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Mo W, Ke Q, Yang Q, Zhou M, Xie G, Qi D, Peng L, Wang X, Wang F, Ni S, Wang A, Huang J, Wen J, Yang Y, Du K, Wang X, Du X, Zhao Z. A Dual-Modal, Label-Free Raman Imaging Method for Rapid Virtual Staining of Large-Area Breast Cancer Tissue Sections. Anal Chem 2024; 96:13410-13420. [PMID: 38967251 DOI: 10.1021/acs.analchem.4c00870] [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: 07/06/2024]
Abstract
As one of the most common cancers, accurate, rapid, and simple histopathological diagnosis is very important for breast cancer. Raman imaging is a powerful technique for label-free analysis of tissue composition and histopathology, but it suffers from slow speed when applied to large-area tissue sections. In this study, we propose a dual-modal Raman imaging method that combines Raman mapping data with microscopy bright-field images to achieve virtual staining of breast cancer tissue sections. We validate our method on various breast tissue sections with different morphologies and biomarker expressions and compare it with the golden standard of histopathological methods. The results demonstrate that our method can effectively distinguish various types and components of tissues, and provide staining images comparable to stained tissue sections. Moreover, our method can improve imaging speed by up to 65 times compared to general spontaneous Raman imaging methods. It is simple, fast, and suitable for clinical applications.
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Affiliation(s)
- Wenbo Mo
- National Key Laboratory of Plasma Physics, Laser Fusion Research Center, China Academy of Engineering Physics, 621900 Mianyang, China
- Department of Engineering Physics, Tsinghua University, 100084 Beijing, China
| | - Qi Ke
- Mianyang Central Hospital, 621000 Mianyang, China
| | - Qiang Yang
- China Academy of Engineering Physics, 621900 Mianyang, China
- Department of Engineering Physics, Tsinghua University, 100084 Beijing, China
| | - Minjie Zhou
- Laser Fusion Research Center, China Academy of Engineering Physics, 621900 Mianyang, China
| | - Gang Xie
- Mianyang Central Hospital, 621000 Mianyang, China
| | - Daojian Qi
- Laser Fusion Research Center, China Academy of Engineering Physics, 621900 Mianyang, China
| | - Lijun Peng
- Mianyang Central Hospital, 621000 Mianyang, China
| | - Xinming Wang
- Laser Fusion Research Center, China Academy of Engineering Physics, 621900 Mianyang, China
| | - Fei Wang
- Mianyang Central Hospital, 621000 Mianyang, China
| | - Shuang Ni
- Laser Fusion Research Center, China Academy of Engineering Physics, 621900 Mianyang, China
| | - Anqun Wang
- Mianyang Central Hospital, 621000 Mianyang, China
| | - Jinglin Huang
- Laser Fusion Research Center, China Academy of Engineering Physics, 621900 Mianyang, China
| | - Jiaxing Wen
- National Key Laboratory of Plasma Physics, Laser Fusion Research Center, China Academy of Engineering Physics, 621900 Mianyang, China
| | - Yue Yang
- National Key Laboratory of Plasma Physics, Laser Fusion Research Center, China Academy of Engineering Physics, 621900 Mianyang, China
| | - Kai Du
- Laser Fusion Research Center, China Academy of Engineering Physics, 621900 Mianyang, China
| | - Xuewu Wang
- Department of Engineering Physics, Tsinghua University, 100084 Beijing, China
| | - Xiaobo Du
- Mianyang Central Hospital, 621000 Mianyang, China
| | - Zongqing Zhao
- National Key Laboratory of Plasma Physics, Laser Fusion Research Center, China Academy of Engineering Physics, 621900 Mianyang, China
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Huang Y, Chen C, Chang C, Cheng Z, Liu Y, Wang X, Chen C, Lv X. SLE diagnosis research based on SERS combined with a multi-modal fusion method. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 315:124296. [PMID: 38640628 DOI: 10.1016/j.saa.2024.124296] [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: 12/09/2023] [Revised: 03/15/2024] [Accepted: 04/14/2024] [Indexed: 04/21/2024]
Abstract
As artificial intelligence technology gains widespread adoption in biomedicine, the exploration of integrating biofluidic Raman spectroscopy for enhanced disease diagnosis opens up new prospects for the practical application of Raman spectroscopy in clinical settings. However, for systemic lupus erythematosus (SLE), origin Raman spectral data (ORS) have relatively weak signals, making it challenging to obtain ideal classification results. Although the surface enhancement technique can enhance the scattering signal of Raman spectroscopic data, the sensitivity of the SERS substrate to airborne impurities and the inhomogeneous distribution of hotspots degrade part of the signal. To fully utilize both kinds of data, this paper proposes a two-branch residual-attention network (DBRAN) fusion technique, which allows the ORS to complement the degraded portion and thus improve the model's classification accuracy. The features are extracted using the residual module, which retains the original features while extracting the deep features. At the same time, the study incorporates the attention module in both the upper and lower branches to handle the weight allocation of the two modal features more efficiently. The experimental results demonstrate that both the low-level fusion method and the intermediate-level fusion method can significantly improve the diagnostic accuracy of SLE disease classification compared with a single modality, in which the intermediate-level fusion of DBRAN achieves 100% classification accuracy, sensitivity, and specificity. The accuracy is improved by 10% and 7% compared with the ORS unimodal and the SERS unimodal modalities, respectively. The experiment, by fusing the multimodal spectral, realized rapid diagnosis of SLE disease by fusing multimodal spectral data, which provides a reference idea in the field of Raman spectroscopy and can be further promoted to clinical practical applications in the future.
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Affiliation(s)
- Yuhao Huang
- College of Software, Xinjiang University, Urumqi 830046, Xinjiang, China
| | - Chen Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China; Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi 830046, China; Xinjiang Cloud Computing Application Laboratory, Xinjiang Cloud Computing Engineering Technology Research Center, Karamay 834000, China
| | - Chenjie Chang
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Zhiyuan Cheng
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Yang Liu
- College of Software, Xinjiang University, Urumqi 830046, Xinjiang, China
| | - Xuehua Wang
- College of Physical Science and Technology, Xinjiang University, Urumqi 830046, China
| | - Cheng Chen
- College of Software, Xinjiang University, Urumqi 830046, Xinjiang, China; Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi 830046, China; Xinjiang Cloud Computing Application Laboratory, Xinjiang Cloud Computing Engineering Technology Research Center, Karamay 834000, China.
| | - Xiaoyi Lv
- College of Software, Xinjiang University, Urumqi 830046, Xinjiang, China; Xinjiang Cloud Computing Application Laboratory, Xinjiang Cloud Computing Engineering Technology Research Center, Karamay 834000, China.
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3
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Bourdillon AT. Computer Vision-Radiomics & Pathognomics. Otolaryngol Clin North Am 2024:S0030-6665(24)00072-0. [PMID: 38910065 DOI: 10.1016/j.otc.2024.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/25/2024]
Abstract
The role of computer vision in extracting radiographic (radiomics) and histopathologic (pathognomics) features is an extension of molecular biomarkers that have been foundational to our understanding across the spectrum of head and neck disorders. Especially within head and neck cancers, machine learning and deep learning applications have yielded advances in the characterization of tumor features, nodal features, and various outcomes. This review aims to overview the landscape of radiomic and pathognomic applications, informing future work to address gaps. Novel methodologies will be needed to potentially engineer ways of integrating multidimensional data inputs to examine disease features to guide prognosis comprehensively and ultimately clinical management.
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Affiliation(s)
- Alexandra T Bourdillon
- Department of Otolaryngology-Head & Neck Surgery, University of California-San Francisco, San Francisco, CA 94115, USA.
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4
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Zhou W, Liu D, Fang T, Chen X, Jia H, Tian X, Hao C, Yue S. Rapid and Precise Diagnosis of Retroperitoneal Liposarcoma with Deep-Learned Label-Free Molecular Microscopy. Anal Chem 2024; 96:9353-9361. [PMID: 38810149 DOI: 10.1021/acs.analchem.3c05417] [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: 05/31/2024]
Abstract
The retroperitoneal liposarcoma (RLPS) is a rare malignancy whose only curative therapy is surgical resection. However, well-differentiated liposarcomas (WDLPSs), one of its most common types, can hardly be distinguished from normal fat during operation without an effective margin assessment method, jeopardizing the prognosis severely with a high recurrence risk. Here, we combined dual label-free nonlinear optical modalities, stimulated Raman scattering (SRS) microscopy and second harmonic generation (SHG) microscopy, to image two predominant tissue biomolecules, lipids and collagen fibers, in 35 RLPSs and 34 normal fat samples collected from 35 patients. The produced dual-modal tissue images were used for RLPS diagnosis based on deep learning. Dramatically decreasing lipids and increasing collagen fibers during tumor progression were reflected. A ResNeXt101-based model achieved 94.7% overall accuracy and 0.987 mean area under the ROC curve (AUC) in differentiating among normal fat, WDLPSs, and dedifferentiated liposarcomas (DDLPSs). In particular, WDLPSs were detected with 94.1% precision and 84.6% sensitivity superior to existing methods. The ablation experiment showed that such performance was attributed to both SRS and SHG microscopies, which increased the sensitivity of recognizing WDLPS by 16.0 and 3.6%, respectively. Furthermore, we utilized this model on RLPS margins to identify the tumor infiltration. Our method holds great potential for accurate intraoperative liposarcoma detection.
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Affiliation(s)
- Wanhui Zhou
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Daoning Liu
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Hepato-Pancreato-Biliary Surgery/Sarcoma Center, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Tinghe Fang
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Xun Chen
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
- School of Engineering Medicine, Beihang University, Beijing 100191, China
| | - Hao Jia
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Xiuyun Tian
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Hepato-Pancreato-Biliary Surgery/Sarcoma Center, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Chunyi Hao
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Hepato-Pancreato-Biliary Surgery/Sarcoma Center, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Shuhua Yue
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
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5
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Wang Z, Han K, Liu W, Wang Z, Shi C, Liu X, Huang M, Sun G, Liu S, Guo Q. Fast Real-Time Brain Tumor Detection Based on Stimulated Raman Histology and Self-Supervised Deep Learning Model. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:1160-1176. [PMID: 38326533 PMCID: PMC11169153 DOI: 10.1007/s10278-024-01001-4] [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: 09/26/2023] [Revised: 12/18/2023] [Accepted: 12/21/2023] [Indexed: 02/09/2024]
Abstract
In intraoperative brain cancer procedures, real-time diagnosis is essential for ensuring safe and effective care. The prevailing workflow, which relies on histological staining with hematoxylin and eosin (H&E) for tissue processing, is resource-intensive, time-consuming, and requires considerable labor. Recently, an innovative approach combining stimulated Raman histology (SRH) and deep convolutional neural networks (CNN) has emerged, creating a new avenue for real-time cancer diagnosis during surgery. While this approach exhibits potential, there exists an opportunity for refinement in the domain of feature extraction. In this study, we employ coherent Raman scattering imaging method and a self-supervised deep learning model (VQVAE2) to enhance the speed of SRH image acquisition and feature representation, thereby enhancing the capability of automated real-time bedside diagnosis. Specifically, we propose the VQSRS network, which integrates vector quantization with a proxy task based on patch annotation for analysis of brain tumor subtypes. Training on images collected from the SRS microscopy system, our VQSRS demonstrates a significant speed enhancement over traditional techniques (e.g., 20-30 min). Comparative studies in dimensionality reduction clustering confirm the diagnostic capacity of VQSRS rivals that of CNN. By learning a hierarchical structure of recognizable histological features, VQSRS classifies major tissue pathological categories in brain tumors. Additionally, an external semantic segmentation method is applied for identifying tumor-infiltrated regions in SRH images. Collectively, these findings indicate that this automated real-time prediction technique holds the potential to streamline intraoperative cancer diagnosis, providing assistance to pathologists in simplifying the process.
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Affiliation(s)
- Zijun Wang
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, 102617, China
| | - Kaitai Han
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, 102617, China
| | - Wu Liu
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, 102617, China
| | - Zhenghui Wang
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, 102617, China
| | - Chaojing Shi
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, 102617, China
| | - Xi Liu
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, 102617, China
| | - Mengyuan Huang
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, 102617, China
| | - Guocheng Sun
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, 102617, China
| | - Shitou Liu
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, 102617, China
| | - Qianjin Guo
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, 102617, China.
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Ruan X, Ao J, Ma M, Jones RR, Liu J, Li K, Ge Q, Xu G, Liu Y, Wang T, Xie L, Wang W, You W, Wang L, Valev VK, Ji M, Zhang L. Nanoplastics Detected in Commercial Sea Salt. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:9091-9101. [PMID: 38709279 PMCID: PMC11196019 DOI: 10.1021/acs.est.3c11021] [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: 12/27/2023] [Revised: 04/08/2024] [Accepted: 04/18/2024] [Indexed: 05/07/2024]
Abstract
People of all ages consume salt every day, but is it really just salt? Plastic nanoparticles [nanoplastics (NPs)] pose an increasing environmental threat and have begun to contaminate everyday salt in consumer goods. Herein, we developed a combined surface enhanced Raman scattering (SERS) and stimulated Raman scattering (SRS) approach that can realize the filtration, enrichment, and detection of NPs in commercial salt. The Au-loaded (50 nm) anodic alumina oxide substrate was used as the SERS substrate to explore the potential types of NP contaminants in salts. SRS was used to conduct imaging and quantify the presence of the NPs. SRS detection was successfully established through standard plastics, and NPs were identified through the match of the hydrocarbon group of the nanoparticles. Simultaneously, the NPs were quantified based on the high spatial resolution and rapid imaging of the SRS imaging platform. NPs in sea salts produced in Asia, Australasia, Europe, and the Atlantic were studied. We estimate that, depending on the location, an average person could be ingesting as many as 6 million NPs per year through the consumption of sea salt alone. The potential health hazards associated with NP ingestion should not be underestimated.
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Affiliation(s)
- Xuejun Ruan
- Shanghai
Key Laboratory of Atmospheric Particle Pollution and Prevention, National
Observations and Research Station for Wetland Ecosystems of the Yangtze
Estuary, IRDR International Center of Excellence on Risk Interconnectivity
and Governance on Weather, Department of Environmental Science &
Engineering, Fudan University, Shanghai 200433, Peoples’ Republic of China
| | - Jianpeng Ao
- State
Key Laboratory of Surface Physics and Department of Physics, Human
Phenome Institute, Academy for Engineering and Technology, Key Laboratory
of Micro and Nano Photonic Structures (Ministry of Education), Yiwu Research Institute of Fudan University, Fudan
University, Shanghai 200433, Peoples’ Republic
of China
| | - Minglu Ma
- Shanghai
Key Laboratory of Atmospheric Particle Pollution and Prevention, National
Observations and Research Station for Wetland Ecosystems of the Yangtze
Estuary, IRDR International Center of Excellence on Risk Interconnectivity
and Governance on Weather, Department of Environmental Science &
Engineering, Fudan University, Shanghai 200433, Peoples’ Republic of China
| | - Robin R. Jones
- Centre
for Photonics and Photonic Materials and Centre for Nanoscience and
Nanotechnology, Department of Physics, University
of Bath, Claverton Down, Bath BA2 7AY, U.K.
