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Bianco V, Valentino M, Pirone D, Miccio L, Memmolo P, Brancato V, Coppola L, Smaldone G, D’Aiuto M, Mossetti G, Salvatore M, Ferraro P. Classifying breast cancer and fibroadenoma tissue biopsies from paraffined stain-free slides by fractal biomarkers in Fourier Ptychographic Microscopy. Comput Struct Biotechnol J 2024; 24:225-236. [PMID: 38572166 PMCID: PMC10990711 DOI: 10.1016/j.csbj.2024.03.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 03/21/2024] [Accepted: 03/21/2024] [Indexed: 04/05/2024] Open
Abstract
Breast cancer is one of the most spread and monitored pathologies in high-income countries. After breast biopsy, histological tissue is stored in paraffin, sectioned and mounted. Conventional inspection of tissue slides under benchtop light microscopes involves paraffin removal and staining, typically with H&E. Then, expert pathologists are called to judge the stained slides. However, paraffin removal and staining are operator-dependent, time and resources consuming processes that can generate ambiguities due to non-uniform staining. Here we propose a novel method that can work directly on paraffined stain-free slides. We use Fourier Ptychography as a quantitative phase-contrast microscopy method, which allows accessing a very wide field of view (i.e., mm2) in one single image while guaranteeing high lateral resolution (i.e., 0.5 µm). This imaging method is multi-scale, since it enables looking at the big picture, i.e. the complex tissue structure and connections, with the possibility to zoom-in up to the single-cell level. To handle this informative image content, we introduce elements of fractal geometry as multi-scale analysis method. We show the effectiveness of fractal features in describing and classifying fibroadenoma and breast cancer tissue slides from ten patients with very high accuracy. We reach 94.0 ± 4.2% test accuracy in classifying single images. Above all, we show that combining the decisions of the single images, each patient's slide can be classified with no error. Besides, fractal geometry returns a guide map to help pathologist to judge the different tissue portions based on the likelihood these can be associated to a breast cancer or fibroadenoma biomarker. The proposed automatic method could significantly simplify the steps of tissue analysis and make it independent from the sample preparation, the skills of the lab operator and the pathologist.
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Affiliation(s)
- Vittorio Bianco
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems “E. Caianiello”, Via Campi Flegrei 34, 80078 Pozzuoli, Napoli, Italy
| | - Marika Valentino
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems “E. Caianiello”, Via Campi Flegrei 34, 80078 Pozzuoli, Napoli, Italy
- DIETI, Department of Electrical Engineering and Information Technologies, University of Naples “Federico II”, via Claudio 21, 80125 Napoli, Italy
| | - Daniele Pirone
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems “E. Caianiello”, Via Campi Flegrei 34, 80078 Pozzuoli, Napoli, Italy
| | - Lisa Miccio
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems “E. Caianiello”, Via Campi Flegrei 34, 80078 Pozzuoli, Napoli, Italy
| | - Pasquale Memmolo
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems “E. Caianiello”, Via Campi Flegrei 34, 80078 Pozzuoli, Napoli, Italy
| | | | - Luigi Coppola
- IRCCS SYNLAB SDN, Via E. Gianturco 113, Napoli 80143, Italy
| | | | | | - Gennaro Mossetti
- Pathological Anatomy Service, Casa di Cura Maria Rosaria, Via Colle San Bartolomeo 50, 80045 Pompei, Napoli, Italy
| | | | - Pietro Ferraro
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems “E. Caianiello”, Via Campi Flegrei 34, 80078 Pozzuoli, Napoli, Italy
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Shang H, Wu Q, Wu J, Zhou S, Wang Z, Wang H, Yin J. Study on breast cancerization and isolated diagnosis in situ by HOF-ATR-MIR spectroscopy with deep learning. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 319:124546. [PMID: 38824755 DOI: 10.1016/j.saa.2024.124546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 05/20/2024] [Accepted: 05/26/2024] [Indexed: 06/04/2024]
Abstract
Mid-infrared (MIR) spectroscopy can characterize the content and structural changes of macromolecular components in different breast tissues, which can be used for feature extraction and model training by machine learning to achieve accurate classification and recognition of different breast tissues. In parallel, the one-dimensional convolutional neural network (1D-CNN) stands out in the field of deep learning for its ability to efficiently process sequential data, such as spectroscopic signals. In this study, MIR spectra of breast tissue were collected in situ by coupling the self-developed MIR hollow optical fiber attenuated total reflection (HOF-ATR) probe with a Fourier transform infrared spectroscopy (FTIR) spectrometer. Staging analysis was conducted on the changes in macromolecular content and structure in breast cancer tissues. For the first time, a trinary classification model was established based on 1D-CNN for recognizing normal, paracancerous and cancerous tissues. The final predication results reveal that the 1D-CNN model based on baseline correction (BC) and data augmentation yields more precise classification results, with a total accuracy of 95.09%, exhibiting superior discrimination ability than machine learning models of SVM-DA (90.00%), SVR (88.89%), PCA-FDA (67.78%) and PCA-KNN (70.00%). The experimental results suggest that the application of 1D-CNN enables accurate classification and recognition of different breast tissues, which can be considered as a precise, efficient and intelligent novel method for breast cancer diagnosis.
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Affiliation(s)
- Hui Shang
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Qingxia Wu
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Jinjin Wu
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Suwei Zhou
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Zihan Wang
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Huijie Wang
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
| | - Jianhua Yin
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
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Jeon G, Kim S, Kim YJ, Kim S, Han K, Oh K, Lee HJ, Choi J. Identification of fluoroquinolone-resistant Mycobacterium tuberculosis through high-level data fusion of Raman and laser-induced breakdown spectroscopy. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2024; 16:6349-6355. [PMID: 39221494 DOI: 10.1039/d4ay01331j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Abstract
Accurate and rapid diagnosis of drug susceptibility of Mycobacterium tuberculosis is crucial for the successful treatment of tuberculosis, a persistent global public health threat. To shorten diagnosis times and enhance accuracy, this study introduces a fusion model combining laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy. This model offers a rapid and accurate method for diagnosing drug-resistance. LIBS and Raman spectroscopy provide complementary information, enabling accurate identification of drug resistance in tuberculosis. Although individual use of LIBS or Raman spectroscopy achieved approximately 90% accuracy in identifying drug resistance, the fusion model significantly improved identification accuracy to 98.3%. Given the fast measurement capabilities of both techniques, this fusion approach is expected to markedly decrease the time required for diagnosis.
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Affiliation(s)
- Gookseon Jeon
- Industrial Transformation Technology Department, Research Institute of Sustainable Development Technology, Korea Institute of Industrial Technology, 89, Yangdaegiro-gil, Ipjang-myeon, Seobuk-gu, Cheonan-Si, Chungcheongnam-do 31056, Republic of Korea.
- Photonic Device Physics Laboratory, Institute of Physics and Applied Physics, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
| | - Soogeun Kim
- Advanced Photonics Research Institute (APRI), Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Republic of Korea
| | - Young Jin Kim
- Department of Laboratory Medicine, Kyung Hee University College of Medicine, Kyung Hee University Hospital, Seoul, Republic of Korea
| | - Seungmo Kim
- Laboratory Medicine Center, Korean National Tuberculosis Association, The Korean Institute of Tuberculosis, Cheongju, Republic of Korea
| | - Kyungmin Han
- Clinical Laboratory Medicine Center, Korean National Tuberculosis Association, Seoul, Republic of Korea.
| | - Kyunghwan Oh
- Photonic Device Physics Laboratory, Institute of Physics and Applied Physics, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
| | - Hee Joo Lee
- Clinical Laboratory Medicine Center, Korean National Tuberculosis Association, Seoul, Republic of Korea.
| | - Janghee Choi
- Industrial Transformation Technology Department, Research Institute of Sustainable Development Technology, Korea Institute of Industrial Technology, 89, Yangdaegiro-gil, Ipjang-myeon, Seobuk-gu, Cheonan-Si, Chungcheongnam-do 31056, Republic of Korea.
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Zhang Y, Li Z, Li Z, Wang H, Regmi D, Zhang J, Feng J, Yao S, Xu J. Employing Raman Spectroscopy and Machine Learning for the Identification of Breast Cancer. Biol Proced Online 2024; 26:28. [PMID: 39266953 PMCID: PMC11396685 DOI: 10.1186/s12575-024-00255-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2024] [Accepted: 09/04/2024] [Indexed: 09/14/2024] Open
Abstract
BACKGROUND Breast cancer poses a significant health risk to women worldwide, with approximately 30% being diagnosed annually in the United States. The identification of cancerous mammary tissues from non-cancerous ones during surgery is crucial for the complete removal of tumors. RESULTS Our study innovatively utilized machine learning techniques (Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN)) alongside Raman spectroscopy to streamline and hasten the differentiation of normal and late-stage cancerous mammary tissues in mice. The classification accuracy rates achieved by these models were 94.47% for RF, 96.76% for SVM, and 97.58% for CNN, respectively. To our best knowledge, this study was the first effort in comparing the effectiveness of these three machine-learning techniques in classifying breast cancer tissues based on their Raman spectra. Moreover, we innovatively identified specific spectral peaks that contribute to the molecular characteristics of the murine cancerous and non-cancerous tissues. CONCLUSIONS Consequently, our integrated approach of machine learning and Raman spectroscopy presents a non-invasive, swift diagnostic tool for breast cancer, offering promising applications in intraoperative settings.
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Affiliation(s)
- Ya Zhang
- Division of Electrical and Computer Engineering, College of Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA
| | - Zheng Li
- Division of Electrical and Computer Engineering, College of Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA
| | - Zhongqiang Li
- Division of Electrical and Computer Engineering, College of Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA
| | - Huaizhi Wang
- Division of Electrical and Computer Engineering, College of Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA
| | - Dinkar Regmi
- Division of Electrical and Computer Engineering, College of Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA
| | - Jian Zhang
- Division of Computer Science & Engineering, College of Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA
| | - Jiming Feng
- Department of Comparative Biomedical Science, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA, 70803, USA
| | - Shaomian Yao
- Department of Comparative Biomedical Science, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA, 70803, USA
| | - Jian Xu
- Division of Electrical and Computer Engineering, College of Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA.
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Li Q, Chen S. Glioma Identification Based on Digital Multimodal Spectra Integrated With Deep Learning Feature Fusion Using a Miniature Raman Spectrometer. APPLIED SPECTROSCOPY 2024:37028241276013. [PMID: 39252509 DOI: 10.1177/00037028241276013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/11/2024]
Abstract
The miniature fiber Raman spectroscopy detection technology can reflect the properties of biomolecules through spectral characteristics and has the advantages of noninvasiveness, real-time, safety, label-free operation, and potential for early cancer diagnosis. This technology holds promise for developing portable, low-cost, intraoperative tumor detection instruments. Glioma is one of the most common malignant tumors of the central nervous system with rapid growth and a short disease course. However, the considerable heterogeneity of the glioma sample leads to substantial intraclass variance in collected spectra, coupled with the miniature Raman spectrometer's low signal-to-noise ratio. These factors diminish the accuracy of the brain glioma recognition model. To address this issue, a glioma identification method based on digital multimodal spectra integrated with deep learning features fusion (DMS-DLFF) using the miniature Raman spectrometer is proposed. Different from existing multimodal tumor detection methods employing multiple spectral instruments, DMS-DLFF enhances tumor identification accuracy without increasing hardware costs. The method mathematically decomposes the original spectra to Raman and fluorescence spectra, so as to augment the biospectral information. Then, the deep learning method is used to extract the feature information of the two kinds of spectra, respectively, and the digital multimodal spectral fusion is realized at the feature level. Moreover, a two-layer pattern recognition model is constructed based on the ensemble strategy, amalgamating the strengths of diverse classifiers. Meanwhile, the bagging strategy is introduced to improve support vector machine algorithms, one of the basic classifiers. Compared with traditional methodologies, DMS-DLFF operates at both the feature level and decision level, employing high-information-density feature vectors to train ensemble classification models for increasing overall recognition accuracy. This study collected 260 Raman spectra of glioma and 151 Raman spectra of normal brain tissue. The accuracy, sensitivity, and specificity were 91.9%, 96.7%, and 80.8%, respectively. The proposed method outperforms traditional algorithms in brain glioma detection, which helps doctors formulate precise surgical plans and thereby improve patient prognosis.
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Affiliation(s)
- Qingbo Li
- School of Instrumentation and Optoelectronic Engineering, Precision Opto-Mechatronics Technology Key Laboratory of Education Ministry, Beihang University, Beijing, China
| | - Shufan Chen
- School of Instrumentation and Optoelectronic Engineering, Precision Opto-Mechatronics Technology Key Laboratory of Education Ministry, Beihang University, Beijing, China
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Lin J, Li Y, Lin X, Che C. Decision-level data fusion based on laser-induced breakdown and Raman spectroscopy: A study of bimodal spectroscopy for diagnosis of lung cancer at different stages. Talanta 2024; 275:126194. [PMID: 38703481 DOI: 10.1016/j.talanta.2024.126194] [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: 03/06/2024] [Revised: 04/22/2024] [Accepted: 04/30/2024] [Indexed: 05/06/2024]
Abstract
Lung cancer staging is crucial for personalized treatment and improved prognosis. We propose a novel bimodal diagnostic approach that integrates LIBS and Raman technologies into a single platform, enabling comprehensive tissue elemental and molecular analysis. This strategy identifies critical staging elements and molecular marker signatures of lung tumors. LIBS detects concentration patterns of elemental lines including Mg (I), Mg (II), Ca (I), Ca (II), Fe (I), and Cu (II). Concurrently, Raman spectroscopy identifies changes in molecular content, such as phenylalanine (1033 cm-1), tyrosine (1174 cm-1), tryptophan (1207 cm-1), amide III (1267 cm-1), and proteins (1126 cm-1 and 1447 cm-1), among others. The bimodal information is fused using a decision-level Bayesian fusion model, significantly enhancing the performance of the convolutional neural network architecture in classification algorithms, with an accuracy of 99.17 %, sensitivity of 99.17 %, and specificity of 99.88 %. This study provides a powerful new tool for the accurate staging and diagnosis of lung tumors.
