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Chow DJX, Tan TCY, Upadhya A, Lim M, Dholakia K, Dunning KR. Viewing early life without labels: optical approaches for imaging the early embryo†. Biol Reprod 2024; 110:1157-1174. [PMID: 38647415 PMCID: PMC11180623 DOI: 10.1093/biolre/ioae062] [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: 01/28/2024] [Revised: 03/26/2024] [Accepted: 04/18/2024] [Indexed: 04/25/2024] Open
Abstract
Embryo quality is an important determinant of successful implantation and a resultant live birth. Current clinical approaches for evaluating embryo quality rely on subjective morphology assessments or an invasive biopsy for genetic testing. However, both approaches can be inherently inaccurate and crucially, fail to improve the live birth rate following the transfer of in vitro produced embryos. Optical imaging offers a potential non-invasive and accurate avenue for assessing embryo viability. Recent advances in various label-free optical imaging approaches have garnered increased interest in the field of reproductive biology due to their ability to rapidly capture images at high resolution, delivering both morphological and molecular information. This burgeoning field holds immense potential for further development, with profound implications for clinical translation. Here, our review aims to: (1) describe the principles of various imaging systems, distinguishing between approaches that capture morphological and molecular information, (2) highlight the recent application of these technologies in the field of reproductive biology, and (3) assess their respective merits and limitations concerning the capacity to evaluate embryo quality. Additionally, the review summarizes challenges in the translation of optical imaging systems into routine clinical practice, providing recommendations for their future development. Finally, we identify suitable imaging approaches for interrogating the mechanisms underpinning successful embryo development.
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Affiliation(s)
- Darren J X Chow
- Robinson Research Institute, School of Biomedicine, The University of Adelaide, Adelaide, Australia
- Institute for Photonics and Advanced Sensing, The University of Adelaide, Adelaide, Australia
- Centre of Light for Life, The University of Adelaide, Adelaide, Australia
| | - Tiffany C Y Tan
- Robinson Research Institute, School of Biomedicine, The University of Adelaide, Adelaide, Australia
- Institute for Photonics and Advanced Sensing, The University of Adelaide, Adelaide, Australia
| | - Avinash Upadhya
- Institute for Photonics and Advanced Sensing, The University of Adelaide, Adelaide, Australia
- Centre of Light for Life, The University of Adelaide, Adelaide, Australia
- School of Biological Sciences, The University of Adelaide, Adelaide, Australia
| | - Megan Lim
- Robinson Research Institute, School of Biomedicine, The University of Adelaide, Adelaide, Australia
- Institute for Photonics and Advanced Sensing, The University of Adelaide, Adelaide, Australia
- Centre of Light for Life, The University of Adelaide, Adelaide, Australia
- School of Biological Sciences, The University of Adelaide, Adelaide, Australia
| | - Kishan Dholakia
- Centre of Light for Life, The University of Adelaide, Adelaide, Australia
- School of Biological Sciences, The University of Adelaide, Adelaide, Australia
- Scottish Universities Physics Alliance, School of Physics and Astronomy, University of St Andrews, St Andrews, United Kingdom
| | - Kylie R Dunning
- Robinson Research Institute, School of Biomedicine, The University of Adelaide, Adelaide, Australia
- Institute for Photonics and Advanced Sensing, The University of Adelaide, Adelaide, Australia
- Centre of Light for Life, The University of Adelaide, Adelaide, Australia
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2
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Boland PA, Hardy NP, Moynihan A, McEntee PD, Loo C, Fenlon H, Cahill RA. Intraoperative near infrared functional imaging of rectal cancer using artificial intelligence methods - now and near future state of the art. Eur J Nucl Med Mol Imaging 2024:10.1007/s00259-024-06731-9. [PMID: 38858280 DOI: 10.1007/s00259-024-06731-9] [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: 12/23/2023] [Accepted: 04/15/2024] [Indexed: 06/12/2024]
Abstract
Colorectal cancer remains a major cause of cancer death and morbidity worldwide. Surgery is a major treatment modality for primary and, increasingly, secondary curative therapy. However, with more patients being diagnosed with early stage and premalignant disease manifesting as large polyps, greater accuracy in diagnostic and therapeutic precision is needed right from the time of first endoscopic encounter. Rapid advancements in the field of artificial intelligence (AI), coupled with widespread availability of near infrared imaging (currently based around indocyanine green (ICG)) can enable colonoscopic tissue classification and prognostic stratification for significant polyps, in a similar manner to contemporary dynamic radiological perfusion imaging but with the advantage of being able to do so directly within interventional procedural time frames. It can provide an explainable method for immediate digital biopsies that could guide or even replace traditional forceps biopsies and provide guidance re margins (both areas where current practice is only approximately 80% accurate prior to definitive excision). Here, we discuss the concept and practice of AI enhanced ICG perfusion analysis for rectal cancer surgery while highlighting recent and essential near-future advancements. These include breakthrough developments in computer vision and time series analysis that allow for real-time quantification and classification of fluorescent perfusion signals of rectal cancer tissue intraoperatively that accurately distinguish between normal, benign, and malignant tissues in situ endoscopically, which are now undergoing international prospective validation (the Horizon Europe CLASSICA study). Next stage advancements may include detailed digital characterisation of small rectal malignancy based on intraoperative assessment of specific intratumoral fluorescent signal pattern. This could include T staging and intratumoral molecular process profiling (e.g. regarding angiogenesis, differentiation, inflammatory component, and tumour to stroma ratio) with the potential to accurately predict the microscopic local response to nonsurgical treatment enabling personalised therapy via decision support tools. Such advancements are also applicable to the next generation fluorophores and imaging agents currently emerging from clinical trials. In addition, by providing an understandable, applicable method for detailed tissue characterisation visually, such technology paves the way for acceptance of other AI methodology during surgery including, potentially, deep learning methods based on whole screen/video detailing.
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Affiliation(s)
- Patrick A Boland
- UCD Centre for Precision Surgery, School of Medicine, University College Dublin, 47 Eccles Street, Dublin 7, Dublin, Ireland
- Department of Colorectal Surgery, Mater Misericordiae University Hospital, Dublin, Ireland
| | - N P Hardy
- UCD Centre for Precision Surgery, School of Medicine, University College Dublin, 47 Eccles Street, Dublin 7, Dublin, Ireland
- Department of Colorectal Surgery, Mater Misericordiae University Hospital, Dublin, Ireland
| | - A Moynihan
- UCD Centre for Precision Surgery, School of Medicine, University College Dublin, 47 Eccles Street, Dublin 7, Dublin, Ireland
- Department of Colorectal Surgery, Mater Misericordiae University Hospital, Dublin, Ireland
| | - P D McEntee
- UCD Centre for Precision Surgery, School of Medicine, University College Dublin, 47 Eccles Street, Dublin 7, Dublin, Ireland
- Department of Colorectal Surgery, Mater Misericordiae University Hospital, Dublin, Ireland
| | - C Loo
- UCD Centre for Precision Surgery, School of Medicine, University College Dublin, 47 Eccles Street, Dublin 7, Dublin, Ireland
| | - H Fenlon
- Department of Radiology, Mater Misericordiae University Hospital, Dublin, Ireland
| | - R A Cahill
- UCD Centre for Precision Surgery, School of Medicine, University College Dublin, 47 Eccles Street, Dublin 7, Dublin, Ireland.
- Department of Colorectal Surgery, Mater Misericordiae University Hospital, Dublin, Ireland.
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3
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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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4
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Ember K, Dallaire F, Plante A, Sheehy G, Guiot MC, Agarwal R, Yadav R, Douet A, Selb J, Tremblay JP, Dupuis A, Marple E, Urmey K, Rizea C, Harb A, McCarthy L, Schupper A, Umphlett M, Tsankova N, Leblond F, Hadjipanayis C, Petrecca K. In situ brain tumor detection using a Raman spectroscopy system-results of a multicenter study. Sci Rep 2024; 14:13309. [PMID: 38858389 PMCID: PMC11164901 DOI: 10.1038/s41598-024-62543-9] [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: 04/19/2024] [Accepted: 05/17/2024] [Indexed: 06/12/2024] Open
Abstract
Safe and effective brain tumor surgery aims to remove tumor tissue, not non-tumoral brain. This is a challenge since tumor cells are often not visually distinguishable from peritumoral brain during surgery. To address this, we conducted a multicenter study testing whether the Sentry System could distinguish the three most common types of brain tumors from brain tissue in a label-free manner. The Sentry System is a new real time, in situ brain tumor detection device that merges Raman spectroscopy with machine learning tissue classifiers. Nine hundred and seventy-six in situ spectroscopy measurements and colocalized tissue specimens were acquired from 67 patients undergoing surgery for glioblastoma, brain metastases, or meningioma to assess tumor classification. The device achieved diagnostic accuracies of 91% for glioblastoma, 97% for brain metastases, and 96% for meningiomas. These data show that the Sentry System discriminated tumor containing tissue from non-tumoral brain in real time and prior to resection.
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Affiliation(s)
- Katherine Ember
- Polytechnique Montréal, Montreal, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, Canada
| | - Frédérick Dallaire
- Polytechnique Montréal, Montreal, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, Canada
| | - Arthur Plante
- Polytechnique Montréal, Montreal, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, Canada
| | - Guillaume Sheehy
- Polytechnique Montréal, Montreal, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, Canada
| | - Marie-Christine Guiot
- Division of Neuropathology, Department of Pathology, Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Frédéric Leblond
- Polytechnique Montréal, Montreal, Canada.
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, Canada.
- Institut du Cancer de Montréal, Montreal, Canada.
| | - Constantinos Hadjipanayis
- Mount Sinai Hospital, New York, NY, USA.
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
| | - Kevin Petrecca
- Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada.
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5
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Farooqi HA, Nabi R, Zahid T, Hayder Z. Breaking new ground: can artificial intelligence and machine learning transform papillary glioneuronal tumor diagnosis? Neurosurg Rev 2024; 47:261. [PMID: 38844709 DOI: 10.1007/s10143-024-02504-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2024] [Revised: 05/27/2024] [Accepted: 06/04/2024] [Indexed: 06/11/2024]
Abstract
Papillary glioneuronal tumors (PGNTs), classified as Grade I by the WHO in 2016, present diagnostic challenges due to their rarity and potential for malignancy. Xiaodan Du et al.'s recent study of 36 confirmed PGNT cases provides critical insights into their imaging characteristics, revealing frequent presentation with headaches, seizures, and mass effect symptoms, predominantly located in the supratentorial region near the lateral ventricles. Lesions often appeared as mixed cystic and solid masses with septations or as cystic masses with mural nodules. Given these complexities, artificial intelligence (AI) and machine learning (ML) offer promising advancements for PGNT diagnosis. Previous studies have demonstrated AI's efficacy in diagnosing various brain tumors, utilizing deep learning and advanced imaging techniques for rapid and accurate identification. Implementing AI in PGNT diagnosis involves assembling comprehensive datasets, preprocessing data, extracting relevant features, and iteratively training models for optimal performance. Despite AI's potential, medical professionals must validate AI predictions, ensuring they complement rather than replace clinical expertise. This integration of AI and ML into PGNT diagnostics could significantly enhance preoperative accuracy, ultimately improving patient outcomes through more precise and timely interventions.
