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Zhu S, Zhu Z, Ni C, Zhou Z, Chen Y, Tang D, Guo K, Yang S, Liu K, Ni Z, Xiang N. Liquid Biopsy Instrument for Ultra-Fast and Label-Free Detection of Circulating Tumor Cells. RESEARCH (WASHINGTON, D.C.) 2024; 7:0431. [PMID: 39050821 PMCID: PMC11266806 DOI: 10.34133/research.0431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 06/27/2024] [Indexed: 07/27/2024]
Abstract
Rapid diagnosis and real-time monitoring are of great important in the fight against cancer. However, most available diagnostic technologies are time-consuming and labor-intensive and are commonly invasive. Here, we describe CytoExam, an automatic liquid biopsy instrument designed based on inertial microfluidics and impedance cytometry, which uses a deep learning algorithm for the analysis of circulating tumor cells (CTCs). In silico and in vitro experiments demonstrated that CytoExam could achieve label-free detection of CTCs in the peripheral blood of cancer patients within 15 min. The clinical applicability of CytoExam was also verified using peripheral blood samples from 10 healthy donors and >50 patients with breast, colorectal, or lung cancer. Significant differences in the number of collected cells and predicted CTCs were observed between the 2 groups, with variations in the dielectric properties of the collected cells from cancer patients also being observed. The ultra-fast and minimally invasive features of CytoExam may pave the way for new paths for cancer diagnosis and scientific research.
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Affiliation(s)
- Shu Zhu
- School of Mechanical Engineering, and Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments,
Southeast University, Nanjing 211189, China
- School of Electrical and Automation Engineering, and Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing,
Nanjing Normal University, Nanjing 210023, China
| | - Zhixian Zhu
- School of Mechanical Engineering, and Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments,
Southeast University, Nanjing 211189, China
| | - Chen Ni
- School of Mechanical Engineering, and Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments,
Southeast University, Nanjing 211189, China
| | - Zheng Zhou
- School of Mechanical Engineering, and Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments,
Southeast University, Nanjing 211189, China
| | - Yao Chen
- School of Mechanical Engineering, and Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments,
Southeast University, Nanjing 211189, China
| | - Dezhi Tang
- School of Mechanical Engineering, and Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments,
Southeast University, Nanjing 211189, China
| | - Kefan Guo
- School of Mechanical Engineering, and Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments,
Southeast University, Nanjing 211189, China
| | - Shuai Yang
- School of Mechanical Engineering, and Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments,
Southeast University, Nanjing 211189, China
| | - Kang Liu
- School of Mechanical Engineering, and Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments,
Southeast University, Nanjing 211189, China
| | - Zhonghua Ni
- School of Mechanical Engineering, and Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments,
Southeast University, Nanjing 211189, China
| | - Nan Xiang
- School of Mechanical Engineering, and Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments,
Southeast University, Nanjing 211189, China
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2
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Bai A, Si M, Xue P, Qu Y, Jiang Y. Artificial intelligence performance in detecting lymphoma from medical imaging: a systematic review and meta-analysis. BMC Med Inform Decis Mak 2024; 24:13. [PMID: 38191361 PMCID: PMC10775443 DOI: 10.1186/s12911-023-02397-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 12/07/2023] [Indexed: 01/10/2024] Open
Abstract
BACKGROUND Accurate diagnosis and early treatment are essential in the fight against lymphatic cancer. The application of artificial intelligence (AI) in the field of medical imaging shows great potential, but the diagnostic accuracy of lymphoma is unclear. This study was done to systematically review and meta-analyse researches concerning the diagnostic performance of AI in detecting lymphoma using medical imaging for the first time. METHODS Searches were conducted in Medline, Embase, IEEE and Cochrane up to December 2023. Data extraction and assessment of the included study quality were independently conducted by two investigators. Studies that reported the diagnostic performance of an AI model/s for the early detection of lymphoma using medical imaging were included in the systemic review. We extracted the binary diagnostic accuracy data to obtain the outcomes of interest: sensitivity (SE), specificity (SP), and Area Under the Curve (AUC). The study was registered with the PROSPERO, CRD42022383386. RESULTS Thirty studies were included in the systematic review, sixteen of which were meta-analyzed with a pooled sensitivity of 87% (95%CI 83-91%), specificity of 94% (92-96%), and AUC of 97% (95-98%). Satisfactory diagnostic performance was observed in subgroup analyses based on algorithms types (machine learning versus deep learning, and whether transfer learning was applied), sample size (≤ 200 or > 200), clinicians versus AI models and geographical distribution of institutions (Asia versus non-Asia). CONCLUSIONS Even if possible overestimation and further studies with a better standards for application of AI algorithms in lymphoma detection are needed, we suggest the AI may be useful in lymphoma diagnosis.
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Affiliation(s)
- Anying Bai
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Mingyu Si
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Peng Xue
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Yimin Qu
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yu Jiang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
- School of Health Policy and Management, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
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Chen L, Yu L, Chen M, Liu Y, Xu H, Wang F, Zhu J, Tian P, Yi K, Zhang Q, Xiao H, Duan Y, Li W, Ma L, Zhou F, Cheng Y, Bai L, Wang F, Xiao X, Zhu Y, Yang Y. A microfluidic hemostatic diagnostics platform: Harnessing coagulation-induced adaptive-bubble behavioral perception. Cell Rep Med 2023; 4:101252. [PMID: 37879336 PMCID: PMC10694630 DOI: 10.1016/j.xcrm.2023.101252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 05/10/2023] [Accepted: 09/29/2023] [Indexed: 10/27/2023]
Abstract
Clinical viscoelastic hemostatic assays, which have been used for decades, rely on measuring biomechanical responses to physical stimuli but face challenges related to high device and test cost, limited portability, and limited scalability.. Here, we report a differential pattern using self-induced adaptive-bubble behavioral perception to refresh it. The adaptive behaviors of bubble deformation during coagulation precisely describe the transformation of viscoelastic hemostatic properties, being free of the precise and complex physical devices. And the integrated bubble array chip allows microassays and enables multi-bubble tests with good reproducibility. Recognition of the developed bubble behaviors empowers automated and user-friendly diagnosis. In a prospective clinical study (clinical model development [n = 273]; clinical assay [n = 44]), we show that the diagnostic accuracies were 99.1% for key viscoelastic hemostatic assay indicators (reaction time [R], kinetics time [K], alpha angle [Angle], maximum amplitude [MA], lysis at 30 min [LY30]; n = 220) and 100% (n = 44) for hypercoagulation, healthy, and hypocoagulation diagnoses. This should provide fresh insight into existing paradigms and help more clinical needs.
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Affiliation(s)
- Longfei Chen
- Department of Clinical Laboratory, Institute of Translational Medicine, Institute of Medicine and Physics, Renmin Hospital of Wuhan University, Key Laboratory of Artificial Micro- and Nano-Structures of Ministry of Education, School of Physics & Technology, Wuhan University, Wuhan 430072, China; Shenzhen Research Institute, Wuhan University, Shenzhen 518000, China
| | - Le Yu
- Department of Clinical Laboratory, Institute of Translational Medicine, Institute of Medicine and Physics, Renmin Hospital of Wuhan University, Key Laboratory of Artificial Micro- and Nano-Structures of Ministry of Education, School of Physics & Technology, Wuhan University, Wuhan 430072, China; Shenzhen Research Institute, Wuhan University, Shenzhen 518000, China
| | - Ming Chen
- Department of Blood Transfusion, Zhongnan Hospital, Wuhan University, Wuhan 430071, China
| | - Yantong Liu
- Department of Clinical Laboratory, Institute of Translational Medicine, Institute of Medicine and Physics, Renmin Hospital of Wuhan University, Key Laboratory of Artificial Micro- and Nano-Structures of Ministry of Education, School of Physics & Technology, Wuhan University, Wuhan 430072, China; Shenzhen Research Institute, Wuhan University, Shenzhen 518000, China
| | - Hongshan Xu
- Department of Clinical Laboratory, Institute of Translational Medicine, Institute of Medicine and Physics, Renmin Hospital of Wuhan University, Key Laboratory of Artificial Micro- and Nano-Structures of Ministry of Education, School of Physics & Technology, Wuhan University, Wuhan 430072, China
| | - Fang Wang
- Department of Clinical Laboratory, Institute of Translational Medicine, Institute of Medicine and Physics, Renmin Hospital of Wuhan University, Key Laboratory of Artificial Micro- and Nano-Structures of Ministry of Education, School of Physics & Technology, Wuhan University, Wuhan 430072, China
| | - Jiaomeng Zhu
- Department of Clinical Laboratory, Institute of Translational Medicine, Institute of Medicine and Physics, Renmin Hospital of Wuhan University, Key Laboratory of Artificial Micro- and Nano-Structures of Ministry of Education, School of Physics & Technology, Wuhan University, Wuhan 430072, China
| | - Pengfu Tian
- Department of Clinical Laboratory, Institute of Translational Medicine, Institute of Medicine and Physics, Renmin Hospital of Wuhan University, Key Laboratory of Artificial Micro- and Nano-Structures of Ministry of Education, School of Physics & Technology, Wuhan University, Wuhan 430072, China
| | - Kezhen Yi
- Department of Laboratory Medicine, Zhongnan Hospital, Wuhan University, Wuhan 430071, China
| | - Qian Zhang
- Department of Laboratory Medicine, Zhongnan Hospital, Wuhan University, Wuhan 430071, China
| | - Hui Xiao
- Department of Hematology, Zhongnan Hospital, Wuhan University, Wuhan 430071, China
| | - Yongwei Duan
- Department of Laboratory Medicine, Zhongnan Hospital, Wuhan University, Wuhan 430071, China
| | - Wei Li
- Department of Clinical Laboratory, Institute of Translational Medicine, Institute of Medicine and Physics, Renmin Hospital of Wuhan University, Key Laboratory of Artificial Micro- and Nano-Structures of Ministry of Education, School of Physics & Technology, Wuhan University, Wuhan 430072, China
| | - Linlu Ma
- Department of Hematology, Zhongnan Hospital, Wuhan University, Wuhan 430071, China
| | - Fuling Zhou
- Department of Hematology, Zhongnan Hospital, Wuhan University, Wuhan 430071, China
| | - Yanxiang Cheng
- School of Medicine, Renmin Hospital, Wuhan University, Wuhan 430060, China
| | - Long Bai
- School of Medicine, Zhejiang University, Zhejiang 310002, China
| | - Fubing Wang
- Department of Laboratory Medicine, Zhongnan Hospital, Wuhan University, Wuhan 430071, China
| | - Xuan Xiao
- Department of Clinical Laboratory, Institute of Translational Medicine, Institute of Medicine and Physics, Renmin Hospital of Wuhan University, Key Laboratory of Artificial Micro- and Nano-Structures of Ministry of Education, School of Physics & Technology, Wuhan University, Wuhan 430072, China
| | - Yimin Zhu
- School of Medicine, Zhejiang University, Zhejiang 310002, China
| | - Yi Yang
- Department of Clinical Laboratory, Institute of Translational Medicine, Institute of Medicine and Physics, Renmin Hospital of Wuhan University, Key Laboratory of Artificial Micro- and Nano-Structures of Ministry of Education, School of Physics & Technology, Wuhan University, Wuhan 430072, China; Shenzhen Research Institute, Wuhan University, Shenzhen 518000, China.
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Zhang A, Wu Z, Wu E, Wu M, Snyder MP, Zou J, Wu JC. Leveraging physiology and artificial intelligence to deliver advancements in health care. Physiol Rev 2023; 103:2423-2450. [PMID: 37104717 PMCID: PMC10390055 DOI: 10.1152/physrev.00033.2022] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 03/06/2023] [Accepted: 04/25/2023] [Indexed: 04/29/2023] Open
Abstract
Artificial intelligence in health care has experienced remarkable innovation and progress in the last decade. Significant advancements can be attributed to the utilization of artificial intelligence to transform physiology data to advance health care. In this review, we explore how past work has shaped the field and defined future challenges and directions. In particular, we focus on three areas of development. First, we give an overview of artificial intelligence, with special attention to the most relevant artificial intelligence models. We then detail how physiology data have been harnessed by artificial intelligence to advance the main areas of health care: automating existing health care tasks, increasing access to care, and augmenting health care capabilities. Finally, we discuss emerging concerns surrounding the use of individual physiology data and detail an increasingly important consideration for the field, namely the challenges of deploying artificial intelligence models to achieve meaningful clinical impact.
