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Zhang Z, Du Y, Shi X, Wang K, Qu Q, Liang Q, Ma X, He K, Chi C, Tang J, Liu B, Ji J, Wang J, Dong J, Hu Z, Tian J. NIR-II light in clinical oncology: opportunities and challenges. Nat Rev Clin Oncol 2024; 21:449-467. [PMID: 38693335 DOI: 10.1038/s41571-024-00892-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/04/2024] [Indexed: 05/03/2024]
Abstract
Novel strategies utilizing light in the second near-infrared region (NIR-II; 900-1,880 nm wavelengths) offer the potential to visualize and treat solid tumours with enhanced precision. Over the past few decades, numerous techniques leveraging NIR-II light have been developed with the aim of precisely eliminating tumours while maximally preserving organ function. During cancer surgery, NIR-II optical imaging enables the visualization of clinically occult lesions and surrounding vital structures with increased sensitivity and resolution, thereby enhancing surgical quality and improving patient prognosis. Furthermore, the use of NIR-II light promises to improve cancer phototherapy by enabling the selective delivery of increased therapeutic energy to tissues at greater depths. Initial clinical studies of NIR-II-based imaging and phototherapy have indicated impressive potential to decrease cancer recurrence, reduce complications and prolong survival. Despite the encouraging results achieved, clinical translation of innovative NIR-II techniques remains challenging and inefficient; multidisciplinary cooperation is necessary to bridge the gap between preclinical research and clinical practice, and thus accelerate the translation of technical advances into clinical benefits. In this Review, we summarize the available clinical data on NIR-II-based imaging and phototherapy, demonstrating the feasibility and utility of integrating these technologies into the treatment of cancer. We also introduce emerging NIR-II-based approaches with substantial potential to further enhance patient outcomes, while also highlighting the challenges associated with imminent clinical studies of these modalities.
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Affiliation(s)
- Zeyu Zhang
- Key Laboratory of Big Data-Based Precision Medicine of Ministry of Industry and Information Technology, School of Engineering Medicine, Beihang University, Beijing, China
| | - Yang Du
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, China
| | - Xiaojing Shi
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, China
| | - Kun Wang
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, China
| | - Qiaojun Qu
- Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Qian Liang
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, China
| | - Xiaopeng Ma
- School of Control Science and Engineering, Shandong University, Jinan, China
| | - Kunshan He
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, China
| | - Chongwei Chi
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, China
| | - Jianqiang Tang
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Bo Liu
- Department of General Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Jiafu Ji
- Department of Gastrointestinal Surgery, Peking University Cancer Hospital and Institute, Beijing, China.
| | - Jun Wang
- Thoracic Oncology Institute/Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China.
| | - Jiahong Dong
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China.
| | - Zhenhua Hu
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, China.
| | - Jie Tian
- Key Laboratory of Big Data-Based Precision Medicine of Ministry of Industry and Information Technology, School of Engineering Medicine, Beihang University, Beijing, China.
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, China.
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China.
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Wang W, Yang L, Sun H, Peng X, Yuan J, Zhong W, Chen J, He X, Ye L, Zeng Y, Gao Z, Li Y, Qu X. Cellular nucleus image-based smarter microscope system for single cell analysis. Biosens Bioelectron 2024; 250:116052. [PMID: 38266616 DOI: 10.1016/j.bios.2024.116052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 12/31/2023] [Accepted: 01/18/2024] [Indexed: 01/26/2024]
Abstract
Cell imaging technology is undoubtedly a powerful tool for studying single-cell heterogeneity due to its non-invasive and visual advantages. It covers microscope hardware, software, and image analysis techniques, which are hindered by low throughput owing to abundant hands-on time and expertise. Herein, a cellular nucleus image-based smarter microscope system for single-cell analysis is reported to achieve high-throughput analysis and high-content detection of cells. By combining the hardware of an automatic fluorescence microscope and multi-object recognition/acquisition software, we have achieved more advanced process automation with the assistance of Robotic Process Automation (RPA), which realizes a high-throughput collection of single-cell images. Automated acquisition of single-cell images has benefits beyond ease and throughout and can lead to uniform standard and higher quality images. We further constructed a single-cell image database-based convolutional neural network (Efficient Convolutional Neural Network, E-CNN) exceeding 20618 single-cell nucleus images. Computational analysis of large and complex data sets enhances the content and efficiency of single-cell analysis with the assistance of Artificial Intelligence (AI), which breaks through the super-resolution microscope's hardware limitation, such as specialized light sources with specific wavelengths, advanced optical components, and high-performance graphics cards. Our system can identify single-cell nucleus images that cannot be artificially distinguished with an accuracy of 95.3%. Overall, we build an ordinary microscope into a high-throughput analysis and high-content smarter microscope system, making it a candidate tool for Imaging cytology.