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Affiliation(s)
- Jingjun Lin
- Changchun University of Technology, Changchun, Jilin, 130012, China
| | - Yao Li
- Changchun University of Technology, Changchun, Jilin, 130012, China
| | - Xiaomei Lin
- Changchun University of Technology, Changchun, Jilin, 130012, China.
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Zhao J, Lui H, Kalia S, Lee TK, Zeng H. Improving skin cancer detection by Raman spectroscopy using convolutional neural networks and data augmentation. Front Oncol 2024; 14:1320220. [PMID: 38962264 PMCID: PMC11219827 DOI: 10.3389/fonc.2024.1320220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 05/23/2024] [Indexed: 07/05/2024] Open
Abstract
Background Our previous studies have demonstrated that Raman spectroscopy could be used for skin cancer detection with good sensitivity and specificity. The objective of this study is to determine if skin cancer detection can be further improved by combining deep neural networks and Raman spectroscopy. Patients and methods Raman spectra of 731 skin lesions were included in this study, containing 340 cancerous and precancerous lesions (melanoma, basal cell carcinoma, squamous cell carcinoma and actinic keratosis) and 391 benign lesions (melanocytic nevus and seborrheic keratosis). One-dimensional convolutional neural networks (1D-CNN) were developed for Raman spectral classification. The stratified samples were divided randomly into training (70%), validation (10%) and test set (20%), and were repeated 56 times using parallel computing. Different data augmentation strategies were implemented for the training dataset, including added random noise, spectral shift, spectral combination and artificially synthesized Raman spectra using one-dimensional generative adversarial networks (1D-GAN). The area under the receiver operating characteristic curve (ROC AUC) was used as a measure of the diagnostic performance. Conventional machine learning approaches, including partial least squares for discriminant analysis (PLS-DA), principal component and linear discriminant analysis (PC-LDA), support vector machine (SVM), and logistic regression (LR) were evaluated for comparison with the same data splitting scheme as the 1D-CNN. Results The ROC AUC of the test dataset based on the original training spectra were 0.886±0.022 (1D-CNN), 0.870±0.028 (PLS-DA), 0.875±0.033 (PC-LDA), 0.864±0.027 (SVM), and 0.525±0.045 (LR), which were improved to 0.909±0.021 (1D-CNN), 0.899±0.022 (PLS-DA), 0.895±0.022 (PC-LDA), 0.901±0.020 (SVM), and 0.897±0.021 (LR) respectively after augmentation of the training dataset (p<0.0001, Wilcoxon test). Paired analyses of 1D-CNN with conventional machine learning approaches showed that 1D-CNN had a 1-3% improvement (p<0.001, Wilcoxon test). Conclusions Data augmentation not only improved the performance of both deep neural networks and conventional machine learning techniques by 2-4%, but also improved the performance of the models on spectra with higher noise or spectral shifting. Convolutional neural networks slightly outperformed conventional machine learning approaches for skin cancer detection by Raman spectroscopy.
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Affiliation(s)
- Jianhua Zhao
- Photomedicine Institute, Department of Dermatology and Skin Science, University of British Columbia and Vancouver Coastal Health Research Institute, Vancouver, BC, Canada
- BC Cancer Research Institute, University of British Columbia, Vancouver, BC, Canada
| | - Harvey Lui
- Photomedicine Institute, Department of Dermatology and Skin Science, University of British Columbia and Vancouver Coastal Health Research Institute, Vancouver, BC, Canada
- BC Cancer Research Institute, University of British Columbia, Vancouver, BC, Canada
| | - Sunil Kalia
- Photomedicine Institute, Department of Dermatology and Skin Science, University of British Columbia and Vancouver Coastal Health Research Institute, Vancouver, BC, Canada
- BC Children’s Hospital Research Institute, Vancouver, BC, Canada
- Centre for Clinical Epidemiology and Evaluation, Vancouver Coastal Health Research Institute, Vancouver, BC, Canada
| | - Tim K. Lee
- Photomedicine Institute, Department of Dermatology and Skin Science, University of British Columbia and Vancouver Coastal Health Research Institute, Vancouver, BC, Canada
- BC Cancer Research Institute, University of British Columbia, Vancouver, BC, Canada
| | - Haishan Zeng
- Photomedicine Institute, Department of Dermatology and Skin Science, University of British Columbia and Vancouver Coastal Health Research Institute, Vancouver, BC, Canada
- BC Cancer Research Institute, University of British Columbia, Vancouver, BC, Canada
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Guo Y, Zhang L, He Y, Lv C, Liu Y, Song H, Lv H, Du Z. Online inspection of blackheart in potatoes using visible-near infrared spectroscopy and interpretable spectrogram-based modified ResNet modeling. FRONTIERS IN PLANT SCIENCE 2024; 15:1403713. [PMID: 38911981 PMCID: PMC11190306 DOI: 10.3389/fpls.2024.1403713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Accepted: 05/09/2024] [Indexed: 06/25/2024]
Abstract
Introduction Blackheart is one of the most common physiological diseases in potatoes during storage. In the initial stage, black spots only occur in tissues near the potato core and cannot be detected from an outward appearance. If not identified and removed in time, the disease will seriously undermine the quality and sale of theentire batch of potatoes. There is an urgent need to develop a method for early detection of blackheart in potatoes. Methods This paper used visible-near infrared (Vis/NIR) spectroscopy to conduct online discriminant analysis on potatoes with varying degrees of blackheart and healthy potatoes to achieve real-time detection. An efficient and lightweight detection model was developed for detecting different degrees of blackheart in potatoes by introducing the depthwise convolution, pointwise convolution, and efficient channel attention modules into the ResNet model. Two discriminative models, the support vector machine (SVM) and the ResNet model were compared with the modified ResNet model. Results and discussion The prediction accuracy for blackheart and healthy potatoes test sets reached 0.971 using the original spectrum combined with a modified ResNet model. Moreover, the modified ResNet model significantly reduced the number of parameters to 1434052, achieving a substantial 62.71% reduction in model complexity. Meanwhile, its performance was evidenced by a 4.18% improvement in accuracy. The Grad-CAM++ visualizations provided a qualitative assessment of the model's focus across different severity grades of blackheart condition, highlighting the importance of different wavelengths in the analysis. In these visualizations, the most significant features were predominantly found in the 650-750 nm range, with a notable peak near 700 nm. This peak was speculated to be associated with the vibrational activities of the C-H bond, specifically the fourth overtone of the C-H functional group, within the molecular structure of the potato components. This research demonstrated that the modified ResNet model combined with Vis/NIR could assist in the detection of different degrees of black in potatoes.
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Affiliation(s)
- Yalin Guo
- Chinese Academy of Agricultural Mechanization Sciences Group Co., Ltd., Beijing, China
| | - Lina Zhang
- Chinese Academy of Agricultural Mechanization Sciences Group Co., Ltd., Beijing, China
| | - Yakai He
- Key Laboratory of Agricultural Products Processing Equipment in the Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Chengxu Lv
- Chinese Academy of Agricultural Mechanization Sciences Group Co., Ltd., Beijing, China
| | - Yijun Liu
- China National Packaging and Food Machinery Corporation, Beijing, China
| | - Haiyun Song
- Chinese Academy of Agricultural Mechanization Sciences Group Co., Ltd., Beijing, China
| | - Huangzhen Lv
- Chinese Academy of Agricultural Mechanization Sciences Group Co., Ltd., Beijing, China
- China National Packaging and Food Machinery Corporation, Beijing, China
| | - Zhilong Du
- Chinese Academy of Agricultural Mechanization Sciences Group Co., Ltd., Beijing, China
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Grajales D, Le WT, Tran T, David S, Dallaire F, Ember K, Leblond F, Ménard C, Kadoury S. Robot-assisted biopsy sampling for online Raman spectroscopy cancer confirmation in the operating room. Int J Comput Assist Radiol Surg 2024; 19:1103-1111. [PMID: 38573566 DOI: 10.1007/s11548-024-03100-7] [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: 01/22/2024] [Accepted: 03/04/2024] [Indexed: 04/05/2024]
Abstract
PURPOSE Cancer confirmation in the operating room (OR) is crucial to improve local control in cancer therapies. Histopathological analysis remains the gold standard, but there is a lack of real-time in situ cancer confirmation to support margin confirmation or remnant tissue. Raman spectroscopy (RS), as a label-free optical technique, has proven its power in cancer detection and, when integrated into a robotic assistance system, can positively impact the efficiency of procedures and the quality of life of patients, avoiding potential recurrence. METHODS A workflow is proposed where a 6-DOF robotic system (optical camera + MECA500 robotic arm) assists the characterization of fresh tissue samples using RS. Three calibration methods are compared for the robot, and the temporal efficiency is compared with standard hand-held analysis. For healthy/cancerous tissue discrimination, a 1D-convolutional neural network is proposed and tested on three ex vivo datasets (brain, breast, and prostate) containing processed RS and histopathology ground truth. RESULTS The robot achieves a minimum error of 0.20 mm (0.12) on a set of 30 test landmarks and demonstrates significant time reduction in 4 of the 5 proposed tasks. The proposed classification model can identify brain, breast, and prostate cancer with an accuracy of 0.83 (0.02), 0.93 (0.01), and 0.71 (0.01), respectively. CONCLUSION Automated RS analysis with deep learning demonstrates promising classification performance compared to commonly used support vector machines. Robotic assistance in tissue characterization can contribute to highly accurate, rapid, and robust biopsy analysis in the OR. These two elements are an important step toward real-time cancer confirmation using RS and OR integration.
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Affiliation(s)
- David Grajales
- Polytechnique Montréal, Montréal, QC, Canada.
- Centre de recherche du Centre Hospitalier de l'Université de Montréal, Montréal, QC, Canada.
| | - William T Le
- Polytechnique Montréal, Montréal, QC, Canada
- Centre de recherche du Centre Hospitalier de l'Université de Montréal, Montréal, QC, Canada
| | - Trang Tran
- Polytechnique Montréal, Montréal, QC, Canada
- Centre de recherche du Centre Hospitalier de l'Université de Montréal, Montréal, QC, Canada
| | - Sandryne David
- Polytechnique Montréal, Montréal, QC, Canada
- Centre de recherche du Centre Hospitalier de l'Université de Montréal, Montréal, QC, Canada
| | - Frédérick Dallaire
- Polytechnique Montréal, Montréal, QC, Canada
- Centre de recherche du Centre Hospitalier de l'Université de Montréal, Montréal, QC, Canada
| | - Katherine Ember
- Polytechnique Montréal, Montréal, QC, Canada
- Centre de recherche du Centre Hospitalier de l'Université de Montréal, Montréal, QC, Canada
| | - Frédéric Leblond
- Polytechnique Montréal, Montréal, QC, Canada
- Centre de recherche du Centre Hospitalier de l'Université de Montréal, Montréal, QC, Canada
- Institut du Cancer de Montréal, Montréal, QC, Canada
| | - Cynthia Ménard
- Centre de recherche du Centre Hospitalier de l'Université de Montréal, Montréal, QC, Canada
| | - Samuel Kadoury
- Polytechnique Montréal, Montréal, QC, Canada
- Centre de recherche du Centre Hospitalier de l'Université de Montréal, Montréal, QC, Canada
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Wu J, Dong J, Bao Y, Shang L, Wu Q, Yang Z, Wang H, Yin J. Synovial fluid research based on SERS and SERRS for enhanced detection of biomarkers in staged osteoarthritis. JOURNAL OF BIOPHOTONICS 2024; 17:e202400024. [PMID: 38566479 DOI: 10.1002/jbio.202400024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 03/10/2024] [Accepted: 03/24/2024] [Indexed: 04/04/2024]
Abstract
Surface-enhanced (resonance) Raman scattering (SER(R)S) can extremely enhance Raman intensity of samples, which is helpful for detecting synovial fluid (SF) that does not show Raman activity under normal conditions. In this study, SER(R)S spectra of SF from three different osteoarthritis (OA) stages were collected and analyzed for OA progress, finding that the content of collagen increased throughout the disease, while non-collagen proteins and polysaccharides decreased sharply at advanced OA stage accompanied by the increase of phospholipid. The spectral features and differences were enhanced by salting-out and centrifugation. Much more information on biomolecules at different OA stages was disclosed by using SERRS for the first time, these main trace components (β-carotene, collagen, hyaluronic acid, nucleotide, and phospholipid) can be used as potential biomarkers. It indicates that SERRS has a more comprehensive ability to assist SERS in seeking micro(trace) biomolecules as biomarkers and facilitating accurate and efficient diagnosis and mechanism research of OA.
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Affiliation(s)
- Jinjin Wu
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Jiachun Dong
- Department of Orthopaedics, Sir Run Run Hospital, Nanjing Medical University, Nanjing, China
| | - Yilin Bao
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Linwei Shang
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Qingxia Wu
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Zichun Yang
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Huijie Wang
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Jianhua Yin
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
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11
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Teng Y, Chen Y, Chen X, Zuo S, Li X, Pan Z, Shao K, Du J, Li Z. Revealing the adulteration of sesame oil products by portable Raman spectrometer and 1D CNN vector regression: A comparative study with chemometrics and colorimetry. Food Chem 2024; 436:137694. [PMID: 37844509 DOI: 10.1016/j.foodchem.2023.137694] [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: 06/28/2023] [Revised: 09/28/2023] [Accepted: 10/06/2023] [Indexed: 10/18/2023]
Abstract
Identification and quantification of sesame oil products are crucial due to the existing problems of adulteration with lower-priced oils and false labeling of sesame proportions. In this study, 1D CNN models were established to achieve discrimination of oil types and multiple quantification of adulteration using portable Raman spectrometer. An improved data augmentation method involving discarding transformations that alter peak positions was proposed, and synchronously injecting noise during geometric transformations. Furthermore, a novel neural network structure was introduced incorporating vector regression to accurately predict each component simultaneously. The proposed method has achieved higher accuracy in detecting multi-component adulteration compared with chemometrics (100 % accuracy in classifying different oils; R2 over 0.99 and RMSE within 2 % in predicting unknown adulterated samples). Finally, commercially available sesame oil products were tested and compared with gas chromatography and colorimetric methods, demonstrating the effectiveness of our proposed model in achieving higher detection accuracy at low-concentration adulteration.