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Affiliation(s)
- Angela Zhang
- Stanford Cardiovascular Institute, School of Medicine, Stanford University, Stanford, California, United States
- Department of Genetics, School of Medicine, Stanford University, Stanford, California, United States
- Greenstone Biosciences, Palo Alto, California, United States
| | - Zhenqin Wu
- Department of Chemistry, Stanford University, Stanford, California, United States
| | - Eric Wu
- Department of Electrical Engineering, Stanford University, Stanford, California, United States
| | - Matthew Wu
- Greenstone Biosciences, Palo Alto, California, United States
| | - Michael P Snyder
- Department of Genetics, School of Medicine, Stanford University, Stanford, California, United States
| | - James Zou
- Department of Biomedical Informatics, School of Medicine, Stanford University, Stanford, California, United States
- Department of Computer Science, Stanford University, Stanford, California, United States
| | - Joseph C Wu
- Stanford Cardiovascular Institute, School of Medicine, Stanford University, Stanford, California, United States
- Greenstone Biosciences, Palo Alto, California, United States
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, California, United States
- Department of Radiology, School of Medicine, Stanford University, Stanford, California, United States
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Chamba C, Mawalla W. The future of lymphoma diagnosis, prognosis, and treatment monitoring in countries with limited access to pathology services. Semin Hematol 2023; 60:215-219. [PMID: 37596119 DOI: 10.1053/j.seminhematol.2023.07.004] [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: 05/31/2023] [Revised: 07/17/2023] [Accepted: 07/24/2023] [Indexed: 08/20/2023]
Abstract
The world is moving towards precision medicine for cancer. This movement goes hand in hand with the development of newer advanced technologies for early, precise diagnosis of cancer and personalized treatment plans with fewer adverse effects for the patient. Liquid biopsy is one such advancement. At the same time, it has the advantage of minimal invasion and avoids serial invasive biopsies. In countries with limited access to pathology services, such as sub-Saharan Africa, liquid biopsy may provide an opportunity for early detection and prognostication of lymphoma. We discuss the current diagnostic modalities for lymphoma, highlighting the existing challenges with tissue biopsy, and how feasible it is for countries with limited pathology resources to leverage advancements made in the clinical application of liquid biopsy to improve lymphoma care.
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Affiliation(s)
- Clara Chamba
- Department of Hematology and Blood Transfusion, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania.
| | - William Mawalla
- Department of Hematology and Blood Transfusion, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania
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Zhao W, Zhou Y, Feng YZ, Niu X, Zhao Y, Zhao J, Dong Y, Tan M, Xianyu Y, Chen Y. Computer Vision-Based Artificial Intelligence-Mediated Encoding-Decoding for Multiplexed Microfluidic Digital Immunoassay. ACS NANO 2023; 17:13700-13714. [PMID: 37458511 DOI: 10.1021/acsnano.3c02941] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/26/2023]
Abstract
Digital immunoassays with multiplexed capacity, ultrahigh sensitivity, and broad affordability are urgently required in clinical diagnosis, food safety, and environmental monitoring. In this work, a multidimensional digital immunoassay has been developed through microparticle-based encoding and artificial intelligence-based decoding, enabling multiplexed detection with high sensitivity and convenient operation. The information encoded in the features of microspheres, including their size, number, and color, allows for the simultaneous identification and accurate quantification of multiple targets. Computer vision-based artificial intelligence can analyze the microscopy images for information decoding and output identification results visually. Moreover, the optical microscopy imaging can be well integrated with the microfluidic platform, allowing for encoding-decoding through the computer vision-based artificial intelligence. This microfluidic digital immunoassay can simultaneously analyze multiple inflammatory markers and antibiotics within 30 min with high sensitivity and a broad detection range from pg/mL to μg/mL, which holds great promise as an intelligent bioassay for next-generation multiplexed biosensing.
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Affiliation(s)
- Weiqi Zhao
- College of Food Science and Technology, Huazhong Agricultural University, Wuhan, 430070, Hubei China
| | - Yang Zhou
- College of Food Science and Technology, Huazhong Agricultural University, Wuhan, 430070, Hubei China
- College of Engineering, Huazhong Agricultural University, Wuhan, 430070, Hubei China
| | - Yao-Ze Feng
- College of Engineering, Huazhong Agricultural University, Wuhan, 430070, Hubei China
| | - Xiaohu Niu
- College of Engineering, Huazhong Agricultural University, Wuhan, 430070, Hubei China
| | - Yongkun Zhao
- College of Food Science and Technology, Huazhong Agricultural University, Wuhan, 430070, Hubei China
- College of Engineering, Huazhong Agricultural University, Wuhan, 430070, Hubei China
| | - Junpeng Zhao
- College of Food Science and Technology, Huazhong Agricultural University, Wuhan, 430070, Hubei China
| | - Yongzhen Dong
- College of Food Science and Technology, Huazhong Agricultural University, Wuhan, 430070, Hubei China
| | - Mingqian Tan
- Academy of Food Interdisciplinary Science, School of Food Science and Technology, Dalian Polytechnic University, Dalian, 116034, Liaoning China
| | - Yunlei Xianyu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, Zhejiang China
| | - Yiping Chen
- College of Food Science and Technology, Huazhong Agricultural University, Wuhan, 430070, Hubei China
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Farah L, Davaze-Schneider J, Martin T, Nguyen P, Borget I, Martelli N. Are current clinical studies on artificial intelligence-based medical devices comprehensive enough to support a full health technology assessment? A systematic review. Artif Intell Med 2023; 140:102547. [PMID: 37210155 DOI: 10.1016/j.artmed.2023.102547] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 03/28/2023] [Accepted: 04/04/2023] [Indexed: 05/22/2023]
Abstract
INTRODUCTION Artificial Intelligence-based Medical Devices (AI-based MDs) are experiencing exponential growth in healthcare. This study aimed to investigate whether current studies assessing AI contain the information required for health technology assessment (HTA) by HTA bodies. METHODS We conducted a systematic literature review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology to extract articles published between 2016 and 2021 related to the assessment of AI-based MDs. Data extraction focused on study characteristics, technology, algorithms, comparators, and results. AI quality assessment and HTA scores were calculated to evaluate whether the items present in the included studies were concordant with the HTA requirements. We performed a linear regression for the HTA and AI scores with the explanatory variables of the impact factor, publication date, and medical specialty. We conducted a univariate analysis of the HTA score and a multivariate analysis of the AI score with an alpha risk of 5 %. RESULTS Of 5578 retrieved records, 56 were included. The mean AI quality assessment score was 67 %; 32 % of articles had an AI quality score ≥ 70 %, 50 % had a score between 50 % and 70 %, and 18 % had a score under 50 %. The highest quality scores were observed for the study design (82 %) and optimisation (69 %) categories, whereas the scores were lowest in the clinical practice category (23 %). The mean HTA score was 52 % for all seven domains. 100 % of the studies assessed clinical effectiveness, whereas only 9 % evaluated safety, and 20 % evaluated economic issues. There was a statistically significant relationship between the impact factor and the HTA and AI scores (both p = 0.046). DISCUSSION Clinical studies on AI-based MDs have limitations and often lack adapted, robust, and complete evidence. High-quality datasets are also required because the output data can only be trusted if the inputs are reliable. The existing assessment frameworks are not specifically designed to assess AI-based MDs. From the perspective of regulatory authorities, we suggest that these frameworks should be adapted to assess the interpretability, explainability, cybersecurity, and safety of ongoing updates. From the perspective of HTA agencies, we highlight that transparency, professional and patient acceptance, ethical issues, and organizational changes are required for the implementation of these devices. Economic assessments of AI should rely on a robust methodology (business impact or health economic models) to provide decision-makers with more reliable evidence. CONCLUSION Currently, AI studies are insufficient to cover HTA prerequisites. HTA processes also need to be adapted because they do not consider the important specificities of AI-based MDs. Specific HTA workflows and accurate assessment tools should be designed to standardise evaluations, generate reliable evidence, and create confidence.
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Affiliation(s)
- Line Farah
- Groupe de Recherche et d'accueil en Droit et Economie de la Santé (GRADES) Department, University Paris-Saclay, Orsay, France; Innovation Center for Medical Devices, Foch Hospital, 40 Rue Worth, 92150 Suresnes, France.
| | - Julie Davaze-Schneider
- Pharmacy Department, Georges Pompidou European Hospital, AP-HP, 20 Rue Leblanc, 75015 Paris, France
| | - Tess Martin
- Groupe de Recherche et d'accueil en Droit et Economie de la Santé (GRADES) Department, University Paris-Saclay, Orsay, France; Pharmacy Department, Georges Pompidou European Hospital, AP-HP, 20 Rue Leblanc, 75015 Paris, France
| | - Pierre Nguyen
- Pharmacy Department, Georges Pompidou European Hospital, AP-HP, 20 Rue Leblanc, 75015 Paris, France
| | - Isabelle Borget
- Groupe de Recherche et d'accueil en Droit et Economie de la Santé (GRADES) Department, University Paris-Saclay, Orsay, France; Department of Biostatistics and Epidemiology, Gustave Roussy, University Paris-Saclay, 94805 Villejuif, France; Oncostat U1018, Inserm, University Paris-Saclay, Équipe Labellisée Ligue Contre le Cancer, Villejuif, France
| | - Nicolas Martelli
- Groupe de Recherche et d'accueil en Droit et Economie de la Santé (GRADES) Department, University Paris-Saclay, Orsay, France; Pharmacy Department, Georges Pompidou European Hospital, AP-HP, 20 Rue Leblanc, 75015 Paris, France
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Wang B, Li Y, Zhou M, Han Y, Zhang M, Gao Z, Liu Z, Chen P, Du W, Zhang X, Feng X, Liu BF. Smartphone-based platforms implementing microfluidic detection with image-based artificial intelligence. Nat Commun 2023; 14:1341. [PMID: 36906581 PMCID: PMC10007670 DOI: 10.1038/s41467-023-36017-x] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 01/10/2023] [Indexed: 03/13/2023] Open
Abstract
The frequent outbreak of global infectious diseases has prompted the development of rapid and effective diagnostic tools for the early screening of potential patients in point-of-care testing scenarios. With advances in mobile computing power and microfluidic technology, the smartphone-based mobile health platform has drawn significant attention from researchers developing point-of-care testing devices that integrate microfluidic optical detection with artificial intelligence analysis. In this article, we summarize recent progress in these mobile health platforms, including the aspects of microfluidic chips, imaging modalities, supporting components, and the development of software algorithms. We document the application of mobile health platforms in terms of the detection objects, including molecules, viruses, cells, and parasites. Finally, we discuss the prospects for future development of mobile health platforms.