| | - Juan Liu
- Shanghai
Key Laboratory of Atmospheric Particle Pollution and Prevention, National
Observations and Research Station for Wetland Ecosystems of the Yangtze
Estuary, IRDR International Center of Excellence on Risk Interconnectivity
and Governance on Weather, Department of Environmental Science &
Engineering, Fudan University, Shanghai 200433, Peoples’ Republic of China
| | - Kejian Li
- Shanghai
Key Laboratory of Atmospheric Particle Pollution and Prevention, National
Observations and Research Station for Wetland Ecosystems of the Yangtze
Estuary, IRDR International Center of Excellence on Risk Interconnectivity
and Governance on Weather, Department of Environmental Science &
Engineering, Fudan University, Shanghai 200433, Peoples’ Republic of China
| | - Qiuyue Ge
- Shanghai
Key Laboratory of Atmospheric Particle Pollution and Prevention, National
Observations and Research Station for Wetland Ecosystems of the Yangtze
Estuary, IRDR International Center of Excellence on Risk Interconnectivity
and Governance on Weather, Department of Environmental Science &
Engineering, Fudan University, Shanghai 200433, Peoples’ Republic of China
| | - Guanjun Xu
- Shanghai
Key Laboratory of Atmospheric Particle Pollution and Prevention, National
Observations and Research Station for Wetland Ecosystems of the Yangtze
Estuary, IRDR International Center of Excellence on Risk Interconnectivity
and Governance on Weather, Department of Environmental Science &
Engineering, Fudan University, Shanghai 200433, Peoples’ Republic of China
| | - Yangyang Liu
- Shanghai
Key Laboratory of Atmospheric Particle Pollution and Prevention, National
Observations and Research Station for Wetland Ecosystems of the Yangtze
Estuary, IRDR International Center of Excellence on Risk Interconnectivity
and Governance on Weather, Department of Environmental Science &
Engineering, Fudan University, Shanghai 200433, Peoples’ Republic of China
| | - Tao Wang
- Shanghai
Key Laboratory of Atmospheric Particle Pollution and Prevention, National
Observations and Research Station for Wetland Ecosystems of the Yangtze
Estuary, IRDR International Center of Excellence on Risk Interconnectivity
and Governance on Weather, Department of Environmental Science &
Engineering, Fudan University, Shanghai 200433, Peoples’ Republic of China
| | - Lifang Xie
- Shanghai
Key Laboratory of Atmospheric Particle Pollution and Prevention, National
Observations and Research Station for Wetland Ecosystems of the Yangtze
Estuary, IRDR International Center of Excellence on Risk Interconnectivity
and Governance on Weather, Department of Environmental Science &
Engineering, Fudan University, Shanghai 200433, Peoples’ Republic of China
| | - Wei Wang
- Shanghai
Key Laboratory of Atmospheric Particle Pollution and Prevention, National
Observations and Research Station for Wetland Ecosystems of the Yangtze
Estuary, IRDR International Center of Excellence on Risk Interconnectivity
and Governance on Weather, Department of Environmental Science &
Engineering, Fudan University, Shanghai 200433, Peoples’ Republic of China
| | - Wenbo You
- Shanghai
Key Laboratory of Atmospheric Particle Pollution and Prevention, National
Observations and Research Station for Wetland Ecosystems of the Yangtze
Estuary, IRDR International Center of Excellence on Risk Interconnectivity
and Governance on Weather, Department of Environmental Science &
Engineering, Fudan University, Shanghai 200433, Peoples’ Republic of China
| | - Licheng Wang
- Shanghai
Key Laboratory of Atmospheric Particle Pollution and Prevention, National
Observations and Research Station for Wetland Ecosystems of the Yangtze
Estuary, IRDR International Center of Excellence on Risk Interconnectivity
and Governance on Weather, Department of Environmental Science &
Engineering, Fudan University, Shanghai 200433, Peoples’ Republic of China
| | - Ventsislav K. Valev
- Centre
for Photonics and Photonic Materials and Centre for Nanoscience and
Nanotechnology, Department of Physics, University
of Bath, Claverton Down, Bath BA2 7AY, U.K.
| | - Minbiao Ji
- State
Key Laboratory of Surface Physics and Department of Physics, Human
Phenome Institute, Academy for Engineering and Technology, Key Laboratory
of Micro and Nano Photonic Structures (Ministry of Education), Yiwu Research Institute of Fudan University, Fudan
University, Shanghai 200433, Peoples’ Republic
of China
| | - Liwu Zhang
- Shanghai
Key Laboratory of Atmospheric Particle Pollution and Prevention, National
Observations and Research Station for Wetland Ecosystems of the Yangtze
Estuary, IRDR International Center of Excellence on Risk Interconnectivity
and Governance on Weather, Department of Environmental Science &
Engineering, Fudan University, Shanghai 200433, Peoples’ Republic of China
- Shanghai
Institute of Pollution Control and Ecological Security, Shanghai 200092, Peoples’ Republic of China
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Ma L, Luo K, Liu Z, Ji M. Stain-Free Histopathology with Stimulated Raman Scattering Microscopy. Anal Chem 2024; 96:7907-7925. [PMID: 38713830 DOI: 10.1021/acs.analchem.4c02061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/09/2024]
Affiliation(s)
- Liyang Ma
- State Key Laboratory of Surface Physics and Department of Physics, Academy for Engineering and Technology, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Shanghai Key Laboratory of Metasurfaces for Light Manipulation, Fudan University, Shanghai 200433, China
| | - Kuan Luo
- State Key Laboratory of Surface Physics and Department of Physics, Academy for Engineering and Technology, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Shanghai Key Laboratory of Metasurfaces for Light Manipulation, Fudan University, Shanghai 200433, China
| | - Zhijie Liu
- State Key Laboratory of Surface Physics and Department of Physics, Academy for Engineering and Technology, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Shanghai Key Laboratory of Metasurfaces for Light Manipulation, Fudan University, Shanghai 200433, China
| | - Minbiao Ji
- State Key Laboratory of Surface Physics and Department of Physics, Academy for Engineering and Technology, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Shanghai Key Laboratory of Metasurfaces for Light Manipulation, Fudan University, Shanghai 200433, China
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8
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Li Z, Li T, Zhang P, Wang X. A practical online prediction platform to predict the survival status of laryngeal squamous cell carcinoma after 5 years. Am J Otolaryngol 2024; 45:104209. [PMID: 38154199 DOI: 10.1016/j.amjoto.2023.104209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 12/16/2023] [Accepted: 12/18/2023] [Indexed: 12/30/2023]
Abstract
OBJECTIVE Currently, there are few practical tools for predicting the prognosis of laryngeal squamous cell carcinoma (LSCC). This study aims to establish a model and a convenient online prediction platform to predict whether LSCC patients will survive 5 years after diagnosis, providing a reference for further evaluation of patient prognosis. METHODS This is a retrospective study based on data collected from two centers. Center 1 included 117 LSCC patients with survival prognosis data, and center 2 included 33 patients, totaling 150 patients. All data were divided into independent training sets (60 %) and testing sets (40 %). Eight machine learning (ML) algorithms were used to establish models with 11 clinical parameters as input features. The accuracy, sensitivity, specificity, and receiver operating characteristic curve (ROC) of the testing set were used to evaluate the models, and the best model was selected. The model was then developed into a website-based 5-year survival status prediction platform for LSCC. In addition, we also used the SHapley Additive exPlanations (SHAP) tool to conduct interpretability analysis on the parameters of the model. RESULTS The LSCC 5-year survival status prediction model using the support vector machine (SVM) algorithm achieved the best results, with accuracy, sensitivity, specificity, and area under the ROC curve (AUC) of 85.0 %, 87.5 %, 75.0 %, and 81.2 % respectively. The online platform for predicting the 5-year survival status of LSCC based on this model was successfully established. The SHAP analysis shows that the clinical stage is the most important feature of the model. CONCLUSION This study successfully established a ML model and a practical online prediction platform to predict the survival status of laryngeal cancer patients after 5 years, which may help clinicians to better evaluate the prognosis of LSCC. LEVEL OF EVIDENCE: 3
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Affiliation(s)
- Zufei Li
- Department of Otorhinolaryngology, Head and Neck Surgery, Beijing Chaoyang Hospital, Capital Medical University, 100020, China
| | - Tiancheng Li
- Department of Otorhinolaryngology, Head and Neck Surgery, Beijing Chaoyang Hospital, Capital Medical University, 100020, China
| | - Pei Zhang
- Department of Oncology, Zibo Central Hospital, 255035, China
| | - Xiao Wang
- Department of Otolaryngology, Head and Neck Surgery, Zibo Central Hospital, 255035, China.
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9
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Meißner AK, Goldbrunner R, Neuschmelting V. [Intraoperative stimulated Raman histology for personalized brain tumor surgery]. CHIRURGIE (HEIDELBERG, GERMANY) 2024; 95:274-279. [PMID: 38334774 DOI: 10.1007/s00104-024-02038-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/11/2024] [Indexed: 02/10/2024]
Abstract
BACKGROUND In brain tumor surgery a personalized surgical approach is crucial to achieve a maximum safe tumor resection. The extent of resection decisively depends on the histological diagnosis. Stimulated Raman histology (SRH), a fiber laser-based optical imaging method, offers the possibility for evaluation of an intraoperative diagnosis in a few minutes. OBJECTIVE To provide an overview on the applications of SRH in neurosurgery and transference of the technique to other surgical disciplines. METHODS Description of the technique and review of the current literature on SRH. RESULTS The SRH technique was successfully used in multiple neuro-oncological tumor entities. Initial pilot projects showed the potential for analysis of extracranial tumors. CONCLUSION The use of SRH provides a near real-time diagnosis with high diagnostic accuracy and provides further developmental potential to improve personalized tumor surgery.
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Affiliation(s)
- Anna-Katharina Meißner
- Klinik für allgemeine Neurochirurgie, Zentrum für Neurochirurgie, Medizinische Fakultät und Universitätsklinik Köln, Universität zu Köln, Kerpener Str. 62, 50937, Köln, Deutschland
| | - Roland Goldbrunner
- Klinik für allgemeine Neurochirurgie, Zentrum für Neurochirurgie, Medizinische Fakultät und Universitätsklinik Köln, Universität zu Köln, Kerpener Str. 62, 50937, Köln, Deutschland
| | - Volker Neuschmelting
- Klinik für allgemeine Neurochirurgie, Zentrum für Neurochirurgie, Medizinische Fakultät und Universitätsklinik Köln, Universität zu Köln, Kerpener Str. 62, 50937, Köln, Deutschland.
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10
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Wang Y, Fang L, Wang Y, Xiong Z. Current Trends of Raman Spectroscopy in Clinic Settings: Opportunities and Challenges. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2300668. [PMID: 38072672 PMCID: PMC10870035 DOI: 10.1002/advs.202300668] [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: 01/30/2023] [Revised: 09/08/2023] [Indexed: 02/17/2024]
Abstract
Early clinical diagnosis, effective intraoperative guidance, and an accurate prognosis can lead to timely and effective medical treatment. The current conventional clinical methods have several limitations. Therefore, there is a need to develop faster and more reliable clinical detection, treatment, and monitoring methods to enhance their clinical applications. Raman spectroscopy is noninvasive and provides highly specific information about the molecular structure and biochemical composition of analytes in a rapid and accurate manner. It has a wide range of applications in biomedicine, materials, and clinical settings. This review primarily focuses on the application of Raman spectroscopy in clinical medicine. The advantages and limitations of Raman spectroscopy over traditional clinical methods are discussed. In addition, the advantages of combining Raman spectroscopy with machine learning, nanoparticles, and probes are demonstrated, thereby extending its applicability to different clinical phases. Examples of the clinical applications of Raman spectroscopy over the last 3 years are also integrated. Finally, various prospective approaches based on Raman spectroscopy in clinical studies are surveyed, and current challenges are discussed.
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Affiliation(s)
- Yumei Wang
- Department of NephrologyUnion HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan430022China
| | - Liuru Fang
- Hubei Province Key Laboratory of Systems Science in Metallurgical ProcessWuhan University of Science and TechnologyWuhan430081China
| | - Yuhua Wang
- Hubei Province Key Laboratory of Systems Science in Metallurgical ProcessWuhan University of Science and TechnologyWuhan430081China
| | - Zuzhao Xiong
- Hubei Province Key Laboratory of Systems Science in Metallurgical ProcessWuhan University of Science and TechnologyWuhan430081China
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11
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Sato J, Matsumoto T, Nakao R, Tanaka H, Nagahara H, Niioka H, Takamatsu T. Deep UV-excited fluorescence microscopy installed with CycleGAN-assisted image translation enhances precise detection of lymph node metastasis towards rapid intraoperative diagnosis. Sci Rep 2023; 13:21363. [PMID: 38049475 PMCID: PMC10696085 DOI: 10.1038/s41598-023-48319-7] [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: 09/28/2023] [Accepted: 11/24/2023] [Indexed: 12/06/2023] Open
Abstract
Rapid and precise intraoperative diagnosing systems are required for improving surgical outcomes and patient prognosis. Because of the poor quality and time-intensive process of the prevalent frozen section procedure, various intraoperative diagnostic imaging systems have been explored. Microscopy with ultraviolet surface excitation (MUSE) is an inexpensive, maintenance-free, and rapid imaging technique that yields images like thin-sectioned samples without sectioning. However, pathologists find it nearly impossible to assign diagnostic labels to MUSE images of unfixed specimens; thus, AI for intraoperative diagnosis cannot be trained in a supervised learning manner. In this study, we propose a deep-learning pipeline model for lymph node metastasis detection, in which CycleGAN translate MUSE images of unfixed lymph nodes to formalin-fixed paraffin-embedded (FFPE) sample, and diagnostic prediction is performed using deep convolutional neural network trained on FFPE sample images. Our pipeline yielded an average accuracy of 84.6% when using each of the three deep convolutional neural networks, which is a 18.3% increase over the classification-only model without CycleGAN. The modality translation to FFPE sample images using CycleGAN can be applied to various intraoperative diagnostic imaging systems and eliminate the difficulty for pathologists in labeling new modality images in clinical sites. We anticipate our pipeline to be a starting point for accurate rapid intraoperative diagnostic systems for new imaging modalities, leading to healthcare quality improvement.
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Affiliation(s)
- Junya Sato
- Graduate School of Information Science and Technology, Osaka University, 1-5, Yamadaoka, Suita, Osaka, 565-0871, Japan
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan
- Department of Artificial Intelligence Diagnostic Radiology, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Tatsuya Matsumoto
- Department of Pathology and Cell Regulation, Kyoto Prefectural University of Medicine, 465 Kajiicho, Kawaramachi-Hirokoji, Kamigyo-ku, Kyoto, 602-8566, Japan
| | - Ryuta Nakao
- Department of Pathology and Cell Regulation, Kyoto Prefectural University of Medicine, 465 Kajiicho, Kawaramachi-Hirokoji, Kamigyo-ku, Kyoto, 602-8566, Japan
| | - Hideo Tanaka
- Department of Pathology and Cell Regulation, Kyoto Prefectural University of Medicine, 465 Kajiicho, Kawaramachi-Hirokoji, Kamigyo-ku, Kyoto, 602-8566, Japan
| | - Hajime Nagahara
- Graduate School of Information Science and Technology, Osaka University, 1-5, Yamadaoka, Suita, Osaka, 565-0871, Japan
- Institute for Datability Science, Osaka University, 2-8 Yamadaoka, Suita, 565-0871, Japan
| | - Hirohiko Niioka
- Graduate School of Information Science and Technology, Osaka University, 1-5, Yamadaoka, Suita, Osaka, 565-0871, Japan.
- Institute for Datability Science, Osaka University, 2-8 Yamadaoka, Suita, 565-0871, Japan.
| | - Tetsuro Takamatsu
- Department of Pathology and Cell Regulation, Kyoto Prefectural University of Medicine, 465 Kajiicho, Kawaramachi-Hirokoji, Kamigyo-ku, Kyoto, 602-8566, Japan.
- Department of Medical Photonics, Kyoto Prefectural University of Medicine, 465 Kajiicho, Kawaramachi-Hirokoji, Kamigyo-ku, Kyoto, 602-8566, Japan.
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12
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Astratov VN, Sahel YB, Eldar YC, Huang L, Ozcan A, Zheludev N, Zhao J, Burns Z, Liu Z, Narimanov E, Goswami N, Popescu G, Pfitzner E, Kukura P, Hsiao YT, Hsieh CL, Abbey B, Diaspro A, LeGratiet A, Bianchini P, Shaked NT, Simon B, Verrier N, Debailleul M, Haeberlé O, Wang S, Liu M, Bai Y, Cheng JX, Kariman BS, Fujita K, Sinvani M, Zalevsky Z, Li X, Huang GJ, Chu SW, Tzang O, Hershkovitz D, Cheshnovsky O, Huttunen MJ, Stanciu SG, Smolyaninova VN, Smolyaninov II, Leonhardt U, Sahebdivan S, Wang Z, Luk’yanchuk B, Wu L, Maslov AV, Jin B, Simovski CR, Perrin S, Montgomery P, Lecler S. Roadmap on Label-Free Super-Resolution Imaging. LASER & PHOTONICS REVIEWS 2023; 17:2200029. [PMID: 38883699 PMCID: PMC11178318 DOI: 10.1002/lpor.202200029] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Indexed: 06/18/2024]
Abstract
Label-free super-resolution (LFSR) imaging relies on light-scattering processes in nanoscale objects without a need for fluorescent (FL) staining required in super-resolved FL microscopy. The objectives of this Roadmap are to present a comprehensive vision of the developments, the state-of-the-art in this field, and to discuss the resolution boundaries and hurdles which need to be overcome to break the classical diffraction limit of the LFSR imaging. The scope of this Roadmap spans from the advanced interference detection techniques, where the diffraction-limited lateral resolution is combined with unsurpassed axial and temporal resolution, to techniques with true lateral super-resolution capability which are based on understanding resolution as an information science problem, on using novel structured illumination, near-field scanning, and nonlinear optics approaches, and on designing superlenses based on nanoplasmonics, metamaterials, transformation optics, and microsphere-assisted approaches. To this end, this Roadmap brings under the same umbrella researchers from the physics and biomedical optics communities in which such studies have often been developing separately. The ultimate intent of this paper is to create a vision for the current and future developments of LFSR imaging based on its physical mechanisms and to create a great opening for the series of articles in this field.