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Affiliation(s)
- Yuanjie Teng
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China.
| | - Yingxin Chen
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
| | - Xiangou Chen
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
| | - Shaohua Zuo
- School of Physics and Electronic Science, East China Normal University, Shanghai 200241, China; Engineering Research Center of Nanoelectronic Integration and Advanced Equipment, Ministry of Education, China.
| | - Xin Li
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
| | - Zaifa Pan
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
| | - Kang Shao
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
| | - Jinglin Du
- Grain and Oil Products Quality Inspection Center of Zhejiang Province, Hangzhou 310012, China
| | - Zuguang Li
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China.
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12
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Jiang S, Wang X, Chong Y, Huang Y, Hu W, Smith PES, Jiang J, Feng S. Spectra-Based Machine Learning for Predicting the Statistical Interaction Properties of CO Adsorbates on Surface. J Phys Chem Lett 2024; 15:2400-2404. [PMID: 38393989 DOI: 10.1021/acs.jpclett.4c00011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2024]
Abstract
Theoretical analyses of small-molecule adsorption on heterogeneous catalyst surfaces often rely on simplified models of molecular adsorption with the most favorable configuration. Given that real-world experimental tests frequently entail multiple molecules interacting with the surface, there is a pressing need for a comprehensive multimolecule adsorption model to bridge the gap between theory and experiment. Using machine learning, we predict the average values of important adsorption properties from conformationally averaged, calculated infrared and Raman spectra and compare these values to those theoretically derived from the conformationally averaged ensemble. Remarkably, our approach yields excellent predictions even when faced with large and indeterminate numbers of surface molecules. These quantitative spectra-averaged property relationships provide a theoretical framework for extracting key interaction properties from the spectra of real chemical environments.
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Affiliation(s)
- Shuang Jiang
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China
| | - Xijun Wang
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China
| | - Yuanyuan Chong
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China
| | - Yan Huang
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China
| | - Wei Hu
- School of Chemistry and Chemical Engineering, Qilu University of Technology (Shandong Academy of Science), Jinan 250353, China
| | | | - Jun Jiang
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China
| | - Shuo Feng
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China
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13
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Contreras J, Mostafapour S, Popp J, Bocklitz T. Siamese Networks for Clinically Relevant Bacteria Classification Based on Raman Spectroscopy. Molecules 2024; 29:1061. [PMID: 38474573 DOI: 10.3390/molecules29051061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 02/07/2024] [Accepted: 02/27/2024] [Indexed: 03/14/2024] Open
Abstract
Identifying bacterial strains is essential in microbiology for various practical applications, such as disease diagnosis and quality monitoring of food and water. Classical machine learning algorithms have been utilized to identify bacteria based on their Raman spectra. However, convolutional neural networks (CNNs) offer higher classification accuracy, but they require extensive training sets and retraining of previous untrained class targets can be costly and time-consuming. Siamese networks have emerged as a promising solution. They are composed of two CNNs with the same structure and a final network that acts as a distance metric, converting the classification problem into a similarity problem. Classical machine learning approaches, shallow and deep CNNs, and two Siamese network variants were tailored and tested on Raman spectral datasets of bacteria. The methods were evaluated based on mean sensitivity, training time, prediction time, and the number of parameters. In this comparison, Siamese-model2 achieved the highest mean sensitivity of 83.61 ± 4.73 and demonstrated remarkable performance in handling unbalanced and limited data scenarios, achieving a prediction accuracy of 73%. Therefore, the choice of model depends on the specific trade-off between accuracy, (prediction/training) time, and resources for the particular application. Classical machine learning models and shallow CNN models may be more suitable if time and computational resources are a concern. Siamese networks are a good choice for small datasets and CNN for extensive data.
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Affiliation(s)
- Jhonatan Contreras
- Institute of Physical Chemistry (IPC) and Abbe Center of Photonics (ACP), Friedrich Schiller University Jena, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Helmholtzweg 4, 07743 Jena, Germany
- Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz, Centre for Photonics in Infection Research (LPI), Albert Einstein Straße 9, 07745 Jena, Germany
| | - Sara Mostafapour
- Institute of Physical Chemistry (IPC) and Abbe Center of Photonics (ACP), Friedrich Schiller University Jena, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Helmholtzweg 4, 07743 Jena, Germany
| | - Jürgen Popp
- Institute of Physical Chemistry (IPC) and Abbe Center of Photonics (ACP), Friedrich Schiller University Jena, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Helmholtzweg 4, 07743 Jena, Germany
- Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz, Centre for Photonics in Infection Research (LPI), Albert Einstein Straße 9, 07745 Jena, Germany
| | - Thomas Bocklitz
- Institute of Physical Chemistry (IPC) and Abbe Center of Photonics (ACP), Friedrich Schiller University Jena, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Helmholtzweg 4, 07743 Jena, Germany
- Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz, Centre for Photonics in Infection Research (LPI), Albert Einstein Straße 9, 07745 Jena, Germany
- Institute of Computer Science, Faculty of Mathematics, Physics & Computer Science, University Bayreuth Universitaetsstraße 30, 95447 Bayreuth, Germany
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14
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Fuentes AM, Milligan K, Wiebe M, Narayan A, Lum JJ, Brolo AG, Andrews JL, Jirasek A. Stratification of tumour cell radiation response and metabolic signatures visualization with Raman spectroscopy and explainable convolutional neural network. Analyst 2024; 149:1645-1657. [PMID: 38312026 DOI: 10.1039/d3an01797d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
Reprogramming of cellular metabolism is a driving factor of tumour progression and radiation therapy resistance. Identifying biochemical signatures associated with tumour radioresistance may assist with the development of targeted treatment strategies to improve clinical outcomes. Raman spectroscopy (RS) can monitor post-irradiation biomolecular changes and signatures of radiation response in tumour cells in a label-free manner. Convolutional Neural Networks (CNN) perform feature extraction directly from data in an end-to-end learning manner, with high classification performance. Furthermore, recently developed CNN explainability techniques help visualize the critical discriminative features captured by the model. In this work, a CNN is developed to characterize tumour response to radiotherapy based on its degree of radioresistance. The model was trained to classify Raman spectra of three human tumour cell lines as radiosensitive (LNCaP) or radioresistant (MCF7, H460) over a range of treatment doses and data collection time points. Additionally, a method based on Gradient-Weighted Class Activation Mapping (Grad-CAM) was used to determine response-specific salient Raman peaks influencing the CNN predictions. The CNN effectively classified the cell spectra, with accuracy, sensitivity, specificity, and F1 score exceeding 99.8%. Grad-CAM heatmaps of H460 and MCF7 cell spectra (radioresistant) exhibited high contributions from Raman bands tentatively assigned to glycogen, amino acids, and nucleic acids. Conversely, heatmaps of LNCaP cells (radiosensitive) revealed activations at lipid and phospholipid bands. Finally, Grad-CAM variable importance scores were derived for glycogen, asparagine, and phosphatidylcholine, and we show that their trends over cell line, dose, and acquisition time agreed with previously established models. Thus, the CNN can accurately detect biomolecular differences in the Raman spectra of tumour cells of varying radiosensitivity without requiring manual feature extraction. Finally, Grad-CAM may help identify metabolic signatures associated with the observed categories, offering the potential for automated clinical tumour radiation response characterization.
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Affiliation(s)
- Alejandra M Fuentes
- Department of Physics, The University of British Columbia Okanagan Campus, Kelowna, Canada.
| | - Kirsty Milligan
- Department of Physics, The University of British Columbia Okanagan Campus, Kelowna, Canada.
| | - Mitchell Wiebe
- Department of Physics, The University of British Columbia Okanagan Campus, Kelowna, Canada.
| | - Apurva Narayan
- Department of Computer Science, Western University, London, Canada
- Department of Computer Science, The University of British Columbia Okanagan Campus, Kelowna, Canada
| | - Julian J Lum
- Department of Biochemistry and Microbiology, The University of Victoria, Victoria, Canada
- Trev and Joyce Deeley Research Centre, BC Cancer, Victoria, Canada
| | - Alexandre G Brolo
- Department of Chemistry, The University of Victoria, Victoria, Canada
| | - Jeffrey L Andrews
- Department of Statistics, The University of British Columbia Okanagan Campus, Kelowna, Canada
| | - Andrew Jirasek
- Department of Physics, The University of British Columbia Okanagan Campus, Kelowna, Canada.
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15
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Yuan G, Ge Z, Zheng J, Yan X, Fu M, Li M, Yang X, Tang L. CNN-based diagnosis model of children's bladder compliance using a single intravesical pressure signal. Comput Methods Biomech Biomed Engin 2024:1-12. [PMID: 38193146 DOI: 10.1080/10255842.2023.2301414] [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: 07/12/2023] [Accepted: 12/20/2023] [Indexed: 01/10/2024]
Abstract
Bladder compliance assessment is crucial for diagnosing bladder functional disorders, with urodynamic study (UDS) being the principal evaluation method. However, the application of UDS is intricate and time-consuming in children. So it'S necessary to develop an efficient bladder compliance screen approach before UDS. In this study, We constructed a dataset based on UDS and designed a 1D-CNN model to optimize and train the network. Then applied the trained model to a dataset obtained solely through a proposed perfusion experiment. Our model outperformed other algorithms. The results demonstrate the potential of our model to alert abnormal bladder compliance accurately and efficiently.
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Affiliation(s)
- Gang Yuan
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Zicong Ge
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Jian Zheng
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Xiangming Yan
- Department of Surgery, Children's Hospital of Soochow University, Soochow University, Suzhou, China
| | - Mingcui Fu
- Department of Surgery, Children's Hospital of Soochow University, Soochow University, Suzhou, China
| | - Ming Li
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Xiaodong Yang
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Liangfeng Tang
- Department of Pediatric Urology, Children's Hospital, Fudan University, Shanghai, China
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16
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Ge H, Gao X, Lin J, Zhao X, Wu X, Zhang H. Label-free SERS detection of prostate cancer based on multi-layer perceptron surrogate model method. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 304:123407. [PMID: 37717486 DOI: 10.1016/j.saa.2023.123407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 08/23/2023] [Accepted: 09/12/2023] [Indexed: 09/19/2023]
Abstract
Prior surface-enhanced Raman spectroscopy (SERS) research has shown that pre-processing is necessary before analysis. Pre-processing also typically serves the dual purposes of removing the auto-fluorescence background and minimizing data volatility. This method allows for a more accurate comparison of spectral traits and relative SERS peak strength. However, because there are so many different kinds of samples, it can take a long time, and there is no assurance that the approach chosen will work well with a particular kind of sample. Therefore, this study employed a deep learning technique called multi-layer perceptron (MLP) to simplify the pre-processing of blood plasma SERS samples in patients with prostate cancer (PC), as well as to enhance the sensitivity and specificity of diagnosis using SERS technology. First of all, significant variations in peak intensity can be observed in the difference spectra, facilitating differentiation between PC and normal groups. Second, the data analysis was carried out in three different stages (raw data, defluorescenced data, and normalized data) using principal component analysis and linear discriminant analysis (PCA-LDA), as well as PCA-multi-layer perceptron (PCA-MLP). Finally, when SERS data was analyzed using PCA-LDA, there were significant differences in classification accuracy across each stage (The classification accuracy of three different stages were 76.90%, 85.60%, 95.20%, respectively). However, when PCA-MLP was utilized for SERS data analysis, the classification accuracy remained consistently high and stable (The classification accuracy of three different stages were 92.00%, 92.40%, 96.70%, respectively). The experimental results of PCA-MLP for classifying specific SERS data indicate that analyzing raw data directly can simplify the experimental process and enhance the efficacy of SERS analysis.
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Affiliation(s)
- Houyang Ge
- School of Opto-electronic and Communication Engineering, Xiamen University of Technology, Xiamen, Fujian, China
| | - Xingen Gao
- School of Opto-electronic and Communication Engineering, Xiamen University of Technology, Xiamen, Fujian, China
| | - Juqiang Lin
- School of Opto-electronic and Communication Engineering, Xiamen University of Technology, Xiamen, Fujian, China.
| | - Xin Zhao
- MOE Key Laboratory of OptoElectronic Science and Technology for Medicine, and Affiliated Hospital, Fujian Normal University, Fuzhou, Fujian, China
| | - Xiang Wu
- Department of Urology, Fujian Provincial Hospital, Fuzhou, Fujian, China
| | - Hongyi Zhang
- School of Opto-electronic and Communication Engineering, Xiamen University of Technology, Xiamen, Fujian, China
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17
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Kalatzis D, Spyratou E, Karnachoriti M, Kouri MA, Stathopoulos I, Danias N, Arkadopoulos N, Orfanoudakis S, Seimenis I, Kontos AG, Efstathopoulos EP. Extended Analysis of Raman Spectra Using Artificial Intelligence Techniques for Colorectal Abnormality Classification. J Imaging 2023; 9:261. [PMID: 38132679 PMCID: PMC10744297 DOI: 10.3390/jimaging9120261] [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: 10/02/2023] [Revised: 11/06/2023] [Accepted: 11/22/2023] [Indexed: 12/23/2023] Open
Abstract
Raman spectroscopy (RS) techniques are attracting attention in the medical field as a promising tool for real-time biochemical analyses. The integration of artificial intelligence (AI) algorithms with RS has greatly enhanced its ability to accurately classify spectral data in vivo. This combination has opened up new possibilities for precise and efficient analysis in medical applications. In this study, healthy and cancerous specimens from 22 patients who underwent open colorectal surgery were collected. By using these spectral data, we investigate an optimal preprocessing pipeline for statistical analysis using AI techniques. This exploration entails proposing preprocessing methods and algorithms to enhance classification outcomes. The research encompasses a thorough ablation study comparing machine learning and deep learning algorithms toward the advancement of the clinical applicability of RS. The results indicate substantial accuracy improvements using techniques like baseline correction, L2 normalization, filtering, and PCA, yielding an overall accuracy enhancement of 15.8%. In comparing various algorithms, machine learning models, such as XGBoost and Random Forest, demonstrate effectiveness in classifying both normal and abnormal tissues. Similarly, deep learning models, such as 1D-Resnet and particularly the 1D-CNN model, exhibit superior performance in classifying abnormal cases. This research contributes valuable insights into the integration of AI in medical diagnostics and expands the potential of RS methods for achieving accurate malignancy classification.