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Affiliation(s)
- Bangfeng Wang
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Yiwei Li
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Mengfan Zhou
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Yulong Han
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA
| | - Mingyu Zhang
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Zhaolong Gao
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Zetai Liu
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Peng Chen
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Wei Du
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Xingcai Zhang
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA.
| | - Xiaojun Feng
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
| | - Bi-Feng Liu
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
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Istasy P, Lee WS, Iansavichene A, Upshur R, Gyawali B, Burkell J, Sadikovic B, Lazo-Langner A, Chin-Yee B. The Impact of Artificial Intelligence on Health Equity in Oncology: Scoping Review. J Med Internet Res 2022; 24:e39748. [PMID: 36005841 PMCID: PMC9667381 DOI: 10.2196/39748] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 08/11/2022] [Accepted: 08/24/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The field of oncology is at the forefront of advances in artificial intelligence (AI) in health care, providing an opportunity to examine the early integration of these technologies in clinical research and patient care. Hope that AI will revolutionize health care delivery and improve clinical outcomes has been accompanied by concerns about the impact of these technologies on health equity. OBJECTIVE We aimed to conduct a scoping review of the literature to address the question, "What are the current and potential impacts of AI technologies on health equity in oncology?" METHODS Following PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines for scoping reviews, we systematically searched MEDLINE and Embase electronic databases from January 2000 to August 2021 for records engaging with key concepts of AI, health equity, and oncology. We included all English-language articles that engaged with the 3 key concepts. Articles were analyzed qualitatively for themes pertaining to the influence of AI on health equity in oncology. RESULTS Of the 14,011 records, 133 (0.95%) identified from our review were included. We identified 3 general themes in the literature: the use of AI to reduce health care disparities (58/133, 43.6%), concerns surrounding AI technologies and bias (16/133, 12.1%), and the use of AI to examine biological and social determinants of health (55/133, 41.4%). A total of 3% (4/133) of articles focused on many of these themes. CONCLUSIONS Our scoping review revealed 3 main themes on the impact of AI on health equity in oncology, which relate to AI's ability to help address health disparities, its potential to mitigate or exacerbate bias, and its capability to help elucidate determinants of health. Gaps in the literature included a lack of discussion of ethical challenges with the application of AI technologies in low- and middle-income countries, lack of discussion of problems of bias in AI algorithms, and a lack of justification for the use of AI technologies over traditional statistical methods to address specific research questions in oncology. Our review highlights a need to address these gaps to ensure a more equitable integration of AI in cancer research and clinical practice. The limitations of our study include its exploratory nature, its focus on oncology as opposed to all health care sectors, and its analysis of solely English-language articles.
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Affiliation(s)
- Paul Istasy
- Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Rotman Institute of Philosophy, Western University, London, ON, Canada
| | - Wen Shen Lee
- Department of Pathology & Laboratory Medicine, Schulich School of Medicine, Western University, London, ON, Canada
| | | | - Ross Upshur
- Division of Clinical Public Health, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Bridgepoint Collaboratory for Research and Innovation, Lunenfeld Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
| | - Bishal Gyawali
- Division of Cancer Care and Epidemiology, Department of Oncology, Queen's University, Kingston, ON, Canada
- Division of Cancer Care and Epidemiology, Department of Public Health Sciences, Queen's University, Kingston, ON, Canada
| | - Jacquelyn Burkell
- Faculty of Information and Media Studies, Western University, London, ON, Canada
| | - Bekim Sadikovic
- Department of Pathology & Laboratory Medicine, Schulich School of Medicine, Western University, London, ON, Canada
| | - Alejandro Lazo-Langner
- Division of Hematology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Benjamin Chin-Yee
- Rotman Institute of Philosophy, Western University, London, ON, Canada
- Division of Hematology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Division of Hematology, Department of Medicine, London Health Sciences Centre, London, ON, Canada
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10
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Keshavamurthy KN, Dylov DV, Yazdanfar S, Patel D, Silk T, Silk M, Jacques F, Petre EN, Gonen M, Rekhtman N, Ostroverkhov V, Scher HI, Solomon SB, Durack JC. Evaluation of an Integrated Spectroscopy and Classification Platform for Point-of-Care Core Needle Biopsy Assessment: Performance Characteristics from Ex Vivo Renal Mass Biopsies. J Vasc Interv Radiol 2022; 33:1408-1415.e3. [PMID: 35940363 PMCID: PMC10204606 DOI: 10.1016/j.jvir.2022.07.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 07/21/2022] [Accepted: 07/29/2022] [Indexed: 12/15/2022] Open
Abstract
PURPOSE To evaluate a transmission optical spectroscopy instrument for rapid ex vivo assessment of core needle cancer biopsies (CNBs) at the point of care. MATERIALS AND METHODS CNBs from surgically resected renal tumors and nontumor regions were scanned on their sampling trays with a custom spectroscopy instrument. After extracting principal spectral components, machine learning was used to train logistic regression, support vector machines, and random decision forest (RF) classifiers on 80% of randomized and stratified data. The algorithms were evaluated on the remaining 20% of the data set held out during training. Binary classification (tumor/nontumor) was performed based on a decision threshold. Multinomial classification was also performed to differentiate between the subtypes of renal cell carcinoma (RCC) and account for potential confounding effects from fat, blood, and necrotic tissue. Classifiers were compared based on sensitivity, specificity, and positive predictive value (PPV) relative to a histopathologic standard. RESULTS A total of 545 CNBs from 102 patients were analyzed, yielding 5,583 spectra after outlier exclusion. At the individual spectra level, the best performing algorithm was RF with sensitivities of 96% and 92% and specificities of 90% and 89%, for the binary and multiclass analyses, respectively. At the full CNB level, RF algorithm also showed the highest sensitivity and specificity (93% and 91%, respectively). For RCC subtypes, the highest sensitivity and PPV were attained for clear cell (93.5%) and chromophobe (98.2%) subtypes, respectively. CONCLUSIONS Ex vivo spectroscopy imaging paired with machine learning can accurately characterize renal mass CNB at the time of tissue acquisition.
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Affiliation(s)
| | - Dmitry V Dylov
- Skolkovo Institute of Science and Technology, Moscow, Russia
| | | | - Dharam Patel
- Novartis Pharmaceutical Corporation, East Hanover, New Jersey
| | - Tarik Silk
- New York University Langone Medical Center, New York, New York
| | - Mikhail Silk
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York
| | | | - Elena N Petre
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Mithat Gonen
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Natasha Rekhtman
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | | | - Howard I Scher
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Stephen B Solomon
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Jeremy C Durack
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York.
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11
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Chen L, Yu L, Liu Y, Xu H, Ma L, Tian P, Zhu J, Wang F, Yi K, Xiao H, Zhou F, Yang Y, Cheng Y, Bai L, Wang F, Zhu Y. Space-time-regulated imaging analyzer for smart coagulation diagnosis. Cell Rep Med 2022; 3:100765. [PMID: 36206751 PMCID: PMC9589004 DOI: 10.1016/j.xcrm.2022.100765] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 07/26/2022] [Accepted: 09/14/2022] [Indexed: 11/07/2022]
Abstract
The development of intelligent blood coagulation diagnoses is awaited to meet the current need for large clinical time-sensitive caseloads due to its efficient and automated diagnoses. Herein, a method is reported and validated to realize it through artificial intelligence (AI)-assisted optical clotting biophysics (OCB) properties identification. The image differential calculation is used for precise acquisition of OCB properties with elimination of initial differences, and the strategy of space-time regulation allows on-demand space time OCB properties identification and enables diverse blood function diagnoses. The integrated applications of smartphones and cloud computing offer a user-friendly automated analysis for accurate and convenient diagnoses. The prospective assays of clinical cases (n = 41) show that the system realizes 97.6%, 95.1%, and 100% accuracy for coagulation factors, fibrinogen function, and comprehensive blood coagulation diagnoses, respectively. This method should enable more low-cost and convenient diagnoses and provide a path for potential diagnostic-markers finding. An ultraportable optofluidic analyzer empowers convenient coagulation diagnoses The system enables optical clotting biophysics (OCB) properties acquisition and process Coagulation function diagnoses uses intelligent OCB properties identification Space-time regulation of OCB properties endow it capability to diverse diagnoses
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Affiliation(s)
- Longfei Chen
- Key Laboratory of Artificial Micro- and Nano- Structures of Ministry of Education, School of Physics & Technology, Wuhan University, Wuhan 430072, China; Renmin Hospital, Wuhan University, Wuhan 430060, China; Shenzhen Research Institute, Wuhan University, Shenzhen 518000, China
| | - Le Yu
- Key Laboratory of Artificial Micro- and Nano- Structures of Ministry of Education, School of Physics & Technology, Wuhan University, Wuhan 430072, China; Renmin Hospital, Wuhan University, Wuhan 430060, China; Shenzhen Research Institute, Wuhan University, Shenzhen 518000, China
| | - Yantong Liu
- Key Laboratory of Artificial Micro- and Nano- Structures of Ministry of Education, School of Physics & Technology, Wuhan University, Wuhan 430072, China; Renmin Hospital, Wuhan University, Wuhan 430060, China; Shenzhen Research Institute, Wuhan University, Shenzhen 518000, China
| | - Hongshan Xu
- Key Laboratory of Artificial Micro- and Nano- Structures of Ministry of Education, School of Physics & Technology, Wuhan University, Wuhan 430072, China
| | - Linlu Ma
- Department of Hematology, Zhongnan Hospital, Wuhan University, Wuhan 430071, China
| | - Pengfu Tian
- Key Laboratory of Artificial Micro- and Nano- Structures of Ministry of Education, School of Physics & Technology, Wuhan University, Wuhan 430072, China
| | - Jiaomeng Zhu
- Key Laboratory of Artificial Micro- and Nano- Structures of Ministry of Education, School of Physics & Technology, Wuhan University, Wuhan 430072, China
| | - Fang Wang
- Key Laboratory of Artificial Micro- and Nano- Structures of Ministry of Education, School of Physics & Technology, Wuhan University, Wuhan 430072, China
| | - Kezhen Yi
- Department of Laboratory Medicine, Zhongnan Hospital, Wuhan University, Wuhan 430071, China
| | - Hui Xiao
- Department of Hematology, Zhongnan Hospital, Wuhan University, Wuhan 430071, China
| | - Fuling Zhou
- Department of Hematology, Zhongnan Hospital, Wuhan University, Wuhan 430071, China
| | - Yi Yang
- Key Laboratory of Artificial Micro- and Nano- Structures of Ministry of Education, School of Physics & Technology, Wuhan University, Wuhan 430072, China; Renmin Hospital, Wuhan University, Wuhan 430060, China; Shenzhen Research Institute, Wuhan University, Shenzhen 518000, China.
| | | | - Long Bai
- School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310002, China
| | - Fubing Wang
- Department of Laboratory Medicine, Zhongnan Hospital, Wuhan University, Wuhan 430071, China
| | - Yimin Zhu
- School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310002, China
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12
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Chin LK, Li H, Choi JH, Iwamoto Y, Oh J, Min J, Beak SK, Yoo D, Castro CM, Lee D, Im H. Hydrogel Stamping for Rapid, Multiplexed, Point-of-Care Immunostaining of Cells and Tissues. ACS APPLIED MATERIALS & INTERFACES 2022; 14:27613-27622. [PMID: 35671240 DOI: 10.1021/acsami.2c05071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
In the era of precision oncology, multicolor fluorescence imaging has become a core technology for multiplexed molecular analysis of cellular and tissue specimens. However, conventional solution-based staining is labor-intensive and time-consuming and requires considerable expertise to yield optimal results, which creates difficulties for employing this technology in resource-limited settings. Here, we report a new immunostaining method based on hydrogel stamping, which is simple, fast, easy to use, and reproducible. We showed that a hydrophilic hydrogel stamp could effectively transfer fluorescent antibodies to targets and withdraw an excess solution when the reaction is completed, obviating the need for extra washing. This unique property allows for quality immunostaining in 5 min for cells using one-eighth of antibody consumption compared to the conventional solution-based method. Furthermore, we implemented fluorescence quenching and immunocycling with hydrogel staining for multiplexed analysis of 9 protein markers at a single cell level. Finally, we applied the immunocycling method to human breast cancer tissue samples and showed quality immunostaining over a large area (∼2 cm2) in 30 min for molecular subtyping of breast cancer. The hydrogel immunostaining could open new opportunities for rapid, automated, and multiplexed profiling in compact point-of-care systems for molecular cancer diagnosis.