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Affiliation(s)
- Vasily N. Astratov
- Department of Physics and Optical Science, University of North Carolina at Charlotte, Charlotte, North Carolina 28223-0001, USA
| | - Yair Ben Sahel
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Yonina C. Eldar
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Luzhe Huang
- Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, USA
- Bioengineering Department, University of California, Los Angeles, California 90095, USA
- California Nano Systems Institute (CNSI), University of California, Los Angeles, California 90095, USA
| | - Aydogan Ozcan
- Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, USA
- Bioengineering Department, University of California, Los Angeles, California 90095, USA
- California Nano Systems Institute (CNSI), University of California, Los Angeles, California 90095, USA
- David Geffen School of Medicine, University of California, Los Angeles, California 90095, USA
| | - Nikolay Zheludev
- Optoelectronics Research Centre, University of Southampton, Southampton, SO17 1BJ, UK
- Centre for Disruptive Photonic Technologies, The Photonics Institute, School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore
| | - Junxiang Zhao
- Department of Electrical and Computer Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA
| | - Zachary Burns
- Department of Electrical and Computer Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA
| | - Zhaowei Liu
- Department of Electrical and Computer Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA
- Material Science and Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA
| | - Evgenii Narimanov
- School of Electrical Engineering, and Birck Nanotechnology Center, Purdue University, West Lafayette, Indiana 47907, USA
| | - Neha Goswami
- Quantitative Light Imaging Laboratory, Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign, Illinois 61801, USA
| | - Gabriel Popescu
- Quantitative Light Imaging Laboratory, Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign, Illinois 61801, USA
| | - Emanuel Pfitzner
- Department of Chemistry, University of Oxford, Oxford OX1 3QZ, United Kingdom
| | - Philipp Kukura
- Department of Chemistry, University of Oxford, Oxford OX1 3QZ, United Kingdom
| | - Yi-Teng Hsiao
- Institute of Atomic and Molecular Sciences (IAMS), Academia Sinica 1, Roosevelt Rd. Sec. 4, Taipei 10617 Taiwan
| | - Chia-Lung Hsieh
- Institute of Atomic and Molecular Sciences (IAMS), Academia Sinica 1, Roosevelt Rd. Sec. 4, Taipei 10617 Taiwan
| | - Brian Abbey
- Australian Research Council Centre of Excellence for Advanced Molecular Imaging, La Trobe University, Melbourne, Victoria, Australia
- Department of Chemistry and Physics, La Trobe Institute for Molecular Science (LIMS), La Trobe University, Melbourne, Victoria, Australia
| | - Alberto Diaspro
- Optical Nanoscopy and NIC@IIT, CHT, Istituto Italiano di Tecnologia, Via Enrico Melen 83B, 16152 Genoa, Italy
- DIFILAB, Department of Physics, University of Genoa, Via Dodecaneso 33, 16146 Genoa, Italy
| | - Aymeric LeGratiet
- Optical Nanoscopy and NIC@IIT, CHT, Istituto Italiano di Tecnologia, Via Enrico Melen 83B, 16152 Genoa, Italy
- Université de Rennes, CNRS, Institut FOTON - UMR 6082, F-22305 Lannion, France
| | - Paolo Bianchini
- Optical Nanoscopy and NIC@IIT, CHT, Istituto Italiano di Tecnologia, Via Enrico Melen 83B, 16152 Genoa, Italy
- DIFILAB, Department of Physics, University of Genoa, Via Dodecaneso 33, 16146 Genoa, Italy
| | - Natan T. Shaked
- Tel Aviv University, Faculty of Engineering, Department of Biomedical Engineering, Tel Aviv 6997801, Israel
| | - Bertrand Simon
- LP2N, Institut d’Optique Graduate School, CNRS UMR 5298, Université de Bordeaux, Talence France
| | - Nicolas Verrier
- IRIMAS UR UHA 7499, Université de Haute-Alsace, Mulhouse, France
| | | | - Olivier Haeberlé
- IRIMAS UR UHA 7499, Université de Haute-Alsace, Mulhouse, France
| | - Sheng Wang
- School of Physics and Technology, Wuhan University, China
- Wuhan Institute of Quantum Technology, China
| | - Mengkun Liu
- Department of Physics and Astronomy, Stony Brook University, USA
- National Synchrotron Light Source II, Brookhaven National Laboratory, USA
| | - Yeran Bai
- Boston University Photonics Center, Boston, MA 02215, USA
| | - Ji-Xin Cheng
- Boston University Photonics Center, Boston, MA 02215, USA
| | - Behjat S. Kariman
- Optical Nanoscopy and NIC@IIT, CHT, Istituto Italiano di Tecnologia, Via Enrico Melen 83B, 16152 Genoa, Italy
- DIFILAB, Department of Physics, University of Genoa, Via Dodecaneso 33, 16146 Genoa, Italy
| | - Katsumasa Fujita
- Department of Applied Physics and the Advanced Photonics and Biosensing Open Innovation Laboratory (AIST); and the Transdimensional Life Imaging Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Osaka, Japan
| | - Moshe Sinvani
- Faculty of Engineering and the Nano-Technology Center, Bar-Ilan University, Ramat Gan, 52900 Israel
| | - Zeev Zalevsky
- Faculty of Engineering and the Nano-Technology Center, Bar-Ilan University, Ramat Gan, 52900 Israel
| | - Xiangping Li
- Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Institute of Photonics Technology, Jinan University, Guangzhou 510632, China
| | - Guan-Jie Huang
- Department of Physics and Molecular Imaging Center, National Taiwan University, Taipei 10617, Taiwan
- Brain Research Center, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Shi-Wei Chu
- Department of Physics and Molecular Imaging Center, National Taiwan University, Taipei 10617, Taiwan
- Brain Research Center, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Omer Tzang
- School of Chemistry, The Sackler faculty of Exact Sciences, and the Center for Light matter Interactions, and the Tel Aviv University Center for Nanoscience and Nanotechnology, Tel Aviv 69978, Israel
| | - Dror Hershkovitz
- School of Chemistry, The Sackler faculty of Exact Sciences, and the Center for Light matter Interactions, and the Tel Aviv University Center for Nanoscience and Nanotechnology, Tel Aviv 69978, Israel
| | - Ori Cheshnovsky
- School of Chemistry, The Sackler faculty of Exact Sciences, and the Center for Light matter Interactions, and the Tel Aviv University Center for Nanoscience and Nanotechnology, Tel Aviv 69978, Israel
| | - Mikko J. Huttunen
- Laboratory of Photonics, Physics Unit, Tampere University, FI-33014, Tampere, Finland
| | - Stefan G. Stanciu
- Center for Microscopy – Microanalysis and Information Processing, Politehnica University of Bucharest, 313 Splaiul Independentei, 060042, Bucharest, Romania
| | - Vera N. Smolyaninova
- Department of Physics Astronomy and Geosciences, Towson University, 8000 York Rd., Towson, MD 21252, USA
| | - Igor I. Smolyaninov
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD 20742, USA
| | - Ulf Leonhardt
- Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Sahar Sahebdivan
- EMTensor GmbH, TechGate, Donau-City-Strasse 1, 1220 Wien, Austria
| | - Zengbo Wang
- School of Computer Science and Electronic Engineering, Bangor University, Bangor, LL57 1UT, United Kingdom
| | - Boris Luk’yanchuk
- Faculty of Physics, Lomonosov Moscow State University, Moscow 119991, Russia
| | - Limin Wu
- Department of Materials Science and State Key Laboratory of Molecular Engineering of Polymers, Fudan University, Shanghai 200433, China
| | - Alexey V. Maslov
- Department of Radiophysics, University of Nizhny Novgorod, Nizhny Novgorod, 603022, Russia
| | - Boya Jin
- Department of Physics and Optical Science, University of North Carolina at Charlotte, Charlotte, North Carolina 28223-0001, USA
| | - Constantin R. Simovski
- Department of Electronics and Nano-Engineering, Aalto University, FI-00076, Espoo, Finland
- Faculty of Physics and Engineering, ITMO University, 199034, St-Petersburg, Russia
| | - Stephane Perrin
- ICube Research Institute, University of Strasbourg - CNRS - INSA de Strasbourg, 300 Bd. Sébastien Brant, 67412 Illkirch, France
| | - Paul Montgomery
- ICube Research Institute, University of Strasbourg - CNRS - INSA de Strasbourg, 300 Bd. Sébastien Brant, 67412 Illkirch, France
| | - Sylvain Lecler
- ICube Research Institute, University of Strasbourg - CNRS - INSA de Strasbourg, 300 Bd. Sébastien Brant, 67412 Illkirch, France
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13
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Yang M, Wang J, Quan S, Xu Q. High-precision bladder cancer diagnosis method: 2D Raman spectrum figures based on maintenance technology combined with automatic weighted feature fusion network. Anal Chim Acta 2023; 1282:341908. [PMID: 37923405 DOI: 10.1016/j.aca.2023.341908] [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: 06/07/2023] [Revised: 08/28/2023] [Accepted: 10/10/2023] [Indexed: 11/07/2023]
Abstract
BACKGROUND Raman spectroscopy has been extensively utilized as a marker-free detection method in the complementary diagnosis of cancer. Multivariate statistical classification analysis is frequently employed for Raman spectral data classification. Nevertheless, traditional multivariate statistical classification analysis performs poorly when analyzing large samples and multicategory spectral data. In addition, with the advancement of computer vision, convolutional neural networks (CNNs) have demonstrated extraordinarily precise analysis of two-dimensional image processing. RESULT Combining 2D Raman spectrograms with automatic weighted feature fusion network (AWFFN) for bladder cancer detection is presented in this paper. Initially, the s-transform (ST) is implemented for the first time to convert 1D Raman data into 2D spectrograms, achieving 99.2% detection accuracy. Second, four upscaling techniques, including short time fourier transform (STFT), recurrence map (RP), markov transform field (MTF), and grammy angle field (GAF), were used to transform the 1D Raman spectral data into a variety of 2D Raman spectrograms. In addition, a particle swarm optimization (PSO) algorithm is combined with VGG19, ResNet50, and ResNet101 to construct a weighted feature fusion network, and this parallel network is employed for evaluating multiple spectrograms. Class activation mapping (CAM) is additionally employed to illustrate and evaluate the process of feature extraction via the three parallel network branches. The results demonstrate that the combination of a 2D Raman spectrogram along with a CNN for the diagnosis of bladder cancer obtains a 99.2% accuracy rate,which indicates that it is an extremely promising auxiliary technology for cancer diagnosis. SIGNIFICANCE The proposed two-dimensional Raman spectroscopy method has an improved precision than one-dimensional spectroscopic data, which presents a potential methodology for assisted cancer detection and providing crucial technical support for assisted diagnosis.
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Affiliation(s)
- Mengge Yang
- School of Information Science and Engineering, Xinjiang University, Urumqi, China
| | - Jiajia Wang
- School of Information Science and Engineering, Xinjiang University, Urumqi, China; The Key Laboratory of Signal Detection and Processing, Xinjiang Uygur Autonomous Region, Xinjiang University, China; Post-doctoral Workstation of Xinjiang Uygur Autonomous Region Institute of Product Quality Supervision and Inspection, Urumqi, China.
| | - Siyu Quan
- School of Information Science and Engineering, Xinjiang University, Urumqi, China
| | - Qiqi Xu
- School of Information Science and Engineering, Xinjiang University, Urumqi, China
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14
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Huang J, Tang X, Chen Z, Li X, Zhang Y, Huang X, Zhang D, An G, Lee HJ. Rapid azoospermia classification by stimulated Raman scattering and second harmonic generation microscopy. BIOMEDICAL OPTICS EXPRESS 2023; 14:5569-5582. [PMID: 38021145 PMCID: PMC10659792 DOI: 10.1364/boe.501623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/26/2023] [Accepted: 09/26/2023] [Indexed: 12/01/2023]
Abstract
Disease diagnosis and classification pose significant challenges due to the limited capabilities of traditional methods to obtain molecular information with spatial distribution. Optical imaging techniques, utilizing (auto)fluorescence and nonlinear optical signals, introduce new dimensions for biomarkers exploration that can improve diagnosis and classification. Nevertheless, these signals often cover only a limited number of species, impeding a comprehensive assessment of the tissue microenvironment, which is crucial for effective disease diagnosis and therapy. To address this challenge, we developed a multimodal platform, termed stimulated Raman scattering and second harmonic generation microscopy (SRASH), capable of simultaneously providing both chemical bonds and structural information of tissues. Applying SRASH imaging to azoospermia patient samples, we successfully identified lipids, protein, and collagen contrasts, unveiling molecular and structural signatures for non-obstructive azoospermia. This achievement is facilitated by LiteBlendNet-Dx (LBNet-Dx), our diagnostic algorithm, which achieved an outstanding 100% sample-level accuracy in classifying azoospermia, surpassing conventional imaging modalities. As a label-free technique, SRASH imaging eliminates the requirement for sample pre-treatment, demonstrating great potential for clinical translation and enabling molecular imaging-based diagnosis and therapy.
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Affiliation(s)
- Jie Huang
- Zhejiang Polytechnic Institute, Polytechnic Institute, Zhejiang University, Hangzhou 310058, China
- College of Biomedical Engineering & Instrument Science; Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310058, China
| | - Xiaobin Tang
- Interdisciplinary Centre for Quantum Information, Zhejiang Province Key Laboratory of Quantum Technology and Device, and Department of Physics, Zhejiang University; Hangzhou 310027, China
| | - Zhicong Chen
- Department of Obstetrics and Gynecology, Center for Reproductive Medicine; Guangdong Provincial Key Laboratory of Major Obstetric Diseases; Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology; Guangdong-Hong Kong-Macao Greater Bay Area Higher Education Joint Laboratory of Maternal-Fetal Medicine; The Third Affiliated Hospital of Guangzhou Medical University; Guangzhou 510150, China
| | - Xiaomin Li
- Department of Obstetrics and Gynecology, Center for Reproductive Medicine; Guangdong Provincial Key Laboratory of Major Obstetric Diseases; Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology; Guangdong-Hong Kong-Macao Greater Bay Area Higher Education Joint Laboratory of Maternal-Fetal Medicine; The Third Affiliated Hospital of Guangzhou Medical University; Guangzhou 510150, China
| | - Yongqing Zhang
- Interdisciplinary Centre for Quantum Information, Zhejiang Province Key Laboratory of Quantum Technology and Device, and Department of Physics, Zhejiang University; Hangzhou 310027, China
| | - Xiangjie Huang
- College of Biomedical Engineering & Instrument Science; Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310058, China
| | - Delong Zhang
- Interdisciplinary Centre for Quantum Information, Zhejiang Province Key Laboratory of Quantum Technology and Device, and Department of Physics, Zhejiang University; Hangzhou 310027, China
| | - Geng An
- Department of Obstetrics and Gynecology, Center for Reproductive Medicine; Guangdong Provincial Key Laboratory of Major Obstetric Diseases; Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology; Guangdong-Hong Kong-Macao Greater Bay Area Higher Education Joint Laboratory of Maternal-Fetal Medicine; The Third Affiliated Hospital of Guangzhou Medical University; Guangzhou 510150, China
| | - Hyeon Jeong Lee
- College of Biomedical Engineering & Instrument Science; Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310058, China
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15
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Reddi DM, Barner LA, Burke W, Gao G, Grady WM, Liu JTC. Nondestructive 3D Pathology Image Atlas of Barrett Esophagus With Open-Top Light-Sheet Microscopy. Arch Pathol Lab Med 2023; 147:1164-1171. [PMID: 36596255 DOI: 10.5858/arpa.2022-0133-oa] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/01/2022] [Indexed: 01/04/2023]
Abstract
CONTEXT.— Anatomic pathologists render diagnosis on tissue samples sectioned onto glass slides and viewed under a bright-field microscope. This approach is destructive to the sample, which can limit its use for ancillary assays that can inform patient management. Furthermore, the subjective interpretation of a relatively small number of 2D tissue sections per sample contributes to low interobserver agreement among pathologists for the assessment (diagnosis and grading) of various lesions. OBJECTIVE.— To evaluate 3D pathology data sets of thick formalin-fixed Barrett esophagus specimens imaged nondestructively with open-top light-sheet (OTLS) microscopy. DESIGN.— Formalin-fixed, paraffin-embedded Barrett esophagus samples (N = 15) were deparaffinized, stained with a fluorescent analog of hematoxylin-eosin, optically cleared, and imaged nondestructively with OTLS microscopy. The OTLS microscopy images were subsequently compared with archived hematoxylin-eosin histology sections from each sample. RESULTS.— Barrett esophagus samples, both small endoscopic forceps biopsies and endoscopic mucosal resections, exhibited similar resolvable structures between OTLS microscopy and conventional light microscopy with up to a ×20 objective (×200 overall magnification). The 3D histologic images generated by OTLS microscopy can enable improved discrimination of cribriform and well-formed gland morphologies. In addition, a much larger amount of tissue is visualized with OTLS microscopy, which enables improved assessment of clinical specimens exhibiting high spatial heterogeneity. CONCLUSIONS.— In esophageal specimens, OTLS microscopy can generate images comparable in quality to conventional light microscopy, with the advantages of providing 3D information for enhanced evaluation of glandular morphologies and enabling much more of the tissue specimen to be visualized nondestructively.