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Affiliation(s)
- Dimitris Kalatzis
- 2nd Department of Radiology, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; (D.K.); (E.S.); (M.A.K.); (I.S.)
| | - Ellas Spyratou
- 2nd Department of Radiology, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; (D.K.); (E.S.); (M.A.K.); (I.S.)
- Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece;
| | - Maria Karnachoriti
- Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece;
- School of Applied Mathematical and Physical Sciences, National Technical University Athens, 15780 Athens, Greece;
| | - Maria Anthi Kouri
- 2nd Department of Radiology, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; (D.K.); (E.S.); (M.A.K.); (I.S.)
- Medical Physics Program, University of Massachusetts Lowell, Lowell, MA 01854, USA
| | - Ioannis Stathopoulos
- 2nd Department of Radiology, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; (D.K.); (E.S.); (M.A.K.); (I.S.)
| | - Nikolaos Danias
- 4th Department of Surgery, School of Medicine, Attikon University Hospital, University of Athens, 12462 Athens, Greece; (N.D.); (N.A.)
| | - Nikolaos Arkadopoulos
- 4th Department of Surgery, School of Medicine, Attikon University Hospital, University of Athens, 12462 Athens, Greece; (N.D.); (N.A.)
| | - Spyros Orfanoudakis
- Alpha Information Technology S.A., Software & System Development, 68131 Alexandroupolis, Greece;
| | - Ioannis Seimenis
- Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece;
| | - Athanassios G. Kontos
- School of Applied Mathematical and Physical Sciences, National Technical University Athens, 15780 Athens, Greece;
| | - Efstathios P. Efstathopoulos
- 2nd Department of Radiology, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; (D.K.); (E.S.); (M.A.K.); (I.S.)
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18
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Du Y, Hu L, Wu G, Tang Y, Cai X, Yin L. Diagnoses in multiple types of cancer based on serum Raman spectroscopy combined with a convolutional neural network: Gastric cancer, colon cancer, rectal cancer, lung cancer. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 298:122743. [PMID: 37119637 DOI: 10.1016/j.saa.2023.122743] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 04/02/2023] [Accepted: 04/11/2023] [Indexed: 05/26/2023]
Abstract
Cancer is one of the major diseases that seriously threaten human health. Timely screening is beneficial to the cure of cancer. There are some shortcomings in current diagnosis methods, so it is very important to find a low-cost, fast, and nondestructive cancer screening technology. In this study, we demonstrated that serum Raman spectroscopy combined with a convolutional neural network model can be used for the diagnosis of four types of cancer including gastric cancer, colon cancer, rectal cancer, and lung cancer. Raman spectra database containing four types of cancer and healthy controls was established and a one-dimensional convolutional neural network (1D-CNN) was constructed. The classification accuracy of the Raman spectra combined with the 1D-CNN model was 94.5%. A convolutional neural network (CNN) is regarded as a black box, and the learning mechanism of the model is not clear. Therefore, we tried to visualize the CNN features of each convolutional layer in the diagnosis of rectal cancer. Overall, Raman spectroscopy combined with the CNN model is an effective tool that can be used to distinguish different cancer from healthy controls.
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Affiliation(s)
- Yu Du
- School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Lin Hu
- Department of Laboratory Medicine, The First Affiliated Hospital of Chongqing Medical University, 400016 Chongqing, China
| | - Guohua Wu
- School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
| | - Yishu Tang
- Department of Laboratory Medicine, The First Affiliated Hospital of Chongqing Medical University, 400016 Chongqing, China.
| | - Xiongwei Cai
- Department of Gynecology, Chongqing Health Center for Women and Children, Women and Children's Hospital of Chongqing Medical University, 400016 Chongqing, China
| | - Longfei Yin
- School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
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19
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Bellantuono L, Tommasi R, Pantaleo E, Verri M, Amoroso N, Crucitti P, Di Gioacchino M, Longo F, Monaco A, Naciu AM, Palermo A, Taffon C, Tangaro S, Crescenzi A, Sodo A, Bellotti R. An eXplainable Artificial Intelligence analysis of Raman spectra for thyroid cancer diagnosis. Sci Rep 2023; 13:16590. [PMID: 37789191 PMCID: PMC10547772 DOI: 10.1038/s41598-023-43856-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 09/29/2023] [Indexed: 10/05/2023] Open
Abstract
Raman spectroscopy shows great potential as a diagnostic tool for thyroid cancer due to its ability to detect biochemical changes during cancer development. This technique is particularly valuable because it is non-invasive and label/dye-free. Compared to molecular tests, Raman spectroscopy analyses can more effectively discriminate malignant features, thus reducing unnecessary surgeries. However, one major hurdle to using Raman spectroscopy as a diagnostic tool is the identification of significant patterns and peaks. In this study, we propose a Machine Learning procedure to discriminate healthy/benign versus malignant nodules that produces interpretable results. We collect Raman spectra obtained from histological samples, select a set of peaks with a data-driven and label independent approach and train the algorithms with the relative prominence of the peaks in the selected set. The performance of the considered models, quantified by area under the Receiver Operating Characteristic curve, exceeds 0.9. To enhance the interpretability of the results, we employ eXplainable Artificial Intelligence and compute the contribution of each feature to the prediction of each sample.
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Affiliation(s)
- Loredana Bellantuono
- Dipartimento di Biomedicina Traslazionale e Neuroscienze (DiBraiN), Università degli Studi di Bari Aldo Moro, 70124, Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125, Bari, Italy
| | - Raffaele Tommasi
- Dipartimento di Biomedicina Traslazionale e Neuroscienze (DiBraiN), Università degli Studi di Bari Aldo Moro, 70124, Bari, Italy
| | - Ester Pantaleo
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125, Bari, Italy
- Dipartimento Interateneo di Fisica, Università degli Studi di Bari Aldo Moro, 70125, Bari, Italy
| | - Martina Verri
- Unit of Endocrine Organs and Neuromuscolar Pathology, Fondazione Policlinico Universitario Campus Bio-Medico, 00128, Rome, Italy
- Dipartimento di Scienze, Università degli Studi Roma Tre, 00146, Roma, Italy
| | - Nicola Amoroso
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125, Bari, Italy
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125, Bari, Italy
| | - Pierfilippo Crucitti
- Unit of Thoracic Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, 00128, Rome, Italy
| | | | - Filippo Longo
- Unit of Thoracic Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, 00128, Rome, Italy
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125, Bari, Italy
- Dipartimento Interateneo di Fisica, Università degli Studi di Bari Aldo Moro, 70125, Bari, Italy
| | - Anda Mihaela Naciu
- Unit of Metabolic Bone and Thyroid Diseases, Fondazione Policlinico Universitario Campus Bio-Medico, 00128, Rome, Italy
| | - Andrea Palermo
- Unit of Metabolic Bone and Thyroid Diseases, Fondazione Policlinico Universitario Campus Bio-Medico, 00128, Rome, Italy
| | - Chiara Taffon
- Unit of Endocrine Organs and Neuromuscolar Pathology, Fondazione Policlinico Universitario Campus Bio-Medico, 00128, Rome, Italy
| | - Sabina Tangaro
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125, Bari, Italy
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, 70125, Bari, Italy
| | - Anna Crescenzi
- Unit of Endocrine Organs and Neuromuscolar Pathology, Fondazione Policlinico Universitario Campus Bio-Medico, 00128, Rome, Italy
| | - Armida Sodo
- Dipartimento di Scienze, Università degli Studi Roma Tre, 00146, Roma, Italy
| | - Roberto Bellotti
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125, Bari, Italy
- Dipartimento Interateneo di Fisica, Università degli Studi di Bari Aldo Moro, 70125, Bari, Italy
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20
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Ren P, Zhou RG, Li Y, Xiong S, Han B. Raman ConvMSANet: A High-Accuracy Neural Network for Raman Spectroscopy Blood and Semen Identification. ACS OMEGA 2023; 8:30421-30431. [PMID: 37636956 PMCID: PMC10448484 DOI: 10.1021/acsomega.3c03572] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 08/01/2023] [Indexed: 08/29/2023]
Abstract
Animal blood and semen analysis plays a significant role in national biological resource management, wildlife conservation, and customs security quarantine. Traditional blood analysis methods have disadvantages, such as complex sample preparation, time consumption, and false positives. Therefore, proposing a rapid and highly accurate analysis method is highly valuable. Raman spectroscopy has been widely used in blood analysis, and efficient and accurate analysis results can be obtained through the machine learning algorithm feature extraction. Recently, the transformer network structure was applied to Raman spectroscopy recognition. However, the multihead self-attention mechanism does not perform well in extracting local feature peaks, although it obtains global feature relations. This paper proposes a neural network based on the combination of one-dimensional convolution and multihead self-attention mechanism (Raman ConvMSANet) to identify 52 species of blood and semen Raman spectra. The network can achieve reliable identification effects in multiclassification and sample imbalance situations, and the average identification accuracy of blood and semen can reach more than 98.5%. The proposed network model can be applied not only to blood and semen identification but also to other biological fields.
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Affiliation(s)
- Pengju Ren
- College
of Information Engineering, Shanghai Maritime
University, Shanghai 201306, China
| | - Ri-gui Zhou
- College
of Information Engineering, Shanghai Maritime
University, Shanghai 201306, China
| | - Yaochong Li
- College
of Information Engineering, Shanghai Maritime
University, Shanghai 201306, China
| | | | - Bing Han
- National
Engineering Research Center of Ship & Shipping Control System, Shanghai Ship and Shipping Research Institute Co.,Ltd, Shanghai 200135, China
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21
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Oshima Y, Haruki T, Koizumi K, Yonezawa S, Taketani A, Kadowaki M, Saito S. Practices, Potential, and Perspectives for Detecting Predisease Using Raman Spectroscopy. Int J Mol Sci 2023; 24:12170. [PMID: 37569541 PMCID: PMC10418989 DOI: 10.3390/ijms241512170] [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: 07/07/2023] [Revised: 07/23/2023] [Accepted: 07/27/2023] [Indexed: 08/13/2023] Open
Abstract
Raman spectroscopy shows great potential for practical clinical applications. By analyzing the structure and composition of molecules through real-time, non-destructive measurements of the scattered light from living cells and tissues, it offers valuable insights. The Raman spectral data directly link to the molecular composition of the cells and tissues and provides a "molecular fingerprint" for various disease states. This review focuses on the practical and clinical applications of Raman spectroscopy, especially in the early detection of human diseases. Identifying predisease, which marks the transition from a healthy to a disease state, is crucial for effective interventions to prevent disease onset. Raman spectroscopy can reveal biological processes occurring during the transition states and may eventually detect the molecular dynamics in predisease conditions.
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Affiliation(s)
- Yusuke Oshima
- Faculty of Engineering, University of Toyama, Toyama 930-8555, Japan
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
- Faculty of Medicine, Oita University, Yufu 879-5593, Japan
| | - Takayuki Haruki
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
- Faculty of Sustainable Design, University of Toyama, Toyama 930-8555, Japan
| | - Keiichi Koizumi
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
- Division of Presymptomatic Disease, Institute of Natural Medicine, University of Toyama, Toyama 930-8555, Japan
| | - Shota Yonezawa
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
| | - Akinori Taketani
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
| | - Makoto Kadowaki
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
| | - Shigeru Saito
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
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22
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Gong L, Martinez O, Mesquita P, Kurtz K, Xu Y, Lin Y. A microfluidic approach for label-free identification of small-sized microplastics in seawater. Sci Rep 2023; 13:11011. [PMID: 37419935 PMCID: PMC10329028 DOI: 10.1038/s41598-023-37900-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 06/29/2023] [Indexed: 07/09/2023] Open
Abstract
Marine microplastics are emerging as a growing environmental concern due to their potential harm to marine biota. The substantial variations in their physical and chemical properties pose a significant challenge when it comes to sampling and characterizing small-sized microplastics. In this study, we introduce a novel microfluidic approach that simplifies the trapping and identification process of microplastics in surface seawater, eliminating the need for labeling. We examine various models, including support vector machine, random forest, convolutional neural network (CNN), and residual neural network (ResNet34), to assess their performance in identifying 11 common plastics. Our findings reveal that the CNN method outperforms the other models, achieving an impressive accuracy of 93% and a mean area under the curve of 98 ± 0.02%. Furthermore, we demonstrate that miniaturized devices can effectively trap and identify microplastics smaller than 50 µm. Overall, this proposed approach facilitates efficient sampling and identification of small-sized microplastics, potentially contributing to crucial long-term monitoring and treatment efforts.
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Affiliation(s)
- Liyuan Gong
- Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI, USA
| | - Omar Martinez
- Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI, USA
| | - Pedro Mesquita
- Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI, USA
| | - Kayla Kurtz
- Department of Civil and Environmental Engineering, University of Rhode Island, Kingston, RI, USA
| | - Yang Xu
- Department of Computer Science, San Diego State University, San Diego, CA, USA
| | - Yang Lin
- Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI, USA.