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Affiliation(s)
| | - Rayyan Nabi
- Islamic International Medical College, Rawalpindi, Pakistan.
- , 332, street 12, phase 4, Bahria Town, Islamabad, 46220, Pakistan.
| | - Tabeer Zahid
- Foundation University Medical College, Rawalpindi, Pakistan
| | - Zeeshan Hayder
- Islamic International Medical College, Rawalpindi, Pakistan
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6
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Sun J, Cheng W, Guo S, Cai R, Liu G, Wu A, Yin J. A ratiometric SERS strategy for the prediction of cancer cell proportion and guidance of glioma surgical resection. Biosens Bioelectron 2024; 261:116475. [PMID: 38852324 DOI: 10.1016/j.bios.2024.116475] [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: 03/29/2024] [Revised: 06/01/2024] [Accepted: 06/05/2024] [Indexed: 06/11/2024]
Abstract
Rapid and accurate identification of tumor boundaries is critical for the cure of glioma, but it is difficult due to the invasive nature of glioma cells. This paper aimed to explore a rapid diagnostic strategy based on a label-free surface-enhanced Raman scattering (SERS) technique for the quantitative detection of glioma cell proportion intraoperatively. With silver nanoparticles as substrate, an in-depth SERS analysis was performed on simulated clinical samples containing normal brain tissue and different concentrations of patient-derived glioma cells. The results revealed two universal characteristic peaks of 655 and 717 cm-1, which strongly correlated with glioma cell proportion regardless of individual differences. Based on the intensity ratio of the two peaks, a ratiometric SERS strategy for the quantification of glioma cells was established by employing an artificial neuron network model and a polynomial regression model. Such a strategy accurately estimated the proportion of glioma cells in simulated clinical samples (R2 = 0.98) and frozen samples (R2 = 0.85). More importantly, it accurately facilitated the delineation of tumor margins in freshly obtained samples. Taken together, this SERS-based method ensured a rapid and more detailed identification of tumor margins during surgical resection, which could be beneficial for intraoperative decision-making and pathological evaluation.
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Affiliation(s)
- Jiaojiao Sun
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, 215163, PR China
| | - Wen Cheng
- Department of Neurosurgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning, 110004, PR China
| | - Songyi Guo
- Department of Neurosurgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning, 110004, PR China
| | - Ruikai Cai
- Department of Neurosurgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning, 110004, PR China
| | - Guangxing Liu
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, 215163, PR China.
| | - Anhua Wu
- Department of Neurosurgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning, 110004, PR China.
| | - Jian Yin
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, 215163, PR China.
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7
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Liu X, Li F, Xu J, Ma J, Duan X, Mao R, Chen M, Chen Z, Huang Y, Jiang J, Huang B, Ye Z. Deep learning model to differentiate Crohn's disease from intestinal tuberculosis using histopathological whole slide images from intestinal specimens. Virchows Arch 2024; 484:965-976. [PMID: 38332051 DOI: 10.1007/s00428-024-03740-9] [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: 11/13/2023] [Revised: 12/29/2023] [Accepted: 01/13/2024] [Indexed: 02/10/2024]
Abstract
Crohn's disease (CD) and intestinal tuberculosis (ITB) share similar histopathological characteristics, and differential diagnosis can be a dilemma for pathologists. This study aimed to apply deep learning (DL) to analyze whole slide images (WSI) of surgical resection specimens to distinguish CD from ITB. Overall, 1973 WSI from 85 cases from 3 centers were obtained. The DL model was established in internal training and validated in external test cohort, evaluated by area under receiver operator characteristic curve (AUC). Diagnostic results of pathologists were compared with those of the DL model using DeLong's test. DL model had case level AUC of 0.886, 0.893 and slide level AUC of 0.954, 0.827 in training and test cohorts. Attention maps highlighted discriminative areas and top 10 features were extracted from CD and ITB. DL model's diagnostic efficiency (AUC = 0.886) was better than junior pathologists (*1 AUC = 0.701, P = 0.088; *2 AUC = 0.861, P = 0.788) and inferior to senior GI pathologists (*3 AUC = 0.910, P = 0.800; *4 AUC = 0.946, P = 0.507) in training cohort. In the test cohort, model (AUC = 0.893) outperformed senior non-GI pathologists (*5 AUC = 0.782, P = 0.327; *6 AUC = 0.821, P = 0.516). We developed a DL model for the classification of CD and ITB, improving pathological diagnosis accuracy effectively.
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Affiliation(s)
- Xinning Liu
- Department of Pathology, The First Affiliated Hospital, Sun Yat-sen University, No. 58 Zhongshan Road 2, Guangzhou, 510080, Guangdong, People's Republic of China
- Department of Pathology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
| | - Fei Li
- Medical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, 1066 Xueyuan Avenue, Shenzhen, 518000, Guangdong, People's Republic of China
| | - Jie Xu
- Department of Pathology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, People's Republic of China
| | - Jinting Ma
- Medical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, 1066 Xueyuan Avenue, Shenzhen, 518000, Guangdong, People's Republic of China
| | - Xiaoyu Duan
- Department of Pathology, The First Affiliated Hospital, Sun Yat-sen University, No. 58 Zhongshan Road 2, Guangzhou, 510080, Guangdong, People's Republic of China
| | - Ren Mao
- Department of Gastroenterology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
| | - Minhu Chen
- Department of Gastroenterology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
| | - Zhihui Chen
- Department of Gastrointestinal and Pancreatic Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
| | - Yan Huang
- Department of Pathology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
| | - Jingyi Jiang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
| | - Bingsheng Huang
- Medical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, 1066 Xueyuan Avenue, Shenzhen, 518000, Guangdong, People's Republic of China.
| | - Ziyin Ye
- Department of Pathology, The First Affiliated Hospital, Sun Yat-sen University, No. 58 Zhongshan Road 2, Guangzhou, 510080, Guangdong, People's Republic of China.
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8
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Chen Z, Wong IHM, Dai W, Lo CTK, Wong TTW. Lung Cancer Diagnosis on Virtual Histologically Stained Tissue Using Weakly Supervised Learning. Mod Pathol 2024; 37:100487. [PMID: 38588884 DOI: 10.1016/j.modpat.2024.100487] [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: 12/16/2023] [Revised: 03/05/2024] [Accepted: 03/30/2024] [Indexed: 04/10/2024]
Abstract
Lung adenocarcinoma (LUAD) is the most common primary lung cancer and accounts for 40% of all lung cancer cases. The current gold standard for lung cancer analysis is based on the pathologists' interpretation of hematoxylin and eosin (H&E)-stained tissue slices viewed under a brightfield microscope or a digital slide scanner. Computational pathology using deep learning has been proposed to detect lung cancer on histology images. However, the histological staining workflow to acquire the H&E-stained images and the subsequent cancer diagnosis procedures are labor-intensive and time-consuming with tedious sample preparation steps and repetitive manual interpretation, respectively. In this work, we propose a weakly supervised learning method for LUAD classification on label-free tissue slices with virtual histological staining. The autofluorescence images of label-free tissue with histopathological information can be converted into virtual H&E-stained images by a weakly supervised deep generative model. For the downstream LUAD classification task, we trained the attention-based multiple-instance learning model with different settings on the open-source LUAD H&E-stained whole-slide images (WSIs) dataset from the Cancer Genome Atlas (TCGA). The model was validated on the 150 H&E-stained WSIs collected from patients in Queen Mary Hospital and Prince of Wales Hospital with an average area under the curve (AUC) of 0.961. The model also achieved an average AUC of 0.973 on 58 virtual H&E-stained WSIs, comparable to the results on 58 standard H&E-stained WSIs with an average AUC of 0.977. The attention heatmaps of virtual H&E-stained WSIs and ground-truth H&E-stained WSIs can indicate tumor regions of LUAD tissue slices. In conclusion, the proposed diagnostic workflow on virtual H&E-stained WSIs of label-free tissue is a rapid, cost effective, and interpretable approach to assist clinicians in postoperative pathological examinations. The method could serve as a blueprint for other label-free imaging modalities and disease contexts.
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Affiliation(s)
- Zhenghui Chen
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Ivy H M Wong
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Weixing Dai
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Claudia T K Lo
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Terence T W Wong
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China.
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9
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Raghunathan R, Vasquez M, Zhang K, Zhao H, Wong STC. Label-free optical imaging for brain cancer assessment. Trends Cancer 2024; 10:557-570. [PMID: 38575412 PMCID: PMC11168891 DOI: 10.1016/j.trecan.2024.03.005] [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: 01/01/2024] [Revised: 03/12/2024] [Accepted: 03/13/2024] [Indexed: 04/06/2024]
Abstract
Advances in label-free optical imaging offer a promising avenue for brain cancer assessment, providing high-resolution, real-time insights without the need for radiation or exogeneous agents. These cost-effective and intricately detailed techniques overcome the limitations inherent in magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) scans by offering superior resolution and more readily accessible imaging options. This comprehensive review explores a variety of such methods, including photoacoustic imaging (PAI), optical coherence tomography (OCT), Raman imaging, and IR microscopy. It focuses on their roles in the detection, diagnosis, and management of brain tumors. By highlighting recent advances in these imaging techniques, the review aims to underscore the importance of label-free optical imaging in enhancing early detection and refining therapeutic strategies for brain cancer.