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Affiliation(s)
- Deepti M Reddi
- From the Department of Laboratory Medicine and Pathology (Reddi, Liu), University of Washington, Seattle
| | - Lindsey A Barner
- Department of Mechanical Engineering (Barner, Gao, Liu), University of Washington, Seattle
| | - Wynn Burke
- Department of Medicine (Burke, Grady), University of Washington, Seattle
- The Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington (Burke, Grady)
| | - Gan Gao
- Department of Mechanical Engineering (Barner, Gao, Liu), University of Washington, Seattle
| | - William M Grady
- Department of Medicine (Burke, Grady), University of Washington, Seattle
- The Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington (Burke, Grady)
| | - Jonathan T C Liu
- From the Department of Laboratory Medicine and Pathology (Reddi, Liu), University of Washington, Seattle
- Department of Mechanical Engineering (Barner, Gao, Liu), University of Washington, Seattle
- Department of Bioengineering (Liu), University of Washington, Seattle
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16
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Falahkheirkhah K, Mukherjee SS, Gupta S, Herrera-Hernandez L, McCarthy MR, Jimenez RE, Cheville JC, Bhargava R. Accelerating Cancer Histopathology Workflows with Chemical Imaging and Machine Learning. CANCER RESEARCH COMMUNICATIONS 2023; 3:1875-1887. [PMID: 37772992 PMCID: PMC10506535 DOI: 10.1158/2767-9764.crc-23-0226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 08/21/2023] [Accepted: 08/21/2023] [Indexed: 09/30/2023]
Abstract
Histopathology has remained a cornerstone for biomedical tissue assessment for over a century, with a resource-intensive workflow involving biopsy or excision, gross examination, sampling, tissue processing to snap frozen or formalin-fixed paraffin-embedded blocks, sectioning, staining, optical imaging, and microscopic assessment. Emerging chemical imaging approaches, including stimulated Raman scattering (SRS) microscopy, can directly measure inherent molecular composition in tissue (thereby dispensing with the need for tissue processing, sectioning, and using dyes) and can use artificial intelligence (AI) algorithms to provide high-quality images. Here we show the integration of SRS microscopy in a pathology workflow to rapidly record chemical information from minimally processed fresh-frozen prostate tissue. Instead of using thin sections, we record data from intact thick tissues and use optical sectioning to generate images from multiple planes. We use a deep learning–based processing pipeline to generate virtual hematoxylin and eosin images. Next, we extend the computational method to generate archival-quality images in minutes, which are equivalent to those obtained from hours/days-long formalin-fixed, paraffin-embedded processing. We assessed the quality of images from the perspective of enabling pathologists to make decisions, demonstrating that the virtual stained image quality was diagnostically useful and the interpathologist agreement on prostate cancer grade was not impacted. Finally, because this method does not wash away lipids and small molecules, we assessed the utility of lipid chemical composition in determining grade. Together, the combination of chemical imaging and AI provides novel capabilities for rapid assessments in pathology by reducing the complexity and burden of current workflows. SIGNIFICANCE Archival-quality (formalin-fixed paraffin-embedded), thin-section diagnostic images are obtained from thick-cut, fresh-frozen prostate tissues without dyes or stains to expedite cancer histopathology by combining SRS microscopy and machine learning.
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Affiliation(s)
- Kianoush Falahkheirkhah
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, Illinois
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, Illinois
| | - Sudipta S. Mukherjee
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, Illinois
| | - Sounak Gupta
- Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | | | | | - Rafael E. Jimenez
- Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - John C. Cheville
- Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Rohit Bhargava
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, Illinois
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, Illinois
- Department of Chemical and Biomolecular Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois
- Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois
- Mechanical Science and Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois
- Cancer Center at Illinois, University of Illinois Urbana-Champaign, Urbana, Illinois
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17
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Li Z, Ding S, Zhong Q, Fang J, Huang J, Huang Z, Zhang Y. A machine learning model for predicting the three-year survival status of patients with hypopharyngeal squamous cell carcinoma using multiple parameters. J Laryngol Otol 2023; 137:1041-1047. [PMID: 36682376 DOI: 10.1017/s0022215123000063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
OBJECTIVE This study aimed to establish a model for predicting the three-year survival status of patients with hypopharyngeal squamous cell carcinoma using artificial intelligence algorithms. METHOD Data from 295 patients with hypopharyngeal squamous cell carcinoma were analysed retrospectively. Training sets comprised 70 per cent of the data and test sets the remaining 30 per cent. A total of 22 clinical parameters were included as training features. In total, 12 different types of machine learning algorithms were used for model construction. Accuracy, sensitivity, specificity, area under the receiver operating characteristic curve and Cohen's kappa co-efficient were used to evaluate model performance. RESULTS The XGBoost algorithm achieved the best model performance. Accuracy, sensitivity, specificity, area under the receiver operating characteristic curve and kappa value of the model were 80.9 per cent, 92.6 per cent, 62.9 per cent, 77.7 per cent and 58.1 per cent, respectively. CONCLUSION This study successfully identified a machine learning model for predicting three-year survival status for patients with hypopharyngeal squamous cell carcinoma that can offer a new prognostic evaluation method for the clinical treatment of these patients.
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Affiliation(s)
- Z Li
- Department of Otorhinolaryngology - Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Key Laboratory of Otolaryngology Head and Neck Surgery, Ministry of Education, Beijing, China
- Department of Otorhinolaryngology - Head and Neck Surgery, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - S Ding
- Department of Otorhinolaryngology - Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Key Laboratory of Otolaryngology Head and Neck Surgery, Ministry of Education, Beijing, China
| | - Q Zhong
- Department of Otorhinolaryngology - Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Key Laboratory of Otolaryngology Head and Neck Surgery, Ministry of Education, Beijing, China
| | - J Fang
- Department of Otorhinolaryngology - Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Key Laboratory of Otolaryngology Head and Neck Surgery, Ministry of Education, Beijing, China
| | - J Huang
- Department of Otorhinolaryngology - Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Key Laboratory of Otolaryngology Head and Neck Surgery, Ministry of Education, Beijing, China
| | - Z Huang
- Department of Otorhinolaryngology - Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Key Laboratory of Otolaryngology Head and Neck Surgery, Ministry of Education, Beijing, China
| | - Y Zhang
- Department of Otorhinolaryngology - Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Key Laboratory of Otolaryngology Head and Neck Surgery, Ministry of Education, Beijing, China
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18
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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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19
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Liang X, Dai J, Zhou X, Liu L, Zhang C, Jiang Y, Li N, Niu T, Xie Y, Dai Z, Wang X. An Unsupervised Learning-Based Regional Deformable Model for Automated Multi-Organ Contour Propagation. J Digit Imaging 2023; 36:923-931. [PMID: 36717520 PMCID: PMC10287868 DOI: 10.1007/s10278-023-00779-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 01/11/2023] [Accepted: 01/13/2023] [Indexed: 02/01/2023] Open
Abstract
The aim of this study is to evaluate a regional deformable model based on a deep unsupervised learning model for automatic contour propagation in breast cone-beam computed tomography-guided adaptive radiation therapy. A deep unsupervised learning model was introduced to map breast's tumor bed, clinical target volume, heart, left lung, right lung, and spinal cord from planning computed tomography to cone-beam CT. To improve the traditional image registration method's performance, we used a regional deformable framework based on the narrow-band mapping, which can mitigate the effect of the image artifacts on the cone-beam CT. We retrospectively selected 373 anonymized cone-beam CT volumes from 111 patients with breast cancer. The cone-beam CTs are divided into three sets. 311 / 20 / 42 cone-beam CT images were used for training, validating, and testing. The manual contour was used as reference for the testing set. We compared the results between the reference and the model prediction for evaluating the performance. The mean Dice between manual reference segmentations and the model predicted segmentations for breast tumor bed, clinical target volume, heart, left lung, right lung, and spinal cord were 0.78 ± 0.09, 0.90 ± 0.03, 0.88 ± 0.04, 0.94 ± 0.03, 0.95 ± 0.02, and 0.77 ± 0.07, respectively. The results demonstrated a good agreement between the reference and the proposed contours. The proposed deep learning-based regional deformable model technique can automatically propagate contours for breast cancer adaptive radiotherapy. Deep learning in contour propagation was promising, but further investigation was warranted.
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Affiliation(s)
- Xiaokun Liang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055 China
| | - Jingjing Dai
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055 China
| | - Xuanru Zhou
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055 China
| | - Lin Liu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055 China
| | - Chulong Zhang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055 China
| | - Yuming Jiang
- Department of Radiation Oncology, Stanford University, Stanford, CA 94305 USA
| | - Na Li
- Department of Biomedical Engineering, Guangdong Medical University, Dongguan, 523808 China
| | - Tianye Niu
- Shenzhen Bay Laboratory, Shenzhen, Guangdong 518118 China
- Peking University Aerospace School of Clinical Medicine, Aerospace Center Hospital, Beijing, 100049 China
| | - Yaoqin Xie
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055 China
| | - Zhenhui Dai
- Department of Radiation Therapy, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510120 China
| | - Xuetao Wang
- Department of Radiation Therapy, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510120 China
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20
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Stanciu SG, König K, Song YM, Wolf L, Charitidis CA, Bianchini P, Goetz M. Toward next-generation endoscopes integrating biomimetic video systems, nonlinear optical microscopy, and deep learning. BIOPHYSICS REVIEWS 2023; 4:021307. [PMID: 38510341 PMCID: PMC10903409 DOI: 10.1063/5.0133027] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 05/26/2023] [Indexed: 03/22/2024]
Abstract
According to the World Health Organization, the proportion of the world's population over 60 years will approximately double by 2050. This progressive increase in the elderly population will lead to a dramatic growth of age-related diseases, resulting in tremendous pressure on the sustainability of healthcare systems globally. In this context, finding more efficient ways to address cancers, a set of diseases whose incidence is correlated with age, is of utmost importance. Prevention of cancers to decrease morbidity relies on the identification of precursor lesions before the onset of the disease, or at least diagnosis at an early stage. In this article, after briefly discussing some of the most prominent endoscopic approaches for gastric cancer diagnostics, we review relevant progress in three emerging technologies that have significant potential to play pivotal roles in next-generation endoscopy systems: biomimetic vision (with special focus on compound eye cameras), non-linear optical microscopies, and Deep Learning. Such systems are urgently needed to enhance the three major steps required for the successful diagnostics of gastrointestinal cancers: detection, characterization, and confirmation of suspicious lesions. In the final part, we discuss challenges that lie en route to translating these technologies to next-generation endoscopes that could enhance gastrointestinal imaging, and depict a possible configuration of a system capable of (i) biomimetic endoscopic vision enabling easier detection of lesions, (ii) label-free in vivo tissue characterization, and (iii) intelligently automated gastrointestinal cancer diagnostic.
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Affiliation(s)
- Stefan G. Stanciu
- Center for Microscopy-Microanalysis and Information Processing, University Politehnica of Bucharest, Bucharest, Romania
| | | | | | - Lior Wolf
- School of Computer Science, Tel Aviv University, Tel-Aviv, Israel
| | - Costas A. Charitidis
- Research Lab of Advanced, Composite, Nano-Materials and Nanotechnology, School of Chemical Engineering, National Technical University of Athens, Athens, Greece
| | - Paolo Bianchini
- Nanoscopy and NIC@IIT, Italian Institute of Technology, Genoa, Italy
| | - Martin Goetz
- Medizinische Klinik IV-Gastroenterologie/Onkologie, Kliniken Böblingen, Klinikverbund Südwest, Böblingen, Germany
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21
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Kong Y, Ao J, Chen Q, Su W, Zhao Y, Fei Y, Ma J, Ji M, Mi L. Evaluating Differentiation Status of Mesenchymal Stem Cells by Label-Free Microscopy System and Machine Learning. Cells 2023; 12:1524. [PMID: 37296645 PMCID: PMC10252613 DOI: 10.3390/cells12111524] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 05/26/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023] Open
Abstract
Mesenchymal stem cells (MSCs) play a crucial role in tissue engineering, as their differentiation status directly affects the quality of the final cultured tissue, which is critical to the success of transplantation therapy. Furthermore, the precise control of MSC differentiation is essential for stem cell therapy in clinical settings, as low-purity stem cells can lead to tumorigenic problems. Therefore, to address the heterogeneity of MSCs during their differentiation into adipogenic or osteogenic lineages, numerous label-free microscopic images were acquired using fluorescence lifetime imaging microscopy (FLIM) and stimulated Raman scattering (SRS), and an automated evaluation model for the differentiation status of MSCs was built based on the K-means machine learning algorithm. The model is capable of highly sensitive analysis of individual cell differentiation status, so it has great potential for stem cell differentiation research.
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Affiliation(s)
- Yawei Kong
- Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Department of Optical Science and Engineering, Shanghai Engineering Research Center of Ultra-Precision Optical Manufacturing, School of Information Science and Technology, Fudan University, Shanghai 200433, China; (Y.K.); (Q.C.); (W.S.); (Y.F.); (J.M.)
| | - Jianpeng Ao
- Department of Physics, Fudan University, Shanghai 200433, China;
| | - Qiushu Chen
- Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Department of Optical Science and Engineering, Shanghai Engineering Research Center of Ultra-Precision Optical Manufacturing, School of Information Science and Technology, Fudan University, Shanghai 200433, China; (Y.K.); (Q.C.); (W.S.); (Y.F.); (J.M.)
| | - Wenhua Su
- Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Department of Optical Science and Engineering, Shanghai Engineering Research Center of Ultra-Precision Optical Manufacturing, School of Information Science and Technology, Fudan University, Shanghai 200433, China; (Y.K.); (Q.C.); (W.S.); (Y.F.); (J.M.)
| | - Yinping Zhao
- Human Phenome Institute, Fudan University, Shanghai 200433, China;
| | - Yiyan Fei
- Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Department of Optical Science and Engineering, Shanghai Engineering Research Center of Ultra-Precision Optical Manufacturing, School of Information Science and Technology, Fudan University, Shanghai 200433, China; (Y.K.); (Q.C.); (W.S.); (Y.F.); (J.M.)
| | - Jiong Ma
- Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Department of Optical Science and Engineering, Shanghai Engineering Research Center of Ultra-Precision Optical Manufacturing, School of Information Science and Technology, Fudan University, Shanghai 200433, China; (Y.K.); (Q.C.); (W.S.); (Y.F.); (J.M.)
- Institute of Biomedical Engineering and Technology, Academy for Engineering and Technology, Fudan University, Shanghai 200433, China
- Shanghai Engineering Research Center of Industrial Microorganisms, The Multiscale Research Institute of Complex Systems (MRICS), School of Life Sciences, Fudan University, Shanghai 200433, China
| | - Minbiao Ji
- Department of Physics, Fudan University, Shanghai 200433, China;
| | - Lan Mi
- Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Department of Optical Science and Engineering, Shanghai Engineering Research Center of Ultra-Precision Optical Manufacturing, School of Information Science and Technology, Fudan University, Shanghai 200433, China; (Y.K.); (Q.C.); (W.S.); (Y.F.); (J.M.)
- Institute of Biomedical Engineering and Technology, Academy for Engineering and Technology, Fudan University, Shanghai 200433, China
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22
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Ito H, Uragami N, Miyazaki T, Shimamura Y, Ikeda H, Nishikawa Y, Onimaru M, Matsuo K, Isozaki M, Yang W, Issha K, Kimura S, Kawamura M, Yokoyama N, Kushima M, Inoue H. Determination of esophageal squamous cell carcinoma and gastric adenocarcinoma on raw tissue using Raman spectroscopy. World J Gastroenterol 2023; 29:3145-3156. [PMID: 37346148 PMCID: PMC10280800 DOI: 10.3748/wjg.v29.i20.3145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 04/10/2023] [Accepted: 04/27/2023] [Indexed: 05/26/2023] Open
Abstract
BACKGROUND Cancer detection is a global research focus, and novel, rapid, and label-free techniques are being developed for routine clinical practice. This has led to the development of new tools and techniques from the bench side to routine clinical practice. In this study, we present a method that uses Raman spectroscopy (RS) to detect cancer in unstained formalin-fixed, resected specimens of the esophagus and stomach. Our method can record a clear Raman-scattered light spectrum in these specimens, confirming that the Raman-scattered light spectrum changes because of the histological differences in the mucosal tissue.