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Affiliation(s)
- Lip Ket Chin
- Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5206, Boston, Massachusetts 02114, United States
- Department of Electrical Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong SAR
| | - Huiyan Li
- Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5206, Boston, Massachusetts 02114, United States
- School of Engineering, University of Guelph, 50 Stone Road East, Guelph N1G2W1, Canada
| | - Jae-Hyeok Choi
- Noul Co. Limited, Gyeonggi-do, Yongin 16942, Republic of Korea
| | - Yoshiko Iwamoto
- Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5206, Boston, Massachusetts 02114, United States
| | - Juhyun Oh
- Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5206, Boston, Massachusetts 02114, United States
| | - Jouha Min
- Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5206, Boston, Massachusetts 02114, United States
| | - Suk Kyung Beak
- Noul Co. Limited, Gyeonggi-do, Yongin 16942, Republic of Korea
| | - Dahyeon Yoo
- Noul Co. Limited, Gyeonggi-do, Yongin 16942, Republic of Korea
| | - Cesar M Castro
- Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5206, Boston, Massachusetts 02114, United States
- Cancer Center, Massachusetts General Hospital, Boston, Massachusetts 02114, United States
| | - Dongyoung Lee
- Noul Co. Limited, Gyeonggi-do, Yongin 16942, Republic of Korea
| | - Hyungsoon Im
- Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5206, Boston, Massachusetts 02114, United States
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts 02114, United States
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13
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Andreou C, Weissleder R, Kircher MF. Multiplexed imaging in oncology. Nat Biomed Eng 2022; 6:527-540. [PMID: 35624151 DOI: 10.1038/s41551-022-00891-5] [Citation(s) in RCA: 44] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Accepted: 09/06/2021] [Indexed: 01/24/2023]
Abstract
In oncology, technologies for clinical molecular imaging are used to diagnose patients, establish the efficacy of treatments and monitor the recurrence of disease. Multiplexed methods increase the number of disease-specific biomarkers that can be detected simultaneously, such as the overexpression of oncogenic proteins, aberrant metabolite uptake and anomalous blood perfusion. The quantitative localization of each biomarker could considerably increase the specificity and the accuracy of technologies for clinical molecular imaging to facilitate granular diagnoses, patient stratification and earlier assessments of the responses to administered therapeutics. In this Review, we discuss established techniques for multiplexed imaging and the most promising emerging multiplexing technologies applied to the imaging of isolated tissues and cells and to non-invasive whole-body imaging. We also highlight advances in radiology that have been made possible by multiplexed imaging.
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Affiliation(s)
- Chrysafis Andreou
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Center for Molecular Imaging and Nanotechnology (CMINT), Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus
| | - Ralph Weissleder
- Center for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. .,Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. .,Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
| | - Moritz F Kircher
- Molecular Pharmacology Program, Sloan Kettering Institute, New York, NY, USA.,Department of Imaging, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.,Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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14
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Wani SUD, Khan NA, Thakur G, Gautam SP, Ali M, Alam P, Alshehri S, Ghoneim MM, Shakeel F. Utilization of Artificial Intelligence in Disease Prevention: Diagnosis, Treatment, and Implications for the Healthcare Workforce. Healthcare (Basel) 2022; 10:608. [PMID: 35455786 PMCID: PMC9026833 DOI: 10.3390/healthcare10040608] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 03/15/2022] [Accepted: 03/18/2022] [Indexed: 01/27/2023] Open
Abstract
Artificial intelligence (AI) has been described as one of the extremely effective and promising scientific tools available to mankind. AI and its associated innovations are becoming more popular in industry and culture, and they are starting to show up in healthcare. Numerous facets of healthcare, as well as regulatory procedures within providers, payers, and pharmaceutical companies, may be transformed by these innovations. As a result, the purpose of this review is to identify the potential machine learning applications in the field of infectious diseases and the general healthcare system. The literature on this topic was extracted from various databases, such as Google, Google Scholar, Pubmed, Scopus, and Web of Science. The articles having important information were selected for this review. The most challenging task for AI in such healthcare sectors is to sustain its adoption in daily clinical practice, regardless of whether the programs are scalable enough to be useful. Based on the summarized data, it has been concluded that AI can assist healthcare staff in expanding their knowledge, allowing them to spend more time providing direct patient care and reducing weariness. Overall, we might conclude that the future of "conventional medicine" is closer than we realize, with patients seeing a computer first and subsequently a doctor.
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Affiliation(s)
- Shahid Ud Din Wani
- Department of Pharmaceutical Sciences, University of Kashmir, Jammu and Kashmir, Srinagar 190006, India;
| | - Nisar Ahmad Khan
- Department of Pharmaceutical Sciences, University of Kashmir, Jammu and Kashmir, Srinagar 190006, India;
| | - Gaurav Thakur
- Department of Pharmaceutics, CT Institute of Pharmaceutical Sciences, CT Group of Institutions, Jalandhar 144020, India; (G.T.); (S.P.G.)
| | - Surya Prakash Gautam
- Department of Pharmaceutics, CT Institute of Pharmaceutical Sciences, CT Group of Institutions, Jalandhar 144020, India; (G.T.); (S.P.G.)
| | - Mohammad Ali
- Department of Pharmacology, School of Pharmaceutical Sciences, University of Science & Technology, Meghalaya 793101, India;
| | - Prawez Alam
- Department of Pharmacognosy, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia;
| | - Sultan Alshehri
- Department of Pharmaceutics, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia;
| | - Mohammed M. Ghoneim
- Department of Pharmacy Practice, College of Pharmacy, AlMaarefa University, Ad Diriyah 13713, Saudi Arabia;
| | - Faiyaz Shakeel
- Department of Pharmaceutics, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia;
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15
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Vaghashiya R, Shin S, Chauhan V, Kapadiya K, Sanghavi S, Seo S, Roy M. Machine Learning Based Lens-Free Shadow Imaging Technique for Field-Portable Cytometry. BIOSENSORS 2022; 12:144. [PMID: 35323414 PMCID: PMC8946550 DOI: 10.3390/bios12030144] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 02/23/2022] [Accepted: 02/25/2022] [Indexed: 06/14/2023]
Abstract
The lens-free shadow imaging technique (LSIT) is a well-established technique for the characterization of microparticles and biological cells. Due to its simplicity and cost-effectiveness, various low-cost solutions have been developed, such as automatic analysis of complete blood count (CBC), cell viability, 2D cell morphology, 3D cell tomography, etc. The developed auto characterization algorithm so far for this custom-developed LSIT cytometer was based on the handcrafted features of the cell diffraction patterns from the LSIT cytometer, that were determined from our empirical findings on thousands of samples of individual cell types, which limit the system in terms of induction of a new cell type for auto classification or characterization. Further, its performance suffers from poor image (cell diffraction pattern) signatures due to their small signal or background noise. In this work, we address these issues by leveraging the artificial intelligence-powered auto signal enhancing scheme such as denoising autoencoder and adaptive cell characterization technique based on the transfer of learning in deep neural networks. The performance of our proposed method shows an increase in accuracy >98% along with the signal enhancement of >5 dB for most of the cell types, such as red blood cell (RBC) and white blood cell (WBC). Furthermore, the model is adaptive to learn new type of samples within a few learning iterations and able to successfully classify the newly introduced sample along with the existing other sample types.
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Affiliation(s)
- Rajkumar Vaghashiya
- Department of Computer Engineering, Pandit Deendayal Energy University, Gandhinagar 382007, India; (R.V.); (V.C.); (K.K.); (S.S.)
| | - Sanghoon Shin
- Department of Electronics and Information Engineering, Korea University, Sejong 30019, Korea;
| | - Varun Chauhan
- Department of Computer Engineering, Pandit Deendayal Energy University, Gandhinagar 382007, India; (R.V.); (V.C.); (K.K.); (S.S.)
| | - Kaushal Kapadiya
- Department of Computer Engineering, Pandit Deendayal Energy University, Gandhinagar 382007, India; (R.V.); (V.C.); (K.K.); (S.S.)
| | - Smit Sanghavi
- Department of Computer Engineering, Pandit Deendayal Energy University, Gandhinagar 382007, India; (R.V.); (V.C.); (K.K.); (S.S.)
| | - Sungkyu Seo
- Department of Electronics and Information Engineering, Korea University, Sejong 30019, Korea;
| | - Mohendra Roy
- Department of Information and Communication Technology, Pandit Deendayal Energy University, Gandhinagar 38207, India
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16
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Min J, Allen M, Castro CM, Lee H, Weissleder R, Im H. Computational Optics for Point-of-Care Breast Cancer Profiling. Methods Mol Biol 2022; 2393:153-162. [PMID: 34837178 PMCID: PMC9283060 DOI: 10.1007/978-1-0716-1803-5_8] [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] [Indexed: 06/13/2023]
Abstract
With the global burden of cancer on the rise, it is critical to developing new modalities that could detect cancer and guide targeted treatments in fast and inexpensive ways. The need for such technologies is vital, especially in underserved regions where severe diagnostic bottlenecks exist. Recently, we developed a low-cost digital diagnostic system for breast cancer using fine-needle aspirates (FNAs). Named, AIDA (artificial intelligence diffraction analysis), the system combines lens-free digital diffraction imaging with deep-learning algorithms to achieve automated, rapid, and high-throughput cellular analyses for breast cancer diagnosis of FNA and subtype classification for better-guided treatments (Min et al. ACS Nano 12:9081-9090, 2018). Although primarily validated for breast cancer and lymphoma (Min et al. ACS Nano 12:9081-9090, 2018; Im et al. Nat Biomed Eng 2:666-674, 2018), the system could be easily adapted to diagnosing other prevalent cancers and thus find widespread use for global health.
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Affiliation(s)
- Jouha Min
- Center for Systems Biology, Massachusetts General Hospital, Boston, MA, USA
| | - Matthew Allen
- Center for Systems Biology, Massachusetts General Hospital, Boston, MA, USA
| | - Cesar M Castro
- Center for Systems Biology, Massachusetts General Hospital, Boston, MA, USA
- Cancer Center, Massachusetts General Hospital, Boston, MA, USA
| | - Hakho Lee
- Center for Systems Biology, Massachusetts General Hospital, Boston, MA, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Ralph Weissleder
- Center for Systems Biology, Massachusetts General Hospital, Boston, MA, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Hyungsoon Im
- Center for Systems Biology, Massachusetts General Hospital, Boston, MA, USA.
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.
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17
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Wang Y, Tan H, Yu T, Ma X, Chen X, Jing F, Zou L, Shi H. The identification of gene signatures in patients with extranodal NK/T-cell lymphoma from a pair of twins. BMC Cancer 2021; 21:1303. [PMID: 34872521 PMCID: PMC8650233 DOI: 10.1186/s12885-021-09023-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 11/17/2021] [Indexed: 02/08/2023] Open
Abstract
Background There is no unified treatment standard for patients with extranodal NK/T-cell lymphoma (ENKTL). Cancer neoantigens are the result of somatic mutations and cancer-specific. Increased number of somatic mutations are associated with anti-cancer effects. Screening out ENKTL-specific neoantigens on the surface of cancer cells relies on the understanding of ENKTL mutation patterns. Hence, it is imperative to identify ENKTL-specific genes for ENKTL diagnosis, the discovery of tumor-specific neoantigens and the development of novel therapeutic strategies. We investigated the gene signatures of ENKTL patients. Methods We collected the peripheral blood of a pair of twins for sequencing to identify unique variant genes. One of the twins is diagnosed with ENKTL. Seventy samples were analyzed by Robust Multi-array Analysis (RMA). Two methods (elastic net and Support Vector Machine-Recursive Feature Elimination) were used to select unique genes. Next, we performed functional enrichment analysis and pathway enrichment analysis. Then, we conducted single-sample gene set enrichment analysis of immune infiltration and validated the expression of the screened markers with limma packages. Results We screened out 126 unique variant genes. Among them, 11 unique genes were selected by the combination of elastic net and Support Vector Machine-Recursive Feature Elimination. Subsequently, GO and KEGG analysis indicated the biological function of identified unique genes. GSEA indicated five immunity-related pathways with high signature scores. In patients with ENKTL and the group with high signature scores, a proportion of functional immune cells are all of great infiltration. We finally found that CDC27, ZNF141, FCGR2C and NES were four significantly differential genes in ENKTL patients. ZNF141, FCGR2C and NES were upregulated in patients with ENKTL, while CDC27 was significantly downregulated. Conclusion We identified four ENKTL markers (ZNF141, FCGR2C, NES and CDC27) in patients with extranodal NK/T-cell lymphoma.