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23
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Xu M, Chen Z, Zheng J, Zhao Q, Yuan Z. Artificial Intelligence-Aided Optical Imaging for Cancer Theranostics. Semin Cancer Biol 2023:S1044-579X(23)00094-9. [PMID: 37302519 DOI: 10.1016/j.semcancer.2023.06.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 06/08/2023] [Accepted: 06/08/2023] [Indexed: 06/13/2023]
Abstract
The use of artificial intelligence (AI) to assist biomedical imaging have demonstrated its high accuracy and high efficiency in medical decision-making for individualized cancer medicine. In particular, optical imaging methods are able to visualize both the structural and functional information of tumors tissues with high contrast, low cost, and noninvasive property. However, no systematic work has been performed to inspect the recent advances on AI-aided optical imaging for cancer theranostics. In this review, we demonstrated how AI can guide optical imaging methods to improve the accuracy on tumor detection, automated analysis and prediction of its histopathological section, its monitoring during treatment, and its prognosis by using computer vision, deep learning and natural language processing. By contrast, the optical imaging techniques involved mainly consisted of various tomography and microscopy imaging methods such as optical endoscopy imaging, optical coherence tomography, photoacoustic imaging, diffuse optical tomography, optical microscopy imaging, Raman imaging, and fluorescent imaging. Meanwhile, existing problems, possible challenges and future prospects for AI-aided optical imaging protocol for cancer theranostics were also discussed. It is expected that the present work can open a new avenue for precision oncology by using AI and optical imaging tools.
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Affiliation(s)
- Mengze Xu
- Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Zhuhai, China; Cancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China; Centre for Cognitive and Brain Sciences, University of Macau, Macau SAR, China
| | - Zhiyi Chen
- Institute of Medical Imaging, Hengyang Medical School, University of South China, Hengyang, China
| | - Junxiao Zheng
- Cancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China; Centre for Cognitive and Brain Sciences, University of Macau, Macau SAR, China
| | - Qi Zhao
- Cancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China
| | - Zhen Yuan
- Cancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China; Centre for Cognitive and Brain Sciences, University of Macau, Macau SAR, China.
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24
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Fernández-Manteca MG, Ocampo-Sosa AA, Ruiz de Alegría-Puig C, Pía Roiz M, Rodríguez-Grande J, Madrazo F, Calvo J, Rodríguez-Cobo L, López-Higuera JM, Fariñas MC, Cobo A. Automatic classification of Candida species using Raman spectroscopy and machine learning. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 290:122270. [PMID: 36580749 DOI: 10.1016/j.saa.2022.122270] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 11/29/2022] [Accepted: 12/20/2022] [Indexed: 06/17/2023]
Abstract
One of the problems that most affect hospitals is infections by pathogenic microorganisms. Rapid identification and adequate, timely treatment can avoid fatal consequences and the development of antibiotic resistance, so it is crucial to use fast, reliable, and not too laborious techniques to obtain quick results. Raman spectroscopy has proven to be a powerful tool for molecular analysis, meeting these requirements better than traditional techniques. In this work, we have used Raman spectroscopy combined with machine learning algorithms to explore the automatic identification of eleven species of the genus Candida, the most common cause of fungal infections worldwide. The Raman spectra were obtained from more than 220 different measurements of dried drops from pure cultures of each Candida species using a Raman Confocal Microscope with a 532 nm laser excitation source. After developing a spectral preprocessing methodology, a study of the quality and variability of the measured spectra at the isolate and species level, and the spectral features contributing to inter-class variations, showed the potential to discriminate between those pathogenic yeasts. Several machine learning and deep learning algorithms were trained using hyperparameter optimization techniques to find the best possible classifier for this spectral data, in terms of accuracy and lowest possible overfitting. We found that a one-dimensional Convolutional Neural Network (1-D CNN) could achieve above 80 % overall accuracy for the eleven classes spectral dataset, with good generalization capabilities.
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Affiliation(s)
| | - Alain A Ocampo-Sosa
- Instituto de Investigación Sanitaria Valdecilla (IDIVAL), Santander, Spain; Servicio de Microbiología, Hospital Universitario Marqués de Valdecilla, Santander, Spain
| | - Carlos Ruiz de Alegría-Puig
- Instituto de Investigación Sanitaria Valdecilla (IDIVAL), Santander, Spain; Servicio de Microbiología, Hospital Universitario Marqués de Valdecilla, Santander, Spain; CIBER de Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, Madrid, Spain
| | - María Pía Roiz
- Instituto de Investigación Sanitaria Valdecilla (IDIVAL), Santander, Spain; Servicio de Microbiología, Hospital Universitario Marqués de Valdecilla, Santander, Spain
| | - Jorge Rodríguez-Grande
- Instituto de Investigación Sanitaria Valdecilla (IDIVAL), Santander, Spain; Servicio de Microbiología, Hospital Universitario Marqués de Valdecilla, Santander, Spain
| | - Fidel Madrazo
- Instituto de Investigación Sanitaria Valdecilla (IDIVAL), Santander, Spain
| | - Jorge Calvo
- Instituto de Investigación Sanitaria Valdecilla (IDIVAL), Santander, Spain; Servicio de Microbiología, Hospital Universitario Marqués de Valdecilla, Santander, Spain; CIBER de Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, Madrid, Spain
| | - Luis Rodríguez-Cobo
- Instituto de Investigación Sanitaria Valdecilla (IDIVAL), Santander, Spain; Photonics Engineering Group, Universidad de Cantabria, Santander, Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
| | - José Miguel López-Higuera
- Instituto de Investigación Sanitaria Valdecilla (IDIVAL), Santander, Spain; Photonics Engineering Group, Universidad de Cantabria, Santander, Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
| | - María Carmen Fariñas
- Instituto de Investigación Sanitaria Valdecilla (IDIVAL), Santander, Spain; CIBER de Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, Madrid, Spain; Servicio de Enfermedades Infecciosas, Hospital Universitario Marqués de Valdecilla, Santander, Spain; Departamento de Medicina y Psiquiatría, Universidad de Cantabria, Santander, Spain
| | - Adolfo Cobo
- Instituto de Investigación Sanitaria Valdecilla (IDIVAL), Santander, Spain; Photonics Engineering Group, Universidad de Cantabria, Santander, Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain.
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25
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Chen X, Wu X, Chen C, Luo C, Shi Y, Li Z, Lv X, Chen C, Su J, Wu L. Raman spectroscopy combined with a support vector machine algorithm as a diagnostic technique for primary Sjögren's syndrome. Sci Rep 2023; 13:5137. [PMID: 36991016 PMCID: PMC10060214 DOI: 10.1038/s41598-023-29943-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 02/13/2023] [Indexed: 03/31/2023] Open
Abstract
The aim of this study was to explore the feasibility of Raman spectroscopy combined with computer algorithms in the diagnosis of primary Sjögren syndrome (pSS). In this study, Raman spectra of 60 serum samples were acquired from 30 patients with pSS and 30 healthy controls (HCs). The means and standard deviations of the raw spectra of patients with pSS and HCs were calculated. Spectral features were assigned based on the literature. Principal component analysis (PCA) was used to extract the spectral features. Then, a particle swarm optimization (PSO)-support vector machine (SVM) was selected as the method of parameter optimization to rapidly classify patients with pSS and HCs. In this study, the SVM algorithm was used as the classification model, and the radial basis kernel function was selected as the kernel function. In addition, the PSO algorithm was used to establish a model for the parameter optimization method. The training set and test set were randomly divided at a ratio of 7:3. After PCA dimension reduction, the specificity, sensitivity and accuracy of the PSO-SVM model were obtained, and the results were 88.89%, 100% and 94.44%, respectively. This study showed that the combination of Raman spectroscopy and a support vector machine algorithm could be used as an effective pSS diagnosis method with broad application value.
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Affiliation(s)
- Xiaomei Chen
- Department of Rheumatology and Immunology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China
- Xinjiang Clinical Research Center for Rheumatoid Arthritis, Urumqi, Xinjiang, China
| | - Xue Wu
- Department of Rheumatology and Immunology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China
- Xinjiang Clinical Research Center for Rheumatoid Arthritis, Urumqi, Xinjiang, China
| | - Chen Chen
- College of Software, Xinjiang University, Urumqi, Xinjiang, China
| | - Cainan Luo
- Department of Rheumatology and Immunology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China
- Xinjiang Clinical Research Center for Rheumatoid Arthritis, Urumqi, Xinjiang, China
| | - Yamei Shi
- Department of Rheumatology and Immunology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China
- Xinjiang Clinical Research Center for Rheumatoid Arthritis, Urumqi, Xinjiang, China
| | - Zhengfang Li
- Department of Rheumatology and Immunology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China
- Xinjiang Clinical Research Center for Rheumatoid Arthritis, Urumqi, Xinjiang, China
| | - Xiaoyi Lv
- College of Software, Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi, Xinjiang, China
| | - Cheng Chen
- College of Software, Xinjiang University, Urumqi, Xinjiang, China
| | - Jinmei Su
- Department of Rheumatology and Immunology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China.
- Department of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Lijun Wu
- Department of Rheumatology and Immunology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China.
- Xinjiang Clinical Research Center for Rheumatoid Arthritis, Urumqi, Xinjiang, China.
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26
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Blake N, Gaifulina R, Griffin LD, Bell IM, Rodriguez-Justo M, Thomas GMH. Deep Learning Applied to Raman Spectroscopy for the Detection of Microsatellite Instability/MMR Deficient Colorectal Cancer. Cancers (Basel) 2023; 15:cancers15061720. [PMID: 36980606 PMCID: PMC10046611 DOI: 10.3390/cancers15061720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 03/08/2023] [Accepted: 03/09/2023] [Indexed: 03/16/2023] Open
Abstract
Defective DNA mismatch repair is one pathogenic pathway to colorectal cancer. It is characterised by microsatellite instability which provides a molecular biomarker for its detection. Clinical guidelines for universal testing of this biomarker are not met due to resource limitations; thus, there is interest in developing novel methods for its detection. Raman spectroscopy (RS) is an analytical tool able to interrogate the molecular vibrations of a sample to provide a unique biochemical fingerprint. The resulting datasets are complex and high-dimensional, making them an ideal candidate for deep learning, though this may be limited by small sample sizes. This study investigates the potential of using RS to distinguish between normal, microsatellite stable (MSS) and microsatellite unstable (MSI-H) adenocarcinoma in human colorectal samples and whether deep learning provides any benefit to this end over traditional machine learning models. A 1D convolutional neural network (CNN) was developed to discriminate between healthy, MSI-H and MSS in human tissue and compared to a principal component analysis–linear discriminant analysis (PCA–LDA) and a support vector machine (SVM) model. A nested cross-validation strategy was used to train 30 samples, 10 from each group, with a total of 1490 Raman spectra. The CNN achieved a sensitivity and specificity of 83% and 45% compared to PCA–LDA, which achieved a sensitivity and specificity of 82% and 51%, respectively. These are competitive with existing guidelines, despite the low sample size, speaking to the molecular discriminative power of RS combined with deep learning. A number of biochemical antecedents responsible for this discrimination are also explored, with Raman peaks associated with nucleic acids and collagen being implicated.
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Affiliation(s)
- Nathan Blake
- Department of Cell and Developmental Biology, University College London, London WC1E 6BT, UK
| | - Riana Gaifulina
- Department of Cell and Developmental Biology, University College London, London WC1E 6BT, UK
| | - Lewis D. Griffin
- Department of Computer Science, University College London, London WC1E 6BT, UK
| | - Ian M. Bell
- Spectroscopy Products Division, Renishaw PLC, Wotton-under-Edge GL12 8JR, UK
| | - Manuel Rodriguez-Justo
- Department of Research Pathology, Cancer Institute, University College London, London WC1E 6DD, UK
| | - Geraint M. H. Thomas
- Department of Cell and Developmental Biology, University College London, London WC1E 6BT, UK
- Correspondence: ; Tel.: +44-20-3549-5456
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27
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Wu X, Xu B, Niu Y, Gao S, Zhao Z, Ma R, Liu H, Zhang Y. Detection of antioxidants in edible oil by two-dimensional correlation spectroscopy combined with convolutional neural network. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2023.105262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2023]
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28
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Shang H, Shang L, Wu J, Xu Z, Zhou S, Wang Z, Wang H, Yin J. NIR spectroscopy combined with 1D-convolutional neural network for breast cancerization analysis and diagnosis. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 287:121990. [PMID: 36327802 DOI: 10.1016/j.saa.2022.121990] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 10/05/2022] [Accepted: 10/10/2022] [Indexed: 06/16/2023]
Abstract
Near-infrared (NIR) spectroscopy with deep penetration can characterize the composition of biological tissue based on the vibration of the X-H group in a rapid and high-specificity way. Deep learning is proven helpful for rapid and automatic identification of tissue cancerization. In this study, NIR spectroscopic detection equipped with the lab-made NIR probe was performed to in situ explore the change of molecular compositions in breast cancerization, where the diffused NIR spectra were efficiently collected at different locations of cancerous and paracancerous areas. The breast cancerous-paracancerous discriminant model was established based on one-dimensional convolutional neural network (1D-CNN). By optimizing the structure of the neural network, the high classification accuracy (94.67%), recall/sensitivity (95.33%), specificity (94.00%), precision (94.08%) and F1 score (0.9470) were achieved, showing the better discrimination ability and reliability than the K-Nearest Neighbor (KNN, 88.34%, 98.21%, 76.11%, 83.59%, 0.9031) and Fisher Discriminant Analysis (FDA, 90.00%, 96.43%, 81.82%, 87.10%, 0.9153) methods. The experimental results indicate that the application of 1D-CNN can discriminate the cancerous and paracancerous breast tissues, and provide an intelligent method for clinical locating, diagnosis and treatment of breast cancer.
