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Wang X, Hou J, Chen C, Jia Z, Zuo E, Chang C, Huang Y, Chen C, Lv X. Non-invasive detection of systemic lupus erythematosus using SERS serum detection technology and deep learning algorithms. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 320:124592. [PMID: 38861826 DOI: 10.1016/j.saa.2024.124592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 05/21/2024] [Accepted: 06/02/2024] [Indexed: 06/13/2024]
Abstract
Systemic lupus erythematosus (SLE) is an autoimmune disease with multiple symptoms, and its rapid screening is the research focus of surface-enhanced Raman scattering (SERS) technology. In this study, gold@silver-porous silicon (Au@Ag-PSi) composite substrates were synthesized by electrochemical etching and in-situ reduction methods, which showed excellent sensitivity and accuracy in the detection of rhodamine 6G (R6G) and serum from SLE patients. SERS technology was combined with deep learning algorithms to model serum features using selected CNN, AlexNet, and RF models. 92 % accuracy was achieved in classifying SLE patients by CNN models, and the reliability of these models in accurately identifying sera was verified by ROC curve analysis. This study highlights the great potential of Au@Ag-PSi substrate in SERS detection and introduces a novel deep learning approach for SERS for accurate screening of SLE. The proposed method and composite substrate provide significant value for rapid, accurate, and noninvasive SLE screening and provide insights into SERS-based diagnostic techniques.
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Affiliation(s)
- Xuehua Wang
- College of Physical Science and Technology, Xinjiang University, Urumqi 830046, China.
| | - Junwei Hou
- State Key Laboratory of Heavy Oil Processing, China University of Petroleum-Beijing at Karamay, Karamay 834000, China.
| | - Chen Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.
| | - Zhenhong Jia
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.
| | - Enguang Zuo
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.
| | - Chenjie Chang
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.
| | - Yuhao Huang
- College of Software, Xinjiang University, Urumqi 830046, China.
| | - Cheng Chen
- College of Software, Xinjiang University, Urumqi 830046, China.
| | - Xiaoyi Lv
- College of Software, Xinjiang University, Urumqi 830046, China.
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Xiao H, Xue X, Zhu M, Jiang X, Xia Q, Chen K, Li H, Long L, Peng K. Deep learning-based lung image registration: A review. Comput Biol Med 2023; 165:107434. [PMID: 37696177 DOI: 10.1016/j.compbiomed.2023.107434] [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] [Revised: 08/13/2023] [Accepted: 08/28/2023] [Indexed: 09/13/2023]
Abstract
Lung image registration can effectively describe the relative motion of lung tissues, thereby helping to solve series problems in clinical applications. Since the lungs are soft and fairly passive organs, they are influenced by respiration and heartbeat, resulting in discontinuity of lung motion and large deformation of anatomic features. This poses great challenges for accurate registration of lung image and its applications. The recent application of deep learning (DL) methods in the field of medical image registration has brought promising results. However, a versatile registration framework has not yet emerged due to diverse challenges of registration for different regions of interest (ROI). DL-based image registration methods used for other ROI cannot achieve satisfactory results in lungs. In addition, there are few review articles available on DL-based lung image registration. In this review, the development of conventional methods for lung image registration is briefly described and a more comprehensive survey of DL-based methods for lung image registration is illustrated. The DL-based methods are classified according to different supervision types, including fully-supervised, weakly-supervised and unsupervised. The contributions of researchers in addressing various challenges are described, as well as the limitations of these approaches. This review also presents a comprehensive statistical analysis of the cited papers in terms of evaluation metrics and loss functions. In addition, publicly available datasets for lung image registration are also summarized. Finally, the remaining challenges and potential trends in DL-based lung image registration are discussed.
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Affiliation(s)
- Hanguang Xiao
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China
| | - Xufeng Xue
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China
| | - Mi Zhu
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China.
| | - Xin Jiang
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China
| | - Qingling Xia
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China
| | - Kai Chen
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China
| | - Huanqi Li
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China
| | - Li Long
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China
| | - Ke Peng
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China.
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Szmul A, Chandy E, Veiga C, Jacob J, Stavropoulou A, Landau D, Hiley CT, McClelland JR. A Novel and Automated Approach to Classify Radiation Induced Lung Tissue Damage on CT Scans. Cancers (Basel) 2022; 14:1341. [PMID: 35267649 PMCID: PMC8909378 DOI: 10.3390/cancers14051341] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 02/18/2022] [Accepted: 02/25/2022] [Indexed: 02/01/2023] Open
Abstract
Radiation-induced lung damage (RILD) is a common side effect of radiotherapy (RT). The ability to automatically segment, classify, and quantify different types of lung parenchymal change is essential to uncover underlying patterns of RILD and their evolution over time. A RILD dedicated tissue classification system was developed to describe lung parenchymal tissue changes on a voxel-wise level. The classification system was automated for segmentation of five lung tissue classes on computed tomography (CT) scans that described incrementally increasing tissue density, ranging from normal lung (Class 1) to consolidation (Class 5). For ground truth data generation, we employed a two-stage data annotation approach, akin to active learning. Manual segmentation was used to train a stage one auto-segmentation method. These results were manually refined and used to train the stage two auto-segmentation algorithm. The stage two auto-segmentation algorithm was an ensemble of six 2D Unets using different loss functions and numbers of input channels. The development dataset used in this study consisted of 40 cases, each with a pre-radiotherapy, 3-, 6-, 12-, and 24-month follow-up CT scans (n = 200 CT scans). The method was assessed on a hold-out test dataset of 6 cases (n = 30 CT scans). The global Dice score coefficients (DSC) achieved for each tissue class were: Class (1) 99% and 98%, Class (2) 71% and 44%, Class (3) 56% and 26%, Class (4) 79% and 47%, and Class (5) 96% and 92%, for development and test subsets, respectively. The lowest values for the test subsets were caused by imaging artefacts or reflected subgroups that occurred infrequently and with smaller overall parenchymal volumes. We performed qualitative evaluation on the test dataset presenting manual and auto-segmentation to a blinded independent radiologist to rate them as 'acceptable', 'minor disagreement' or 'major disagreement'. The auto-segmentation ratings were similar to the manual segmentation, both having approximately 90% of cases rated as acceptable. The proposed framework for auto-segmentation of different lung tissue classes produces acceptable results in the majority of cases and has the potential to facilitate future large studies of RILD.