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Affiliation(s)
- Raksha Raghunathan
- Department of Systems Medicine and Bioengineering and T.T. and W.F. Chao Center for BRAIN, Houston Methodist Neal Cancer Center, Houston Methodist Hospital, Houston, TX 77030, USA; Advanced Cellular and Tissue Microscopy Core, Houston Methodist Neal Cancer Center and Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Matthew Vasquez
- Department of Systems Medicine and Bioengineering and T.T. and W.F. Chao Center for BRAIN, Houston Methodist Neal Cancer Center, Houston Methodist Hospital, Houston, TX 77030, USA; Advanced Cellular and Tissue Microscopy Core, Houston Methodist Neal Cancer Center and Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Katherine Zhang
- Department of Systems Medicine and Bioengineering and T.T. and W.F. Chao Center for BRAIN, Houston Methodist Neal Cancer Center, Houston Methodist Hospital, Houston, TX 77030, USA; Advanced Cellular and Tissue Microscopy Core, Houston Methodist Neal Cancer Center and Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Hong Zhao
- Department of Systems Medicine and Bioengineering and T.T. and W.F. Chao Center for BRAIN, Houston Methodist Neal Cancer Center, Houston Methodist Hospital, Houston, TX 77030, USA; Advanced Cellular and Tissue Microscopy Core, Houston Methodist Neal Cancer Center and Houston Methodist Research Institute, Houston, TX 77030, USA; Department of Medicine, Weill Cornell Medicine, New York, NY 10065, USA.
| | - Stephen T C Wong
- Department of Systems Medicine and Bioengineering and T.T. and W.F. Chao Center for BRAIN, Houston Methodist Neal Cancer Center, Houston Methodist Hospital, Houston, TX 77030, USA; Advanced Cellular and Tissue Microscopy Core, Houston Methodist Neal Cancer Center and Houston Methodist Research Institute, Houston, TX 77030, USA; Departments of Radiology, Pathology, and Laboratory Medicine and Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10065, USA
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10
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Becker N, Camelo-Piragua S, Conway KS. A Contemporary Approach to Intraoperative Evaluation in Neuropathology. Arch Pathol Lab Med 2024; 148:649-658. [PMID: 37694565 DOI: 10.5858/arpa.2023-0097-ra] [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: 06/27/2023] [Indexed: 09/12/2023]
Abstract
CONTEXT.— Although the basic principles of intraoperative diagnosis in surgical neuropathology have not changed in the last century, the last several decades have seen dramatic changes in tumor classification, terminology, molecular classification, and modalities used for intraoperative diagnosis. As many neuropathologic intraoperative diagnoses are performed by general surgical pathologists, awareness of these recent changes is important for the most accurate intraoperative diagnosis. OBJECTIVE.— To describe recent changes in the practice of intraoperative surgical neuropathology, with an emphasis on new entities, tumor classification, and anticipated ancillary tests, including molecular testing. DATA SOURCES.— The sources for this review include the fifth edition of the World Health Organization Classification of Tumours of the Central Nervous System, primary literature on intraoperative diagnosis and newly described tumor entities, and the authors' clinical experience. CONCLUSIONS.— A significant majority of neuropathologic diagnoses require ancillary testing, including molecular analysis, for appropriate classification. Therefore, the primary goal for any neurosurgical intraoperative diagnosis is the identification of diagnostic tissue and the preservation of the appropriate tissue for molecular testing. The intraoperative pathologist should seek to place a tumor in the most accurate diagnostic category possible, but specific diagnosis at the time of an intraoperative diagnosis is often not possible. Many entities have seen adjustments to grading criteria, including the incorporation of molecular features into grading. Awareness of these changes can help to avoid overgrading or undergrading at the time of intraoperative evaluation.
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Affiliation(s)
- Nicole Becker
- From the Department of Pathology, University of Iowa, Iowa City (Becker)
| | - Sandra Camelo-Piragua
- the Department of Pathology, University of Michigan, Ann Arbor (Camelo-Piragua, Conway)
| | - Kyle S Conway
- the Department of Pathology, University of Michigan, Ann Arbor (Camelo-Piragua, Conway)
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11
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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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12
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Zhou Z, Islam MT, Xing L. Multibranch CNN With MLP-Mixer-Based Feature Exploration for High-Performance Disease Diagnosis. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:7351-7362. [PMID: 37028335 DOI: 10.1109/tnnls.2023.3250490] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Deep learning-based diagnosis is becoming an indispensable part of modern healthcare. For high-performance diagnosis, the optimal design of deep neural networks (DNNs) is a prerequisite. Despite its success in image analysis, existing supervised DNNs based on convolutional layers often suffer from their rudimentary feature exploration ability caused by the limited receptive field and biased feature extraction of conventional convolutional neural networks (CNNs), which compromises the network performance. Here, we propose a novel feature exploration network named manifold embedded multilayer perceptron (MLP) mixer (ME-Mixer), which utilizes both supervised and unsupervised features for disease diagnosis. In the proposed approach, a manifold embedding network is employed to extract class-discriminative features; then, two MLP-Mixer-based feature projectors are adopted to encode the extracted features with the global reception field. Our ME-Mixer network is quite general and can be added as a plugin to any existing CNN. Comprehensive evaluations on two medical datasets are performed. The results demonstrate that their approach greatly enhances the classification accuracy in comparison with different configurations of DNNs with acceptable computational complexity.
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13
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Murphy ZR, Bianchini EC, Smith A, Körner LI, Russell T, Reinecke D, Wang Y, Snuderl M, Orringer DA, Evrony GD. Ultra-Rapid Droplet Digital PCR Enables Intraoperative Tumor Quantification. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.29.24308126. [PMID: 38854127 PMCID: PMC11160868 DOI: 10.1101/2024.05.29.24308126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
The diagnosis and treatment of tumors often depends on molecular-genetic data. However, rapid and iterative access to molecular data is not currently feasible during surgery, complicating intraoperative diagnosis and precluding measurement of tumor cell burdens at surgical margins to guide resections. To address this gap, we developed Ultra-Rapid droplet digital PCR (UR-ddPCR), which can be completed in 15 minutes from tissue to result with an accuracy comparable to standard ddPCR. We demonstrate UR-ddPCR assays for the IDH1 R132H and BRAF V600E clonal mutations that are present in many low-grade gliomas and melanomas, respectively. We illustrate the clinical feasibility of UR-ddPCR by performing it intraoperatively for 13 glioma cases. We further combine UR-ddPCR measurements with UR-stimulated Raman histology intraoperatively to estimate tumor cell densities in addition to tumor cell percentages. We anticipate that UR-ddPCR, along with future refinements in assay instrumentation, will enable novel point-of-care diagnostics and the development of molecularly-guided surgeries that improve clinical outcomes.
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Affiliation(s)
- Zachary R. Murphy
- Center for Human Genetics and Genomics, New York University Grossman School of Medicine, USA
- Department of Pediatrics, Department of Neuroscience & Physiology, Institute for Systems Genetics, Laura and Isaac Perlmutter Cancer Center, and Neuroscience Institute, New York University Grossman School of Medicine, USA
| | - Emilia C. Bianchini
- Center for Human Genetics and Genomics, New York University Grossman School of Medicine, USA
- Department of Pediatrics, Department of Neuroscience & Physiology, Institute for Systems Genetics, Laura and Isaac Perlmutter Cancer Center, and Neuroscience Institute, New York University Grossman School of Medicine, USA
| | - Andrew Smith
- Department of Neurosurgery, New York University Grossman School of Medicine, USA
| | - Lisa I. Körner
- Department of Neurosurgery, New York University Grossman School of Medicine, USA
| | - Teresa Russell
- Department of Neurosurgery, New York University Grossman School of Medicine, USA
| | - David Reinecke
- Department of Neurosurgery, New York University Grossman School of Medicine, USA
| | - Yuxiu Wang
- Department of Pathology, New York University Grossman School of Medicine, USA
- Brain and Spine Tumor Center, Laura and Isaac Perlmutter Cancer Center, New York University Langone Health
| | - Matija Snuderl
- Department of Pathology, New York University Grossman School of Medicine, USA
- Brain and Spine Tumor Center, Laura and Isaac Perlmutter Cancer Center, New York University Langone Health
| | - Daniel A. Orringer
- Department of Neurosurgery, New York University Grossman School of Medicine, USA
- Brain and Spine Tumor Center, Laura and Isaac Perlmutter Cancer Center, New York University Langone Health
| | - Gilad D. Evrony
- Center for Human Genetics and Genomics, New York University Grossman School of Medicine, USA
- Department of Pediatrics, Department of Neuroscience & Physiology, Institute for Systems Genetics, Laura and Isaac Perlmutter Cancer Center, and Neuroscience Institute, New York University Grossman School of Medicine, USA
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14
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Iqbal MS, Belal Bin Heyat M, Parveen S, Ammar Bin Hayat M, Roshanzamir M, Alizadehsani R, Akhtar F, Sayeed E, Hussain S, Hussein HS, Sawan M. Progress and trends in neurological disorders research based on deep learning. Comput Med Imaging Graph 2024; 116:102400. [PMID: 38851079 DOI: 10.1016/j.compmedimag.2024.102400] [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: 01/02/2024] [Revised: 05/07/2024] [Accepted: 05/13/2024] [Indexed: 06/10/2024]
Abstract
In recent years, deep learning (DL) has emerged as a powerful tool in clinical imaging, offering unprecedented opportunities for the diagnosis and treatment of neurological disorders (NDs). This comprehensive review explores the multifaceted role of DL techniques in leveraging vast datasets to advance our understanding of NDs and improve clinical outcomes. Beginning with a systematic literature review, we delve into the utilization of DL, particularly focusing on multimodal neuroimaging data analysis-a domain that has witnessed rapid progress and garnered significant scientific interest. Our study categorizes and critically analyses numerous DL models, including Convolutional Neural Networks (CNNs), LSTM-CNN, GAN, and VGG, to understand their performance across different types of Neurology Diseases. Through particular analysis, we identify key benchmarks and datasets utilized in training and testing DL models, shedding light on the challenges and opportunities in clinical neuroimaging research. Moreover, we discuss the effectiveness of DL in real-world clinical scenarios, emphasizing its potential to revolutionize ND diagnosis and therapy. By synthesizing existing literature and describing future directions, this review not only provides insights into the current state of DL applications in ND analysis but also covers the way for the development of more efficient and accessible DL techniques. Finally, our findings underscore the transformative impact of DL in reshaping the landscape of clinical neuroimaging, offering hope for enhanced patient care and groundbreaking discoveries in the field of neurology. This review paper is beneficial for neuropathologists and new researchers in this field.
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Affiliation(s)
- Muhammad Shahid Iqbal
- Department of Computer Science and Information Technology, Women University of Azad Jammu & Kashmir, Bagh, Pakistan.
| | - Md Belal Bin Heyat
- CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, Zhejiang, China.
| | - Saba Parveen
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, China.
| | | | - Mohamad Roshanzamir
- Department of Computer Engineering, Faculty of Engineering, Fasa University, Fasa, Iran.
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation, Deakin University, VIC 3216, Australia.
| | - Faijan Akhtar
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China.
| | - Eram Sayeed
- Kisan Inter College, Dhaurahara, Kushinagar, India.
| | - Sadiq Hussain
- Department of Examination, Dibrugarh University, Assam 786004, India.
| | - Hany S Hussein
- Electrical Engineering Department, Faculty of Engineering, King Khalid University, Abha 61411, Saudi Arabia; Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan 81528, Egypt.
| | - Mohamad Sawan
- CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, Zhejiang, China.