AIM To evaluate the use of Raman-scattered light spectrum for detecting endoscop-ically resected specimens of esophageal squamous cell carcinoma (SCC) and gastric adenocarcinoma (AC).
METHODS We created a Raman device that is suitable for observing living tissues, and attempted to acquire Raman-scattered light spectra in endoscopically resected specimens of six esophageal tissues and 12 gastric tissues. We evaluated formalin-fixed tissues using this technique and captured shifts at multiple locations based on feasibility, ranging from six to 19 locations 200 microns apart in the vertical and horizontal directions. Furthermore, a correlation between the obtained Raman scattered light spectra and histopathological diagnosis was performed.
RESULTS We successfully obtained Raman scattered light spectra from all six esophageal and 12 gastric specimens. After data capture, the tissue specimens were sent for histopathological analysis for further processing because RS is a label-free methodology that does not cause tissue destruction or alterations. Based on data analysis of molecular-level substrates, we established cut-off values for the diagnosis of esophageal SCC and gastric AC. By analyzing specific Raman shifts, we developed an algorithm to identify the range of esophageal SCC and gastric AC with an accuracy close to that of histopathological diagnoses.
CONCLUSION Our technique provides qualitative information for real-time morphological diagnosis. However, further in vivo evaluations require an excitation light source with low human toxicity and large amounts of data for validation.
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Affiliation(s)
- Hiroaki Ito
- Digestive Disease Center, Showa University Koto Toyosu Hospital, Tokyo 135-8577, Japan
| | - Naoyuki Uragami
- Digestive Disease Center, Showa University Koto Toyosu Hospital, Tokyo 135-8577, Japan
| | | | - Yuto Shimamura
- Digestive Disease Center, Showa University Koto Toyosu Hospital, Tokyo 135-8577, Japan
| | - Haruo Ikeda
- Digestive Disease Center, Showa University Koto Toyosu Hospital, Tokyo 135-8577, Japan
| | - Yohei Nishikawa
- Digestive Disease Center, Showa University Koto Toyosu Hospital, Tokyo 135-8577, Japan
| | - Manabu Onimaru
- Digestive Disease Center, Showa University Koto Toyosu Hospital, Tokyo 135-8577, Japan
| | - Kai Matsuo
- Digestive Disease Center, Showa University Koto Toyosu Hospital, Tokyo 135-8577, Japan
| | - Masayuki Isozaki
- Digestive Disease Center, Showa University Koto Toyosu Hospital, Tokyo 135-8577, Japan
| | - William Yang
- Bay Spec Inc., San Jose, CA 95131, United States
| | - Kenji Issha
- Fuji Technical Research Inc., Yokohama 220-6215, Japan
| | - Satoshi Kimura
- Department of Laboratory Medicine and Central Clinical Laboratory, Showa University Northern Yokohama Hospital, Yokohama 224-8503, Japan
| | - Machiko Kawamura
- Department of Hematology, Saitama Cancer Center, Inamachi 362-0806, Japan
| | - Noboru Yokoyama
- Digestive Disease Center, Showa University Koto Toyosu Hospital, Tokyo 135-8577, Japan
| | - Miki Kushima
- Department of Pathology, Showa University Koto Toyosu Hospital, Tokyo 135-8577, Japan
| | - Haruhiro Inoue
- Digestive Disease Center, Showa University Koto Toyosu Hospital, Tokyo 135-8577, Japan
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23
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Chen X, Wu Z, He Y, Hao Z, Wang Q, Zhou K, Zhou W, Wang P, Shan F, Li Z, Ji J, Fan Y, Li Z, Yue S. Accurate and Rapid Detection of Peritoneal Metastasis from Gastric Cancer by AI-Assisted Stimulated Raman Molecular Cytology. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023:e2300961. [PMID: 37114845 PMCID: PMC10375130 DOI: 10.1002/advs.202300961] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 04/04/2023] [Indexed: 06/19/2023]
Abstract
Peritoneal metastasis (PM) is the mostcommon form of distant metastasis and one of the leading causes of death in gastriccancer (GC). For locally advanced GC, clinical guidelines recommend peritoneal lavage cytology for intraoperative PM detection. Unfortunately, current peritoneal lavage cytology is limited by low sensitivity (<60%). Here the authors established the stimulated Raman molecular cytology (SRMC), a chemical microscopy-based intelligent cytology. The authors firstly imaged 53 951 exfoliated cells in ascites obtained from 80 GC patients (27 PM positive, 53 PM negative). Then, the authors revealed 12 single cell features of morphology and composition that are significantly different between PM positive and negative specimens, including cellular area, lipid protein ratio, etc. Importantly, the authors developed a single cell phenotyping algorithm to further transform the above raw features to feature matrix. Such matrix is crucial to identify the significant marker cell cluster, the divergence of which is finally used to differentiate the PM positive and negative. Compared with histopathology, the gold standard of PM detection, their SRMC method could reach 81.5% sensitivity, 84.9% specificity, and the AUC of 0.85, within 20 minutes for each patient. Together, their SRMC method shows great potential for accurate and rapid detection of PM from GC.
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Affiliation(s)
- Xun Chen
- Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, 100191, Beijing, China
- School of Engineering Medicine, Beihang University, 100191, Beijing, China
| | - Zhouqiao Wu
- Gastrointestinal Cancer Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, 100142, Beijing, China
| | - Yexuan He
- Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, 100191, Beijing, China
| | - Zhe Hao
- Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, 100191, Beijing, China
| | - Qi Wang
- Gastrointestinal Cancer Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, 100142, Beijing, China
| | - Keji Zhou
- Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, 100191, Beijing, China
| | - Wanhui Zhou
- Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, 100191, Beijing, China
| | - Pu Wang
- Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, 100191, Beijing, China
| | - Fei Shan
- Gastrointestinal Cancer Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, 100142, Beijing, China
| | - Zhongwu Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Pathology, Peking University Cancer Hospital and Institute, 100142, Beijing, China
| | - Jiafu Ji
- Gastrointestinal Cancer Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, 100142, Beijing, China
| | - Yubo Fan
- Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, 100191, Beijing, China
- School of Engineering Medicine, Beihang University, 100191, Beijing, China
| | - Ziyu Li
- Gastrointestinal Cancer Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, 100142, Beijing, China
| | - Shuhua Yue
- Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, 100191, Beijing, China
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24
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Bensoussan Y, Vanstrum EB, Johns MM, Rameau A. Artificial Intelligence and Laryngeal Cancer: From Screening to Prognosis: A State of the Art Review. Otolaryngol Head Neck Surg 2023; 168:319-329. [PMID: 35787073 DOI: 10.1177/01945998221110839] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 06/13/2022] [Indexed: 11/16/2022]
Abstract
OBJECTIVE This state of the art review aims to examine contemporary advances in applications of artificial intelligence (AI) to the screening, detection, management, and prognostication of laryngeal cancer (LC). DATA SOURCES Four bibliographic databases were searched: PubMed, EMBASE, Cochrane, and IEEE. REVIEW METHODS A structured review of the current literature (up to January 2022) was performed. Search terms related to topics of AI in LC were identified and queried by 2 independent reviewers. Citations of selected studies and review articles were also evaluated to ensure comprehensiveness. CONCLUSIONS AI applications in LC have encompassed a variety of data modalities, including radiomics, genomics, acoustics, clinical data, and videomics, to support screening, diagnosis, therapeutic decision making, and prognosis. However, most studies remain at the proof-of-concept level, as AI algorithms are trained on single-institution databases with limited data sets and a single data modality. IMPLICATIONS FOR PRACTICE AI algorithms in LC will need to be trained on large multi-institutional data sets and integrate multimodal data for optimal performance and clinical utility from screening to prognosis. Out of the data types reviewed, genomics has the most potential to provide generalizable models thanks to available large multi-institutional open access genomic data sets. Voice acoustic data represent an inexpensive and accurate biomarker, which is easy and noninvasive to capture, offering a unique opportunity for screening and monitoring of LA, especially in low-resource settings.
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Affiliation(s)
- Yael Bensoussan
- Department of Otolaryngology-Head and Neck Surgery, University of South Florida, Tampa, Florida, USA
| | - Erik B Vanstrum
- Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Michael M Johns
- Department of Otolaryngology-Head and Neck Surgery, University of Southern California, Los Angeles, California, USA
| | - Anaïs Rameau
- Department of Otolaryngology-Head and Neck Surgery, Sean Parker Institute for the Voice, Weill Cornell Medical College, New York, New York, USA
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25
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Xiang H, Zhang B, Wang Y, Xu N, Zhang F, Luo R, Ji M, Ding C. Region-resolved multi-omics of the mouse eye. Cell Rep 2023; 42:112121. [PMID: 36790928 DOI: 10.1016/j.celrep.2023.112121] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 12/19/2022] [Accepted: 01/31/2023] [Indexed: 02/16/2023] Open
Abstract
The eye is a complex organ consisting of multiple compartments with unique and specialized properties, and small disturbances in one eye region can result in impaired vision and blindness. Although there have been advancements in ocular research, the hierarchical molecular network in region-wide resolution, indicating the division of labor and crosstalk among different eye regions, is not yet comprehensively illuminated. Here, we present an atlas of region-resolved proteome and lipidome of mouse eye. Multiphoton microscopy-guided laser microdissection combined with in-depth label-free proteomics identifies 13,536 proteins across various mouse eye regions. Further integrative analysis of spectral imaging, label-free proteome, and imaging mass spectrometry of the lipidome and phosphoproteome reveals distinctive molecular features, including proteins and lipids of various anatomical mouse eye regions. These deposited datasets and our open proteome server integrating all information provide a valuable resource for future functional and mechanistic studies of mouse eye and ocular disease.
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Affiliation(s)
- Hang Xiang
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai 200433, China
| | - Bohan Zhang
- State Key Laboratory of Surface Physics and Department of Physics, Human Phenome Institute, Multiscale Research Institute of Complex Systems, Academy for Engineering and Technology, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Fudan University, Shanghai 200433, China
| | - Yunzhi Wang
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai 200433, China
| | - Ning Xu
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai 200433, China
| | - Fan Zhang
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai 200433, China
| | - Rongkui Luo
- Department of Pathology, Zhongshan Hospital Fudan University, Shanghai, China
| | - Minbiao Ji
- State Key Laboratory of Surface Physics and Department of Physics, Human Phenome Institute, Multiscale Research Institute of Complex Systems, Academy for Engineering and Technology, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Fudan University, Shanghai 200433, China.
| | - Chen Ding
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai 200433, China.
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Hernández IC, Yau J, Rishøj L, Cui N, Minderler S, Jowett N. Tutorial: multiphoton microscopy to advance neuroscience research. Methods Appl Fluoresc 2023; 11. [PMID: 36753763 DOI: 10.1088/2050-6120/acba66] [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: 10/03/2022] [Accepted: 02/08/2023] [Indexed: 02/10/2023]
Abstract
Multiphoton microscopy (MPM) employs ultrafast infrared lasers for high-resolution deep three-dimensional imaging of live biological samples. The goal of this tutorial is to provide a practical guide to MPM imaging for novice microscopy developers and life-science users. Principles of MPM, microscope setup, and labeling strategies are discussed. Use of MPM to achieve unprecedented imaging depth of whole mounted explants and intravital imaging via implantable glass windows of the mammalian nervous system is demonstrated.
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Affiliation(s)
- Iván Coto Hernández
- Surgical Photonics & Engineering Laboratory, Mass Eye and Ear, Harvard Medical School, 243 Charles St, Boston, MA, United States of America
| | - Jenny Yau
- Surgical Photonics & Engineering Laboratory, Mass Eye and Ear, Harvard Medical School, 243 Charles St, Boston, MA, United States of America
| | - Lars Rishøj
- Technical University of Denmark, DTU Electro, Ørsteds Plads 343, 2800 Kgs. Lyngby, Denmark
| | - Nanke Cui
- Surgical Photonics & Engineering Laboratory, Mass Eye and Ear, Harvard Medical School, 243 Charles St, Boston, MA, United States of America
| | - Steven Minderler
- Surgical Photonics & Engineering Laboratory, Mass Eye and Ear, Harvard Medical School, 243 Charles St, Boston, MA, United States of America
| | - Nate Jowett
- Surgical Photonics & Engineering Laboratory, Mass Eye and Ear, Harvard Medical School, 243 Charles St, Boston, MA, United States of America
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27
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Ao J, Shao X, Liu Z, Liu Q, Xia J, Shi Y, Qi L, Pan J, Ji M. Stimulated Raman Scattering Microscopy Enables Gleason Scoring of Prostate Core Needle Biopsy by a Convolutional Neural Network. Cancer Res 2023; 83:641-651. [PMID: 36594873 PMCID: PMC9929517 DOI: 10.1158/0008-5472.can-22-2146] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 10/19/2022] [Accepted: 12/29/2022] [Indexed: 01/04/2023]
Abstract
Focal therapy (FT) has been proposed as an approach to eradicate clinically significant prostate cancer while preserving the normal surrounding tissues to minimize treatment-related toxicity. Rapid histology of core needle biopsies is essential to ensure the precise FT for localized lesions and to determine tumor grades. However, it is difficult to achieve both high accuracy and speed with currently available histopathology methods. Here, we demonstrated that stimulated Raman scattering (SRS) microscopy could reveal the largely heterogeneous histologic features of fresh prostatic biopsy tissues in a label-free and near real-time manner. A diagnostic convolutional neural network (CNN) built based on images from 61 patients could classify Gleason patterns of prostate cancer with an accuracy of 85.7%. An additional 22 independent cases introduced as external test dataset validated the CNN performance with 84.4% accuracy. Gleason scores of core needle biopsies from 21 cases were calculated using the deep learning SRS system and showed a 71% diagnostic consistency with grading from three pathologists. This study demonstrates the potential of a deep learning-assisted SRS platform in evaluating the tumor grade of prostate cancer, which could help simplify the diagnostic workflow and provide timely histopathology compatible with FT treatment. SIGNIFICANCE A platform combining stimulated Raman scattering microscopy and a convolutional neural network provides rapid histopathology and automated Gleason scoring on fresh prostate core needle biopsies without complex tissue processing.
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Affiliation(s)
- Jianpeng Ao
- State Key Laboratory of Surface Physics and Department of Physics, Human Phenome Institute, Academy for Engineering and Technology, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Yiwu Research Institute of Fudan University, Fudan University, Shanghai, P.R. China
| | - Xiaoguang Shao
- Department of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Zhijie Liu
- State Key Laboratory of Surface Physics and Department of Physics, Human Phenome Institute, Academy for Engineering and Technology, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Yiwu Research Institute of Fudan University, Fudan University, Shanghai, P.R. China
| | - Qiang Liu
- Department of Pathology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Jun Xia
- Department of Pathology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Yongheng Shi
- Department of Pathology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Lin Qi
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, P.R. China
| | - Jiahua Pan
- Department of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Minbiao Ji
- State Key Laboratory of Surface Physics and Department of Physics, Human Phenome Institute, Academy for Engineering and Technology, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Yiwu Research Institute of Fudan University, Fudan University, Shanghai, P.R. China
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28
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Mo W, Ke Q, Zhou M, Xie G, Huang J, Gao F, Ni S, Yang X, Qi D, Wang A, Wen J, Yang Y, Jing M, Du K, Wang X, Du X, Zhao Z. Combined Morphological and Spectroscopic Diagnostic of HER2 Expression in Breast Cancer Tissues Based on Label-Free Surface-Enhanced Raman Scattering. Anal Chem 2023; 95:3019-3027. [PMID: 36706440 DOI: 10.1021/acs.analchem.2c05067] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Breast cancer is the most commonly diagnosed cancer type worldwide. Overexpression of human epidermal growth factor receptor 2 (HER2) is an important subtype of breast cancer and results in an increased risk of recurrence and metastasis in patients. At present, immunohistochemistry (IHC) is used to detect the expression of HER2 in breast cancer tissues as the golden standard. However, IHC has some shortcomings, such as large subjective impact, long time consumption, expensive reagents, etc. In this paper, a combined morphological and spectroscopic diagnostic method based on label-free surface-enhanced Raman scattering (SERS) for HER2 expression in breast cancer is proposed. It can not only quantitively detect HER2 expression in breast cancer tissues by spectroscopic measurements but also give morphological images reflecting the distribution of HER2 in tissues. The results show that the consistency between this method and IHC is 95% and achieves the annotation of tumor regions on tissue sections. This method is time-consuming, quantifiable, intuitive, scalable, and easy to understand. Combined with deep learning approaches, it is expected to promote the development of clinical detection and diagnosis technology for breast cancer and other cancers.