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Affiliation(s)
- Yang Wang
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, No. 17, Block 3, Southern Renmin Road, Chengdu, Sichuan, 610041, PR China
| | - Huaicheng Tan
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, No. 17, Block 3, Southern Renmin Road, Chengdu, Sichuan, 610041, PR China
| | - Ting Yu
- Department of Pathology and Laboratory of Pathology, State Key Laboratory of Biotherapy, West China Hospital, West China School of Medicine, Sichuan University, Chengdu, China
| | - Xuelei Ma
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.,Department of Radiotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Xiaoxuan Chen
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, China
| | - Fangqi Jing
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, China
| | - Liqun Zou
- Department of Head and Neck Cancer, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Huashan Shi
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China. .,Department of Radiotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China.
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18
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You JB, McCallum C, Wang Y, Riordon J, Nosrati R, Sinton D. Machine learning for sperm selection. Nat Rev Urol 2021; 18:387-403. [PMID: 34002070 DOI: 10.1038/s41585-021-00465-1] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/30/2021] [Indexed: 02/04/2023]
Abstract
Infertility rates and the number of couples seeking fertility care have increased worldwide over the past few decades. Over 2.5 million cycles of assisted reproductive technologies are being performed globally every year, but the success rate has remained at ~33%. Machine learning, an automated method of data analysis based on patterns and inference, is increasingly being deployed within the health-care sector to improve diagnostics and therapeutics. This technique is already aiding embryo selection in some fertility clinics, and has also been applied in research laboratories to improve sperm analysis and selection. Tremendous opportunities exist for machine learning to advance male fertility treatments. The fundamental challenge of sperm selection - selecting the most promising candidate from 108 gametes - presents a challenge that is uniquely well-suited to the high-throughput capabilities of machine learning algorithms paired with modern data processing capabilities.
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Affiliation(s)
- Jae Bem You
- Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, ON, Canada.,Department of Chemical Engineering, Kyungpook National University, Daegu, Republic of Korea
| | - Christopher McCallum
- Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, ON, Canada
| | - Yihe Wang
- Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, ON, Canada
| | - Jason Riordon
- Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, ON, Canada
| | - Reza Nosrati
- Department of Mechanical & Aerospace Engineering, Monash University, Clayton, VIC, Australia
| | - David Sinton
- Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, ON, Canada.
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19
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Jaquet A, Boni SP, Boidy K, Tine J, Tchounga B, Touré SA, Koffi JJ, Dial C, Monnereau A, Diomande I, Tanon A, Seydi M, Dabis F, Diop S, Koffi G. Chronic viral hepatitis, HIV infection and Non-Hodgkin lymphomas in West Africa, a case-control study. Int J Cancer 2021; 149:1536-1543. [PMID: 34124779 DOI: 10.1002/ijc.33709] [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: 04/14/2021] [Revised: 05/17/2021] [Accepted: 05/28/2021] [Indexed: 12/09/2022]
Abstract
Non-Hodgkin lymphomas (NHL) are underestimated causes of cancer in West Africa where chronic viral hepatitis and HIV are endemic. While the association with HIV infection has already been characterized, limited information is available on the association between chronic viral hepatitis and NHL in sub-Saharan Africa. A case-control study was conducted in referral hospitals of Abidjan (Cote d'Ivoire) and Dakar (Senegal). Cases of NHL were matched with controls on age, gender and participating site. The diagnosis of NHL relied on local pathological examination completed with immunohistochemistry. HIV, HBV and HCV serology tests were systematically performed. A conditional logistic regression model estimated the associations by the Odds Ratio (OR) with their 95% confidence interval (CI). A total of 117 NHL cases (Abidjan n = 97, Dakar n = 20) and their 234 matched controls were enrolled. Cases were predominantly men (68.4%) and had a median age of 50 years (IQR 37-57). While Diffuse Large B-cell lymphoma were the most reported morphological type (n = 35) among mature B-cell NHL, the proportion mature T-cell NHL (30%) was high. The prevalence figures of HBV, HCV and HIV infection were 12.8%, 7.7% and 14.5%, respectively among cases of NHL. In multivariate analysis, HBV, HCV and HIV were independently associated with NHL with OR of 2.23 (CI 1.05-4.75), 4.82 (CI 1.52-15.29) and 3.32 (CI 1.54-7.16), respectively. Chronic viral hepatitis B and C were significantly associated with NHL in West Africa. Timely preventive measures against HBV infection and access to curative anti-HCV treatment might prevent a significant number of NHL.
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Affiliation(s)
- Antoine Jaquet
- University of Bordeaux, Inserm, French National Research Institute for Sustainable Development (IRD), Bordeaux, France
| | - Simon P Boni
- Programme National de Lutte Contre le Cancer (PNLCa), Abidjan, Côte d'Ivoire.,Programme PACCI/Site ANRS Abidjan, Abidjan, Côte d'Ivoire
| | - Kouakou Boidy
- Service d'hématologie, Centre Hospitalier Universitaire de Yopougon, Abidjan, Côte d'Ivoire
| | - Judicaël Tine
- Service des Maladies Infectieuses et Tropicales, Centre Hospitalier National Universitaire de Fann, Dakar, Senegal
| | - Boris Tchounga
- Programme PACCI/Site ANRS Abidjan, Abidjan, Côte d'Ivoire.,Elizabeth Glaser Pediatric AIDS Foundation, Yaoundé, Cameroon
| | - Sokhna A Touré
- Service d'hématologie, Centre National de Transfusion Sanguine, Dakar, Senegal
| | | | - Cherif Dial
- Service Anatomopathologie, Hôpital de Grand Yoff, Dakar, Senegal
| | - Alain Monnereau
- University of Bordeaux, Inserm, French National Research Institute for Sustainable Development (IRD), Bordeaux, France
| | - Isidore Diomande
- Service Anatomopathologie, Centre Hospitalier Universitaire Cocody, Abidjan, Côte d'Ivoire
| | - Aristophane Tanon
- Service des Maladies Infectieuses et Tropicales, Centre Hospitalier Universitaire de Treichville, Abidjan, Côte d'Ivoire
| | - Moussa Seydi
- Service des Maladies Infectieuses et Tropicales, Centre Hospitalier National Universitaire de Fann, Dakar, Senegal
| | - François Dabis
- University of Bordeaux, Inserm, French National Research Institute for Sustainable Development (IRD), Bordeaux, France
| | - Saliou Diop
- Service d'hématologie, Centre National de Transfusion Sanguine, Dakar, Senegal
| | - Gustave Koffi
- Service d'hématologie, Centre Hospitalier Universitaire de Yopougon, Abidjan, Côte d'Ivoire
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20
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Manco L, Maffei N, Strolin S, Vichi S, Bottazzi L, Strigari L. Basic of machine learning and deep learning in imaging for medical physicists. Phys Med 2021; 83:194-205. [DOI: 10.1016/j.ejmp.2021.03.026] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 03/07/2021] [Accepted: 03/16/2021] [Indexed: 02/08/2023] Open
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21
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Chen L, Liu Y, Xu H, Ma L, Wang Y, Wang F, Zhu J, Hu X, Yi K, Yang Y, Shen H, Zhou F, Gao X, Cheng Y, Bai L, Duan Y, Wang F, Zhu Y. Touchable cell biophysics property recognition platforms enable multifunctional blood smart health care. MICROSYSTEMS & NANOENGINEERING 2021; 7:103. [PMID: 34963817 PMCID: PMC8651774 DOI: 10.1038/s41378-021-00329-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 10/25/2021] [Accepted: 11/06/2021] [Indexed: 05/10/2023]
Abstract
As a crucial biophysical property, red blood cell (RBC) deformability is pathologically altered in numerous disease states, and biochemical and structural changes occur over time in stored samples of otherwise normal RBCs. However, there is still a gap in applying it further to point-of-care blood devices due to the large external equipment (high-resolution microscope and microfluidic pump), associated operational difficulties, and professional analysis. Herein, we revolutionarily propose a smart optofluidic system to provide a differential diagnosis for blood testing via precise cell biophysics property recognition both mechanically and morphologically. Deformation of the RBC population is caused by pressing the hydrogel via an integrated mechanical transfer device. The biophysical properties of the cell population are obtained by the designed smartphone algorithm. Artificial intelligence-based modeling of cell biophysics properties related to blood diseases and quality was developed for online testing. We currently achieve 100% diagnostic accuracy for five typical clinical blood diseases (90 megaloblastic anemia, 78 myelofibrosis, 84 iron deficiency anemia, 48 thrombotic thrombocytopenic purpura, and 48 thalassemias) via real-world prospective implementation; furthermore, personalized blood quality (for transfusion in cardiac surgery) monitoring is achieved with an accuracy of 96.9%. This work suggests a potential basis for next-generation blood smart health care devices.
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Affiliation(s)
- Longfei Chen
- Affiliations School of Physics & Technology, Key Laboratory of Artificial Micro/Nano Structure of Ministry of Education, Wuhan University, Wuhan, 430072 China
- Shenzhen Research Institute, Wuhan University, Shenzhen, 518000 China
| | - Yantong Liu
- Affiliations School of Physics & Technology, Key Laboratory of Artificial Micro/Nano Structure of Ministry of Education, Wuhan University, Wuhan, 430072 China
- Shenzhen Research Institute, Wuhan University, Shenzhen, 518000 China
| | - Hongshan Xu
- Affiliations School of Physics & Technology, Key Laboratory of Artificial Micro/Nano Structure of Ministry of Education, Wuhan University, Wuhan, 430072 China
| | - Linlu Ma
- Department of Hematology, Zhongnan Hospital, Wuhan University, Wuhan, 430071 China
| | - Yifan Wang
- Affiliations School of Physics & Technology, Key Laboratory of Artificial Micro/Nano Structure of Ministry of Education, Wuhan University, Wuhan, 430072 China
| | - Fang Wang
- Affiliations School of Physics & Technology, Key Laboratory of Artificial Micro/Nano Structure of Ministry of Education, Wuhan University, Wuhan, 430072 China
| | - Jiaomeng Zhu
- Affiliations School of Physics & Technology, Key Laboratory of Artificial Micro/Nano Structure of Ministry of Education, Wuhan University, Wuhan, 430072 China
| | - Xuejia Hu
- Affiliations School of Physics & Technology, Key Laboratory of Artificial Micro/Nano Structure of Ministry of Education, Wuhan University, Wuhan, 430072 China
| | - Kezhen Yi
- Department of Laboratory Medicine, Zhongnan Hospital, Wuhan University, Wuhan, 430071 China
| | - Yi Yang
- Affiliations School of Physics & Technology, Key Laboratory of Artificial Micro/Nano Structure of Ministry of Education, Wuhan University, Wuhan, 430072 China
- Shenzhen Research Institute, Wuhan University, Shenzhen, 518000 China
| | - Hui Shen
- Department of Hematology, Zhongnan Hospital, Wuhan University, Wuhan, 430071 China
| | - Fuling Zhou
- Department of Hematology, Zhongnan Hospital, Wuhan University, Wuhan, 430071 China
| | - Xiaoqi Gao
- Affiliations School of Physics & Technology, Key Laboratory of Artificial Micro/Nano Structure of Ministry of Education, Wuhan University, Wuhan, 430072 China
| | - Yanxiang Cheng
- Remin Hospital of Wuhan University, Wuhan University, Wuhan, 430060 China
| | - Long Bai
- School of Medicine, Zhejiang University, Hangzhou, Zhejiang, 310002 China
| | - Yongwei Duan
- Department of Laboratory Medicine, Zhongnan Hospital, Wuhan University, Wuhan, 430071 China
| | - Fubing Wang
- Department of Laboratory Medicine, Zhongnan Hospital, Wuhan University, Wuhan, 430071 China
| | - Yimin Zhu
- School of Medicine, Zhejiang University, Hangzhou, Zhejiang, 310002 China
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22
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23
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Radakovich N, Nagy M, Nazha A. Artificial Intelligence in Hematology: Current Challenges and Opportunities. Curr Hematol Malig Rep 2020; 15:203-210. [PMID: 32239350 DOI: 10.1007/s11899-020-00575-4] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
PURPOSE OF REVIEW Artificial intelligence (AI), and in particular its subcategory machine learning, is finding an increasing number of applications in medicine, driven in large part by an abundance of data and powerful, accessible tools that have made AI accessible to a larger circle of investigators. RECENT FINDINGS AI has been employed in the analysis of hematopathological, radiographic, laboratory, genomic, pharmacological, and chemical data to better inform diagnosis, prognosis, treatment planning, and foundational knowledge related to benign and malignant hematology. As more widespread implementation of clinical AI nears, attention has also turned to the effects this will have on other areas in medicine. AI offers many promising tools to clinicians broadly, and specifically in the practice of hematology. Ongoing research into its various applications will likely result in an increasing utilization of AI by a broader swath of clinicians.