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15
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Rich K, Tosefsky K, Martin KC, Bashashati A, Yip S. Practical Application of Deep Learning in Diagnostic Neuropathology-Reimagining a Histological Asset in the Era of Precision Medicine. Cancers (Basel) 2024; 16:1976. [PMID: 38893099 PMCID: PMC11171052 DOI: 10.3390/cancers16111976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2024] [Revised: 05/10/2024] [Accepted: 05/20/2024] [Indexed: 06/21/2024] Open
Abstract
In the past few decades, neuropathology has experienced several paradigm shifts with the introduction of new technologies. Deep learning, a rapidly progressing subfield of machine learning, seems to be the next innovation to alter the diagnostic workflow. In this review, we will explore the recent changes in the field of neuropathology and how this has led to an increased focus on molecular features in diagnosis and prognosis. Then, we will examine the work carried out to train deep learning models for various diagnostic tasks in neuropathology, as well as the machine learning frameworks they used. Focus will be given to both the challenges and successes highlighted therein, as well as what these trends may tell us about future roadblocks in the widespread adoption of this new technology. Finally, we will touch on recent trends in deep learning, as applied to digital pathology more generally, and what this may tell us about the future of deep learning applications in neuropathology.
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Affiliation(s)
- Katherine Rich
- Bioinformatics Graduate Program, University of British Columbia, Vancouver, BC V6T 1Z4, Canada;
| | - Kira Tosefsky
- Faculty of Medicine, University of British Columbia, Vancouver, BC V6T 1Z4, Canada; (K.T.); (K.C.M.)
| | - Karina C. Martin
- Faculty of Medicine, University of British Columbia, Vancouver, BC V6T 1Z4, Canada; (K.T.); (K.C.M.)
- Department of Pathology & Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Ali Bashashati
- Department of Pathology & Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Stephen Yip
- Faculty of Medicine, University of British Columbia, Vancouver, BC V6T 1Z4, Canada; (K.T.); (K.C.M.)
- Department of Pathology & Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
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16
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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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17
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Huang X, Xue Z, Zhang D, Lee HJ. Pinpointing Fat Molecules: Advances in Coherent Raman Scattering Microscopy for Lipid Metabolism. Anal Chem 2024; 96:7945-7958. [PMID: 38700460 DOI: 10.1021/acs.analchem.4c01398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Affiliation(s)
- Xiangjie Huang
- College of Biomedical Engineering & Instrument Science, and Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027, China
| | - Zexin Xue
- College of Biomedical Engineering & Instrument Science, and Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027, China
| | - Delong Zhang
- MOE Frontier Science Center for Brain Science & Brain-Machine Integration, Zhejiang University, Hangzhou 310027, China
- Zhejiang Key Laboratory of Micro-nano Quantum Chips and Quantum Control, and School of Physics, Zhejiang University, Hangzhou 310027, China
| | - Hyeon Jeong Lee
- College of Biomedical Engineering & Instrument Science, and Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027, China
- MOE Frontier Science Center for Brain Science & Brain-Machine Integration, Zhejiang University, Hangzhou 310027, China
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18
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Burström G, Amini M, El-Hajj VG, Arfan A, Gharios M, Buwaider A, Losch MS, Manni F, Edström E, Elmi-Terander A. Optical Methods for Brain Tumor Detection: A Systematic Review. J Clin Med 2024; 13:2676. [PMID: 38731204 PMCID: PMC11084501 DOI: 10.3390/jcm13092676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 04/28/2024] [Accepted: 04/30/2024] [Indexed: 05/13/2024] Open
Abstract
Background: In brain tumor surgery, maximal tumor resection is typically desired. This is complicated by infiltrative tumor cells which cannot be visually distinguished from healthy brain tissue. Optical methods are an emerging field that can potentially revolutionize brain tumor surgery through intraoperative differentiation between healthy and tumor tissues. Methods: This study aimed to systematically explore and summarize the existing literature on the use of Raman Spectroscopy (RS), Hyperspectral Imaging (HSI), Optical Coherence Tomography (OCT), and Diffuse Reflectance Spectroscopy (DRS) for brain tumor detection. MEDLINE, Embase, and Web of Science were searched for studies evaluating the accuracy of these systems for brain tumor detection. Outcome measures included accuracy, sensitivity, and specificity. Results: In total, 44 studies were included, covering a range of tumor types and technologies. Accuracy metrics in the studies ranged between 54 and 100% for RS, 69 and 99% for HSI, 82 and 99% for OCT, and 42 and 100% for DRS. Conclusions: This review provides insightful evidence on the use of optical methods in distinguishing tumor from healthy brain tissue.
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Affiliation(s)
- Gustav Burström
- Department of Clinical Neuroscience, Karolinska Institute, 171 77 Stockholm, Sweden; (G.B.); (M.A.); (A.A.); (M.G.); (A.B.); (E.E.)
| | - Misha Amini
- Department of Clinical Neuroscience, Karolinska Institute, 171 77 Stockholm, Sweden; (G.B.); (M.A.); (A.A.); (M.G.); (A.B.); (E.E.)
| | - Victor Gabriel El-Hajj
- Department of Clinical Neuroscience, Karolinska Institute, 171 77 Stockholm, Sweden; (G.B.); (M.A.); (A.A.); (M.G.); (A.B.); (E.E.)
| | - Arooj Arfan
- Department of Clinical Neuroscience, Karolinska Institute, 171 77 Stockholm, Sweden; (G.B.); (M.A.); (A.A.); (M.G.); (A.B.); (E.E.)
| | - Maria Gharios
- Department of Clinical Neuroscience, Karolinska Institute, 171 77 Stockholm, Sweden; (G.B.); (M.A.); (A.A.); (M.G.); (A.B.); (E.E.)
| | - Ali Buwaider
- Department of Clinical Neuroscience, Karolinska Institute, 171 77 Stockholm, Sweden; (G.B.); (M.A.); (A.A.); (M.G.); (A.B.); (E.E.)
| | - Merle S. Losch
- Department of Biomechanical Engineering, Faculty of Mechanical Engineering, Delft University of Technology, 2627 Delft, The Netherlands
| | - Francesca Manni
- Department of Electrical Engineering, Eindhoven University of Technology (TU/e), 5612 Eindhoven, The Netherlands;
| | - Erik Edström
- Department of Clinical Neuroscience, Karolinska Institute, 171 77 Stockholm, Sweden; (G.B.); (M.A.); (A.A.); (M.G.); (A.B.); (E.E.)
- Capio Spine Center Stockholm, Löwenströmska Hospital, 194 80 Upplands-Väsby, Sweden
- Department of Medical Sciences, Örebro University, 701 85 Örebro, Sweden
| | - Adrian Elmi-Terander
- Department of Clinical Neuroscience, Karolinska Institute, 171 77 Stockholm, Sweden; (G.B.); (M.A.); (A.A.); (M.G.); (A.B.); (E.E.)
- Capio Spine Center Stockholm, Löwenströmska Hospital, 194 80 Upplands-Väsby, Sweden
- Department of Medical Sciences, Örebro University, 701 85 Örebro, Sweden
- Department of Surgical Sciences, Uppsala University, 751 35 Uppsala, Sweden
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19
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Lotter W, Hassett MJ, Schultz N, Kehl KL, Van Allen EM, Cerami E. Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions. Cancer Discov 2024; 14:711-726. [PMID: 38597966 PMCID: PMC11131133 DOI: 10.1158/2159-8290.cd-23-1199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 01/29/2024] [Accepted: 02/28/2024] [Indexed: 04/11/2024]
Abstract
Artificial intelligence (AI) in oncology is advancing beyond algorithm development to integration into clinical practice. This review describes the current state of the field, with a specific focus on clinical integration. AI applications are structured according to cancer type and clinical domain, focusing on the four most common cancers and tasks of detection, diagnosis, and treatment. These applications encompass various data modalities, including imaging, genomics, and medical records. We conclude with a summary of existing challenges, evolving solutions, and potential future directions for the field. SIGNIFICANCE AI is increasingly being applied to all aspects of oncology, where several applications are maturing beyond research and development to direct clinical integration. This review summarizes the current state of the field through the lens of clinical translation along the clinical care continuum. Emerging areas are also highlighted, along with common challenges, evolving solutions, and potential future directions for the field.
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Affiliation(s)
- William Lotter
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Michael J. Hassett
- Harvard Medical School, Boston, MA, USA
- Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Nikolaus Schultz
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center; New York, NY, USA
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Kenneth L. Kehl
- Harvard Medical School, Boston, MA, USA
- Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Eliezer M. Van Allen
- Harvard Medical School, Boston, MA, USA
- Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ethan Cerami
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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20
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Pillar N, Li Y, Zhang Y, Ozcan A. Virtual Staining of Nonfixed Tissue Histology. Mod Pathol 2024; 37:100444. [PMID: 38325706 DOI: 10.1016/j.modpat.2024.100444] [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: 11/02/2023] [Revised: 01/19/2024] [Accepted: 01/29/2024] [Indexed: 02/09/2024]
Abstract
Surgical pathology workflow involves multiple labor-intensive steps, such as tissue removal, fixation, embedding, sectioning, staining, and microscopic examination. This process is time-consuming and costly and requires skilled technicians. In certain clinical scenarios, such as intraoperative consultations, there is a need for faster histologic evaluation to provide real-time surgical guidance. Currently, frozen section techniques involving hematoxylin and eosin (H&E) staining are used for intraoperative pathology consultations. However, these techniques have limitations, including a turnaround time of 20 to 30 minutes, staining artifacts, and potential tissue loss, negatively impacting accurate diagnosis. To address these challenges, researchers are exploring alternative optical imaging modalities for rapid microscopic tissue imaging. These modalities differ in optical characteristics, tissue preparation requirements, imaging equipment, and output image quality and format. Some of these imaging methods have been combined with computational algorithms to generate H&E-like images, which could greatly facilitate their adoption by pathologists. Here, we provide a comprehensive, organ-specific review of the latest advancements in emerging imaging modalities applied to nonfixed human tissue. We focused on studies that generated H&E-like images evaluated by pathologists. By presenting up-to-date research progress and clinical utility, this review serves as a valuable resource for scholars and clinicians, covering some of the major technical developments in this rapidly evolving field. It also offers insights into the potential benefits and drawbacks of alternative imaging modalities and their implications for improving patient care.
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Affiliation(s)
- Nir Pillar
- Electrical and Computer Engineering Department, University of California, Los Angeles, California; Bioengineering Department, University of California, Los Angeles, California; California NanoSystems Institute (CNSI), University of California, Los Angeles, California
| | - Yuzhu Li
- Electrical and Computer Engineering Department, University of California, Los Angeles, California; Bioengineering Department, University of California, Los Angeles, California; California NanoSystems Institute (CNSI), University of California, Los Angeles, California
| | - Yijie Zhang
- Electrical and Computer Engineering Department, University of California, Los Angeles, California; Bioengineering Department, University of California, Los Angeles, California; California NanoSystems Institute (CNSI), University of California, Los Angeles, California
| | - Aydogan Ozcan
- Electrical and Computer Engineering Department, University of California, Los Angeles, California; Bioengineering Department, University of California, Los Angeles, California; California NanoSystems Institute (CNSI), University of California, Los Angeles, California.