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Affiliation(s)
- Wenbo Mo
- China Academy of Engineering Physics, Laser Fusion Research Center, 621900 Mianyang, China.,Department of Engineering Physics, Tsinghua University, 100084 Beijing, China
| | - Qi Ke
- Mianyang Central Hospital, 621000 Mianyang, China
| | - Minjie Zhou
- China Academy of Engineering Physics, Laser Fusion Research Center, 621900 Mianyang, China
| | - Gang Xie
- Mianyang Central Hospital, 621000 Mianyang, China
| | - Jinglin Huang
- China Academy of Engineering Physics, Laser Fusion Research Center, 621900 Mianyang, China
| | - Feng Gao
- Mianyang Central Hospital, 621000 Mianyang, China
| | - Shuang Ni
- China Academy of Engineering Physics, Laser Fusion Research Center, 621900 Mianyang, China
| | - Xiyue Yang
- Mianyang Central Hospital, 621000 Mianyang, China
| | - Daojian Qi
- China Academy of Engineering Physics, Laser Fusion Research Center, 621900 Mianyang, China
| | - Anqun Wang
- Mianyang Central Hospital, 621000 Mianyang, China
| | - Jiaxing Wen
- China Academy of Engineering Physics, Laser Fusion Research Center, 621900 Mianyang, China
| | - Yue Yang
- China Academy of Engineering Physics, Laser Fusion Research Center, 621900 Mianyang, China
| | - Meng Jing
- Mianyang Central Hospital, 621000 Mianyang, China
| | - Kai Du
- China Academy of Engineering Physics, Laser Fusion Research Center, 621900 Mianyang, China
| | - Xuewu Wang
- Department of Engineering Physics, Tsinghua University, 100084 Beijing, China
| | - Xiaobo Du
- Mianyang Central Hospital, 621000 Mianyang, China
| | - Zongqing Zhao
- China Academy of Engineering Physics, Laser Fusion Research Center, 621900 Mianyang, China
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29
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Shah D, Gehani A, Mahajan A, Chakrabarty N. Advanced Techniques in Head and Neck Cancer Imaging: Guide to Precision Cancer Management. Crit Rev Oncog 2023; 28:45-62. [PMID: 37830215 DOI: 10.1615/critrevoncog.2023047799] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
Abstract
Precision treatment requires precision imaging. With the advent of various advanced techniques in head and neck cancer treatment, imaging has become an integral part of the multidisciplinary approach to head and neck cancer care from diagnosis to staging and also plays a vital role in response evaluation in various tumors. Conventional anatomic imaging (CT scan, MRI, ultrasound) remains basic and focuses on defining the anatomical extent of the disease and its spread. Accurate assessment of the biological behavior of tumors, including tumor cellularity, growth, and response evaluation, is evolving with recent advances in molecular, functional, and hybrid/multiplex imaging. Integration of these various advanced diagnostic imaging and nonimaging methods aids understanding of cancer pathophysiology and provides a more comprehensive evaluation in this era of precision treatment. Here we discuss the current status of various advanced imaging techniques and their applications in head and neck cancer imaging.
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Affiliation(s)
- Diva Shah
- Senior Consultant Radiologist, Department of Radiodiagnosis, HCG Cancer Centre, Ahmedabad, 380060, Gujarat, India
| | - Anisha Gehani
- Department of Radiology and Imaging Sciences, Tata Medical Centre, New Town, WB 700160, India
| | - Abhishek Mahajan
- Department of Radiology, The Clatterbridge Cancer Centre NHS Foundation Trust, Liverpool, L7 8YA, United Kingdom
| | - Nivedita Chakrabarty
- Department of Radiodiagnosis, Tata Memorial Hospital, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), 400012, Mumbai, India
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30
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Yang Y, Liu Z, Huang J, Sun X, Ao J, Zheng B, Chen W, Shao Z, Hu H, Yang Y, Ji M. Histological diagnosis of unprocessed breast core-needle biopsy via stimulated Raman scattering microscopy and multi-instance learning. Theranostics 2023; 13:1342-1354. [PMID: 36923541 PMCID: PMC10008736 DOI: 10.7150/thno.81784] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 01/09/2023] [Indexed: 03/14/2023] Open
Abstract
Core-needle biopsy (CNB) plays a vital role in the initial diagnosis of breast cancer. However, the complex tissue processing and global shortage of pathologists have hindered traditional histopathology from timely diagnosis on fresh biopsies. In this work, we developed a full digital platform by integrating label-free stimulated Raman scattering (SRS) microscopy with weakly-supervised learning for rapid and automated cancer diagnosis on un-labelled breast CNB. Methods: We first compared the results of SRS imaging with standard hematoxylin and eosin (H&E) staining on adjacent frozen tissue sections. Then fresh unprocessed biopsy tissues were imaged by SRS to reveal diagnostic histoarchitectures. Next, weakly-supervised learning, i.e., the multi-instance learning (MIL) model was conducted to evaluate the ability to differentiate between benign and malignant cases, and compared with the performance of supervised learning model. Finally, gradient-weighted class activation mapping (Grad-CAM) and semantic segmentation were performed to spatially resolve benign/malignant areas with high efficiency. Results: We verified the ability of SRS in revealing essential histological hallmarks of breast cancer in both thin frozen sections and fresh unprocessed biopsy, generating histoarchitectures well correlated with H&E staining. Moreover, we demonstrated that weakly-supervised MIL model could achieve superior classification performance to supervised learnings, reaching diagnostic accuracy of 95% on 61 biopsy specimens. Furthermore, Grad-CAM allowed the trained MIL model to visualize the histological heterogeneity within the CNB. Conclusion: Our results indicate that MIL-assisted SRS microscopy provides rapid and accurate diagnosis on histologically heterogeneous breast CNB, and could potentially help the subsequent management of patients.
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Affiliation(s)
- Yifan Yang
- State Key Laboratory of Surface Physics and Department of Physics, Human Phenome Institute, Academy for Engineering and Technology, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Yiwu Research Institute, Fudan University, Shanghai 200433, China
| | - Zhijie Liu
- State Key Laboratory of Surface Physics and Department of Physics, Human Phenome Institute, Academy for Engineering and Technology, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Yiwu Research Institute, Fudan University, Shanghai 200433, China
| | - Jing Huang
- State Key Laboratory of Surface Physics and Department of Physics, Human Phenome Institute, Academy for Engineering and Technology, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Yiwu Research Institute, Fudan University, Shanghai 200433, China
| | - Xiangjie Sun
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai 200032, China
| | - Jianpeng Ao
- State Key Laboratory of Surface Physics and Department of Physics, Human Phenome Institute, Academy for Engineering and Technology, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Yiwu Research Institute, Fudan University, Shanghai 200433, China
| | - Bin Zheng
- Otolaryngology & Head and Neck Center, Cancer Center, Department of Otolaryngology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Wanyuan Chen
- Cancer Center, Department of Pathology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Zhiming Shao
- Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Hao Hu
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, 200032 China
| | - Yinlong Yang
- Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Minbiao Ji
- State Key Laboratory of Surface Physics and Department of Physics, Human Phenome Institute, Academy for Engineering and Technology, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Yiwu Research Institute, Fudan University, Shanghai 200433, China
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31
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Wang P, Chen J, Wu X, Tian Y, Zhang R, Sun J, Zhang Z, Wang C, Bai P, Guo L, Gao J. Determination of blood species using echelle Raman spectrometer and surface enhanced Raman spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 281:121640. [PMID: 35868053 DOI: 10.1016/j.saa.2022.121640] [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: 04/24/2022] [Revised: 07/13/2022] [Accepted: 07/14/2022] [Indexed: 06/15/2023]
Abstract
Blood species identification of human and animals has attracted much attention in the areas of customs inspection and forensic science. The combination of vibrational spectroscopy and machine learning has been proven to be feasible and effective for this purpose. However, the popularization of this technology needs instrument which is compact, robust and more suitable for field application. Besides the quantity of the blood sample should be as little as possible. In this study, we proposed a system using echelle Raman spectrometer combined with surface enhanced Raman spectroscopy (SERS), which protocol combines the advantages of broadband and high resolution of echelle Raman spectrometer with the advantages of high SERS spectral sensitivity. The SERS spectra of 26 species including human were collected with echelle Raman spectrometer, and the convolutional neural network was used for species identification, with an accuracy rate of over 94%. The feasibility, validity and reliability of the combination of echelle Raman spectrometer and SERS for blood species identification were realized.
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Affiliation(s)
- Peng Wang
- Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, China
| | - Jiansheng Chen
- Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, China
| | - Xiaodong Wu
- Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, China
| | - Yubing Tian
- Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, China
| | - Rui Zhang
- Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, China
| | - Jiaojiao Sun
- Suzhou Guoke Medical Science & Technology Development Co. Ltd., Suzhou 215163, China
| | - Zhiqiang Zhang
- Suzhou Guoke Medical Science & Technology Development Co. Ltd., Suzhou 215163, China
| | - Ce Wang
- Suzhou Guoke Medical Science & Technology Development Co. Ltd., Suzhou 215163, China
| | - Pengli Bai
- Suzhou Guoke Medical Science & Technology Development Co. Ltd., Suzhou 215163, China
| | - Liangsheng Guo
- Department of Gynecology and Obstetrics, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215004, China.
| | - Jing Gao
- Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, China; Suzhou Guoke Medical Science & Technology Development Co. Ltd., Suzhou 215163, China.
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32
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Fitzgerald C, Dogan S, Bou-Nassif R, Mclean T, Woods R, Cracchiolo JR, Ganly I, Tabar V, Cohen MA. Stimulated Raman Histology for Rapid Intra-Operative Diagnosis of Sinonasal and Skull Base Tumors. Laryngoscope 2022; 132:2142-2147. [PMID: 35634892 PMCID: PMC10291728 DOI: 10.1002/lary.30233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 04/07/2022] [Accepted: 05/06/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Intra-operative stimulated Raman histology (SRH) is a novel technology that uses laser spectroscopy and color-matching algorithms to create images similar to the formalin-fixed paraffin-embedded (FFPE) section. We aim to assess the accuracy of SRH in a novel range of sinonasal and skull base tumors. METHODS Select patients undergoing sinonasal and skull base surgery using the Invenio Imaging™ Nio™ Laser Imaging SRH system between June 2020 and September 2021 were assessed. The SRH images were reviewed for pathologic features similar to frozen section (FS) and FFPE. Time taken for results and diagnostic concordance was assessed. RESULTS Sixty-seven SRH images from 7 tumor types in 12 patients were assessed. Pathologies included squamous cell carcinoma, rhabdomyosarcoma, inverted papilloma, adenoid cystic carcinoma, SMARCB1-deficient sinonasal carcinoma, mucosal melanoma, metastatic colonic adenocarcinoma, and meningioma. Tumor was identified in 100% of lesional specimens, with characteristic diagnostic features readily appreciable on SRH. Median time for diagnosis was significantly faster for SRH (4.3 min) versus FS (44.5 min; p = <.0001). Where SRH sample site matched precisely to FS (n = 32/67, 47.8%), the same diagnosis was confirmed in 93.8%. Sensitivity, specificity, precision, and overall accuracy of SRH were 93.3%, 94.1%, 93.8%, and 93.3%, respectively. Near-perfect concordance was seen between SRH and FS (Cohen's kappa [κ] = 0.89). CONCLUSION Stimulated Raman histology can rapidly produce images similar to FFPE H&E in sinonasal and skull base tumors. This technology has the potential to act as an adjunct or alternative to standard FS. LEVEL OF EVIDENCE 4 Laryngoscope, 132:2142-2147, 2022.
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Affiliation(s)
- Conall Fitzgerald
- Head and Neck Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Snjezana Dogan
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Rabih Bou-Nassif
- Department of Neurosurgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Tim Mclean
- Head and Neck Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Robbie Woods
- Head and Neck Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jennifer R. Cracchiolo
- Head and Neck Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ian Ganly
- Head and Neck Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Viviane Tabar
- Department of Neurosurgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Marc A. Cohen
- Head and Neck Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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Wei Z, Liu X, Yan R, Sun G, Yu W, Liu Q, Guo Q. Pixel-level multimodal fusion deep networks for predicting subcellular organelle localization from label-free live-cell imaging. Front Genet 2022; 13:1002327. [PMID: 36386823 PMCID: PMC9644055 DOI: 10.3389/fgene.2022.1002327] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 09/26/2022] [Indexed: 01/25/2023] Open
Abstract
Complex intracellular organizations are commonly represented by dividing the metabolic process of cells into different organelles. Therefore, identifying sub-cellular organelle architecture is significant for understanding intracellular structural properties, specific functions, and biological processes in cells. However, the discrimination of these structures in the natural organizational environment and their functional consequences are not clear. In this article, we propose a new pixel-level multimodal fusion (PLMF) deep network which can be used to predict the location of cellular organelle using label-free cell optical microscopy images followed by deep-learning-based automated image denoising. It provides valuable insights that can be of tremendous help in improving the specificity of label-free cell optical microscopy by using the Transformer-Unet network to predict the ground truth imaging which corresponds to different sub-cellular organelle architectures. The new prediction method proposed in this article combines the advantages of a transformer's global prediction and CNN's local detail analytic ability of background features for label-free cell optical microscopy images, so as to improve the prediction accuracy. Our experimental results showed that the PLMF network can achieve over 0.91 Pearson's correlation coefficient (PCC) correlation between estimated and true fractions on lung cancer cell-imaging datasets. In addition, we applied the PLMF network method on the cell images for label-free prediction of several different subcellular components simultaneously, rather than using several fluorescent labels. These results open up a new way for the time-resolved study of subcellular components in different cells, especially for cancer cells.
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Affiliation(s)
- Zhihao Wei
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China
| | - Xi Liu
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China
| | - Ruiqing Yan
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China
| | - Guocheng Sun
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China,School of Mechanical Engineering & Hydrogen Energy Research Centre, Beijing Institute of Petrochemical Technology, Beijing, China
| | - Weiyong Yu
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China
| | - Qiang Liu
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China
| | - Qianjin Guo
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China,School of Mechanical Engineering & Hydrogen Energy Research Centre, Beijing Institute of Petrochemical Technology, Beijing, China,*Correspondence: Qianjin Guo,
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Smolen JA, Wooley KL. Fluorescence lifetime image microscopy prediction with convolutional neural networks for cell detection and classification in tissues. PNAS NEXUS 2022; 1:pgac235. [PMID: 36712353 PMCID: PMC9802238 DOI: 10.1093/pnasnexus/pgac235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 10/11/2022] [Indexed: 12/05/2022]
Abstract
Convolutional neural networks (CNNs) and other deep-learning models have proven to be transformative tools for the automated analysis of microscopy images, particularly in the domain of cellular and tissue imaging. These computer-vision models have primarily been applied with traditional microscopy imaging modalities (e.g. brightfield and fluorescence), likely due to the availability of large datasets in these regimes. However, more advanced microscopy imaging techniques could, potentially, allow for improved model performance in various computational histopathology tasks. In this work, we demonstrate that CNNs can achieve high accuracy in cell detection and classification without large amounts of data when applied to histology images acquired by fluorescence lifetime imaging microscopy (FLIM). This accuracy is higher than what would be achieved with regular single or dual-channel fluorescence images under the same settings, particularly for CNNs pretrained on publicly available fluorescent cell or general image datasets. Additionally, generated FLIM images could be predicted from just the fluorescence image data by using a dense U-Net CNN model trained on a subset of ground-truth FLIM images. These U-Net CNN generated FLIM images demonstrated high similarity to ground truth and improved accuracy in cell detection and classification over fluorescence alone when used as input to a variety of commonly used CNNs. This improved accuracy was maintained even when the FLIM images were generated by a U-Net CNN trained on only a few example FLIM images.
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Affiliation(s)
| | - Karen L Wooley
- Departments of Chemistry, Chemical Engineering, and Materials Science and Engineering, Texas A&M University, College Station, TX 77842, USA
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35
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Wako BD, Dese K, Ulfata RE, Nigatu TA, Turunbedu SK, Kwa T. Squamous Cell Carcinoma of Skin Cancer Margin Classification From Digital Histopathology Images Using Deep Learning. Cancer Control 2022; 29:10732748221132528. [PMID: 36194624 PMCID: PMC9536105 DOI: 10.1177/10732748221132528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Objectives Now a days, squamous cell carcinoma (SCC) margin assessment is done by examining histopathology images and inspection of whole slide images (WSI) using a conventional microscope. This is time-consuming, tedious, and depends on experts’ experience which may lead to misdiagnosis and mistreatment plans. This study aims to develop a system for the automatic diagnosis of skin cancer margin for squamous cell carcinoma from histopathology microscopic images by applying deep learning techniques. Methods The system was trained, validated, and tested using histopathology images of SCC cancer locally acquired from Jimma Medical Center Pathology Department from seven different skin sites using an Olympus digital microscope. All images were preprocessed and trained with transfer learning pre-trained models by fine-tuning the hyper-parameter of the selected models. Results The overall best training accuracy of the models become 95.3%, 97.1%, 89.8%, and 89.9% on EffecientNetB0, MobileNetv2, ResNet50, VGG16 respectively. In addition to this, the best validation accuracy of the models was 94.7%, 91.8%, 87.8%, and 86.7% respectively. The best testing accuracy of the models at the same epoch was 95.2%, 91.5%, 87%, and 85.5% respectively. From these models, EfficientNetB0 showed the best average training and testing accuracy than the other models. Conclusions The system assists the pathologist during the margin assessment of SCC by decreasing the diagnosis time from an average of 25 minutes to less than a minute.