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Affiliation(s)
- Nathan Radakovich
- Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH, USA
| | - Matthew Nagy
- Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH, USA
| | - Aziz Nazha
- Center for Clinical Artificial Intelligence, Cleveland Clinic, Cleveland, OH, USA.
- Department of Hematology and Medical Oncology, Taussig Cancer Institute, Cleveland Clinic, Desk R35 9500 Euclid Ave., Cleveland, OH, 44195, USA.
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24
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Dabbagh SR, Rabbi F, Doğan Z, Yetisen AK, Tasoglu S. Machine learning-enabled multiplexed microfluidic sensors. BIOMICROFLUIDICS 2020; 14:061506. [PMID: 33343782 PMCID: PMC7733540 DOI: 10.1063/5.0025462] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2020] [Accepted: 12/01/2020] [Indexed: 05/02/2023]
Abstract
High-throughput, cost-effective, and portable devices can enhance the performance of point-of-care tests. Such devices are able to acquire images from samples at a high rate in combination with microfluidic chips in point-of-care applications. However, interpreting and analyzing the large amount of acquired data is not only a labor-intensive and time-consuming process, but also prone to the bias of the user and low accuracy. Integrating machine learning (ML) with the image acquisition capability of smartphones as well as increasing computing power could address the need for high-throughput, accurate, and automatized detection, data processing, and quantification of results. Here, ML-supported diagnostic technologies are presented. These technologies include quantification of colorimetric tests, classification of biological samples (cells and sperms), soft sensors, assay type detection, and recognition of the fluid properties. Challenges regarding the implementation of ML methods, including the required number of data points, image acquisition prerequisites, and execution of data-limited experiments are also discussed.
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Affiliation(s)
| | - Fazle Rabbi
- Department of Mechanical Engineering, Koç University, Sariyer, Istanbul 34450, Turkey
| | | | - Ali Kemal Yetisen
- Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom
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25
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Pai SI, Faquin WC, Sadow PM, Pittet MJ, Weissleder R. New technology on the horizon: Fast analytical screening technique FNA (FAST-FNA) enables rapid, multiplex biomarker analysis in head and neck cancers. Cancer Cytopathol 2020; 128:782-791. [PMID: 32841527 PMCID: PMC8276888 DOI: 10.1002/cncy.22305] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 04/08/2020] [Accepted: 04/10/2020] [Indexed: 01/26/2023]
Abstract
PD-L1 profiling was recently approved by the US Food and Drug Administration as a companion diagnostic for anti-PD1 treatment in patients with head and neck cancer, ushering in a new era for precision medicine. However, the routine development and implementation of such testing is still limited by current clinical workflows and the lack of better and more comprehensive alternatives. In this review, the authors discuss the real-world challenges of clinically based biomarker testing and highlight the advantages of developing fine-needle aspiration (FNA)-based biomarker testing that would enable frequent and serial tumor sampling. A conceptual and technological innovation is introduced, fast analytical screening technique (FAST)-FNA (FAST chemistry-enabled FNA), which is being developed to inform immunotherapy treatment options in patients with head and neck cancer and to assist with the development of the next generation of predictive biomarkers.
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Affiliation(s)
- Sara I. Pai
- Division of Surgical Oncology, Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - William C. Faquin
- Division of Head and Neck Pathology, Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Peter M. Sadow
- Division of Head and Neck Pathology, Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Mikael J. Pittet
- Center for Systems Biology, Massachusetts General Hospital, Boston, Massachusetts
| | - Ralph Weissleder
- Center for Systems Biology, Massachusetts General Hospital, Boston, Massachusetts
- Division of Interventional Radiology, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts
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26
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Molinski J, Tadimety A, Burklund A, Zhang JXJ. Scalable Signature-Based Molecular Diagnostics Through On-chip Biomarker Profiling Coupled with Machine Learning. Ann Biomed Eng 2020; 48:2377-2399. [PMID: 32816167 PMCID: PMC7785517 DOI: 10.1007/s10439-020-02593-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 08/11/2020] [Indexed: 02/07/2023]
Abstract
Molecular diagnostics have traditionally relied on discrete biological substances as diagnostic markers. In recent years however, advances in on-chip biomarker screening technologies and data analytics have enabled signature-based diagnostics. Such diagnostics aim to utilize unique combinations of multiple biomarkers or diagnostic 'fingerprints' rather than discrete analyte measurements. This approach has shown to improve both diagnostic accuracy and diagnostic specificity. In this review, signature-based diagnostics enabled by microfluidic and micro-/nano- technologies will be reviewed with a focus on device design and data analysis pipelines and methodologies. With increasing amounts of data available from microfluidic biomarker screening, isolation, and detection platforms, advanced data handling and analytics approaches can be employed. Thus, current data analysis approaches including machine learning and recent advances with image processing, along with potential future directions will be explored. Lastly, the needs and gaps in current literature will be elucidated to inform future efforts towards development of molecular diagnostics and biomarker screening technologies.
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Affiliation(s)
- John Molinski
- Thayer School of Engineering at Dartmouth, 14 Engineering Drive, Hanover, NH, 03755, USA
| | - Amogha Tadimety
- Thayer School of Engineering at Dartmouth, 14 Engineering Drive, Hanover, NH, 03755, USA
| | - Alison Burklund
- Thayer School of Engineering at Dartmouth, 14 Engineering Drive, Hanover, NH, 03755, USA
| | - John X J Zhang
- Thayer School of Engineering at Dartmouth, 14 Engineering Drive, Hanover, NH, 03755, USA.
- Norris Cotton Cancer Center, Dartmouth Hitchcock Medical Center, Lebanon, NH, USA.
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27
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Min J, Chin LK, Oh J, Landeros C, Vinegoni C, Lee J, Lee SJ, Park JY, Liu AQ, Castro CM, Lee H, Im H, Weissleder R. CytoPAN-Portable cellular analyses for rapid point-of-care cancer diagnosis. Sci Transl Med 2020; 12:eaaz9746. [PMID: 32759277 PMCID: PMC8217912 DOI: 10.1126/scitranslmed.aaz9746] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Revised: 03/06/2020] [Accepted: 06/19/2020] [Indexed: 12/18/2022]
Abstract
Rapid, automated, point-of-care cellular diagnosis of cancer remains difficult in remote settings due to lack of specialists and medical infrastructure. To address the need for same-day diagnosis, we developed an automated image cytometry system (CytoPAN) that allows rapid breast cancer diagnosis of scant cellular specimens obtained by fine needle aspiration (FNA) of palpable mass lesions. The system is devoid of moving parts for stable operations, harnesses optimized antibody kits for multiplexed analysis, and offers a user-friendly interface with automated analysis for rapid diagnoses. Through extensive optimization and validation using cell lines and mouse models, we established breast cancer diagnosis and receptor subtyping in 1 hour using as few as 50 harvested cells. In a prospective patient cohort study (n = 68), we showed that the diagnostic accuracy was 100% for cancer detection and the receptor subtyping accuracy was 96% for human epidermal growth factor receptor 2 and 93% for hormonal receptors (ER/PR), two key biomarkers associated with breast cancer. A combination of FNA and CytoPAN offers faster, less invasive cancer diagnoses than the current standard (core biopsy and histopathology). This approach should enable the ability to more rapidly diagnose breast cancer in global and remote settings.
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Affiliation(s)
- Jouha Min
- Center for Systems Biology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Lip Ket Chin
- Center for Systems Biology, Massachusetts General Hospital, Boston, MA 02114, USA
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore
| | - Juhyun Oh
- Center for Systems Biology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Christian Landeros
- Center for Systems Biology, Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard-MIT Program in Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Claudio Vinegoni
- Center for Systems Biology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Jeeyeon Lee
- Department of Surgery, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, Daegu 41404, Republic of Korea
| | - Soo Jung Lee
- Department of Oncology/Hematology, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, Daegu 41404, Republic of Korea
| | - Jee Young Park
- Department of Pathology, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, Daegu 41404, Republic of Korea
| | - Ai-Qun Liu
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore
| | - Cesar M Castro
- Center for Systems Biology, Massachusetts General Hospital, Boston, MA 02114, USA
- Cancer Center, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Hakho Lee
- Center for Systems Biology, Massachusetts General Hospital, Boston, MA 02114, USA.
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Hyungsoon Im
- Center for Systems Biology, Massachusetts General Hospital, Boston, MA 02114, USA.
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Ralph Weissleder
- Center for Systems Biology, Massachusetts General Hospital, Boston, MA 02114, USA.
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
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28
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Park Y, Han SH, Byun W, Kim JH, Lee HC, Kim SJ. A Real-Time Depth of Anesthesia Monitoring System Based on Deep Neural Network With Large EDO Tolerant EEG Analog Front-End. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2020; 14:825-837. [PMID: 32746339 DOI: 10.1109/tbcas.2020.2998172] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
In this article, we present a real-time electroencephalogram (EEG) based depth of anesthesia (DoA) monitoring system in conjunction with a deep learning framework, AnesNET. An EEG analog front-end (AFE) that can compensate ±380-mV electrode DC offset using a coarse digital DC servo loop is implemented in the proposed system. The EEG-based MAC, EEGMAC, is introduced as a novel index to accurately predict the DoA, which is designed for applying to patients anesthetized by both volatile and intravenous agents. The proposed deep learning protocol consists of four layers of convolutional neural network and two dense layers. In addition, we optimize the complexity of the deep neural network (DNN) to operate on a microcomputer such as the Raspberry Pi 3, realizing a cost-effective small-size DoA monitoring system. Fabricated in 110-nm CMOS, the prototype AFE consumes 4.33 μW per channel and has the input-referred noise of 0.29 μVrms from 0.5 to 100 Hz with the noise efficiency factor of 2.2. The proposed DNN was evaluated with pre-recorded EEG data from 374 subjects administrated by inhalational anesthetics under surgery, achieving an average squared and absolute errors of 0.048 and 0.05, respectively. The EEGMAC with subjects anesthetized by an intravenous agent also showed a good agreement with the bispectral index value, confirming the proposed DoA index is applicable to both anesthetics. The implemented monitoring system with the Raspberry Pi 3 estimates the EEGMAC within 20 ms, which is about thousand-fold faster than the BIS estimation in literature.
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29
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Zhang J, Cui W, Guo X, Wang B, Wang Z. Classification of digital pathological images of non-Hodgkin's lymphoma subtypes based on the fusion of transfer learning and principal component analysis. Med Phys 2020; 47:4241-4253. [PMID: 32593219 DOI: 10.1002/mp.14357] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Revised: 05/31/2020] [Accepted: 06/19/2020] [Indexed: 11/06/2022] Open
Abstract
PURPOSE Non-Hodgkin's lymphoma (NHL) is a serious malignant disease. Delayed diagnosis will cause anemia, increased intracranial pressure, organ failure, and even lead to death. The current main trend in this area is to use deep learning (DL) for disease diagnosis. Extracting classification information from the digital pathology images by DL may realize the automated qualitative and quantitative analysis of NHL. Previously, DL has been used to classify NHL digital pathology images with some success. However, shortcomings still exist in the data preprocessing methods and feature extraction. Therefore, this paper presents a method for the classification of NHL subtypes based on the fusion of transfer learning (TL) and principal component analysis (PCA). METHODS First, the NHL digital pathology images were preprocessed by image division and segmentation and then input into the transfer models for fine-tuning and feature extraction. Second, PCA was used to map the extracted features. Finally, a neural network was used as a classifier to classify the mapped features. During the fine-tuning of the transfer models, two methods, freezing all feature extraction layers and fine-tuning all layers, were employed to select the optimal model with the best classification result among all the preselected transfer models. On this basis, the use of freezing the layers' location was discussed and analyzed. RESULTS The results show that the proposed method achieved average fivefold cross-validation accuracies of 100%, 99.73%, and 99.20% for chronic lymphocytic leukemia (CLL), follicular lymphoma (FL), and mantle cell lymphoma (MCL) tumor, and each category has standard deviations 0.00, 0.53, and 0.65, respectively, in the NHL reference dataset. The overall classification accuracy for fivefold cross-validation is 98.93%, which is an increase of 1.26% compared to the latest reported methods, having a lower standard deviation (1.00). CONCLUSION The method proposed in this paper achieves a high classification accuracy and strong model generalization for the classification of NHL, which makes it possible to conduct intelligent classification of NHL in clinical practice. Our proposed method has definite clinical value and research significance.
