtained from the Clarivate Analytics Web of Science database in January 2021. A total of 525 documents were assessed by document type, research areas, source titles, organizations, and countries. SciMAT and VOSviewer package were used to perform scientific mapping analysis. Geographical distribution showed the USA and People's Republic of China as the most productive countries, reporting up to 190 (36.19%) of all documents. A third-degree polynomic equation predicts that future global production in this area will be three-fold the current number, near 2031. Thematically, current research is focused on the integration of digital image analysis and genomic sequencing in Non-Hodgkin lymphomas, prediction of chemotherapy response and validation of new prognostic models. These findings can serve pathology departments to depict future clinical and research avenues, but also, public institutions and administrations to promote synergies and optimize funding allocation.
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Affiliation(s)
- Julia Moran-Sanchez
- Division of Hematology and Hemotherapy, Puerta del Mar Hospital, 11009 Cadiz, Spain;
- Ph.D Program of Clinical Medicine and Surgery, University of Cadiz, 11009 Cadiz, Spain
| | - Antonio Santisteban-Espejo
- Pathology Department, Puerta del Mar Hospital, 11009 Cadiz, Spain; (J.P.-R.); (M.G.-R.)
- Institute of Research and Innovation in Biomedical Sciences of the Province of Cadiz (INiBICA), University of Cadiz, 11009 Cadiz, Spain
| | | | - Jose Perez-Requena
- Pathology Department, Puerta del Mar Hospital, 11009 Cadiz, Spain; (J.P.-R.); (M.G.-R.)
| | - Marcial Garcia-Rojo
- Pathology Department, Puerta del Mar Hospital, 11009 Cadiz, Spain; (J.P.-R.); (M.G.-R.)
- Institute of Research and Innovation in Biomedical Sciences of the Province of Cadiz (INiBICA), University of Cadiz, 11009 Cadiz, Spain
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34
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Su J, Han J, Song J. A benchmark bone marrow aspirate smear dataset and a multi-scale cell detection model for the diagnosis of hematological disorders. Comput Med Imaging Graph 2021; 90:101912. [PMID: 33892388 DOI: 10.1016/j.compmedimag.2021.101912] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 01/22/2021] [Accepted: 03/26/2021] [Indexed: 01/18/2023]
Abstract
Research on pathological diagnosis of hematopoietic disorders based on bone marrow aspirate smear images has attracted more and more attention with the development of deep learning methods. However, high quality bone marrow aspirate smear image datasets are not readily available because of the time, the efforts, and the medical knowledge required in the acquisition and manual annotation images. In order to facilitate the research of automated diagnosis of hematological disorders, we constructed a high quality Bone Marrow Aspirate Smear Image Dataset (BMASID), which contains 230 bone marrow aspirate smear images, all with the corresponding labeled images. We used additional clinical images as testing data, which are more challenging because of image noise, cell overlap, cell adhesion, blurred borders of cells and ambiguous types of cells. We also proposed a Cell Recognition Network (CRNet) that was trained on this benchmark dataset. CRNet is comprised of a cell detector to locate and recognize cells in the bone marrow aspirate images, and a cell classifier to classify the types of cells. New anchors and novel evaluation metrics are proposed and applied in CRNet. Benchmark evaluations of the proposed CRNet demonstrated the satisfactory performance of our state-of-the-art methods. Experimental results show that the detection precision by detector is more than 83%, and it is better when compared with other detection methods. After the cell type confirmation by the cell classifier, the precision is more than 95%. Compared with the most popular evaluation metrics Intersection over Union (IoU) and the newly proposed Generalized Intersection over Union (GIoU) used in the object detection benchmarks, our evaluation metrics are more suitable for the cell detection task with ambiguous cell boundaries. The proposed bone marrow aspirate smear image dataset and the proposed evaluation metrics can be used in the training and the evaluation of cell detection models, which contributes to future research in the pathological analysis and auxiliary diagnostic methods of hematological disorders. The codes are available at: https://github.com/SuJie-Med/hematolgical-disorders.
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Affiliation(s)
- Jie Su
- School of Computer Science and Engineering, University of Jinan, Jinan, China; Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, China.
| | - Jinjun Han
- School of Computer Science and Engineering, University of Jinan, Jinan, China; Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, China
| | - Jinming Song
- Department of Hematopathology and Lab Medicines, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
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Royston D, Mead AJ, Psaila B. Application of Single-Cell Approaches to Study Myeloproliferative Neoplasm Biology. Hematol Oncol Clin North Am 2021; 35:279-293. [PMID: 33641869 PMCID: PMC7935666 DOI: 10.1016/j.hoc.2021.01.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Philadelphia-negative myeloproliferative neoplasms (MPNs) are an excellent tractable disease model of a number of aspects of human cancer biology, including genetic evolution, tissue-associated fibrosis, and cancer stem cells. In this review, we discuss recent insights into MPN biology gained from the application of a number of new single-cell technologies to study human disease, with a specific focus on single-cell genomics, single-cell transcriptomics, and digital pathology.