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Affiliation(s)
- Beshatu Debela Wako
- School of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia,Center of Biomedical Engineering, Jimma University Medical Center, Jimma, Ethiopia
| | - Kokeb Dese
- School of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia,Artificial Intelligence and Biomedical Imaging Research Lab, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia,Kokeb Dese, Department of Biomedical Engineering, Jimma University, Jimma 378, Ethiopia. ,
| | - Roba Elala Ulfata
- Department of Pathology, Jimma Institute of Health, Jimma University, Jimma, Ethiopia,Department of Pathology, Adama General Hospital and Medical College, Adama, Ethiopia
| | - Tilahun Alemayehu Nigatu
- Department of Biomedical Sciences (Anatomy Course Unit), Jimma Institute of Health, Jimma University, Jimma, Ethiopia
| | | | - Timothy Kwa
- School of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia,Department of Biomedical Engineering, University of California, 451 Health Sciences, Davis, CA, USA,Medtronic MiniMed, 18000 Devonshire St., Northridge, Los Angeles, CA, USA,Timothy Kwa, Department of Biomedical Engineering, Jimma University, Jimma 378, Ethiopia.
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Multiple Parallel Fusion Network for Predicting Protein Subcellular Localization from Stimulated Raman Scattering (SRS) Microscopy Images in Living Cells. Int J Mol Sci 2022; 23:ijms231810827. [PMID: 36142736 PMCID: PMC9504098 DOI: 10.3390/ijms231810827] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 09/10/2022] [Accepted: 09/13/2022] [Indexed: 11/23/2022] Open
Abstract
Stimulated Raman Scattering Microscopy (SRS) is a powerful tool for label-free detailed recognition and investigation of the cellular and subcellular structures of living cells. Determining subcellular protein localization from the cell level of SRS images is one of the basic goals of cell biology, which can not only provide useful clues for their functions and biological processes but also help to determine the priority and select the appropriate target for drug development. However, the bottleneck in predicting subcellular protein locations of SRS cell imaging lies in modeling complicated relationships concealed beneath the original cell imaging data owing to the spectral overlap information from different protein molecules. In this work, a multiple parallel fusion network, MPFnetwork, is proposed to study the subcellular locations from SRS images. This model used a multiple parallel fusion model to construct feature representations and combined multiple nonlinear decomposing algorithms as the automated subcellular detection method. Our experimental results showed that the MPFnetwork could achieve over 0.93 dice correlation between estimated and true fractions on SRS lung cancer cell datasets. In addition, we applied the MPFnetwork method to cell images for label-free prediction of several different subcellular components simultaneously, rather than using several fluorescent labels. These results open up a new method for the time-resolved study of subcellular components in different cells, especially cancer cells.
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郝 哲, 岳 蜀, 周 利. [Application of Raman-based technologies in the detection of urological tumors]. BEIJING DA XUE XUE BAO. YI XUE BAN = JOURNAL OF PEKING UNIVERSITY. HEALTH SCIENCES 2022; 54:779-784. [PMID: 35950408 PMCID: PMC9385527 DOI: 10.19723/j.issn.1671-167x.2022.04.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Indexed: 06/15/2023]
Abstract
Urinary system tumors affect a huge number of individuals, and are frequently recurrent and progressing following surgery, necessitating lifelong surveillance. As a result, early and precise diagnosis of urinary system cancers is important for prevention and therapy. Histopathology is now the golden stan-dard for the diagnosis, but it is invasive, time-consuming, and inconvenient for initial diagnosis and re-gular follow-up assessment. Endoscopy can directly witness the tumor's structure, but intrusive detection is likely to cause harm to the patient's organs, and it is apt to create other hazards in frequently examined patients. Imaging is a valuable non-invasive and quick assessment tool; however, it can be difficult to define the type of lesions and has limited sensitivity for early tumor detection. The conventional approaches for detecting tumors have their own set of limitations. Thus, detection methods that combine non-invasive detection, label-free detection, high sensitivity and high specificity are urgently needed to aid clinical diagnosis. Optical diagnostics and imaging are increasingly being employed in healthcare settings in a variety of sectors. Raman scattering can assess changes in molecular signatures in cancer cells or tissues based on the interaction with vibrational modes of common molecular bonds. Due to the advantages of label-free, strong chemical selectivity, and high sensitivity, Raman scattering, especially coherent Raman scattering microscopy imaging with high spatial resolution, has been widely used in biomedical research. And quantity studies have shown that it has a good application in the detection and diagnosis of bladder can-cer, renal clear cell carcinoma, prostate cancer, and other cancers. In this paper, several nonlinear imaging techniques based on Raman scattering technology are briefly described, including Raman spectroscopy, coherent anti-Stokes Raman scattering, stimulated Raman scattering, and surface-enhanced Raman spectroscopy. And we will discuss the application of these techniques for detecting urologic malignancy. Future research directions are predicted using the advantages and limitations of the aforesaid methodologies in the research. For clinical practice, Raman scattering technology is intended to enable more accurate, rapid, and non-invasive in early diagnosis, intraoperative margins, and pathological grading basis for clinical practice.
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Affiliation(s)
- 哲 郝
- 北京航空航天大学生物与医学工程学院,北京市生物医学工程高精尖创新中心,生物力学与力生物学教育部重点实验室,医用光子学研究所,北京 100083School of Biological and Medical Engineering, Beihang University, Beijing Advanced Innovation Center for Biomedical Engineering, Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Institute of Medical Photonics, Beijing 100083, China
| | - 蜀华 岳
- 北京航空航天大学生物与医学工程学院,北京市生物医学工程高精尖创新中心,生物力学与力生物学教育部重点实验室,医用光子学研究所,北京 100083School of Biological and Medical Engineering, Beihang University, Beijing Advanced Innovation Center for Biomedical Engineering, Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Institute of Medical Photonics, Beijing 100083, China
| | - 利群 周
- 北京航空航天大学生物与医学工程学院,北京市生物医学工程高精尖创新中心,生物力学与力生物学教育部重点实验室,医用光子学研究所,北京 100083School of Biological and Medical Engineering, Beihang University, Beijing Advanced Innovation Center for Biomedical Engineering, Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Institute of Medical Photonics, Beijing 100083, China
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Instant diagnosis of gastroscopic biopsy via deep-learned single-shot femtosecond stimulated Raman histology. Nat Commun 2022; 13:4050. [PMID: 35831299 PMCID: PMC9279377 DOI: 10.1038/s41467-022-31339-8] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 06/15/2022] [Indexed: 12/18/2022] Open
Abstract
Gastroscopic biopsy provides the only effective method for gastric cancer diagnosis, but the gold standard histopathology is time-consuming and incompatible with gastroscopy. Conventional stimulated Raman scattering (SRS) microscopy has shown promise in label-free diagnosis on human tissues, yet it requires the tuning of picosecond lasers to achieve chemical specificity at the cost of time and complexity. Here, we demonstrate that single-shot femtosecond SRS (femto-SRS) reaches the maximum speed and sensitivity with preserved chemical resolution by integrating with U-Net. Fresh gastroscopic biopsy is imaged in <60 s, revealing essential histoarchitectural hallmarks perfectly agreed with standard histopathology. Moreover, a diagnostic neural network (CNN) is constructed based on images from 279 patients that predicts gastric cancer with accuracy >96%. We further demonstrate semantic segmentation of intratumor heterogeneity and evaluation of resection margins of endoscopic submucosal dissection (ESD) tissues to simulate rapid and automated intraoperative diagnosis. Our method holds potential for synchronizing gastroscopy and histopathological diagnosis. Diagnosis of gastric cancer currently requires gastroscopic biopsy, which requires time and expertize to perform. Here, the authors demonstrate a femto-SRS imaging method which showed high accuracy in diagnosing gastric cancer without the need for pathologistbased diagnosis.
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Manifold B, Fu D. Quantitative Stimulated Raman Scattering Microscopy: Promises and Pitfalls. ANNUAL REVIEW OF ANALYTICAL CHEMISTRY (PALO ALTO, CALIF.) 2022; 15:269-289. [PMID: 35300525 PMCID: PMC10083020 DOI: 10.1146/annurev-anchem-061020-015110] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Since its first demonstration, stimulated Raman scattering (SRS) microscopy has become a powerful chemical imaging tool that shows promise in numerous biological and biomedical applications. The spectroscopic capability of SRS enables identification and tracking of specific molecules or classes of molecules, often without labeling. SRS microscopy also has the hallmark advantage of signal strength that is directly proportional to molecular concentration, allowing for in situ quantitative analysis of chemical composition of heterogeneous samples with submicron spatial resolution and subminute temporal resolution. However, it is important to recognize that quantification through SRS microscopy requires assumptions regarding both system and sample. Such assumptions are often taken axiomatically, which may lead to erroneous conclusions without proper validation. In this review, we focus on the tacitly accepted, yet complex, quantitative aspect of SRS microscopy. We discuss the various approaches to quantitative analysis, examples of such approaches, challenges in different systems, and potential solutions. Through our examination of published literature, we conclude that a scrupulous approach to experimental design can further expand the powerful and incisive quantitative capabilities of SRS microscopy.
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Affiliation(s)
- Bryce Manifold
- Department of Chemistry, University of Washington, Seattle, Washington, USA;
| | - Dan Fu
- Department of Chemistry, University of Washington, Seattle, Washington, USA;
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Carota AG, Campanella B, Del Carratore R, Bongioanni P, Giannelli R, Legnaioli S. Raman spectroscopy and multivariate analysis as potential tool to follow Alzheimer's disease progression. Anal Bioanal Chem 2022; 414:4667-4675. [PMID: 35587826 PMCID: PMC9117601 DOI: 10.1007/s00216-022-04087-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 04/13/2022] [Accepted: 04/19/2022] [Indexed: 11/27/2022]
Abstract
Raman spectroscopy is an emerging tool in the research and diagnosis of different diseases, including neurodegenerative disorders. In this work, blood serum samples collected from healthy controls and dementia patients were analysed by Raman spectroscopy to develop a classification model for the diagnosis of dementia of Alzheimer’s type (DAT). Raman spectra were processed by means of multivariate tools for multivariate analysis. Lower concentration levels of carotenoids were detected in blood serum from patients, which allowed for a good discrimination with respect to controls, such as 93% of correct predictions on the test set with random forest. We also hypothesize that carotenoid levels might be informative about the severity and progression of the disease, since the intensity of carotenoid signals decreased from the early stage to more severe patients. These encouraging results suggest the possibility to use Raman spectroscopy for the analysis of alternative biofluids (e.g. saliva) and the unobtrusive diagnosis of other neurodegenerative disorders.
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Affiliation(s)
- Angela Gilda Carota
- Department of Chemistry and Industrial Chemistry, University of Pisa, Pisa, Italy
- Institute of Chemistry of Organometallic Compounds, ICCOM-CNR-Pisa, Pisa, Italy
| | - Beatrice Campanella
- Institute of Chemistry of Organometallic Compounds, ICCOM-CNR-Pisa, Pisa, Italy
| | | | - Paolo Bongioanni
- Spinal Cord Injuries Section, Azienda Ospedaliero-Universitaria, Pisa, Italy
| | - Roberta Giannelli
- Institute of Clinical Physiology Research, IFC-CNR-Pisa, Pisa, Italy
| | - Stefano Legnaioli
- Institute of Chemistry of Organometallic Compounds, ICCOM-CNR-Pisa, Pisa, Italy.
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Qian L, Ni T, Fei B, Sun H, Ni H. An immune-related lncRNA pairs signature to identify the prognosis and predict the immune landscape of laryngeal squamous cell carcinoma. BMC Cancer 2022; 22:545. [PMID: 35568824 PMCID: PMC9107277 DOI: 10.1186/s12885-022-09524-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Accepted: 04/08/2022] [Indexed: 01/13/2023] Open
Abstract
Background
Laryngeal squamous cell carcinoma (LSCC) is the most common squamous cell carcinoma. Though significant effort has been focused on molecular pathogenesis, development, and recurrence of LSCC, little is known about its relationship with the immune-related long non-coding RNA (lncRNA) pairs. Methods After obtaining the transcriptome profiling data sets and the corresponding clinical characteristics of LSCC patients and normal samples from The Cancer Genome Atlas (TCGA) database, a series of bioinformatic analysis was conducted to select the differently expressed immune-related lncRNAs and build a signature of immune-related lncRNA pairs. Then, the effectiveness of the signature was validated. Results A total of 111 LSCC patients and 12 normal samples’ transcriptome profiling data sets were retrieved from TCGA. 301 differently expressed immune-related lncRNAs were identified and 35,225 lncRNA pairs were established. After univariate Cox analysis, LASSO regression and multivariate Cox analysis, 7 lncRNA pairs were eventually selected to construct a signature. The riskscore was computed using the following formula: Riskscore = 0.95 × (AL133330.1|AC132872.3) + (-1.23) × (LINC01094|LINC02154) + 0.65 × (LINC02575|AC122685.1) + (-1.15) × (MIR9-3HG|LINC01748) + 1.45 × (AC092687.3|SNHG12) + (-0.87) × (AC090204.1|AL158166.1) + 0.64 × (LINC01063|Z82243.1). Patients were classified into the high-risk group (> 1.366) and the low-risk group (< 1.366) according to the cutoff value (1.366), which is based on the 5-year riskscore ROC curve. The survival analysis showed that the low-risk group had a better prognosis (P < 0.001). The riskscore was better than other clinical characteristics in prognostic prediction and the area under the curves (AUCs) for the 1-, 3-, and 5-year survivals were 0.796, 0.946, and 0.895, respectively. Combining age, gender, grade, stage, and riskscore, a nomograph was developed to predict survival probability in LSCC patients. Then, the riskscore was confirmed to be related with the content of tumor-infiltration immune cells and the model could serve as a potential predictor for chemosensitivity. Conclusion We successfully established a more stable signature of 7 immune-related lncRNA pairs, which has demonstrated a better prognostic ability for LSCC patients and may assist clinicians to precisely prescribe chemo drugs. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-022-09524-1.
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Affiliation(s)
- Lvsheng Qian
- Department of Otolaryngology, Affiliated Hospital of Nantong University, Nantong, 226001, Jiangsu, China
| | - Tingting Ni
- Department of Oncology, Nantong Tumor Hospital, Nantong, 226001, Jiangsu, China
| | - Bing Fei
- Department of Otorhinolaryngology Head and Neck Surgery, Affiliated Huai'an Hospital of Xuzhou Medical University, Huai'an, 223002, Jiangsu, China
| | - Hui Sun
- Department of Pathology, Affiliated Hospital of Nantong University, Nantong, 226001, Jiangsu, China
| | - Haosheng Ni
- Department of Otolaryngology, Affiliated Hospital of Nantong University, Nantong, 226001, Jiangsu, China.