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Affiliation(s)
- Jianfei Zhang
- School of computer and control engineering, Qiqihar university, Qiqihar, 161006, China
| | - Wensheng Cui
- School of computer and control engineering, Qiqihar university, Qiqihar, 161006, China
| | - Xiaoyan Guo
- School of computer and control engineering, Qiqihar university, Qiqihar, 161006, China
| | - Bo Wang
- School of computer and control engineering, Qiqihar university, Qiqihar, 161006, China
| | - Zhen Wang
- School of computer and control engineering, Qiqihar university, Qiqihar, 161006, China
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30
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Machine learning in haematological malignancies. LANCET HAEMATOLOGY 2020; 7:e541-e550. [PMID: 32589980 DOI: 10.1016/s2352-3026(20)30121-6] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 04/02/2020] [Accepted: 04/14/2020] [Indexed: 02/06/2023]
Abstract
Machine learning is a branch of computer science and statistics that generates predictive or descriptive models by learning from training data rather than by being rigidly programmed. It has attracted substantial attention for its many applications in medicine, both as a catalyst for research and as a means of improving clinical care across the cycle of diagnosis, prognosis, and treatment of disease. These applications include the management of haematological malignancy, in which machine learning has created inroads in pathology, radiology, genomics, and the analysis of electronic health record data. As computational power becomes cheaper and the tools for implementing machine learning become increasingly democratised, it is likely to become increasingly integrated into the research and practice landscape of haematology. As such, machine learning merits understanding and attention from researchers and clinicians alike. This narrative Review describes important concepts in machine learning for unfamiliar readers, details machine learning's current applications in haematological malignancy, and summarises important concepts for clinicians to be aware of when appraising research that uses machine learning.
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Shouval R, Fein JA, Savani B, Mohty M, Nagler A. Machine learning and artificial intelligence in haematology. Br J Haematol 2020; 192:239-250. [PMID: 32602593 DOI: 10.1111/bjh.16915] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Digitalization of the medical record and integration of genomic methods into clinical practice have resulted in an unprecedented wealth of data. Machine learning is a subdomain of artificial intelligence that attempts to computationally extract meaningful insights from complex data structures. Applications of machine learning in haematological scenarios are steadily increasing. However, basic concepts are often unfamiliar to clinicians and investigators. The purpose of this review is to provide readers with tools to interpret and critically appraise machine learning literature. We begin with the elucidation of standard terminology and then review examples in haematology. Guidelines for designing and evaluating machine-learning studies are provided. Finally, we discuss limitations of the machine-learning approach.
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Affiliation(s)
- Roni Shouval
- Adult Bone Marrow Transplant Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Hematology and Bone Marrow Transplantation Division, Chaim Sheba Medical Center, Tel-Hashomer, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Joshua A Fein
- University of Connecticut Medical Center, Farmington, CT, USA
| | - Bipin Savani
- Division of Hematology-Oncology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Mohamad Mohty
- European Society for Blood and Marrow Transplantation Paris Study Office/CEREST-TC, Paris, France.,Service d'Hématologie Clinique et de Thérapie Cellulaire, Hôpital Saint Antoine, AP-HP, Paris, France
| | - Arnon Nagler
- Hematology and Bone Marrow Transplantation Division, Chaim Sheba Medical Center, Tel-Hashomer, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
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Abstract
Infectious diseases are caused by microorganisms belonging to the class of bacteria, viruses, fungi, or parasites. These pathogens are transmitted, directly or indirectly, and can lead to epidemics or even pandemics. The resulting infection may lead to mild-to-severe symptoms such as life-threatening fever or diarrhea. Infectious diseases may be asymptomatic in some individuals but may lead to disastrous effects in others. Despite the advances in medicine, infectious diseases are a leading cause of death worldwide, especially in low-income countries. With the advent of mathematical tools, scientists are now able to better predict epidemics, understand the specificity of each pathogen, and identify potential targets for drug development. Artificial intelligence and its components have been widely publicized for their ability to better diagnose certain types of cancer from imaging data. This chapter aims at identifying potential applications of machine learning in the field of infectious diseases. We are deliberately focusing on key aspects of infection: diagnosis, transmission, response to treatment, and resistance. We are proposing the use of extreme values as an avenue of interest for future developments in the field of infectious diseases. This chapter covers a series of applications selectively chosen to showcase how artificial intelligence is moving the field of infectious disease further and how it helps institutions to better tackles them, especially in low-income countries.
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Affiliation(s)
- Said Agrebi
- Yobitrust, Technopark El Gazala, Ariana, Tunisia
| | - Anis Larbi
- Singapore Immunology Network, Agency for Science, Technology and Research, Singapore, Singapore,Department of Microbiology & Immunology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
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Das Saha N, Sasmal R, Meethal SK, Vats S, Gopinathan PV, Jash O, Manjithaya R, Gagey-Eilstein N, Agasti SS. Multichannel DNA Sensor Array Fingerprints Cell States and Identifies Pharmacological Effectors of Catabolic Processes. ACS Sens 2019; 4:3124-3132. [PMID: 31763818 DOI: 10.1021/acssensors.9b01009] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Cells at disease onset are often associated with subtle changes in the expression level of a single or few molecular components, making traditionally used biomarker-driven clinical diagnosis a challenging task. We demonstrate here the design of a DNA nanosensor array with multichannel output that identifies the normal or pathological state of a cell based on the alteration of its global proteomic signature. Fluorophore-encoded single-stranded DNA (ssDNA) strands were coupled via supramolecular interaction with a surface-functionalized gold nanoparticle quencher to generate this integrated sensor array. In this design, ssDNA sequences exhibit dual roles, where they provide differential affinities with the receptor gold nanoparticle as well as act as transducer elements. The unique interaction mode of the analyte molecules disrupts the noncovalent supramolecular complexation, generating simultaneous multichannel fluorescence output to enable signature-based analyte identification via a linear discriminant analysis-based machine learning algorithm. Different cell types, particularly normal and cancerous cells, were effectively distinguished using their fluorescent fingerprints. Additionally, this DNA sensor array displayed excellent sensitivity to identify cellular alterations associated with chemical modulation of catabolic processes. Importantly, pharmacological effectors, which could modulate autophagic flux, have been effectively distinguished by generating responses from their global protein signatures. Taken together, these studies demonstrate that our multichannel DNA nanosensor is well suited for rapid identification of subtle changes in a complex mixture and thus can be readily expanded for point-of-care clinical diagnosis, high-throughput drug screening, or predicting the therapeutic outcome from a limited sample volume.
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Affiliation(s)
| | | | | | | | | | | | | | - Nathalie Gagey-Eilstein
- UMR-S 1139, INSERM, 3PHM, Université Paris Descartes, Faculté des Sciences Pharmaceutiques et Biologiques, Sorbonne Paris Cité, 4 avenue de l’Observatoire, 75006 Paris, France
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Pathania D, Landeros C, Rohrer L, D'Agostino V, Hong S, Degani I, Avila-Wallace M, Pivovarov M, Randall T, Weissleder R, Lee H, Im H, Castro CM. Point-of-care cervical cancer screening using deep learning-based microholography. Am J Cancer Res 2019; 9:8438-8447. [PMID: 31879529 PMCID: PMC6924258 DOI: 10.7150/thno.37187] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Accepted: 09/24/2019] [Indexed: 12/11/2022] Open
Abstract
Most deaths (80%) from cervical cancer occur in regions lacking adequate screening infrastructures or ready access to them. In contrast, most developed countries now embrace human papillomavirus (HPV) analyses as standalone screening; this transition threatens to further widen the resource gap. Methods: We describe the development of a DNA-focused digital microholography platform for point-of-care HPV screening, with automated readouts driven by customized deep-learning algorithms. In the presence of high-risk HPV 16 or 18 DNA, microbeads were designed to bind the DNA targets and form microbead dimers. The resulting holographic signature of the microbeads was recorded and analyzed. Results: The HPV DNA assay showed excellent sensitivity (down to a single cell) and specificity (100% concordance) in detecting HPV 16 and 18 DNA from cell lines. Our deep learning approach was 120-folder faster than the traditional reconstruction method and completed the analysis in < 2 min using a single CPU. In a blinded clinical study using patient cervical brushings, we successfully benchmarked our platform's performance to an FDA-approved HPV assay. Conclusions: Reliable and decentralized HPV testing will facilitate cataloguing the high-risk HPV landscape in underserved populations, revealing HPV coverage gaps in existing vaccination strategies and informing future iterations.
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Sunny S, Baby A, James BL, Balaji D, N. V. A, Rana MH, Gurpur P, Skandarajah A, D’Ambrosio M, Ramanjinappa RD, Mohan SP, Raghavan N, Kandasarma U, N. S, Raghavan S, Hedne N, Koch F, Fletcher DA, Selvam S, Kollegal M, N. PB, Ladic L, Suresh A, Pandya HJ, Kuriakose MA. A smart tele-cytology point-of-care platform for oral cancer screening. PLoS One 2019; 14:e0224885. [PMID: 31730638 PMCID: PMC6857853 DOI: 10.1371/journal.pone.0224885] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Accepted: 10/23/2019] [Indexed: 12/14/2022] Open
Abstract
Early detection of oral cancer necessitates a minimally invasive, tissue-specific diagnostic tool that facilitates screening/surveillance. Brush biopsy, though minimally invasive, demands skilled cyto-pathologist expertise. In this study, we explored the clinical utility/efficacy of a tele-cytology system in combination with Artificial Neural Network (ANN) based risk-stratification model for early detection of oral potentially malignant (OPML)/malignant lesion. A portable, automated tablet-based tele-cytology platform capable of digitization of cytology slides was evaluated for its efficacy in the detection of OPML/malignant lesions (n = 82) in comparison with conventional cytology and histology. Then, an image pre-processing algorithm was established to segregate cells, ANN was trained with images (n = 11,981) and a risk-stratification model developed. The specificity, sensitivity and accuracy of platform/ stratification model were computed, and agreement was examined using Kappa statistics. The tele-cytology platform, Cellscope, showed an overall accuracy of 84–86% with no difference between tele-cytology and conventional cytology in detection of oral lesions (kappa, 0.67–0.72). However, OPML could be detected with low sensitivity (18%) in accordance with the limitations of conventional cytology. The integration of image processing and development of an ANN-based risk stratification model improved the detection sensitivity of malignant lesions (93%) and high grade OPML (73%), thereby increasing the overall accuracy by 30%. Tele-cytology integrated with the risk stratification model, a novel strategy established in this study, can be an invaluable Point-of-Care (PoC) tool for early detection/screening in oral cancer. This study hence establishes the applicability of tele-cytology for accurate, remote diagnosis and use of automated ANN-based analysis in improving its efficacy.
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Affiliation(s)
- Sumsum Sunny
- Head and Neck Oncology, Mazumdar Shaw Medical Centre, NH Health city, Bangalore, India
- Integrated Head and Neck Oncology Program (DSRG-5), Mazumdar Shaw Medical Foundation, NH Health city, Bangalore, India
- Manipal Academy of Higher Education, Manipal, Karnataka, India
- Biomedical and Electronic (10-10) Engineering Systems Laboratory, Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, India
| | - Arun Baby
- Biomedical and Electronic (10-10) Engineering Systems Laboratory, Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, India
| | - Bonney Lee James
- Integrated Head and Neck Oncology Program (DSRG-5), Mazumdar Shaw Medical Foundation, NH Health city, Bangalore, India
| | - Dev Balaji
- Biomedical and Electronic (10-10) Engineering Systems Laboratory, Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, India
| | - Aparna N. V.