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Affiliation(s)
- Hui Shang
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Linwei Shang
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Jinjin Wu
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Zhibing Xu
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Suwei Zhou
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Zihan Wang
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Huijie Wang
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
| | - Jianhua Yin
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
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29
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Variabilities in global DNA methylation and β-sheet richness establish spectroscopic landscapes among subtypes of pancreatic cancer. Eur J Nucl Med Mol Imaging 2023; 50:1792-1810. [PMID: 36757432 PMCID: PMC10119063 DOI: 10.1007/s00259-023-06121-7] [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: 09/27/2022] [Accepted: 01/21/2023] [Indexed: 02/10/2023]
Abstract
PURPOSE Knowledge about pancreatic cancer (PC) biology has been growing rapidly in recent decades. Nevertheless, the survival of PC patients has not greatly improved. The development of a novel methodology suitable for deep investigation of the nature of PC tumors is of great importance. Molecular imaging techniques, such as Fourier transform infrared (FTIR) spectroscopy and Raman hyperspectral mapping (RHM) combined with advanced multivariate data analysis, were useful in studying the biochemical composition of PC tissue. METHODS Here, we evaluated the potential of molecular imaging in differentiating three groups of PC tumors, which originate from different precursor lesions. Specifically, we comprehensively investigated adenocarcinomas (ACs): conventional ductal AC, intraductal papillary mucinous carcinoma, and ampulla of Vater AC. FTIR microspectroscopy and RHM maps of 24 PC tissue slides were obtained, and comprehensive advanced statistical analyses, such as hierarchical clustering and nonnegative matrix factorization, were performed on a total of 211,355 Raman spectra. Additionally, we employed deep learning technology for the same task of PC subtyping to enable automation. The so-called convolutional neural network (CNN) was trained to recognize spectra specific to each PC group and then employed to generate CNN-prediction-based tissue maps. To identify the DNA methylation spectral markers, we used differently methylated, isolated DNA and compared the observed spectral differences with the results obtained from cellular nuclei regions of PC tissues. RESULTS The results showed significant differences among cancer tissues of the studied PC groups. The main findings are the varying content of β-sheet-rich proteins within the PC cells and alterations in the relative DNA methylation level. Our CNN model efficiently differentiated PC groups with 94% accuracy. The usage of CNN in the classification task did not require Raman spectral data preprocessing and eliminated the need for extensive knowledge of statistical methodologies. CONCLUSIONS Molecular spectroscopy combined with CNN technology is a powerful tool for PC detection and subtyping. The molecular fingerprint of DNA methylation and β-sheet cytoplasmic proteins established by our results is different for the main PC groups and allowed the subtyping of pancreatic tumors, which can improve patient management and increase their survival. Our observations are of key importance in understanding the variability of PC and allow translation of the methodology into clinical practice by utilizing liquid biopsy testing.
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30
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Wang Z, Qu L, Chen Q, Zhou Y, Duan H, Li B, Weng Y, Su J, Yi W. Deep learning-based multifeature integration robustly predicts central lymph node metastasis in papillary thyroid cancer. BMC Cancer 2023; 23:128. [PMID: 36750791 PMCID: PMC9906958 DOI: 10.1186/s12885-023-10598-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Accepted: 02/01/2023] [Indexed: 02/09/2023] Open
Abstract
BACKGROUND Few highly accurate tests can diagnose central lymph node metastasis (CLNM) of papillary thyroid cancer (PTC). Genetic sequencing of tumor tissue has allowed the targeting of certain genetic variants for personalized cancer therapy development. METHODS This study included 488 patients diagnosed with PTC by ultrasound-guided fine-needle aspiration biopsy, collected clinicopathological data, analyzed the correlation between CLNM and clinicopathological features using univariate analysis and binary logistic regression, and constructed prediction models. RESULTS Binary logistic regression analysis showed that age, maximum diameter of thyroid nodules, capsular invasion, and BRAF V600E gene mutation were independent risk factors for CLNM, and statistically significant indicators were included to construct a nomogram prediction model, which had an area under the curve (AUC) of 0.778. A convolutional neural network (CNN) prediction model built with an artificial intelligence (AI) deep learning algorithm achieved AUCs of 0.89 in the training set and 0.78 in the test set, which indicated a high prediction efficacy for CLNM. In addition, the prediction models were validated in the subclinical metastasis and clinical metastasis groups with high sensitivity and specificity, suggesting the broad applicability of the models. Furthermore, CNN prediction models were constructed for patients with nodule diameters less than 1 cm. The AUCs in the training set and test set were 0.87 and 0.76, respectively, indicating high prediction efficacy. CONCLUSIONS The deep learning-based multifeature integration prediction model provides a reference for the clinical diagnosis and treatment of PTC.
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Affiliation(s)
- Zhongzhi Wang
- grid.216417.70000 0001 0379 7164Department of General Surgery, the Affiliated Zhuzhou Hospital Xiangya Medical College, Central South University, Zhuzhou, Hunan China
| | - Limeng Qu
- grid.452708.c0000 0004 1803 0208Department of General Surgery, The Second Xiangya Hospital of Central South University, No. 139, Renmin Central Road, Changsha, 410011 P.R. China
| | - Qitong Chen
- grid.452708.c0000 0004 1803 0208Department of General Surgery, The Second Xiangya Hospital of Central South University, No. 139, Renmin Central Road, Changsha, 410011 P.R. China
| | - Yong Zhou
- grid.216417.70000 0001 0379 7164Department of General Surgery, the Affiliated Zhuzhou Hospital Xiangya Medical College, Central South University, Zhuzhou, Hunan China
| | - Hongtao Duan
- grid.216417.70000 0001 0379 7164Department of Ultrasound Diagnosis, the Affiliated Zhuzhou Hospital Xiangya Medical College, Central South University, Zhuzhou, Hunan China
| | - Baifeng Li
- grid.216417.70000 0001 0379 7164Department of General Surgery, the Affiliated Zhuzhou Hospital Xiangya Medical College, Central South University, Zhuzhou, Hunan China
| | - Yao Weng
- grid.216417.70000 0001 0379 7164Department of Metabolic Endocrinology, the Affiliated Zhuzhou Hospital Xiangya Medical College, Central South University, Zhuzhou, Hunan China
| | - Juan Su
- Department of Medical Administration, the Affiliated Zhuzhou Hospital Xiangya Medical College, Central South University, No.116, Changjiang South Road, Zhuzhou, 412007, P.R. China.
| | - Wenjun Yi
- Department of General Surgery, The Second Xiangya Hospital of Central South University, No. 139, Renmin Central Road, Changsha, 410011, P.R. China.
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Zeng Q, Chen C, Chen C, Song H, Li M, Yan J, Lv X. Serum Raman spectroscopy combined with convolutional neural network for rapid diagnosis of HER2-positive and triple-negative breast cancer. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 286:122000. [PMID: 36279798 DOI: 10.1016/j.saa.2022.122000] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 09/26/2022] [Accepted: 10/13/2022] [Indexed: 06/16/2023]
Abstract
Breast cancer is common in women, and its number of patients ranks first among female malignant tumors. Breast cancer is highly heterogeneous, and different types of breast cancer have different biological behaviors and prognoses. Therefore, identifying the different types of breast cancer is of great help in formulating individualized treatment plans. Based on serum Raman spectroscopy and deep learning algorithms, we propose a fast and low-cost diagnosis method for screening triple-negative breast cancer, human epidermal growth factor receptor 2 (HER2)-positive breast cancer, and healthy controls. We collected 75 serum samples in this study, including 23 triple-negative breast cancers, 22 HER2-positive breast cancers, and 30 healthy controls. Using the preprocessed Raman spectra as the input of deep learning, three deep learning models, neural network language model (NNLM), bidirectional long-short-term memory network (BiLSTM), and convolutional neural network (CNN), were established, and the accuracy rates of the three models were 87.78%, 90.37%, and 91.11%, respectively. The experimental results demonstrate the feasibility of serum Raman spectroscopy combined with deep learning algorithms to diagnose breast cancer, which can be used as an effective auxiliary diagnosis method for breast cancer.
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Affiliation(s)
- Qinggang Zeng
- College of Information Science and Engineering Xinjiang University, Urumqi 830046, China
| | - Cheng Chen
- College of Software, Xinjiang University, Urumqi 830046, China.
| | - Chen Chen
- College of Information Science and Engineering Xinjiang University, Urumqi 830046, China; Xinjiang Cloud Computing Application Laboratory, Karamay 834099, China
| | - Haitao Song
- Department of Breast, Head and Neck Surgery, Xinjiang Medical University Affiliated Tumor Hospital, Urumqi, China
| | - Min Li
- College of Information Science and Engineering Xinjiang University, Urumqi 830046, China
| | - Junyi Yan
- College of Software, Xinjiang University, Urumqi 830046, China
| | - Xiaoyi Lv
- College of Software, Xinjiang University, Urumqi 830046, China; Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi 830046, Xinjiang, China
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32
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Fuentes AM, Narayan A, Milligan K, Lum JJ, Brolo AG, Andrews JL, Jirasek A. Raman spectroscopy and convolutional neural networks for monitoring biochemical radiation response in breast tumour xenografts. Sci Rep 2023; 13:1530. [PMID: 36707535 PMCID: PMC9883395 DOI: 10.1038/s41598-023-28479-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 01/19/2023] [Indexed: 01/29/2023] Open
Abstract
Tumour cells exhibit altered metabolic pathways that lead to radiation resistance and disease progression. Raman spectroscopy (RS) is a label-free optical modality that can monitor post-irradiation biomolecular signatures in tumour cells and tissues. Convolutional Neural Networks (CNN) perform automated feature extraction directly from data, with classification accuracy exceeding that of traditional machine learning, in cases where data is abundant and feature extraction is challenging. We are interested in developing a CNN-based predictive model to characterize clinical tumour response to radiation therapy based on their degree of radiosensitivity or radioresistance. In this work, a CNN architecture is built for identifying post-irradiation spectral changes in Raman spectra of tumour tissue. The model was trained to classify irradiated versus non-irradiated tissue using Raman spectra of breast tumour xenografts. The CNN effectively classified the tissue spectra, with accuracies exceeding 92.1% for data collected 3 days post-irradiation, and 85.0% at day 1 post-irradiation. Furthermore, the CNN was evaluated using a leave-one-out- (mouse, section or Raman map) validation approach to investigate its generalization to new test subjects. The CNN retained good predictive accuracy (average accuracies 83.7%, 91.4%, and 92.7%, respectively) when little to no information for a specific subject was given during training. Finally, the classification performance of the CNN was compared to that of a previously developed model based on group and basis restricted non-negative matrix factorization and random forest (GBR-NMF-RF) classification. We found that CNN yielded higher classification accuracy, sensitivity, and specificity in mice assessed 3 days post-irradiation, as compared with the GBR-NMF-RF approach. Overall, the CNN can detect biochemical spectral changes in tumour tissue at an early time point following irradiation, without the need for previous manual feature extraction. This study lays the foundation for developing a predictive framework for patient radiation response monitoring.
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Affiliation(s)
- Alejandra M Fuentes
- Department of Physics, The University of British Columbia Okanagan Campus, Kelowna, Canada
| | - Apurva Narayan
- Department of Computer Science, Western University, London, Canada
- Department of Computer Science, The University of British Columbia Okanagan Campus, Kelowna, Canada
| | - Kirsty Milligan
- Department of Physics, The University of British Columbia Okanagan Campus, Kelowna, Canada
| | - Julian J Lum
- Department of Biochemistry and Microbiology, The University of Victoria, Victoria, Canada
| | - Alex G Brolo
- Department of Chemistry, The University of Victoria, Victoria, Canada
| | - Jeffrey L Andrews
- Department of Statistics, The University of British Columbia Okanagan Campus, Kelowna, Canada
| | - Andrew Jirasek
- Department of Physics, The University of British Columbia Okanagan Campus, Kelowna, Canada.
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Szymoński K, Chmura Ł, Lipiec E, Adamek D. Vibrational spectroscopy – are we close to finding a solution for early pancreatic cancer diagnosis? World J Gastroenterol 2023; 29:96-109. [PMID: 36683712 PMCID: PMC9850953 DOI: 10.3748/wjg.v29.i1.96] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 10/03/2022] [Accepted: 10/31/2022] [Indexed: 01/04/2023] Open
Abstract
Pancreatic cancer (PC) is an aggressive and lethal neoplasm, ranking seventh in the world for cancer deaths, with an overall 5-year survival rate of below 10%. The knowledge about PC pathogenesis is rapidly expanding. New aspects of tumor biology, including its molecular and morphological heterogeneity, have been reported to explain the complicated “cross-talk” that occurs between the cancer cells and the tumor stroma or the nature of pancreatic ductal adenocarcinoma-associated neural remodeling. Nevertheless, currently, there are no specific and sensitive diagnosis options for PC. Vibrational spectroscopy (VS) shows a promising role in the development of early diagnosis technology. In this review, we summarize recent reports about improvements in spectroscopic methodologies, briefly explain and highlight the drawbacks of each of them, and discuss available solutions. The important aspects of spectroscopic data evaluation with multivariate analysis and a convolutional neural network methodology are depicted. We conclude by presenting a study design for systemic verification of the VS-based methods in the diagnosis of PC.
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Affiliation(s)
- Krzysztof Szymoński
- Department of Pathomorphology, Jagiellonian University Medical College, Cracow 33-332, Poland
- Department of Pathomorphology, University Hospital in Cracow, Cracow 31-501, Poland
| | - Łukasz Chmura
- Department of Pathomorphology, Jagiellonian University Medical College, Cracow 33-332, Poland
- Department of Pathomorphology, University Hospital in Cracow, Cracow 31-501, Poland
| | - Ewelina Lipiec
- M. Smoluchowski Institute of Physics, Jagiellonian University, Cracow 30-348, Poland
| | - Dariusz Adamek
- Department of Pathomorphology, University Hospital in Cracow, Cracow 31-501, Poland
- Department of Neuropathology, Jagiellonian University Medical College, Cracow 33-332, Poland
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Shang L, Tang J, Wu J, Shang H, Huang X, Bao Y, Xu Z, Wang H, Yin J. Polarized Micro-Raman Spectroscopy and 2D Convolutional Neural Network Applied to Structural Analysis and Discrimination of Breast Cancer. BIOSENSORS 2022; 13:65. [PMID: 36671896 PMCID: PMC9856190 DOI: 10.3390/bios13010065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 12/23/2022] [Accepted: 12/28/2022] [Indexed: 06/17/2023]
Abstract
Raman spectroscopy has been efficiently used to recognize breast cancer tissue by detecting the characteristic changes in tissue composition in cancerization. In addition to chemical composition, the change in bio-structure may be easily obtained via polarized micro-Raman spectroscopy, aiding in identifying the cancerization process and diagnosis. In this study, a polarized Raman spectral technique is employed to obtain rich structural features and, combined with deep learning technology, to achieve discrimination of breast cancer tissue. The results reconfirm that the orientation of collagen fibers changes from parallel to vertical during breast cancerization, and there are significant structural differences between cancerous and normal tissues, which is consistent with previous reports. Optical anisotropy of collagen fibers weakens in cancer tissue, which is closely related with the tumor's progression. To distinguish breast cancer tissue, a discrimination model is established based on a two-dimensional convolutional neural network (2D-CNN), where the input is a matrix containing the Raman spectra acquired at a set of linear polarization angles varying from 0° to 360°. As a result, an average discrimination accuracy of 96.01% for test samples is achieved, better than that of the KNN classifier and 1D-CNN that are based on non-polarized Raman spectra. This study implies that polarized Raman spectroscopy combined with 2D-CNN can effectively detect changes in the structure and components of tissues, innovatively improving the identification and automatic diagnosis of breast cancer with label-free probing and analysis.