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Affiliation(s)
- Adam Szmul
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; (E.C.); (C.V.); (J.J.); (A.S.); (J.R.M.)
| | - Edward Chandy
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; (E.C.); (C.V.); (J.J.); (A.S.); (J.R.M.)
- Sussex Cancer Centre, Royal Sussex County Hospital, Brighton BN2 5BE, UK
- UCL Cancer Institute, University College London, London WC1E 6BT, UK; (D.L.); (C.T.H.)
| | - Catarina Veiga
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; (E.C.); (C.V.); (J.J.); (A.S.); (J.R.M.)
| | - Joseph Jacob
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; (E.C.); (C.V.); (J.J.); (A.S.); (J.R.M.)
- UCL Respiratory Department, University College London Hospital, London NW1 2PG, UK
| | - Alkisti Stavropoulou
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; (E.C.); (C.V.); (J.J.); (A.S.); (J.R.M.)
| | - David Landau
- UCL Cancer Institute, University College London, London WC1E 6BT, UK; (D.L.); (C.T.H.)
| | - Crispin T. Hiley
- UCL Cancer Institute, University College London, London WC1E 6BT, UK; (D.L.); (C.T.H.)
- University College Hospital, University College London, London NW1 2BU, UK
| | - Jamie R. McClelland
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; (E.C.); (C.V.); (J.J.); (A.S.); (J.R.M.)
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Chandy E, Szmul A, Stavropoulou A, Jacob J, Veiga C, Landau D, Wilson J, Gulliford S, Fenwick JD, Hawkins MA, Hiley C, McClelland JR. Quantitative Analysis of Radiation-Associated Parenchymal Lung Change. Cancers (Basel) 2022; 14:946. [PMID: 35205693 PMCID: PMC8870325 DOI: 10.3390/cancers14040946] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 02/07/2022] [Accepted: 02/08/2022] [Indexed: 02/01/2023] Open
Abstract
We present a novel classification system of the parenchymal features of radiation-induced lung damage (RILD). We developed a deep learning network to automate the delineation of five classes of parenchymal textures. We quantify the volumetric change in classes after radiotherapy in order to allow detailed, quantitative descriptions of the evolution of lung parenchyma up to 24 months after RT, and correlate these with radiotherapy dose and respiratory outcomes. Diagnostic CTs were available pre-RT, and at 3, 6, 12 and 24 months post-RT, for 46 subjects enrolled in a clinical trial of chemoradiotherapy for non-small cell lung cancer. All 230 CT scans were segmented using our network. The five parenchymal classes showed distinct temporal patterns. Moderate correlation was seen between change in tissue class volume and clinical and dosimetric parameters, e.g., the Pearson correlation coefficient was ≤0.49 between V30 and change in Class 2, and was 0.39 between change in Class 1 and decline in FVC. The effect of the local dose on tissue class revealed a strong dose-dependent relationship. Respiratory function measured by spirometry and MRC dyspnoea scores after radiotherapy correlated with the measured radiological RILD. We demonstrate the potential of using our approach to analyse and understand the morphological and functional evolution of RILD in greater detail than previously possible.
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Affiliation(s)
- Edward Chandy
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; (A.S.); (A.S.); (J.J.); (C.V.); (J.R.M.)
- UCL Cancer Institute, University College London, London WC1E 6BT, UK; (D.L.); (C.H.)
- Sussex Cancer Centre, Royal Sussex County Hospital, Brighton BN2 5BE, UK
| | - Adam Szmul
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; (A.S.); (A.S.); (J.J.); (C.V.); (J.R.M.)
| | - Alkisti Stavropoulou
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; (A.S.); (A.S.); (J.J.); (C.V.); (J.R.M.)
| | - Joseph Jacob
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; (A.S.); (A.S.); (J.J.); (C.V.); (J.R.M.)
- UCL Respiratory Department, University College London Hospital, London NW1 2PG, UK
| | - Catarina Veiga
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; (A.S.); (A.S.); (J.J.); (C.V.); (J.R.M.)
| | - David Landau
- UCL Cancer Institute, University College London, London WC1E 6BT, UK; (D.L.); (C.H.)
| | - James Wilson
- Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; (J.W.); (S.G.); (M.A.H.)
| | - Sarah Gulliford
- Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; (J.W.); (S.G.); (M.A.H.)
| | - John D. Fenwick
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 3GE, UK;
| | - Maria A. Hawkins
- Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; (J.W.); (S.G.); (M.A.H.)
| | - Crispin Hiley
- UCL Cancer Institute, University College London, London WC1E 6BT, UK; (D.L.); (C.H.)
| | - Jamie R. McClelland
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; (A.S.); (A.S.); (J.J.); (C.V.); (J.R.M.)
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