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21
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Dono A, Zhu P, Takayasu T, Arevalo O, Riascos R, Tandon N, Ballester LY, Esquenazi Y. Extent of Resection Thresholds in Molecular Subgroups of Newly Diagnosed Isocitrate Dehydrogenase-Wildtype Glioblastoma. Neurosurgery 2024:00006123-990000000-01153. [PMID: 38687046 DOI: 10.1227/neu.0000000000002964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 03/05/2024] [Indexed: 05/02/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Maximizing the extent of resection (EOR) improves outcomes in glioblastoma (GBM). However, previous GBM studies have not addressed the EOR impact in molecular subgroups beyond IDH1/IDH2 status. In the current article, we evaluate whether EOR confers a benefit in all GBM subtypes or only in particular molecular subgroups. METHODS A retrospective cohort of newly diagnosed GBM isocitrate dehydrogenase (IDH)-wildtype undergoing resection were prospectively included in a database (n = 138). EOR and residual tumor volume (RTV) were quantified with semiautomated software. Formalin-fixed paraffin-embedded tumor tissues were analyzed by targeted next-generation sequencing. The association between recurrent genomic alterations and EOR/RTV was evaluated using a recursive partitioning analysis to identify thresholds of EOR or RTV that may predict survival. The Kaplan-Meier methods and multivariable Cox proportional hazards regression methods were applied for survival analysis. RESULTS Patients with EOR ≥88% experienced 44% prolonged overall survival (OS) in multivariable analysis (hazard ratio: 0.56, P = .030). Patients with alterations in the TP53 pathway and EOR <89% showed reduced OS compared to TP53 pathway altered patients with EOR>89% (10.5 vs 18.8 months; HR: 2.78, P = .013); however, EOR/RTV was not associated with OS in patients without alterations in the TP53 pathway. Meanwhile, in all patients with EOR <88%, PTEN-altered had significantly worse OS than PTEN-wildtype (9.5 vs 15.4 months; HR: 4.53, P < .001). CONCLUSION Our results suggest that a subset of molecularly defined GBM IDH-wildtype may benefit more from aggressive resections. Re-resections to optimize EOR might be beneficial in a subset of molecularly defined GBMs. Molecular alterations should be taken into consideration for surgical treatment decisions in GBM IDH-wildtype.
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Affiliation(s)
- Antonio Dono
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, the University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Ping Zhu
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, the University of Texas Health Science Center at Houston, Houston, Texas, USA
| | | | - Octavio Arevalo
- Department of Diagnostic and Interventional Imaging, McGovern Medical School, the University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Roy Riascos
- Department of Diagnostic and Interventional Imaging, McGovern Medical School, the University of Texas Health Science Center at Houston, Houston, Texas, USA
- Memorial Hermann Hospital - TMC, Houston, Texas, USA
| | - Nitin Tandon
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, the University of Texas Health Science Center at Houston, Houston, Texas, USA
- Memorial Hermann Hospital - TMC, Houston, Texas, USA
| | - Leomar Y Ballester
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Yoshua Esquenazi
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, the University of Texas Health Science Center at Houston, Houston, Texas, USA
- Memorial Hermann Hospital - TMC, Houston, Texas, USA
- Center for Precision Health, School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, Texas, USA
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22
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Manciu FS, Guerrero J, Pence BC, Martinez Lopez LV, Das S. Assessment of Drug Activities against Giardia Using Hyperspectral Raman Microscopy. Pathogens 2024; 13:358. [PMID: 38787210 PMCID: PMC11124377 DOI: 10.3390/pathogens13050358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2024] [Revised: 04/19/2024] [Accepted: 04/24/2024] [Indexed: 05/25/2024] Open
Abstract
This study demonstrates the capability of Raman microscopy for detecting structural differences in Giardia cells exposed to different drugs and incubation times. While metronidazole (MTZ) visibly affects the cells by inducing extracellular vesicle releases of toxic iron intermediates and modified triple-bond moieties, oseltamivir (OSM) alters the phenylalanine and lipid structures. Modifications in the heme protein environment and the transformation of iron from ferric to ferrous observed for both drug treatments are more notable for MTZ. Different contents and amounts of vesicle excretion are detected for 24 h or 48 h with MTZ incubation. At a shorter drug exposure, releases of altered proteins, glycogen, and phospholipids dominate. Agglomerates of transformed iron complexes from heme proteins and multiple-bond moieties prevail at 48 h of treatment. No such vesicle releases are present in the case of OSM usage. Drug incorporations into the cells and their impact on the plasma membrane and the dynamics of lipid raft confirmed by confocal fluorescence microscopy reveal a more destructive extent by OSM, corroborating the Raman results. Raman microscopy provides a broader understanding of the multifaceted factors and mechanisms responsible for giardiasis treatment or drug resistance by enabling a label-free, simultaneous monitoring of structural changes at the cellular and molecular levels.
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Affiliation(s)
- Felicia S. Manciu
- Department of Physics, University of Texas at El Paso, El Paso, TX 79968, USA; (J.G.); (L.V.M.L.)
- Border Biomedical Research Center, University of Texas at El Paso, El Paso, TX 79968, USA
| | - Jose Guerrero
- Department of Physics, University of Texas at El Paso, El Paso, TX 79968, USA; (J.G.); (L.V.M.L.)
| | - Breanna C. Pence
- Department of Biological Sciences, University of Texas at El Paso, El Paso, TX 79968, USA;
| | | | - Siddhartha Das
- Border Biomedical Research Center, University of Texas at El Paso, El Paso, TX 79968, USA
- Department of Biological Sciences, University of Texas at El Paso, El Paso, TX 79968, USA;
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23
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Matthies L, Amir-Kabirian H, Gebrekidan MT, Braeuer AS, Speth US, Smeets R, Hagel C, Gosau M, Knipfer C, Friedrich RE. Raman difference spectroscopy and U-Net convolutional neural network for molecular analysis of cutaneous neurofibroma. PLoS One 2024; 19:e0302017. [PMID: 38603731 PMCID: PMC11008861 DOI: 10.1371/journal.pone.0302017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 03/26/2024] [Indexed: 04/13/2024] Open
Abstract
In Neurofibromatosis type 1 (NF1), peripheral nerve sheaths tumors are common, with cutaneous neurofibromas resulting in significant aesthetic, painful and functional problems requiring surgical removal. To date, determination of adequate surgical resection margins-complete tumor removal while attempting to preserve viable tissue-remains largely subjective. Thus, residual tumor extension beyond surgical margins or recurrence of the disease may frequently be observed. Here, we introduce Shifted-Excitation Raman Spectroscopy in combination with deep neural networks for the future perspective of objective, real-time diagnosis, and guided surgical ablation. The obtained results are validated through established histological methods. In this study, we evaluated the discrimination between cutaneous neurofibroma (n = 9) and adjacent physiological tissues (n = 25) in 34 surgical pathological specimens ex vivo at a total of 82 distinct measurement loci. Based on a convolutional neural network (U-Net), the mean raw Raman spectra (n = 8,200) were processed and refined, and afterwards the spectral peaks were assigned to their respective molecular origin. Principal component and linear discriminant analysis was used to discriminate cutaneous neurofibromas from physiological tissues with a sensitivity of 100%, specificity of 97.3%, and overall classification accuracy of 97.6%. The results enable the presented optical, non-invasive technique in combination with artificial intelligence as a promising candidate to ameliorate both, diagnosis and treatment of patients affected by cutaneous neurofibroma and NF1.
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Affiliation(s)
- Levi Matthies
- Department of Oral and Maxillofacial Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Mildred Scheel Cancer Career Center HaTriCS4, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Hendrik Amir-Kabirian
- Department of Oral and Maxillofacial Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Medhanie T. Gebrekidan
- Institute of Thermal-, Environmental- and Resources‘ Process Engineering, Technische Universität Bergakademie Freiberg, Freiberg, Germany
| | - Andreas S. Braeuer
- Institute of Thermal-, Environmental- and Resources‘ Process Engineering, Technische Universität Bergakademie Freiberg, Freiberg, Germany
| | - Ulrike S. Speth
- Department of Oral and Maxillofacial Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Ralf Smeets
- Department of Oral and Maxillofacial Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Division of “Regenerative Orofacial Medicine”, Department of Oral and Maxillofacial Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Christian Hagel
- Institute of Neuropathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Martin Gosau
- Department of Oral and Maxillofacial Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Christian Knipfer
- Department of Oral and Maxillofacial Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Reinhard E. Friedrich
- Department of Oral and Maxillofacial Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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24
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Abraham TM, Levenson R. Current Landscape of Advanced Imaging Tools for Pathology Diagnostics. Mod Pathol 2024; 37:100443. [PMID: 38311312 DOI: 10.1016/j.modpat.2024.100443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 12/13/2023] [Accepted: 01/26/2024] [Indexed: 02/10/2024]
Abstract
Histopathology relies on century-old workflows of formalin fixation, paraffin embedding, sectioning, and staining tissue specimens on glass slides. Despite being robust, this conventional process is slow, labor-intensive, and limited to providing two-dimensional views. Emerging technologies promise to enhance and accelerate histopathology. Slide-free microscopy allows rapid imaging of fresh, unsectioned specimens, overcoming slide preparation delays. Methods such as fluorescence confocal microscopy, multiphoton microscopy, along with more recent innovations including microscopy with UV surface excitation and fluorescence-imitating brightfield imaging can generate images resembling conventional histology directly from the surface of tissue specimens. Slide-free microscopy enable applications such as rapid intraoperative margin assessment and, with appropriate technology, three-dimensional histopathology. Multiomics profiling techniques, including imaging mass spectrometry and Raman spectroscopy, provide highly multiplexed molecular maps of tissues, although clinical translation remains challenging. Artificial intelligence is aiding the adoption of new imaging modalities via virtual staining, which converts methods such as slide-free microscopy into synthetic brightfield-like or even molecularly informed images. Although not yet commonplace, these emerging technologies collectively demonstrate the potential to modernize histopathology. Artificial intelligence-assisted workflows will ease the transition to new imaging modalities. With further validation, these advances may transform the century-old conventional histopathology pipeline to better serve 21st-century medicine. This review provides an overview of these enabling technology platforms and discusses their potential impact.
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Affiliation(s)
- Tanishq Mathew Abraham
- Department of Biomedical Engineering, University of California, Davis, Davis, California
| | - Richard Levenson
- Department of Pathology and Laboratory Medicine, UC Davis Health, Sacramento, California.