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Affiliation(s)
- Linwei Shang
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Jinlan Tang
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Jinjin Wu
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Hui Shang
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Xing Huang
- Department of Pathology, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research & the Affiliated Cancer Hospital of Nanjing Medical University, Nanjing 210016, China
| | - Yilin Bao
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Zhibing Xu
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Huijie Wang
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Jianhua Yin
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
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Ranasinghe JC, Wang Z, Huang S. Raman Spectroscopy on Brain Disorders: Transition from Fundamental Research to Clinical Applications. BIOSENSORS 2022; 13:27. [PMID: 36671862 PMCID: PMC9855372 DOI: 10.3390/bios13010027] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 12/13/2022] [Accepted: 12/23/2022] [Indexed: 06/17/2023]
Abstract
Brain disorders such as brain tumors and neurodegenerative diseases (NDs) are accompanied by chemical alterations in the tissues. Early diagnosis of these diseases will provide key benefits for patients and opportunities for preventive treatments. To detect these sophisticated diseases, various imaging modalities have been developed such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). However, they provide inadequate molecule-specific information. In comparison, Raman spectroscopy (RS) is an analytical tool that provides rich information about molecular fingerprints. It is also inexpensive and rapid compared to CT, MRI, and PET. While intrinsic RS suffers from low yield, in recent years, through the adoption of Raman enhancement technologies and advanced data analysis approaches, RS has undergone significant advancements in its ability to probe biological tissues, including the brain. This review discusses recent clinical and biomedical applications of RS and related techniques applicable to brain tumors and NDs.
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Affiliation(s)
| | | | - Shengxi Huang
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
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36
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Li H, Wang S, Zeng Q, Chen C, Lv X, Ma M, Su H, Ma B, Chen C, Fang J. Serum Raman spectroscopy combined with multiple classification models for rapid diagnosis of breast cancer. Photodiagnosis Photodyn Ther 2022; 40:103115. [PMID: 36096439 DOI: 10.1016/j.pdpdt.2022.103115] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 09/03/2022] [Accepted: 09/07/2022] [Indexed: 12/14/2022]
Abstract
Breast cancer is a malignant tumor with the highest incidence rate in women. Current diagnostic methods are time-consuming, costly, and dependent on physician experience. In this study, we used serum Raman spectroscopy combined with multiple classification algorithms to implement an auxiliary diagnosis method for breast cancer, which will help in the early diagnosis of breast cancer patients. We analyzed the serum Raman spectra of 171 invasive ductal carcinoma (IDC) and 100 healthy volunteers; The analysis showed differences in nucleic acids, carotenoids, amino acids, and lipid concentrations in their blood. These differences provide a theoretical basis for this experiment. First, we used adaptive iteratively reweighted penalized least squares (airPLS) and Savitzky-Golay (SG) for baseline correction and smoothing denoising to remove the effect of noise on the experiment. Then, the Principal component analysis (PCA) algorithm was used to extract features. Finally, we built four classification models: support vector machine (SVM), decision tree (DT), linear discriminant analysis (LDA), and Neural Network Language Model (NNLM). The LDA, SVM, and NNLM achieve 100% accuracy. As supplementary, we added the classification experiment of the raw data. By comparing the experimental results of the two groups, We concluded that the NNLM was the best model. The results show the reliability of the combination of serum Raman spectroscopy and classification models under large sample conditions.
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Affiliation(s)
- Hongtao Li
- Department of Breast, Head and Neck Surgery, Xinjiang Medical University Affiliated Tumor Hospital, Urumqi, China
| | | | - Qinggang Zeng
- College of Software, Xinjiang University, Urumqi 830046, China; College of Information Science and Engineering Xinjiang University, Urumqi 830046, China
| | - Chen Chen
- College of Information Science and Engineering Xinjiang University, Urumqi 830046, China; Xinjiang Cloud Computing Application Laboratory, Karamay 834099, China
| | - Xiaoyi Lv
- College of Software, Xinjiang University, Urumqi 830046, China; College of Information Science and Engineering Xinjiang University, Urumqi 830046, China
| | - Mingrui Ma
- Department of Breast, Head and Neck Surgery, Xinjiang Medical University Affiliated Tumor Hospital, Urumqi, China
| | - Haihua Su
- Hospital of Xinjiang Production and Construction Corps, Urumqi 830092, China
| | - Binlin Ma
- Department of Breast, Head and Neck Surgery, Xinjiang Medical University Affiliated Tumor Hospital, Urumqi, China.
| | - Cheng Chen
- College of Software, Xinjiang University, Urumqi 830046, China.
| | - Jingjing Fang
- Department of Breast, Head and Neck Surgery, Xinjiang Medical University Affiliated Tumor Hospital, Urumqi, China
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37
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Qiu X, He T, Wu X, Wang P, Wang X, Fu Q, Fang X, Li S, Li Y. Combining fiber optical tweezers and Raman spectroscopy for rapid identification of melanoma. JOURNAL OF BIOPHOTONICS 2022; 15:e202200158. [PMID: 36053940 DOI: 10.1002/jbio.202200158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 08/21/2022] [Accepted: 08/22/2022] [Indexed: 06/15/2023]
Abstract
Cutaneous melanoma is a skin tumor with a high degree of malignancy and fatality rate, the incidence of which has increased in recent years. Therefore, a rapid and sensitive diagnostic technique of melanoma cells is urgently needed. In this paper, we present a new approach using fiber optical tweezers to manipulate melanoma cells to measure their Raman spectra. Then, combined with Principal Component Analysis and Support Vector Machines (PCA-SVM) classification model, to achieve the classification of common mutant, wild-type and drug-resistant melanoma cells. A total of 150 Raman spectra of 30 cells were collected from mutant, wild-type and drug-resistant melanoma cell lines, and the classification accuracy was 92%, 94%, 97.5%, respectively. These results suggest that the study of tumor cells based on fiber optical tweezers and Raman spectroscopy is a promising method for early and rapid identification and diagnosis of tumor cells.
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Affiliation(s)
- Xun Qiu
- College of Medical Technology, Guangdong Medical University, Dongguan, China
| | - Tao He
- Department of Biology, School of Basic Medical Sciences, Guangdong Medical University, Dongguan, China
| | - Xingda Wu
- Biomedical Photonics Laboratory, School of Biomedical Engineering, Guangdong Medical University, Dongguan, China
| | - Peng Wang
- College of Medical Technology, Guangdong Medical University, Dongguan, China
| | - Xin Wang
- College of Medical Technology, Guangdong Medical University, Dongguan, China
| | - Qiuyue Fu
- College of Medical Technology, Guangdong Medical University, Dongguan, China
| | - Xianglin Fang
- Biomedical Photonics Laboratory, School of Biomedical Engineering, Guangdong Medical University, Dongguan, China
| | - Shaoxin Li
- Biomedical Photonics Laboratory, School of Biomedical Engineering, Guangdong Medical University, Dongguan, China
| | - Ying Li
- Biomedical Photonics Laboratory, School of Biomedical Engineering, Guangdong Medical University, Dongguan, China
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38
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Determination of Wheat Heading Stage Using Convolutional Neural Networks on Multispectral UAV Imaging Data. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:3655804. [DOI: 10.1155/2022/3655804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 10/18/2022] [Accepted: 11/03/2022] [Indexed: 11/25/2022]
Abstract
The heading and flowering stages are crucial for wheat growth and should be used for fusarium head blight (FHB) and other plant prevention operations. Rapid and accurate monitoring of wheat growth in hilly areas is critical for determining plant protection operations and strategies. Currently, the operation time for FHB prevention and plant protection is primarily determined by manual tour inspection of plant growth, which has the disadvantages of low information gathering and subjectivity. In this study, an unmanned aerial vehicle (UAV) equipped with a multispectral camera was used to collect wheat canopy multispectral images and heading rate information during the heading and flowering stages in order to develop a method for detecting the appropriate time for preventive control of FHB. A 1D convolutional neural network + decision tree model (1D CNN + DT) was designed. All the multispectral information was input into the model for feature extraction and result regression. The regression revealed that the coefficient of determination (R2) between multispectral information in the wheat canopy and the heading rate was 0.95, and the root mean square error of prediction (RMSE) was 0.24. This result was superior to that obtained by directly inputting multispectral data into neural networks (NN) or by inputting multispectral data into NN via traditional VI calculation, support vector machines regression (SVR), or decision tree (DT). On the basis of FHB prevention and control production guidelines and field research results, a discrimination model for FHB prevention and plant protection operation time was developed. After the output values of the regression model were input into the discrimination model, a 97.50% precision was obtained. The method proposed in this study can efficiently monitor the growth status of wheat during the heading and flowering stages and provide crop growth information for determining the timing and strategy of FHB prevention and plant protection operations.
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39
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Manganelli Conforti P, D’Acunto M, Russo P. Deep Learning for Chondrogenic Tumor Classification through Wavelet Transform of Raman Spectra. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22197492. [PMID: 36236597 PMCID: PMC9571786 DOI: 10.3390/s22197492] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 09/16/2022] [Accepted: 09/23/2022] [Indexed: 05/22/2023]
Abstract
The grading of cancer tissues is still one of the main challenges for pathologists. The development of enhanced analysis strategies hence becomes crucial to accurately identify and further deal with each individual case. Raman spectroscopy (RS) is a promising tool for the classification of tumor tissues as it allows us to obtain the biochemical maps of the tissues under analysis and to observe their evolution in terms of biomolecules, proteins, lipid structures, DNA, vitamins, and so on. However, its potential could be further improved by providing a classification system which would be able to recognize the sample tumor category by taking as input the raw Raman spectroscopy signal; this could provide more reliable responses in shorter time scales and could reduce or eliminate false-positive or -negative diagnoses. Deep Learning techniques have become ubiquitous in recent years, with models able to perform classification with high accuracy in most diverse fields of research, e.g., natural language processing, computer vision, medical imaging. However, deep models often rely on huge labeled datasets to produce reasonable accuracy, otherwise occurring in overfitting issues when the training data is insufficient. In this paper, we propose a chondrogenic tumor CLAssification through wavelet transform of RAman spectra (CLARA), which is able to classify with high accuracy Raman spectra obtained from bone tissues. CLARA recognizes and grades the tumors in the evaluated dataset with 97% accuracy by exploiting a classification pipeline consisting of the division of the original task in two binary classification steps, where the first is performed on the original RS signals while the latter is accomplished through the use of a hybrid temporal-frequency 2D transform.
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Affiliation(s)
| | - Mario D’Acunto
- CNR-IBF, Istituto di Biofisica, Via Moruzzi 1, 56124 Pisa, Italy
| | - Paolo Russo
- DIAG Department, Sapienza University of Rome, Via Ariosto 25, 00185 Roma, Italy
- Correspondence:
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40
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Wang MH, Liu X, Wang Q, Zhang HW. Diagnosis accuracy of Raman spectroscopy in the diagnosis of breast cancer: a meta-analysis. Anal Bioanal Chem 2022; 414:7911-7922. [PMID: 36138121 DOI: 10.1007/s00216-022-04326-7] [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: 07/08/2022] [Revised: 08/31/2022] [Accepted: 09/05/2022] [Indexed: 11/25/2022]
Abstract
To investigate the diagnostic efficiency of Raman spectroscopy for the diagnosis of breast cancer, we searched PubMed, Web of Science, Cochrane Library, and Embase for articles published from the database establishment to May 20, 2022. Pooled sensitivity, specificity, diagnostic odds ratio, and area under the receiver pooled operating characteristic curve were derived for the included studies as outcome measures. The methodological quality was assessed according to the questionnaires and criteria suggested by the Diagnostic Accuracy Research Quality Assessment-2 tool. Sixteen studies were included in this meta-analysis. The pooled sensitivity and specificity of Raman spectroscopy for breast cancer diagnosis were 0.97 (95% CI, [0.92-0.99]) and 0.96 (95% CI, [0.91-0.98]). The diagnostic odds ratio was 720.89 (95% CI, [135.73-3828.88]) and the area under the curve of summary receiver operating characteristic curves was 0.99 (95% CI, [0.98-1]). Subgroup analysis revealed that all subgroup types in our analysis, including different races, sample types, diagnostic algorithms, number of spectra, instrument types, and laser wavelengths, turned out to have a sensitivity and specificity greater than 0.9. Significant heterogeneity was found between studies. Deeks' funnel plot demonstrated that publication bias was acceptable. This meta-analysis suggests that Raman spectroscopy may be an effective and accurate tool to differentiate breast cancer from normal breast tissue, which will help us diagnose and treat breast cancer.