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Affiliation(s)
- Daniel Royston
- Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine and NIHR Biomedical Research Centre, University of Oxford, Headley Way, Oxford OX39DS, UK
| | - Adam J Mead
- Medical Research Council (MRC) Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, NIHR Biomedical Research Centre, University of Oxford, Headley Way, Oxford OX3 9DS, UK.
| | - Bethan Psaila
- Medical Research Council (MRC) Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, NIHR Biomedical Research Centre, University of Oxford, Headley Way, Oxford OX3 9DS, UK
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Boldú L, Merino A, Acevedo A, Molina A, Rodellar J. A deep learning model (ALNet) for the diagnosis of acute leukaemia lineage using peripheral blood cell images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 202:105999. [PMID: 33618145 DOI: 10.1016/j.cmpb.2021.105999] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 02/08/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVES Morphological differentiation among blasts circulating in blood in acute leukaemia is challenging. Artificial intelligence decision support systems hold substantial promise as part of clinical practise in detecting haematological malignancy. This study aims to develop a deep learning-based system to predict the diagnosis of acute leukaemia using blood cell images. METHODS A set of 731 blood smears containing 16,450 single-cell images was analysed from 100 healthy controls, 191 patients with viral infections and 148 with acute leukaemia. Training and testing sets were arranged with 85% and 15% of these smears, respectively. To find the best architecture for acute leukaemia classification VGG16, ResNet101, DenseNet121 and SENet154 were evaluated. Fine-tuning was implemented to these pre-trained CNNs to adapt their layers to our data. Once the best architecture was chosen, a system with two modules working sequentially was configured (ALNet). The first module recognised abnormal promyelocytes among other mononuclear blood cell images, such as lymphocytes, monocytes, reactive lymphocytes and blasts. The second distinguished if blasts were myeloid or lymphoid lineage. The final strategy was to predict patients' initial diagnosis of acute leukaemia lineage using the blood smear review. ALNet was assessed with smears of the testing set. RESULTS ALNet provided the correct diagnostic prediction of all patients with promyelocytic and myeloid leukaemia. Sensitivity, specificity and precision values of 100%, 92.3% and 93.7%, respectively, were obtained for myeloid leukaemia. Regarding lymphoid leukaemia, a sensitivity of 89% and specificity and precision values of 100% were obtained. CONCLUSIONS ALNet is a predictive model designed with two serially connected convolutional networks. It is proposed to assist clinical pathologists in the diagnosis of acute leukaemia during the blood smear review. It has been proved to distinguish neoplastic (leukaemia) and non-neoplastic (infections) diseases, as well as recognise the leukaemia lineage.
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Affiliation(s)
- Laura Boldú
- Hospital Clínic de Barcelona-IDIBAPS, Haematology and Cytology Unit, CORE Laboratory, Biomedical Diagnostic Centre, Spain
| | - Anna Merino
- Hospital Clínic de Barcelona-IDIBAPS, Haematology and Cytology Unit, CORE Laboratory, Biomedical Diagnostic Centre, Spain.
| | - Andrea Acevedo
- Hospital Clínic de Barcelona-IDIBAPS, Haematology and Cytology Unit, CORE Laboratory, Biomedical Diagnostic Centre, Spain; Technical University of Catalonia, Barcelona East Engineering School, Department of Mathematics, Spain
| | - Angel Molina
- Hospital Clínic de Barcelona-IDIBAPS, Haematology and Cytology Unit, CORE Laboratory, Biomedical Diagnostic Centre, Spain
| | - José Rodellar
- Technical University of Catalonia, Barcelona East Engineering School, Department of Mathematics, Spain
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Developing and Preliminary Validating an Automatic Cell Classification System for Bone Marrow Smears: a Pilot Study. J Med Syst 2020; 44:184. [PMID: 32894360 PMCID: PMC7476995 DOI: 10.1007/s10916-020-01654-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Accepted: 08/25/2020] [Indexed: 01/17/2023]
Abstract
Bone marrow smear examination is an indispensable diagnostic tool in the evaluation of hematological diseases, but the process of manual differential count is labor extensive. In this study, we developed an automatic system with integrated scanning hardware and machine learning-based software to perform differential cell count on bone marrow smears to assist diagnosis. The initial development of the artificial neural network was based on 3000 marrow smear samples retrospectively archived from Sir Run Run Shaw Hospital affiliated to Zhejiang University School of Medicine between June 2016 and December 2018. The preliminary field validating test of the system was based on 124 marrow smears newly collected from the Second Affiliated Hospital of Harbin Medical University between April 2019 and November 2019. The study was performed in parallel of machine automatic recognition with conventional manual differential count by pathologists using the microscope. We selected representative 600,000 marrow cell images as training set of the algorithm, followed by random captured 30,867 cell images for validation. In validation, the overall accuracy of automatic cell classification was 90.1% (95% CI, 89.8-90.5%). In a preliminary field validating test, the reliability coefficient (ICC) of cell series proportion between the two analysis methods were high (ICC ≥ 0.883, P < 0.0001) and the results by the two analysis methods were consistent for granulocytes and erythrocytes. The system was effective in cell classification and differential cell count on marrow smears. It provides a useful digital tool in the screening and evaluation of various hematological disorders.
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Meijering E. A bird's-eye view of deep learning in bioimage analysis. Comput Struct Biotechnol J 2020; 18:2312-2325. [PMID: 32994890 PMCID: PMC7494605 DOI: 10.1016/j.csbj.2020.08.003] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 07/26/2020] [Accepted: 08/01/2020] [Indexed: 02/07/2023] Open
Abstract
Deep learning of artificial neural networks has become the de facto standard approach to solving data analysis problems in virtually all fields of science and engineering. Also in biology and medicine, deep learning technologies are fundamentally transforming how we acquire, process, analyze, and interpret data, with potentially far-reaching consequences for healthcare. In this mini-review, we take a bird's-eye view at the past, present, and future developments of deep learning, starting from science at large, to biomedical imaging, and bioimage analysis in particular.
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Affiliation(s)
- Erik Meijering
- School of Computer Science and Engineering & Graduate School of Biomedical Engineering, University of New South Wales, Sydney, Australia
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