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25
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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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26
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Klamminger GG, Mombaerts L, Kemp F, Jelke F, Klein K, Slimani R, Mirizzi G, Husch A, Hertel F, Mittelbronn M, Kleine Borgmann FB. Machine Learning-Assisted Classification of Paraffin-Embedded Brain Tumors with Raman Spectroscopy. Brain Sci 2024; 14:301. [PMID: 38671953 PMCID: PMC11048578 DOI: 10.3390/brainsci14040301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 03/15/2024] [Accepted: 03/22/2024] [Indexed: 04/28/2024] Open
Abstract
Raman spectroscopy (RS) has demonstrated its utility in neurooncological diagnostics, spanning from intraoperative tumor detection to the analysis of tissue samples peri- and postoperatively. In this study, we employed Raman spectroscopy (RS) to monitor alterations in the molecular vibrational characteristics of a broad range of formalin-fixed, paraffin-embedded (FFPE) intracranial neoplasms (including primary brain tumors and meningiomas, as well as brain metastases) and considered specific challenges when employing RS on FFPE tissue during the routine neuropathological workflow. We spectroscopically measured 82 intracranial neoplasms on CaF2 slides (in total, 679 individual measurements) and set up a machine learning framework to classify spectral characteristics by splitting our data into training cohorts and external validation cohorts. The effectiveness of our machine learning algorithms was assessed by using common performance metrics such as AUROC and AUPR values. With our trained random forest algorithms, we distinguished among various types of gliomas and identified the primary origin in cases of brain metastases. Moreover, we spectroscopically diagnosed tumor types by using biopsy fragments of pure necrotic tissue, a task unattainable through conventional light microscopy. In order to address misclassifications and enhance the assessment of our models, we sought out significant Raman bands suitable for tumor identification. Through the validation phase, we affirmed a considerable complexity within the spectroscopic data, potentially arising not only from the biological tissue subjected to a rigorous chemical procedure but also from residual components of the fixation and paraffin-embedding process. The present study demonstrates not only the potential applications but also the constraints of RS as a diagnostic tool in neuropathology, considering the challenges associated with conducting vibrational spectroscopic analysis on formalin-fixed, paraffin-embedded (FFPE) tissue.
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Affiliation(s)
- Gilbert Georg Klamminger
- Department of General and Special Pathology, Saarland University (USAAR), 66424 Homburg, Germany
- Department of General and Special Pathology, Saarland University Medical Center (UKS), 66424 Homburg, Germany
- National Center of Pathology (NCP), Laboratoire National de Santé (LNS), 3555 Dudelange, Luxembourg
| | - Laurent Mombaerts
- Luxembourg Center of Neuropathology (LCNP), 3555 Dudelange, Luxembourg
| | - Françoise Kemp
- Luxembourg Center of Neuropathology (LCNP), 3555 Dudelange, Luxembourg
| | - Finn Jelke
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg
- Doctoral School in Science and Engineering (DSSE), University of Luxembourg (UL), 4362 Esch-sur-Alzette, Luxembourg
| | - Karoline Klein
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg
- Faculty of Medicine, Saarland University (USAAR), 66424 Homburg, Germany
| | - Rédouane Slimani
- Doctoral School in Science and Engineering (DSSE), University of Luxembourg (UL), 4362 Esch-sur-Alzette, Luxembourg
- Department of Cancer Research (DoCR), Luxembourg Institute of Health (LIH), 1210 Luxembourg, Luxembourg
| | - Giulia Mirizzi
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg
- Faculty of Medicine, Saarland University (USAAR), 66424 Homburg, Germany
| | - Andreas Husch
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg (UL), 4362 Esch-sur-Alzette, Luxembourg
| | - Frank Hertel
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg
- Faculty of Medicine, Saarland University (USAAR), 66424 Homburg, Germany
| | - Michel Mittelbronn
- Luxembourg Center of Neuropathology (LCNP), 3555 Dudelange, Luxembourg
- Department of Cancer Research (DoCR), Luxembourg Institute of Health (LIH), 1210 Luxembourg, Luxembourg
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg (UL), 4362 Esch-sur-Alzette, Luxembourg
- Department of Life Sciences and Medicine (DLSM), University of Luxembourg, 4365 Esch-sur-Alzette, Luxembourg
- Faculty of Science, Technology and Medicine (FSTM), University of Luxembourg, 4365 Esch-sur-Alzette, Luxembourg
| | - Felix B. Kleine Borgmann
- National Center of Pathology (NCP), Laboratoire National de Santé (LNS), 3555 Dudelange, Luxembourg
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg
- Department of Cancer Research (DoCR), Luxembourg Institute of Health (LIH), 1210 Luxembourg, Luxembourg
- Hôpitaux Robert Schuman, 1130 Luxembourg, Luxembourg
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27
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Bou-Nassif R, Reiner AS, Pease M, Bale T, Cohen MA, Rosenblum M, Tabar V. Development and prospective validation of an artificial intelligence-based smartphone app for rapid intraoperative pituitary adenoma identification. COMMUNICATIONS MEDICINE 2024; 4:45. [PMID: 38480833 PMCID: PMC10937994 DOI: 10.1038/s43856-024-00469-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 02/28/2024] [Indexed: 03/17/2024] Open
Abstract
BACKGROUND Intraoperative pathology consultation plays a crucial role in tumor surgery. The ability to accurately and rapidly distinguish tumor from normal tissue can greatly impact intraoperative surgical oncology management. However, this is dependent on the availability of a specialized pathologist for a reliable diagnosis. We developed and prospectively validated an artificial intelligence-based smartphone app capable of differentiating between pituitary adenoma and normal pituitary gland using stimulated Raman histology, almost instantly. METHODS The study consisted of three parts. After data collection (part 1) and development of a deep learning-based smartphone app (part 2), we conducted a prospective study that included 40 consecutive patients with 194 samples to evaluate the app in real-time in a surgical setting (part 3). The smartphone app's sensitivity, specificity, positive predictive value, and negative predictive value were evaluated by comparing the diagnosis rendered by the app to the ground-truth diagnosis set by a neuropathologist. RESULTS The app exhibits a sensitivity of 96.1% (95% CI: 89.9-99.0%), specificity of 92.7% (95% CI: 74-99.3%), positive predictive value of 98% (95% CI: 92.2-99.8%), and negative predictive value of 86.4% (95% CI: 66.2-96.8%). An external validation of the smartphone app on 40 different adenoma tumors and a total of 191 scanned SRH specimens from a public database shows a sensitivity of 93.7% (95% CI: 89.3-96.7%). CONCLUSIONS The app can be readily expanded and repurposed to work on different types of tumors and optical images. Rapid recognition of normal versus tumor tissue during surgery may contribute to improved intraoperative surgical management and oncologic outcomes. In addition to the accelerated pathological assessments during surgery, this platform can be of great benefit in community hospitals and developing countries, where immediate access to a specialized pathologist during surgery is limited.
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Affiliation(s)
- Rabih Bou-Nassif
- Department of Neurosurgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Multidisciplinary Pituitary and Skull Base Tumor Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Anne S Reiner
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Matthew Pease
- Department of Neurosurgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Multidisciplinary Pituitary and Skull Base Tumor Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Tejus Bale
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Marc A Cohen
- Department of Neurosurgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Multidisciplinary Pituitary and Skull Base Tumor Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Surgery, Head and Neck Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Marc Rosenblum
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Viviane Tabar
- Department of Neurosurgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Multidisciplinary Pituitary and Skull Base Tumor Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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28
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Shen B, Li Z, Pan Y, Guo Y, Yin Z, Hu R, Qu J, Liu L. Noninvasive Nonlinear Optical Computational Histology. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2308630. [PMID: 38095543 PMCID: PMC10916666 DOI: 10.1002/advs.202308630] [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: 11/11/2023] [Revised: 11/28/2023] [Indexed: 03/07/2024]
Abstract
Cancer remains a global health challenge, demanding early detection and accurate diagnosis for improved patient outcomes. An intelligent paradigm is introduced that elevates label-free nonlinear optical imaging with contrastive patch-wise learning, yielding stain-free nonlinear optical computational histology (NOCH). NOCH enables swift, precise diagnostic analysis of fresh tissues, reducing patient anxiety and healthcare costs. Nonlinear modalities are evaluated, including stimulated Raman scattering and multiphoton imaging, for their ability to enhance tumor microenvironment sensitivity, pathological analysis, and cancer examination. Quantitative analysis confirmed that NOCH images accurately reproduce nuclear morphometric features across different cancer stages. Key diagnostic features, such as nuclear morphology, size, and nuclear-cytoplasmic contrast, are well preserved. NOCH models also demonstrate promising generalization when applied to other pathological tissues. The study unites label-free nonlinear optical imaging with histopathology using contrastive learning to establish stain-free computational histology. NOCH provides a rapid, non-invasive, and precise approach to surgical pathology, holding immense potential for revolutionizing cancer diagnosis and surgical interventions.
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Affiliation(s)
- Binglin Shen
- Key Laboratory of Optoelectronic Devices and Systems of Guangdong Province and Ministry of EducationCollege of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhen518060China
| | - Zhenglin Li
- Key Laboratory of Optoelectronic Devices and Systems of Guangdong Province and Ministry of EducationCollege of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhen518060China
| | - Ying Pan
- China–Japan Union Hospital of Jilin UniversityChangchun130033China
| | - Yuan Guo
- Shaanxi Provincial Cancer HospitalXi'an710065China
| | - Zongyi Yin
- Shenzhen University General HospitalShenzhen518055China
| | - Rui Hu
- Key Laboratory of Optoelectronic Devices and Systems of Guangdong Province and Ministry of EducationCollege of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhen518060China
| | - Junle Qu
- Key Laboratory of Optoelectronic Devices and Systems of Guangdong Province and Ministry of EducationCollege of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhen518060China
| | - Liwei Liu
- Key Laboratory of Optoelectronic Devices and Systems of Guangdong Province and Ministry of EducationCollege of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhen518060China
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29
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Klein K, Klamminger GG, Mombaerts L, Jelke F, Arroteia IF, Slimani R, Mirizzi G, Husch A, Frauenknecht KBM, Mittelbronn M, Hertel F, Kleine Borgmann FB. Computational Assessment of Spectral Heterogeneity within Fresh Glioblastoma Tissue Using Raman Spectroscopy and Machine Learning Algorithms. Molecules 2024; 29:979. [PMID: 38474491 DOI: 10.3390/molecules29050979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 12/24/2023] [Accepted: 02/07/2024] [Indexed: 03/14/2024] Open
Abstract
Understanding and classifying inherent tumor heterogeneity is a multimodal approach, which can be undertaken at the genetic, biochemical, or morphological level, among others. Optical spectral methods such as Raman spectroscopy aim at rapid and non-destructive tissue analysis, where each spectrum generated reflects the individual molecular composition of an examined spot within a (heterogenous) tissue sample. Using a combination of supervised and unsupervised machine learning methods as well as a solid database of Raman spectra of native glioblastoma samples, we succeed not only in distinguishing explicit tumor areas-vital tumor tissue and necrotic tumor tissue can correctly be predicted with an accuracy of 76%-but also in determining and classifying different spectral entities within the histomorphologically distinct class of vital tumor tissue. Measurements of non-pathological, autoptic brain tissue hereby serve as a healthy control since their respective spectroscopic properties form an individual and reproducible cluster within the spectral heterogeneity of a vital tumor sample. The demonstrated decipherment of a spectral glioblastoma heterogeneity will be valuable, especially in the field of spectroscopically guided surgery to delineate tumor margins and to assist resection control.