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Affiliation(s)
- Wentao Wang
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, Guangdong Province, 518017, China
| | - Lin Yang
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, Guangdong Province, 518017, China
| | - Hang Sun
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, Guangdong Province, 518017, China
| | - Xiaohong Peng
- YueYang Central Hospital, YueYang, Hunan Province, 414000, China
| | - Junjie Yuan
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, Guangdong Province, 518017, China
| | - Wenhao Zhong
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, Guangdong Province, 518017, China
| | - Jinqi Chen
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, Guangdong Province, 518017, China
| | - Xin He
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, Guangdong Province, 518017, China
| | - Lingzhi Ye
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, Guangdong Province, 518017, China
| | - Yi Zeng
- College of Chemistry and Chemical Engineering, Huanggang Normal University, Huanggang, 438000, China
| | - Zhifan Gao
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, Guangdong Province, 518017, China.
| | - Yunhui Li
- Department of Laboratory Medical Center, General Hospital of Northern Theater Command, No.83, Wenhua Road, Shenhe District, Shenyang, Liaoning Province, 110016, China.
| | - Xiangmeng Qu
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, Guangdong Province, 518017, China.
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Nazarian S, Gkouzionis I, Murphy J, Darzi A, Patel N, Peters CJ, Elson DS. Real-time classification of tumour and non-tumour tissue in colorectal cancer using diffuse reflectance spectroscopy and neural networks to aid margin assessment. Int J Surg 2024; 110:01279778-990000000-01004. [PMID: 38241421 PMCID: PMC11020003 DOI: 10.1097/js9.0000000000001102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 01/08/2024] [Indexed: 01/21/2024]
Abstract
BACKGROUND Colorectal cancer is the third most commonly diagnosed malignancy and the second leading cause of mortality worldwide. A positive resection margin following surgery for colorectal cancer is linked with higher rates of local recurrence and poorer survival. We investigated diffuse reflectance spectroscopy (DRS) to distinguish tumour and non-tumour tissue in ex vivo colorectal specimens, to aid margin assessment and provide augmented visual maps to the surgeon in real-time. METHODS Patients undergoing elective colorectal cancer resection surgery at a London-based hospital were prospectively recruited. A hand-held DRS probe was used on the surface of freshly resected ex vivo colorectal tissue. Spectral data was acquired for tumour and non-tumour tissue. Binary classification was achieved using conventional machine learning classifiers and a convolutional neural network (CNN), which were evaluated in terms of sensitivity, specificity, accuracy and the area under the curve. RESULTS A total of 7692 mean spectra were obtained for tumour and non-tumour colorectal tissue. The CNN-based classifier was the best performing machine learning algorithm, when compared to contrastive approaches, for differentiating tumour and non-tumour colorectal tissue, with an overall diagnostic accuracy of 90.8% and area under the curve of 96.8%. Live on-screen classification of tissue type was achieved using a graduated colourmap. CONCLUSION A high diagnostic accuracy for a DRS probe and tracking system to differentiate ex vivo tumour and non-tumour colorectal tissue in real-time with on-screen visual feedback was highlighted by this study. Further in vivo studies are needed to ensure integration into a surgical workflow.
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Affiliation(s)
| | - Ioannis Gkouzionis
- Department of Surgery and Cancer
- Hamlyn Centre for Robotics Surgery, Imperial College London, London, UK
| | | | - Ara Darzi
- Department of Surgery and Cancer
- Hamlyn Centre for Robotics Surgery, Imperial College London, London, UK
| | | | | | - Daniel S. Elson
- Department of Surgery and Cancer
- Hamlyn Centre for Robotics Surgery, Imperial College London, London, UK
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Xie X, Shen C, Zhang X, Wu G, Yang B, Qi Z, Tang Q, Wang Y, Ding H, Shi Z, Yu J. Rapid intraoperative multi-molecular diagnosis of glioma with ultrasound radio frequency signals and deep learning. EBioMedicine 2023; 98:104899. [PMID: 38041959 PMCID: PMC10711390 DOI: 10.1016/j.ebiom.2023.104899] [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: 07/06/2023] [Revised: 11/16/2023] [Accepted: 11/17/2023] [Indexed: 12/04/2023] Open
Abstract
BACKGROUND Molecular diagnosis is crucial for biomarker-assisted glioma resection and management. However, some limitations of current molecular diagnostic techniques prevent their widespread use intraoperatively. With the unique advantages of ultrasound, this study developed a rapid intraoperative molecular diagnostic method based on ultrasound radio-frequency signals. METHODS We built a brain tumor ultrasound bank with 169 cases enrolled since July 2020, of which 43483 RF signal patches from 67 cases with a pathological diagnosis of glioma were a retrospective cohort for model training and validation. IDH1 and TERT promoter (TERTp) mutations and 1p/19q co-deletion were detected by next-generation sequencing. We designed a spatial-temporal integration model (STIM) to diagnose the three molecular biomarkers, thus establishing a rapid intraoperative molecular diagnostic