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Hernández IC, Mohan S, Jowett N. Automated Stain-Free Histomorphometry of Peripheral Nerve by Contrast-Enhancing Techniques and Artificial Intelligence. J Neurosci Methods 2022; 375:109598. [DOI: 10.1016/j.jneumeth.2022.109598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 03/25/2022] [Accepted: 04/08/2022] [Indexed: 10/18/2022]
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Melanthota SK, Gopal D, Chakrabarti S, Kashyap AA, Radhakrishnan R, Mazumder N. Deep learning-based image processing in optical microscopy. Biophys Rev 2022; 14:463-481. [PMID: 35528030 PMCID: PMC9043085 DOI: 10.1007/s12551-022-00949-3] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 03/14/2022] [Indexed: 12/19/2022] Open
Abstract
Optical microscopy has emerged as a key driver of fundamental research since it provides the ability to probe into imperceptible structures in the biomedical world. For the detailed investigation of samples, a high-resolution image with enhanced contrast and minimal damage is preferred. To achieve this, an automated image analysis method is preferable over manual analysis in terms of both speed of acquisition and reduced error accumulation. In this regard, deep learning (DL)-based image processing can be highly beneficial. The review summarises and critiques the use of DL in image processing for the data collected using various optical microscopic techniques. In tandem with optical microscopy, DL has already found applications in various problems related to image classification and segmentation. It has also performed well in enhancing image resolution in smartphone-based microscopy, which in turn enablse crucial medical assistance in remote places. Graphical abstract
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Affiliation(s)
- Sindhoora Kaniyala Melanthota
- Department of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, Karnataka 576104 India
| | - Dharshini Gopal
- Department of Bioinformatics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, Karnataka 576104 India
| | - Shweta Chakrabarti
- Department of Bioinformatics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, Karnataka 576104 India
| | - Anirudh Ameya Kashyap
- Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka 576104 India
| | - Raghu Radhakrishnan
- Department of Oral Pathology, Manipal College of Dental Sciences, Manipal, Manipal Academy of Higher Education, Manipal, 576104 India
| | - Nirmal Mazumder
- Department of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, Karnataka 576104 India
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Cheng HB, Zhang S, Bai E, Cao X, Wang J, Qi J, Liu J, Zhao J, Zhang L, Yoon J. Future-Oriented Advanced Diarylethene Photoswitches: From Molecular Design to Spontaneous Assembly Systems. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2108289. [PMID: 34866257 DOI: 10.1002/adma.202108289] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Revised: 11/20/2021] [Indexed: 06/13/2023]
Abstract
Diarylethene (DAE) photoswitch is a new and promising family of photochromic molecules and has shown superior performance as a smart trigger in stimulus-responsive materials. During the past few decades, the DAE family has achieved a leap from simple molecules to functional molecules and developed toward validity as a universal switching building block. In recent years, the introduction of DAE into an assembly system has been an attractive strategy that enables the photochromic behavior of the building blocks to be manifested at the level of the entire system, beyond the DAE unit itself. This assembly-based strategy will bring many unexpected results that promote the design and manufacture of a new generation of advanced materials. Here, recent advances in the design and fabrication of diarylethene as a trigger in materials science, chemistry, and biomedicine are reviewed.
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Affiliation(s)
- Hong-Bo Cheng
- State Key Laboratory of Organic-Inorganic Composites, Beijing Laboratory of Biomedical Materials, College of Materials Science and Engineering, Beijing University of Chemical Technology, 15 North Third Ring Road, Beijing, 100029, P. R. China
| | - Shuchun Zhang
- State Key Laboratory of Organic-Inorganic Composites, Beijing Laboratory of Biomedical Materials, College of Materials Science and Engineering, Beijing University of Chemical Technology, 15 North Third Ring Road, Beijing, 100029, P. R. China
| | - Enying Bai
- State Key Laboratory of Organic-Inorganic Composites, Beijing Laboratory of Biomedical Materials, College of Materials Science and Engineering, Beijing University of Chemical Technology, 15 North Third Ring Road, Beijing, 100029, P. R. China
| | - Xiaoqiao Cao
- State Key Laboratory of Organic-Inorganic Composites, Beijing Laboratory of Biomedical Materials, College of Materials Science and Engineering, Beijing University of Chemical Technology, 15 North Third Ring Road, Beijing, 100029, P. R. China
| | - Jiaqi Wang
- State Key Laboratory of Organic-Inorganic Composites, Beijing Laboratory of Biomedical Materials, College of Materials Science and Engineering, Beijing University of Chemical Technology, 15 North Third Ring Road, Beijing, 100029, P. R. China
| | - Ji Qi
- State Key Laboratory of Organic-Inorganic Composites, Beijing Laboratory of Biomedical Materials, College of Materials Science and Engineering, Beijing University of Chemical Technology, 15 North Third Ring Road, Beijing, 100029, P. R. China
| | - Jun Liu
- State Key Laboratory of Organic-Inorganic Composites, Beijing Laboratory of Biomedical Materials, College of Materials Science and Engineering, Beijing University of Chemical Technology, 15 North Third Ring Road, Beijing, 100029, P. R. China
| | - Jing Zhao
- State Key Laboratory of Organic-Inorganic Composites, Beijing Laboratory of Biomedical Materials, College of Materials Science and Engineering, Beijing University of Chemical Technology, 15 North Third Ring Road, Beijing, 100029, P. R. China
| | - Liqun Zhang
- State Key Laboratory of Organic-Inorganic Composites, Beijing Laboratory of Biomedical Materials, College of Materials Science and Engineering, Beijing University of Chemical Technology, 15 North Third Ring Road, Beijing, 100029, P. R. China
| | - Juyoung Yoon
- Department of Chemistry and Nanoscience, Ewha Womans University, Seoul, 03760, Korea
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Qi Y, Zhang G, Yang L, Liu B, Zeng H, Xue Q, Liu D, Zheng Q, Liu Y. High-Precision Intelligent Cancer Diagnosis Method: 2D Raman Figures Combined with Deep Learning. Anal Chem 2022; 94:6491-6501. [PMID: 35271250 DOI: 10.1021/acs.analchem.1c05098] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Raman spectroscopy, as a label-free detection technology, has been widely used in tumor diagnosis. However, most tumor diagnosis procedures utilize multivariate statistical analysis methods for classification, which poses a major bottleneck toward achieving high accuracy. Here, we propose a concept called the two-dimensional (2D) Raman figure combined with convolutional neural network (CNN) to improve the accuracy. Two-dimensional Raman figures can be obtained from four transformation methods: spectral recurrence plot (SRP), spectral Gramian angular field (SGAF), spectral short-time Fourier transform (SSTFT), and spectral Markov transition field (SMTF). Two-dimensional CNN models all yield more than 95% accuracy, which is higher than the PCA-LDA method and the Raman-spectrum-CNN method, indicating that 2D Raman figure inputs combined with CNN may be one reason for gaining excellent performances. Among 2D-CNN models, the main difference is the conversion, where SRP is based on the structure of wavenumber series with the best performances (98.9% accuracy, 99.5% sensitivity, 98.3% specificity), followed by SGAF on the wavenumber series, SSTFT on wavenumber and intensity information, and SMTF on wavenumber position information. The inclusion of external information in the conversion may be another reason for improvement in the accuracy. The excellent capability shows huge potential for tumor diagnosis via 2D Raman figures and may be applied in other spectroscopy analytical fields.
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Affiliation(s)
- Yafeng Qi
- State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China
| | - Guochao Zhang
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Lin Yang
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Bangxu Liu
- State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China
| | - Hui Zeng
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Qi Xue
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Dameng Liu
- State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China
| | - Qingfeng Zheng
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Yuhong Liu
- State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China
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Shin KS, Men S, Wong A, Cobb-Bruno C, Chen EY, Fu D. Quantitative Chemical Imaging of Bone Tissue for Intraoperative and Diagnostic Applications. Anal Chem 2022; 94:3791-3799. [PMID: 35188370 PMCID: PMC8944199 DOI: 10.1021/acs.analchem.1c04354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Bone is difficult to image using traditional histopathological methods, leading to challenges in intraoperative pathological evaluation that is critical in guiding surgical treatment, particularly in orthopedic oncology. In this study, we demonstrate that a multimodal quantitative imaging approach that combines stimulated Raman scattering (SRS) microscopy, two-photon fluorescence (TPF) microscopy, and second-harmonic generation (SHG) microscopy can provide useful diagnostic information regarding intact bone tissue fragments from surgical excision or biopsy specimens. We imaged bone samples from 17 patient cases and performed quantitative chemical and morphological analyses of both mineral and organic components of bone. Our main findings show that carbonate content combined with morphometric analysis of bone organic matrix can separate several major classes of bone cancer-associated diagnostic categories with an average accuracy of 92%. This proof-of-principle study demonstrates that quantitative multimodal imaging and machine learning-based analysis of bony tissue can provide crucial diagnostic information for guiding clinical decisions in orthopedic oncology. Moreover, the general methodology of morphological and chemical imaging combined with machine learning can be readily extended to other tissue types for tissue diagnosis in intraoperative and other clinical settings.
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Affiliation(s)
- Kseniya S Shin
- Department of Chemistry, University of Washington, Seattle, Washington 98195, United States.,School of Medicine, University of Washington, Seattle, Washington 98195, United States
| | - Shuaiqian Men
- Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Angel Wong
- Department of Bioengineering, University of Washington, Seattle, Washington 98195, United States
| | - Colburn Cobb-Bruno
- Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Eleanor Y Chen
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington 98195, United States
| | - Dan Fu
- Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
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Qi Y, Yang L, Liu B, Liu L, Liu Y, Zheng Q, Liu D, Luo J. Highly accurate diagnosis of lung adenocarcinoma and squamous cell carcinoma tissues by deep learning. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 265:120400. [PMID: 34547683 DOI: 10.1016/j.saa.2021.120400] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 09/07/2021] [Accepted: 09/10/2021] [Indexed: 06/13/2023]
Abstract
Intraoperative detection of the marginal tissues is the last and most important step to complete the resection of adenocarcinoma and squamous cell carcinoma. However, the current intraoperative diagnosis is time-consuming and requires numerous steps including staining. In this paper, we present the use of Raman spectroscopy with deep learning to achieve accurate diagnosis with stain-free process. To make the spectrum more suitable for deep learning, we utilize an unusual way of thinking which regards Raman spectral signal as a sequence and then converts it into two-dimensional Raman spectrogram by short-time Fourier transform as input. The normal-adenocarcinoma deep learning model and normal-squamous carcinoma deep learning model both achieve more than 96% accuracy, 95% sensitivity and 98% specificity when test, which higher than the conventional principal components analysis-linear discriminant analysis method with normal-adenocarcinoma model (0.896 accuracy, 0.867 sensitivity, 0.926 specificity) and normal-squamous carcinoma model (0.821 accuracy, 0.776 sensitivity, 1.000 specificity). The high performance of deep learning models provides a reliable way for intraoperative detection of marginal tissue, and is expected to reduce the detection time and save human lives.
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Affiliation(s)
- Yafeng Qi
- State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China
| | - Lin Yang
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Bangxu Liu
- State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China
| | - Li Liu
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Yuhong Liu
- State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China.
| | - Qingfeng Zheng
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
| | - Dameng Liu
- State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China.
| | - Jianbin Luo
- State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China
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Zou F, Zhang L, Zou X, Huang J, Nie C, Jiang J, Guo C, Wang H, Ma X, Ji M. Differential characterization of lumbar spine associated tissue histology with nonlinear optical microscopy. BIOMEDICAL OPTICS EXPRESS 2022; 13:474-484. [PMID: 35154886 PMCID: PMC8803016 DOI: 10.1364/boe.446351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 12/12/2021] [Accepted: 12/13/2021] [Indexed: 06/14/2023]
Abstract
Percutaneous endoscopic lumbar discectomy (PELD) is the major effective treatment for lumbar disc herniation, and rapid histological identification of dissected tissue is critical to guide the discectomy. In this work, we revealed the histological features of different types of peridural tissues of the lumbar spine by label-free multi-modal nonlinear optical microscopy. Stimulated Raman scattering (SRS) was used to extract lipid and protein distributions, while second harmonic generation (SHG) and two-photon excited fluorescence (TPEF) signals were applied to image the collagen and elastin fibers at the same time. Our results demonstrated that the nonlinear optical features of the dura and adjacent soft tissues were significantly different, showing the potentials of our method for intraoperative differentiation of these critical tissues and improving the surgical outcome of PELD.
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Affiliation(s)
- Fei Zou
- Department of Orthopaedics, Huashan Hospital, Fudan University, Shanghai 200040, China
- These authors contributed equally
| | - Lili Zhang
- State Key Laboratory of Surface Physics and Department of Physics, Human Phenome Institute, Multiscale Research Institute of Complex Systems, Academy for Engineering and Technology, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Fudan University, Shanghai 200433, China
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
- These authors contributed equally
| | - Xiang Zou
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200040, China
- These authors contributed equally
| | - Jing Huang
- State Key Laboratory of Surface Physics and Department of Physics, Human Phenome Institute, Multiscale Research Institute of Complex Systems, Academy for Engineering and Technology, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Fudan University, Shanghai 200433, China
| | - Cong Nie
- Department of Orthopaedics, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Jianyuan Jiang
- Department of Orthopaedics, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Chongyuan Guo
- Shanghai Starriver Bilingual School, Shanghai 201108, China
| | - Hongli Wang
- Department of Orthopaedics, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Xiaosheng Ma
- Department of Orthopaedics, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Minbiao Ji
- State Key Laboratory of Surface Physics and Department of Physics, Human Phenome Institute, Multiscale Research Institute of Complex Systems, Academy for Engineering and Technology, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Fudan University, Shanghai 200433, China
- Yiwu Research Institute of Fudan University, Chengbei Road, Yiwu City, Zhejiang 322000, China
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49
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Esmaeili N, Sharaf E, Gomes Ataide EJ, Illanes A, Boese A, Davaris N, Arens C, Navab N, Friebe M. Deep Convolution Neural Network for Laryngeal Cancer Classification on Contact Endoscopy-Narrow Band Imaging. SENSORS 2021; 21:s21238157. [PMID: 34884166 PMCID: PMC8662427 DOI: 10.3390/s21238157] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 12/02/2021] [Accepted: 12/03/2021] [Indexed: 12/14/2022]
Abstract
(1) Background: Contact Endoscopy (CE) and Narrow Band Imaging (NBI) are optical imaging modalities that can provide enhanced and magnified visualization of the superficial vascular networks in the laryngeal mucosa. The similarity of vascular structures between benign and malignant lesions causes a challenge in the visual assessment of CE-NBI images. The main objective of this study is to use Deep Convolutional Neural Networks (DCNN) for the automatic classification of CE-NBI images into benign and malignant groups with minimal human intervention. (2) Methods: A pretrained Res-Net50 model combined with the cut-off-layer technique was selected as the DCNN architecture. A dataset of 8181 CE-NBI images was used during the fine-tuning process in three experiments where several models were generated and validated. The accuracy, sensitivity, and specificity were calculated as the performance metrics in each validation and testing scenario. (3) Results: Out of a total of 72 trained and tested models in all experiments, Model 5 showed high performance. This model is considerably smaller than the full ResNet50 architecture and achieved the testing accuracy of 0.835 on the unseen data during the last experiment. (4) Conclusion: The proposed fine-tuned ResNet50 model showed a high performance to classify CE-NBI images into the benign and malignant groups and has the potential to be part of an assisted system for automatic laryngeal cancer detection.
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Affiliation(s)
- Nazila Esmaeili
- INKA—Innovation Laboratory for Image Guided Therapy, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany; (E.S.); (E.J.G.A.); (A.I.); (A.B.); (M.F.)
- Chair for Computer Aided Medical Procedures and Augmented Reality, Technical University of Munich, 85748 Munich, Germany;
- Correspondence:
| | - Esam Sharaf
- INKA—Innovation Laboratory for Image Guided Therapy, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany; (E.S.); (E.J.G.A.); (A.I.); (A.B.); (M.F.)
| | - Elmer Jeto Gomes Ataide
- INKA—Innovation Laboratory for Image Guided Therapy, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany; (E.S.); (E.J.G.A.); (A.I.); (A.B.); (M.F.)
- Department of Nuclear Medicine, Medical Faculty, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany
| | - Alfredo Illanes
- INKA—Innovation Laboratory for Image Guided Therapy, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany; (E.S.); (E.J.G.A.); (A.I.); (A.B.); (M.F.)
| | - Axel Boese
- INKA—Innovation Laboratory for Image Guided Therapy, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany; (E.S.); (E.J.G.A.); (A.I.); (A.B.); (M.F.)
| | - Nikolaos Davaris
- Department of Otorhinolaryngology, Head and Neck Surgery, Magdeburg University Hospital, 39120 Magdeburg, Germany;
| | - Christoph Arens
- Department of Otorhinolaryngology, Head and Neck Surgery, Giessen University Hospital, 35392 Giessen, Germany;
| | - Nassir Navab
- Chair for Computer Aided Medical Procedures and Augmented Reality, Technical University of Munich, 85748 Munich, Germany;
| | - Michael Friebe
- INKA—Innovation Laboratory for Image Guided Therapy, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany; (E.S.); (E.J.G.A.); (A.I.); (A.B.); (M.F.)
- IDTM GmbH, 45657 Recklinghausen, Germany
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50
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El-Mashtoly SF, Gerwert K. Diagnostics and Therapy Assessment Using Label-Free Raman Imaging. Anal Chem 2021; 94:120-142. [PMID: 34852454 DOI: 10.1021/acs.analchem.1c04483] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Affiliation(s)
- Samir F El-Mashtoly
- Center for Protein Diagnostics, Ruhr University Bochum, 44801 Bochum, Germany.,Department of Biophysics, Faculty of Biology and Biotechnology, Ruhr University Bochum, 44801 Bochum, Germany
| | - Klaus Gerwert
- Center for Protein Diagnostics, Ruhr University Bochum, 44801 Bochum, Germany.,Department of Biophysics, Faculty of Biology and Biotechnology, Ruhr University Bochum, 44801 Bochum, Germany
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