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Affiliation(s)
- Karoline Klein
- Faculty of Medicine, Saarland University (USAAR), 66424 Homburg, Germany
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg
| | - Gilbert Georg Klamminger
- Department of General and Special Pathology, Saarland University (USAAR), 66424 Homburg, Germany
- Department of General and Special Pathology, Saarland University Medical Center (UKS), 66424 Homburg, Germany
- National Center of Pathology (NCP), Laboratoire National de Santé (LNS), 3555 Dudelange, Luxembourg
| | - Laurent Mombaerts
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg (UL), 4362 Esch-sur-Alzette, Luxembourg
| | - Finn Jelke
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg
- Doctoral School in Science and Engineering (DSSE), University of Luxembourg (UL), 4362 Esch-sur-Alzette, Luxembourg
| | - Isabel Fernandes Arroteia
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg
| | - Rédouane Slimani
- Doctoral School in Science and Engineering (DSSE), University of Luxembourg (UL), 4362 Esch-sur-Alzette, Luxembourg
- Luxembourg Center of Neuropathology (LCNP), 3555 Dudelange, Luxembourg
- Department of Cancer Research (DoCR), Luxembourg Institute of Health (LIH), 1210 Luxembourg, Luxembourg
| | - Giulia Mirizzi
- Faculty of Medicine, Saarland University (USAAR), 66424 Homburg, Germany
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg
| | - Andreas Husch
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg (UL), 4362 Esch-sur-Alzette, Luxembourg
| | - Katrin B M Frauenknecht
- National Center of Pathology (NCP), Laboratoire National de Santé (LNS), 3555 Dudelange, Luxembourg
- Luxembourg Center of Neuropathology (LCNP), 3555 Dudelange, Luxembourg
- Department of Cancer Research (DoCR), Luxembourg Institute of Health (LIH), 1210 Luxembourg, Luxembourg
| | - Michel Mittelbronn
- National Center of Pathology (NCP), Laboratoire National de Santé (LNS), 3555 Dudelange, Luxembourg
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg (UL), 4362 Esch-sur-Alzette, Luxembourg
- Luxembourg Center of Neuropathology (LCNP), 3555 Dudelange, Luxembourg
- Department of Cancer Research (DoCR), Luxembourg Institute of Health (LIH), 1210 Luxembourg, Luxembourg
- Department of Life Sciences and Medicine (DLSM), University of Luxembourg, 4365 Esch-sur-Alzette, Luxembourg
- Faculty of Science, Technology and Medicine (FSTM), University of Luxembourg, 4365 Esch-sur-Alzette, Luxembourg
| | - Frank Hertel
- Faculty of Medicine, Saarland University (USAAR), 66424 Homburg, Germany
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg
| | - Felix B Kleine Borgmann
- Faculty of Medicine, Saarland University (USAAR), 66424 Homburg, Germany
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg
- Department of Cancer Research (DoCR), Luxembourg Institute of Health (LIH), 1210 Luxembourg, Luxembourg
- Hôpitaux Robert Schuman, 1130 Luxembourg, Luxembourg
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Rethemiotaki I. Brain tumour detection from magnetic resonance imaging using convolutional neural networks. Contemp Oncol (Pozn) 2024; 27:230-241. [PMID: 38405206 PMCID: PMC10883197 DOI: 10.5114/wo.2023.135320] [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] [Received: 08/03/2023] [Accepted: 01/02/2024] [Indexed: 02/27/2024] Open
Abstract
Introduction The aim of this work is to detect and classify brain tumours using computational intelligence techniques on magnetic resonance imaging (MRI) images. Material and methods A dataset of 3264 MRI brain images consisting of 4 categories: unspecified glioma, meningioma, pituitary, and healthy brain, was used in this study. Twelve convolutional neural networks (GoogleNet, MobileNetV2, Xception, DesNet-BC, ResNet 50, SqueezeNet, ShuffleNet, VGG-16, AlexNet, Enet, EfficientB0, and MobileNetV2 with meta pseudo-labels) were used to classify gliomas, meningiomas, pituitary tumours, and healthy brains to find the most appropriate model. The experiments included image preprocessing and hyperparameter tuning. The performance of each neural network was evaluated based on accuracy, precision, recall, and F-measure for each type of brain tumour. Results The experimental results show that the MobileNetV2 convolutional neural network (CNN) model was able to diagnose brain tumours with 99% accuracy, 98% recall, and 99% F1 score. On the other hand, the validation data analysis shows that the CNN model GoogleNet has the highest accuracy (97%) among CNNs and seems to be the best choice for brain tumour classification. Conclusions The results of this work highlight the importance of artificial intelligence and machine learning for brain tumour prediction. Furthermore, this study achieved the highest accuracy in brain tumour classification to date, and it is also the only study to compare the performance of so many neural networks simultaneously.
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Affiliation(s)
- Irene Rethemiotaki
- School of Electrical and Computer Engineering, Technical University of Crete, Chania, Crete, Greece
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31
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Weber A, Enderle-Ammour K, Kurowski K, Metzger MC, Poxleitner P, Werner M, Rothweiler R, Beck J, Straehle J, Schmelzeisen R, Steybe D, Bronsert P. AI-Based Detection of Oral Squamous Cell Carcinoma with Raman Histology. Cancers (Basel) 2024; 16:689. [PMID: 38398080 PMCID: PMC10886627 DOI: 10.3390/cancers16040689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 02/02/2024] [Accepted: 02/02/2024] [Indexed: 02/25/2024] Open
Abstract
Stimulated Raman Histology (SRH) employs the stimulated Raman scattering (SRS) of photons at biomolecules in tissue samples to generate histological images. Subsequent pathological analysis allows for an intraoperative evaluation without the need for sectioning and staining. The objective of this study was to investigate a deep learning-based classification of oral squamous cell carcinoma (OSCC) and the sub-classification of non-malignant tissue types, as well as to compare the performances of the classifier between SRS and SRH images. Raman shifts were measured at wavenumbers k1 = 2845 cm-1 and k2 = 2930 cm-1. SRS images were transformed into SRH images resembling traditional H&E-stained frozen sections. The annotation of 6 tissue types was performed on images obtained from 80 tissue samples from eight OSCC patients. A VGG19-based convolutional neural network was then trained on 64 SRS images (and corresponding SRH images) and tested on 16. A balanced accuracy of 0.90 (0.87 for SRH images) and F1-scores of 0.91 (0.91 for SRH) for stroma, 0.98 (0.96 for SRH) for adipose tissue, 0.90 (0.87 for SRH) for squamous epithelium, 0.92 (0.76 for SRH) for muscle, 0.87 (0.90 for SRH) for glandular tissue, and 0.88 (0.87 for SRH) for tumor were achieved. The results of this study demonstrate the suitability of deep learning for the intraoperative identification of tissue types directly on SRS and SRH images.
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Affiliation(s)
- Andreas Weber
- Institute for Surgical Pathology, Medical Center, University of Freiburg, 79106 Freiburg, Germany
- Faculty of Biology, University of Freiburg, 79104 Freiburg, Germany
| | - Kathrin Enderle-Ammour
- Institute for Surgical Pathology, Medical Center, University of Freiburg, 79106 Freiburg, Germany
| | - Konrad Kurowski
- Institute for Surgical Pathology, Medical Center, University of Freiburg, 79106 Freiburg, Germany
- Tumorbank Comprehensive Cancer Center Freiburg, Medical Center, University of Freiburg, 79106 Freiburg, Germany
- Core Facility for Histopathology and Digital Pathology, Medical Center, University of Freiburg, 79106 Freiburg, Germany
| | - Marc C. Metzger
- Department of Oral and Maxillofacial Surgery, Medical Center, University of Freiburg, 79106 Freiburg, Germany
| | - Philipp Poxleitner
- Department of Oral and Maxillofacial Surgery, Medical Center, University of Freiburg, 79106 Freiburg, Germany
- Center for Advanced Surgical Tissue Analysis (CAST), University of Freiburg, 79106 Freiburg, Germany
- Department of Oral and Maxillofacial Surgery and Facial Plastic Surgery, University Hospital, LMU Munich, 80337 Munich, Germany
| | - Martin Werner
- Institute for Surgical Pathology, Medical Center, University of Freiburg, 79106 Freiburg, Germany
- Tumorbank Comprehensive Cancer Center Freiburg, Medical Center, University of Freiburg, 79106 Freiburg, Germany
| | - René Rothweiler
- Department of Oral and Maxillofacial Surgery, Medical Center, University of Freiburg, 79106 Freiburg, Germany
| | - Jürgen Beck
- Center for Advanced Surgical Tissue Analysis (CAST), University of Freiburg, 79106 Freiburg, Germany
- Department of Neurosurgery, Medical Center, University of Freiburg, 79106 Freiburg, Germany
| | - Jakob Straehle
- Center for Advanced Surgical Tissue Analysis (CAST), University of Freiburg, 79106 Freiburg, Germany
- Department of Neurosurgery, Medical Center, University of Freiburg, 79106 Freiburg, Germany
| | - Rainer Schmelzeisen
- Department of Oral and Maxillofacial Surgery, Medical Center, University of Freiburg, 79106 Freiburg, Germany
- Center for Advanced Surgical Tissue Analysis (CAST), University of Freiburg, 79106 Freiburg, Germany
| | - David Steybe
- Department of Oral and Maxillofacial Surgery, Medical Center, University of Freiburg, 79106 Freiburg, Germany
- Center for Advanced Surgical Tissue Analysis (CAST), University of Freiburg, 79106 Freiburg, Germany
- Department of Oral and Maxillofacial Surgery and Facial Plastic Surgery, University Hospital, LMU Munich, 80337 Munich, Germany
| | - Peter Bronsert
- Institute for Surgical Pathology, Medical Center, University of Freiburg, 79106 Freiburg, Germany
- Tumorbank Comprehensive Cancer Center Freiburg, Medical Center, University of Freiburg, 79106 Freiburg, Germany
- Core Facility for Histopathology and Digital Pathology, Medical Center, University of Freiburg, 79106 Freiburg, Germany
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Ni H, Dessai CP, Lin H, Wang W, Chen S, Yuan Y, Ge X, Ao J, Vild N, Cheng JX. High-content stimulated Raman histology of human breast cancer. Theranostics 2024; 14:1361-1370. [PMID: 38389847 PMCID: PMC10879861 DOI: 10.7150/thno.90336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 12/17/2023] [Indexed: 02/24/2024] Open
Abstract
Histological examination is crucial for cancer diagnosis, however, the labor-intensive sample preparation involved in the histology impedes the speed of diagnosis. Recently developed two-color stimulated Raman histology could bypass the complex tissue processing to generates result close to hematoxylin and eosin staining, which is one of the golden standards in cancer histology. Yet, the underlying chemical features are not revealed in two-color stimulated Raman histology, compromising the effectiveness of prognostic stratification. Here, we present a high-content stimulated Raman histology (HC-SRH) platform that provides both morphological and chemical information for cancer diagnosis based on un-stained breast tissues. Methods: By utilizing both hyperspectral SRS imaging in the C-H vibration window and sparsity-penalized unmixing of overlapped spectral profiles, HC-SRH enabled high-content chemical mapping of saturated lipids, unsaturated lipids, cellular protein, extracellular matrix (ECM), and water. Spectral selective sampling was further implemented to boost the speed of HC-SRH. To show the potential for clinical use, HC-SRH using a compact fiber laser-based stimulated Raman microscope was demonstrated. Harnessing the wide and rapid tuning capability of the fiber laser, both C-H and fingerprint vibration windows were accessed. Results: HC-SRH successfully mapped unsaturated lipids, cellular protein, extracellular matrix, saturated lipid, and water in breast tissue. With these five chemical maps, HC-SRH provided distinct contrast for tissue components including duct, stroma, fat cell, necrosis, and vessel. With selective spectral sampling, the speed of HC-SRH was improved by one order of magnitude. The fiber-laser-based HC-SRH produced the same image quality in the C-H window as the state-of-the-art solid laser. In the fingerprint window, nucleic acid and solid-state ester contrast was demonstrated. Conclusions: HC-SRH provides both morphological and chemical information of tissue in a label-free manner. The chemical information detected is beyond the reach of traditional hematoxylin and eosin staining and heralds the potential of HC-SRH for biomarker discovery.