system for glioma, and further analysed its consistency with the fifth edition of the WHO Classification of Tumors of the Central Nervous System (WHO CNS5). We tested STIM in 16-case prospective cohorts, which contained a total of 10384 RF signal patches. Two other RF-based classical models were used for comparison. Further, we included 20 cases additional prospective data for robustness test (ClinicalTrials.govNCT05656053). FINDINGS In the retrospective cohort, STIM achieved a mean accuracy and AUC of 0.9190 and 0.9650 (95% CI, 0.94-0.99) respectively for the three molecular biomarkers, with a total time of 3 s and a 96% match to WHO CNS5. In the prospective cohort, the diagnostic accuracy of STIM is 0.85 ± 0.04 (mean ± SD) for IDH1, 0.84 ± 0.05 for TERTp, and 0.88 ± 0.04 for 1p/19q. The AUC is 0.89 ± 0.02 (95% CI, 0.84-0.94) for IDH1, 0.80 ± 0.04 (95% CI, 0.71-0.89) for TERTp, and 0.85 ± 0.06 (95% CI, 0.73-0.98) for 1p/19q. Compared to the second best available method based on RF signal, the diagnostic accuracy of STIM is improved by 16.70% and the AUC is improved by 19.23% on average. INTERPRETATION STIM is a rapid, cost-effective, and easy-to-manipulate AI method to perform real-time intraoperative molecular diagnosis. In the future, it may help neurosurgeons designate personalized surgical plans and predict survival outcomes. FUNDING A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.
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Affiliation(s)
- Xuan Xie
- School of Information Science and Technology, Fudan University, Shanghai, China; Neurosurgical Institute of Fudan University, Shanghai, China
| | - Chao Shen
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China; National Center for Neurological Disorders, Shanghai, China; Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China; Neurosurgical Institute of Fudan University, Shanghai, China
| | - Xiandi Zhang
- Department of Ultrasound, Huashan Hospital, Fudan University, Shanghai, China
| | - Guoqing Wu
- School of Information Science and Technology, Fudan University, Shanghai, China; Neurosurgical Institute of Fudan University, Shanghai, China
| | - Bojie Yang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China; National Center for Neurological Disorders, Shanghai, China; Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China; Neurosurgical Institute of Fudan University, Shanghai, China
| | - Zengxin Qi
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China; National Center for Neurological Disorders, Shanghai, China; Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China; Neurosurgical Institute of Fudan University, Shanghai, China
| | - Qisheng Tang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China; National Center for Neurological Disorders, Shanghai, China; Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China; Neurosurgical Institute of Fudan University, Shanghai, China
| | - Yuanyuan Wang
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Hong Ding
- Department of Ultrasound, Huashan Hospital, Fudan University, Shanghai, China.
| | - Zhifeng Shi
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China; National Center for Neurological Disorders, Shanghai, China; Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China; Neurosurgical Institute of Fudan University, Shanghai, China.
| | - Jinhua Yu
- School of Information Science and Technology, Fudan University, Shanghai, China; Neurosurgical Institute of Fudan University, Shanghai, China.
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Sun W, Song C, Tang C, Pan C, Xue P, Fan J, Qiao Y. Performance of deep learning algorithms to distinguish high-grade glioma from low-grade glioma: A systematic review and meta-analysis. iScience 2023; 26:106815. [PMID: 37250800 PMCID: PMC10209541 DOI: 10.1016/j.isci.2023.106815] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 03/23/2023] [Accepted: 05/02/2023] [Indexed: 05/31/2023] Open
Abstract
This study aims to evaluate deep learning (DL) performance in differentiating low- and high-grade glioma. Search online database for studies continuously published from 1st January 2015 until 16th August 2022. The random-effects model was used for synthesis, based on pooled sensitivity (SE), specificity (SP), and area under the curve (AUC). Heterogeneity was estimated using the Higgins inconsistency index (I2). 33 were ultimately included in the meta-analysis. The overall pooled SE and SP were 94% and 93%, with an AUC of 0.98. There was great heterogeneity in this field. Our evidence-based study shows DL achieves high accuracy in glioma grading. Subgroup analysis reveals several limitations in this field: 1) Diagnostic trials require standard method for data merging for AI; 2) small sample size; 3) poor-quality image preprocessing; 4) not standard algorithm development; 5) not standard data report; 6) different definition of HGG and LGG; and 7) poor extrapolation.
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Affiliation(s)
- Wanyi Sun
- Department of Cancer Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Cheng Song
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chao Tang
- Shenzhen Maternity & Child Healthcare Hospital, Shenzhen, China
| | - Chenghao Pan
- Department of Cancer Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Peng Xue
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jinhu Fan
- Department of Cancer Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Youlin Qiao
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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