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Affiliation(s)
- Mei-Huan Wang
- Department of Ultrasound, Shandong Provincial Hospital affiliated to Shandong First Medical University, No. 324 Jing 5 Rd, Shandong Provincial Hospital, Jinan, Shandong, 250021, People's Republic of China
| | - Xiao Liu
- Department of Ultrasound, Shandong Provincial Hospital affiliated to Shandong First Medical University, No. 324 Jing 5 Rd, Shandong Provincial Hospital, Jinan, Shandong, 250021, People's Republic of China
| | - Qian Wang
- Department of Ultrasound, Shandong Provincial Hospital affiliated to Shandong First Medical University, No. 324 Jing 5 Rd, Shandong Provincial Hospital, Jinan, Shandong, 250021, People's Republic of China.
- Department of Ultrasound, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250021, Shandong, China.
| | - Hua-Wei Zhang
- Department of Ultrasound, Shandong Provincial Hospital affiliated to Shandong First Medical University, No. 324 Jing 5 Rd, Shandong Provincial Hospital, Jinan, Shandong, 250021, People's Republic of China.
- Department of Ultrasound, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250021, Shandong, China.
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41
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Huang J, He H, Lv R, Zhang G, Zhou Z, Wang X. Non-destructive detection and classification of textile fibres based on hyperspectral imaging and 1D-CNN. Anal Chim Acta 2022; 1224:340238. [PMID: 35998989 DOI: 10.1016/j.aca.2022.340238] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 06/28/2022] [Accepted: 08/01/2022] [Indexed: 11/27/2022]
Abstract
Textile fibre is very common in daily life, and its classification and identification play an important role in textile recycling, archaeology, public security, and other industries. However, traditional identification methods are time-consuming, laborious, and often destructive to the samples. In order to quickly, accurately, and nondestructively classify and recognize textile fibres, this study established a textile fibre classification and recognition method based on hyperspectral imaging (HSI) and a one-dimensional convolutional neural network (1D-CNN) model. Hyperspectral images of 25 kinds of commercial textile fibres were collected and denoised by pixel fusion. Four traditional machine learning classification models, k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), and partial least squares-discriminant analysis (PLS-DA), were used to identify the data. The results show that RF has the highest classification accuracy, reaching 91.4%. Then a back propagation neural network (BPNN) model and a one-dimensional convolutional neural network (1D-CNN) model were constructed and compared with the traditional machine learning methods. The results show that the 1D-CNN models have 97.9% and 98.6% accuracy on the training and test sets, respectively. The precision (Pr), sensitivity (Se), specificity (Sp), and F1 score (F1 score) of the models reached 98.7%, 98.6%, 99.9%, and 98.6%, respectively, which were significantly better than the four traditional machine learning models. It seems that 1D-CNN combined with the HSI technique may be a potential method in the detection and classification of textile fibres.
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Affiliation(s)
- Jiadong Huang
- School of Criminal Investigation, People's Public Security University of China, Beijing, China
| | - Hongyuan He
- School of Criminal Investigation, People's Public Security University of China, Beijing, China.
| | - Rulin Lv
- School of Criminal Investigation, People's Public Security University of China, Beijing, China
| | - Guangteng Zhang
- School of Criminal Investigation, People's Public Security University of China, Beijing, China
| | - Zongxian Zhou
- School of Criminal Investigation, People's Public Security University of China, Beijing, China
| | - Xiaobin Wang
- School of Criminal Investigation, People's Public Security University of China, Beijing, China
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Lin J, Weng Y, Lin X, Qiu S, Huang Z, Pan C, Li Y, Kong KV, Zhang X, Feng S. Highly Efficient Blood Protein Analysis Using Membrane Purification Technique and Super-Hydrophobic SERS Platform for Precise Screening and Staging of Nasopharyngeal Carcinoma. NANOMATERIALS (BASEL, SWITZERLAND) 2022; 12:2724. [PMID: 35957154 PMCID: PMC9370769 DOI: 10.3390/nano12152724] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 07/18/2022] [Accepted: 08/04/2022] [Indexed: 06/15/2023]
Abstract
Early screening and precise staging are crucial for reducing mortality in patients with nasopharyngeal carcinoma (NPC). This study aimed to assess the performance of blood protein surface-enhanced Raman scattering (SERS) spectroscopy, combined with deep learning, for the precise detection of NPC. A highly efficient protein SERS analysis, based on a membrane purification technique and super-hydrophobic platform, was developed and applied to blood samples from 1164 subjects, including 225 healthy volunteers, 120 stage I, 249 stage II, 291 stage III, and 279 stage IV NPC patients. The proteins were rapidly purified from only 10 µL of blood plasma using the membrane purification technique. Then, the super-hydrophobic platform was prepared to pre-concentrate tiny amounts of proteins by forming a uniform deposition to provide repeatable SERS spectra. A total of 1164 high-quality protein SERS spectra were rapidly collected using a self-developed macro-Raman system. A convolutional neural network-based deep-learning algorithm was used to classify the spectra. An accuracy of 100% was achieved for distinguishing between the healthy and NPC groups, and accuracies of 96%, 96%, 100%, and 100% were found for the differential classification among the four NPC stages. This study demonstrated the great promise of SERS- and deep-learning-based blood protein testing for rapid, non-invasive, and precise screening and staging of NPC.
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Affiliation(s)
- Jinyong Lin
- Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou 350007, China
- Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou 350014, China
| | - Youliang Weng
- Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou 350014, China
| | - Xueliang Lin
- Fujian Provincial Key Laboratory for Advanced Micro-Nano Photonics Technology and Devices, Research Center for Photonics Technology, Quanzhou Normal University, Quanzhou 362046, China
| | - Sufang Qiu
- Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou 350014, China
| | - Zufang Huang
- Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou 350007, China
| | - Changbin Pan
- Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou 350007, China
| | - Ying Li
- Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou 350014, China
| | - Kien Voon Kong
- Department of Chemistry, National Taiwan University, Taipei 10617, Taiwan
| | - Xianzeng Zhang
- Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou 350007, China
| | - Shangyuan Feng
- Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou 350007, China
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Blake N, Gaifulina R, Griffin LD, Bell IM, Thomas GMH. Machine Learning of Raman Spectroscopy Data for Classifying Cancers: A Review of the Recent Literature. Diagnostics (Basel) 2022; 12:diagnostics12061491. [PMID: 35741300 PMCID: PMC9222091 DOI: 10.3390/diagnostics12061491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 06/13/2022] [Accepted: 06/14/2022] [Indexed: 11/16/2022] Open
Abstract
Raman Spectroscopy has long been anticipated to augment clinical decision making, such as classifying oncological samples. Unfortunately, the complexity of Raman data has thus far inhibited their routine use in clinical settings. Traditional machine learning models have been used to help exploit this information, but recent advances in deep learning have the potential to improve the field. However, there are a number of potential pitfalls with both traditional and deep learning models. We conduct a literature review to ascertain the recent machine learning methods used to classify cancers using Raman spectral data. We find that while deep learning models are popular, and ostensibly outperform traditional learning models, there are many methodological considerations which may be leading to an over-estimation of performance; primarily, small sample sizes which compound sub-optimal choices regarding sampling and validation strategies. Amongst several recommendations is a call to collate large benchmark Raman datasets, similar to those that have helped transform digital pathology, which researchers can use to develop and refine deep learning models.
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Affiliation(s)
- Nathan Blake
- Department of Cell and Developmental Biology, University College London, London WC1E 6BT, UK; (N.B.); (R.G.)
| | - Riana Gaifulina
- Department of Cell and Developmental Biology, University College London, London WC1E 6BT, UK; (N.B.); (R.G.)
| | - Lewis D. Griffin
- Department of Computer Science, University College London, London WC1E 6BT, UK;
| | - Ian M. Bell
- Spectroscopy Products Division, Renishaw plc, Wotton-under-Edge GL12 8JR, UK;
| | - Geraint M. H. Thomas
- Department of Cell and Developmental Biology, University College London, London WC1E 6BT, UK; (N.B.); (R.G.)
- Correspondence: ; Tel.: +44-20-3549-5456
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Wu X, Xu B, Ma R, Niu Y, Gao S, Liu H, Zhang Y. Identification and quantification of adulterated honey by Raman spectroscopy combined with convolutional neural network and chemometrics. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 274:121133. [PMID: 35299093 DOI: 10.1016/j.saa.2022.121133] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 02/23/2022] [Accepted: 03/07/2022] [Indexed: 06/14/2023]
Abstract
In this study, Raman spectroscopy combined with convolutional neural network (CNN) and chemometrics was used to achieve the identification and quantification of honey samples adulterated with high fructose corn syrup, rice syrup, maltose syrup and blended syrup, respectively. The shallow CNNs utilized to analyze honey mixed with single-variety syrup classified samples into four categories by the adulteration concentration with more than 97% accuracy, and the general CNN model for simultaneously detecting honey adulterated with any type of syrup obtained an accuracy of 94.79%. The established CNNs had the best performance compared with several chemometric classification algorithms. In addition, partial least square regression (PLS) successfully predicted the purity of honey mixed with single syrup, while coefficients of determination and root mean square errors of prediction were greater than 0.98 and less than 3.50, respectively. Therefore, the proposed methods based on Raman spectra have important practical significance for food safety and quality control of honey products.
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Affiliation(s)
- Xijun Wu
- Measurement Technology & Instrumentation Key Laboratory of Hebei Province, Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004 China
| | - Baoran Xu
- Measurement Technology & Instrumentation Key Laboratory of Hebei Province, Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004 China.
| | - Renqi Ma
- Measurement Technology & Instrumentation Key Laboratory of Hebei Province, Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004 China
| | - Yudong Niu
- Measurement Technology & Instrumentation Key Laboratory of Hebei Province, Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004 China
| | - Shibo Gao
- Measurement Technology & Instrumentation Key Laboratory of Hebei Province, Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004 China
| | - Hailong Liu
- Measurement Technology & Instrumentation Key Laboratory of Hebei Province, Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004 China
| | - Yungang Zhang
- Measurement Technology & Instrumentation Key Laboratory of Hebei Province, Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004 China
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Raman spectroscopy: current applications in breast cancer diagnosis, challenges and future prospects. Br J Cancer 2022; 126:1125-1139. [PMID: 34893761 PMCID: PMC8661339 DOI: 10.1038/s41416-021-01659-5] [Citation(s) in RCA: 48] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 11/11/2021] [Accepted: 11/25/2021] [Indexed: 12/26/2022] Open
Abstract
Despite significant improvements in the way breast cancer is managed and treated, it continues to persist as a leading cause of death worldwide. If detected and diagnosed early, when tumours are small and localised, there is a considerably higher chance of survival. However, current methods for detection and diagnosis lack the required sensitivity and specificity for identifying breast cancer at the asymptomatic or very early stages. Thus, there is a need to develop more rapid and reliable methods, capable of detecting disease earlier, for improved disease management and patient outcome. Raman spectroscopy is a non-destructive analytical technique that can rapidly provide highly specific information on the biochemical composition and molecular structure of samples. In cancer, it has the capacity to probe very early biochemical changes that accompany malignant transformation, even prior to the onset of morphological changes, to produce a fingerprint of disease. This review explores the application of Raman spectroscopy in breast cancer, including discussion on its capabilities in analysing both ex-vivo tissue and liquid biopsy samples, and its potential in vivo applications. The review also addresses current challenges and potential future uses of this technology in cancer research and translational clinical application.
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Prediction of Pulmonary Function Parameters Based on a Combination Algorithm. Bioengineering (Basel) 2022; 9:bioengineering9040136. [PMID: 35447696 PMCID: PMC9032560 DOI: 10.3390/bioengineering9040136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 03/18/2022] [Accepted: 03/23/2022] [Indexed: 11/19/2022] Open
Abstract
Objective: Pulmonary function parameters play a pivotal role in the assessment of respiratory diseases. However, the accuracy of the existing methods for the prediction of pulmonary function parameters is low. This study proposes a combination algorithm to improve the accuracy of pulmonary function parameter prediction. Methods: We first established a system to collect volumetric capnography and then processed the data with a combination algorithm to predict pulmonary function parameters. The algorithm consists of three main parts: a medical feature regression structure consisting of support vector machines (SVM) and extreme gradient boosting (XGBoost) algorithms, a sequence feature regression structure consisting of one-dimensional convolutional neural network (1D-CNN), and an error correction structure using improved K-nearest neighbor (KNN) algorithm. Results: The root mean square error (RMSE) of the pulmonary function parameters predicted by the combination algorithm was less than 0.39L and the R2 was found to be greater than 0.85 through a ten-fold cross-validation experiment. Conclusion: Compared with the existing methods for predicting pulmonary function parameters, the present algorithm can achieve a higher accuracy rate. At the same time, this algorithm uses specific processing structures for different features, and the interpretability of the algorithm is ensured while mining the feature depth information.
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Automatic Breast Tumor Diagnosis in MRI Based on a Hybrid CNN and Feature-Based Method Using Improved Deer Hunting Optimization Algorithm. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:5396327. [PMID: 34326868 PMCID: PMC8302380 DOI: 10.1155/2021/5396327] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Revised: 06/12/2021] [Accepted: 07/06/2021] [Indexed: 11/18/2022]
Abstract
Breast cancer is an unusual mass of the breast texture. It begins with an abnormal change in cell structure. This disease may increase uncontrollably and affects neighboring textures. Early diagnosis of this cancer (abnormal cell changes) can help definitively treat it. Also, prevention of this cancer can help to decrease the high cost of medical caring for breast cancer patients. In recent years, the computer-aided technique is an important active field for automatic cancer detection. In this study, an automatic breast tumor diagnosis system is introduced. An improved Deer Hunting Optimization Algorithm (DHOA) is used as the optimization algorithm. The presented method utilized a hybrid feature-based technique and a new optimized convolutional neural network (CNN). Simulations are applied to the DCE-MRI dataset based on some performance indexes. The novel contribution of this paper is to apply the preprocessing stage to simplifying the classification. Besides, we used a new metaheuristic algorithm. Also, the feature extraction by Haralick texture and local binary pattern (LBP) is recommended. Due to the obtained results, the accuracy of this method is 98.89%, which represents the high potential and efficiency of this method.
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