- Biomedical and Electronic (10-10) Engineering Systems Laboratory, Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, India
| | - Maitreya H. Rana
- Biomedical and Electronic (10-10) Engineering Systems Laboratory, Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, India
| | | | - Arunan Skandarajah
- Department of Bioengineering & Biophysics Program, University of California, Berkeley, California, United States of America
| | - Michael D’Ambrosio
- Department of Bioengineering & Biophysics Program, University of California, Berkeley, California, United States of America
| | | | - Sunil Paramel Mohan
- Department of Oral and Maxillofacial pathology, Sree Anjaneya Dental College, Kozhikode, Kerala, India
| | - Nisheena Raghavan
- Department of Pathology, Mazumdar Shaw Medical Centre, NH Health city, Bangalore, India
| | - Uma Kandasarma
- Department of Oral and Maxillofacial Pathology, KLE Society’s Institute of Dental Sciences, Bangalore, India
| | - Sangeetha N.
- Department of oral medicine and radiology, KLE Society’s Institute of Dental Sciences, Bangalore, India
| | - Subhasini Raghavan
- Department of oral medicine and radiology, KLE Society’s Institute of Dental Sciences, Bangalore, India
| | - Naveen Hedne
- Head and Neck Oncology, Mazumdar Shaw Medical Centre, NH Health city, Bangalore, India
| | - Felix Koch
- University of Mainz, 55099, Mainz, Germany
| | - Daniel A. Fletcher
- Department of Bioengineering & Biophysics Program, University of California, Berkeley, California, United States of America
| | - Sumithra Selvam
- Division of Epidemiology and Biostatistics, St. John’s Research Institute, St. John’s National Academy of Health Sciences, Bangalore, India
| | | | - Praveen Birur N.
- Head and Neck Oncology, Mazumdar Shaw Medical Centre, NH Health city, Bangalore, India
- Department of oral medicine and radiology, KLE Society’s Institute of Dental Sciences, Bangalore, India
| | - Lance Ladic
- Siemens Healthineers, Malvern, Pennsylvania, United States of America
| | - Amritha Suresh
- Head and Neck Oncology, Mazumdar Shaw Medical Centre, NH Health city, Bangalore, India
- Integrated Head and Neck Oncology Program (DSRG-5), Mazumdar Shaw Medical Foundation, NH Health city, Bangalore, India
| | - Hardik J. Pandya
- Biomedical and Electronic (10-10) Engineering Systems Laboratory, Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, India
- * E-mail: (HJP); (MAK)
| | - Moni Abraham Kuriakose
- Head and Neck Oncology, Mazumdar Shaw Medical Centre, NH Health city, Bangalore, India
- Integrated Head and Neck Oncology Program (DSRG-5), Mazumdar Shaw Medical Foundation, NH Health city, Bangalore, India
- * E-mail: (HJP); (MAK)
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Saleh M, Naik G, Mwirigi A, Shaikh AJ, Sayani S, Ghesani M, Asaria S, Sohani AR, Sayed S, Moloo Z, Budhwani KI, Talib Z. Bridging the Gap in Training and Clinical Practice in Sub-Saharan Africa. CURRENT BREAST CANCER REPORTS 2019. [DOI: 10.1007/s12609-019-00322-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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Marquard AN, Carlson JCT, Weissleder R. Glass Chemistry to Analyze Human Cells under Adverse Conditions. ACS OMEGA 2019; 4:11515-11521. [PMID: 31460257 PMCID: PMC6682085 DOI: 10.1021/acsomega.9b01036] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Accepted: 06/19/2019] [Indexed: 05/17/2023]
Abstract
Emerging point-of-care diagnostic tests capable of analyzing whole mammalian cells often rely on the attachment of harvested cells to glass surfaces for subsequent molecular characterization. We set out to develop and optimize a kit for the diagnosis of lymphoma in low- and middle-income countries where access to advanced healthcare testing is often absent or prone to error. Here, we optimized a process for the lyophilization of neutravidin-coated glass and cocktails of antibodies relevant to lymphoma diagnosis to establish long-term stability of reagents required for point-of-care cell capture technology. Lyophilized glass slides showed no decline in their performance compared to freshly prepared neutravidin glass and preserved capture efficiency for 5 weeks under easily attainable storage conditions. We demonstrate the successful performance of the low-cost, lyophilized kit in a cell capture assay to enable true point-of-care analyses under adverse conditions. We anticipate that the strategy can be expanded to other cancer cell types or cell-derived vesicle analysis.
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Affiliation(s)
- Angela N. Marquard
- Center for Systems
Biology, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5206, Boston, Massachusetts 02114, United States
| | - Jonathan C. T. Carlson
- Center for Systems
Biology, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5206, Boston, Massachusetts 02114, United States
- MGH Cancer
Center, Massachusetts General Hospital, Boston, Massachusetts 02114, United States
- E-mail: (J.C.T.C.)
| | - Ralph Weissleder
- Center for Systems
Biology, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5206, Boston, Massachusetts 02114, United States
- Department of Systems Biology, Harvard
Medical School, 200
Longwood Avenue, Boston, Massachusetts 02115, United States
- E-mail: (R.W.)
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McCallum C, Riordon J, Wang Y, Kong T, You JB, Sanner S, Lagunov A, Hannam TG, Jarvi K, Sinton D. Deep learning-based selection of human sperm with high DNA integrity. Commun Biol 2019; 2:250. [PMID: 31286067 PMCID: PMC6610103 DOI: 10.1038/s42003-019-0491-6] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Accepted: 06/05/2019] [Indexed: 12/13/2022] Open
Abstract
Despite the importance of sperm DNA to human reproduction, currently no method exists to assess individual sperm DNA quality prior to clinical selection. Traditionally, skilled clinicians select sperm based on a variety of morphological and motility criteria, but without direct knowledge of their DNA cargo. Here, we show how a deep convolutional neural network can be trained on a collection of ~1000 sperm cells of known DNA quality, to predict DNA quality from brightfield images alone. Our results demonstrate moderate correlation (bivariate correlation ~0.43) between a sperm cell image and DNA quality and the ability to identify higher DNA integrity cells relative to the median. This deep learning selection process is directly compatible with current, manual microscopy-based sperm selection and could assist clinicians, by providing rapid DNA quality predictions (under 10 ms per cell) and sperm selection within the 86th percentile from a given sample.
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Affiliation(s)
- Christopher McCallum
- Department of Mechanical and Industrial Engineering, University of Toronto, 5 King’s College Road, Toronto, ON Canada M5S 3G8
| | - Jason Riordon
- Department of Mechanical and Industrial Engineering, University of Toronto, 5 King’s College Road, Toronto, ON Canada M5S 3G8
| | - Yihe Wang
- Department of Mechanical and Industrial Engineering, University of Toronto, 5 King’s College Road, Toronto, ON Canada M5S 3G8
| | - Tian Kong
- Department of Mechanical and Industrial Engineering, University of Toronto, 5 King’s College Road, Toronto, ON Canada M5S 3G8
| | - Jae Bem You
- Department of Mechanical and Industrial Engineering, University of Toronto, 5 King’s College Road, Toronto, ON Canada M5S 3G8
| | - Scott Sanner
- Department of Mechanical and Industrial Engineering, University of Toronto, 5 King’s College Road, Toronto, ON Canada M5S 3G8
| | - Alexander Lagunov
- Hannam Fertility Centre, 160 Bloor St. East, Toronto, ON Canada M4W 3R2
| | - Thomas G. Hannam
- Hannam Fertility Centre, 160 Bloor St. East, Toronto, ON Canada M4W 3R2
| | - Keith Jarvi
- Department of Surgery, Division of Urology, Mount Sinai Hospital, University of Toronto, 60 Murray Street, 6th Floor, Toronto, ON Canada M5T 3L9
| | - David Sinton
- Department of Mechanical and Industrial Engineering, University of Toronto, 5 King’s College Road, Toronto, ON Canada M5S 3G8
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Wang W, Saeed M, Zhou Y, Yang L, Wang D, Yu H. Non‐viral gene delivery for cancer immunotherapy. J Gene Med 2019; 21:e3092. [DOI: 10.1002/jgm.3092] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Revised: 04/05/2019] [Accepted: 04/09/2019] [Indexed: 12/11/2022] Open
Affiliation(s)
- Weiqi Wang
- School of PharmacyNantong University Nantong China
- State Key Laboratory of Drug Research & Center of PharmaceuticsShanghai Institute of Materia Medica, Chinese Academy of Sciences Shanghai China
| | - Madiha Saeed
- State Key Laboratory of Drug Research & Center of PharmaceuticsShanghai Institute of Materia Medica, Chinese Academy of Sciences Shanghai China
| | - Yao Zhou
- School of PharmacyNantong University Nantong China
| | - Lili Yang
- School of PharmacyNantong University Nantong China
| | - Dangge Wang
- State Key Laboratory of Drug Research & Center of PharmaceuticsShanghai Institute of Materia Medica, Chinese Academy of Sciences Shanghai China
| | - Haijun Yu
- State Key Laboratory of Drug Research & Center of PharmaceuticsShanghai Institute of Materia Medica, Chinese Academy of Sciences Shanghai China
- University of Chinese Academy of Sciences Beijing China
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Low-cost thermophoretic profiling of extracellular-vesicle surface proteins for the early detection and classification of cancers. Nat Biomed Eng 2019; 3:183-193. [PMID: 30948809 DOI: 10.1038/s41551-018-0343-6] [Citation(s) in RCA: 269] [Impact Index Per Article: 53.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2018] [Accepted: 12/06/2018] [Indexed: 12/21/2022]
Abstract
Non-invasive assays for early cancer screening are hampered by challenges in the isolation and profiling of circulating biomarkers. Here, we show that surface proteins from serum extracellular vesicles labelled with a panel of seven fluorescent aptamers can be profiled, via thermophoretic enrichment and linear discriminant analysis, for cancer detection and classification. In a cohort of 102 patients, including 6 cancer types at stages I-IV, the assay detected stage I cancers with 95% sensitivity (95% confidence interval (CI): 74-100%) and 100% specificity (95% CI: 80-100%), and classified the cancer type with an overall accuracy of 68% (95% CI: 59-77%). For patients who underwent prostate biopsies, the assay was superior to the analysis of prostate-specific antigen levels (area under the curve: 0.94 versus 0.68; 33 patients) for the discrimination of prostate cancer and benign prostate enlargement, and also in the assessment of biochemical cancer recurrence after radical prostatectomy. The assay is inexpensive, fast, and requires small serum volumes (<1 µl), and if validated in larger cohorts may facilitate cancer screening, classification and monitoring.
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Schoenberg SO, Attenberger UI, Solomon SB, Weissleder R. Developing a Roadmap for Interventional Oncology. Oncologist 2018; 23:1162-1170. [PMID: 29959284 PMCID: PMC6263130 DOI: 10.1634/theoncologist.2017-0654] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2017] [Accepted: 04/05/2018] [Indexed: 01/05/2023] Open
Abstract
Interventional oncology uses image-guided procedures to enhance cancer care. Today, this specialty plays an increasingly critical role in cancer diagnosis (e.g., biopsy), cancer therapy (e.g., ablation or embolization), and cancer symptom palliation (e.g., nephrostomies or biliary drainages). Although the number of procedures and technical capabilities has improved over the last few years, challenges remain. In this article we discuss the need to advance existing procedures, develop new ones, and focus on several operational aspects that will dictate future interventional techniques to enhance cancer care, particularly by accelerating drug development and improving patient outcomes. IMPLICATIONS FOR PRACTICE Interventional oncology is vital for cancer diagnosis, therapy, and symptom palliation. This report focuses on current interventional procedures and techniques with a look toward future improvements that will improve cancer care and patient outcomes.
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Affiliation(s)
- Stefan O Schoenberg
- Department of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Ulrike I Attenberger
- Department of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Stephen B Solomon
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
| | - Ralph Weissleder
- Division of Interventional Radiology, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Center for Systems Biology, Massachusetts General Hospital, Boston, Massachusetts, USA
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