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Affiliation(s)
- Hongli Ni
- Department of Electrical and Computer Engineering, Boston University, 8 St. Mary's St., Boston, MA, 02215, USA
| | | | - Haonan Lin
- Department of Electrical and Computer Engineering, Boston University, 8 St. Mary's St., Boston, MA, 02215, USA
| | - Wei Wang
- Hologic Inc., 250 campus drive, Marlborough, MA 01752, USA
| | - Shaoxiong Chen
- Indiana University School of Medicine 340 West 10th Street, Fairbanks Hall, Suite 6200, IN 46202, USA
| | - Yuhao Yuan
- Department of Electrical and Computer Engineering, Boston University, 8 St. Mary's St., Boston, MA, 02215, USA
| | - Xiaowei Ge
- Department of Electrical and Computer Engineering, Boston University, 8 St. Mary's St., Boston, MA, 02215, USA
| | - Jianpeng Ao
- Department of Electrical and Computer Engineering, Boston University, 8 St. Mary's St., Boston, MA, 02215, USA
| | - Nolan Vild
- Department of Electrical and Computer Engineering, Boston University, 8 St. Mary's St., Boston, MA, 02215, USA
| | - Ji-Xin Cheng
- Department of Electrical and Computer Engineering, Boston University, 8 St. Mary's St., Boston, MA, 02215, USA
- Department of Biomedical Engineering, Boston University, 44 Cummington Mall, MA 02215, USA
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He H, Cao M, Gao Y, Zheng P, Yan S, Zhong JH, Wang L, Jin D, Ren B. Noise learning of instruments for high-contrast, high-resolution and fast hyperspectral microscopy and nanoscopy. Nat Commun 2024; 15:754. [PMID: 38272927 PMCID: PMC10810791 DOI: 10.1038/s41467-024-44864-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 01/05/2024] [Indexed: 01/27/2024] Open
Abstract
The low scattering efficiency of Raman scattering makes it challenging to simultaneously achieve good signal-to-noise ratio (SNR), high imaging speed, and adequate spatial and spectral resolutions. Here, we report a noise learning (NL) approach that estimates the intrinsic noise distribution of each instrument by statistically learning the noise in the pixel-spatial frequency domain. The estimated noise is then removed from the noisy spectra. This enhances the SNR by ca. 10 folds, and suppresses the mean-square error by almost 150 folds. NL allows us to improve the positioning accuracy and spatial resolution and largely eliminates the impact of thermal drift on tip-enhanced Raman spectroscopic nanoimaging. NL is also applicable to enhance SNR in fluorescence and photoluminescence imaging. Our method manages the ground truth spectra and the instrumental noise simultaneously within the training dataset, which bypasses the tedious labelling of huge dataset required in conventional deep learning, potentially shifting deep learning from sample-dependent to instrument-dependent.
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Affiliation(s)
- Hao He
- Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen, 361005, China
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
- Department of Biomedical Engineering, College of Engineering, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, China
| | - Maofeng Cao
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Yun Gao
- Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen, 361005, China
| | - Peng Zheng
- Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen, 361005, China
| | - Sen Yan
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Jin-Hui Zhong
- Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China.
| | - Lei Wang
- Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen, 361005, China.
| | - Dayong Jin
- Department of Biomedical Engineering, College of Engineering, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, China
- Institute for Biomedical Materials & Devices (IBMD), University of Technology Sydney, Sydney, NSW, 2007, Australia
| | - Bin Ren
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China.
- Tan Kah Kee Innovation Laboratory, Xiamen, 361104, China.
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Qian N, Gao X, Lang X, Deng H, Bratu TM, Chen Q, Stapleton P, Yan B, Min W. Rapid single-particle chemical imaging of nanoplastics by SRS microscopy. Proc Natl Acad Sci U S A 2024; 121:e2300582121. [PMID: 38190543 PMCID: PMC10801917 DOI: 10.1073/pnas.2300582121] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 10/24/2023] [Indexed: 01/10/2024] Open
Abstract
Plastics are now omnipresent in our daily lives. The existence of microplastics (1 µm to 5 mm in length) and possibly even nanoplastics (<1 μm) has recently raised health concerns. In particular, nanoplastics are believed to be more toxic since their smaller size renders them much more amenable, compared to microplastics, to enter the human body. However, detecting nanoplastics imposes tremendous analytical challenges on both the nano-level sensitivity and the plastic-identifying specificity, leading to a knowledge gap in this mysterious nanoworld surrounding us. To address these challenges, we developed a hyperspectral stimulated Raman scattering (SRS) imaging platform with an automated plastic identification algorithm that allows micro-nano plastic analysis at the single-particle level with high chemical specificity and throughput. We first validated the sensitivity enhancement of the narrow band of SRS to enable high-speed single nanoplastic detection below 100 nm. We then devised a data-driven spectral matching algorithm to address spectral identification challenges imposed by sensitive narrow-band hyperspectral imaging and achieve robust determination of common plastic polymers. With the established technique, we studied the micro-nano plastics from bottled water as a model system. We successfully detected and identified nanoplastics from major plastic types. Micro-nano plastics concentrations were estimated to be about 2.4 ± 1.3 × 105 particles per liter of bottled water, about 90% of which are nanoplastics. This is orders of magnitude more than the microplastic abundance reported previously in bottled water. High-throughput single-particle counting revealed extraordinary particle heterogeneity and nonorthogonality between plastic composition and morphologies; the resulting multidimensional profiling sheds light on the science of nanoplastics.
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Affiliation(s)
- Naixin Qian
- Department of Chemistry, Columbia University, New York, NY10027
| | - Xin Gao
- Department of Chemistry, Columbia University, New York, NY10027
| | - Xiaoqi Lang
- Department of Chemistry, Columbia University, New York, NY10027
| | - Huiping Deng
- Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY10964
| | | | - Qixuan Chen
- Department of Biostatistics, Columbia University Mailman School of Public Health, New York, NY10032
| | - Phoebe Stapleton
- Department of Pharmacology and Toxicology, Ernest Mario School of Pharmacy, Environmental and Occupational Health Sciences Institute, Rutgers University, New Brunswick, NJ08854
| | - Beizhan Yan
- Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY10964
| | - Wei Min
- Department of Chemistry, Columbia University, New York, NY10027
- Department of Biomedical Engineering, Columbia University, New York, NY10027
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35
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Levy JJ, Davis MJ, Chacko RS, Davis MJ, Fu LJ, Goel T, Pamal A, Nafi I, Angirekula A, Suvarna A, Vempati R, Christensen BC, Hayden MS, Vaickus LJ, LeBoeuf MR. Intraoperative margin assessment for basal cell carcinoma with deep learning and histologic tumor mapping to surgical site. NPJ Precis Oncol 2024; 8:2. [PMID: 38172524 PMCID: PMC10764333 DOI: 10.1038/s41698-023-00477-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 11/14/2023] [Indexed: 01/05/2024] Open
Abstract
Successful treatment of solid cancers relies on complete surgical excision of the tumor either for definitive treatment or before adjuvant therapy. Intraoperative and postoperative radial sectioning, the most common form of margin assessment, can lead to incomplete excision and increase the risk of recurrence and repeat procedures. Mohs Micrographic Surgery is associated with complete removal of basal cell and squamous cell carcinoma through real-time margin assessment of 100% of the peripheral and deep margins. Real-time assessment in many tumor types is constrained by tissue size, complexity, and specimen processing / assessment time during general anesthesia. We developed an artificial intelligence platform to reduce the tissue preprocessing and histological assessment time through automated grossing recommendations, mapping and orientation of tumor to the surgical specimen. Using basal cell carcinoma as a model system, results demonstrate that this approach can address surgical laboratory efficiency bottlenecks for rapid and complete intraoperative margin assessment.
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Affiliation(s)
- Joshua J Levy
- Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA.
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA.
- Department of Dermatology, Geisel School of Medicine at Dartmouth, Hanover, NH, 03756, USA.
- Emerging Diagnostic and Investigative Technologies, Clinical Genomics and Advanced Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, 03756, USA.
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, 03756, USA.
- Program in Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth, Hanover, NH, 03756, USA.
| | - Matthew J Davis
- Department of Dermatology, Geisel School of Medicine at Dartmouth, Hanover, NH, 03756, USA
| | | | - Michael J Davis
- Department of Dermatology, Geisel School of Medicine at Dartmouth, Hanover, NH, 03756, USA
| | - Lucy J Fu
- Geisel School of Medicine at Dartmouth, Hanover, NH, 03755, USA
| | - Tarushii Goel
- Thomas Jefferson High School for Science and Technology, Alexandria, VA, 22312, USA
- Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Akash Pamal
- Thomas Jefferson High School for Science and Technology, Alexandria, VA, 22312, USA
- University of Virginia, Charlottesville, VA, 22903, USA
| | - Irfan Nafi
- Thomas Jefferson High School for Science and Technology, Alexandria, VA, 22312, USA
- Stanford University, Palo Alto, CA, 94305, USA
| | - Abhinav Angirekula
- Thomas Jefferson High School for Science and Technology, Alexandria, VA, 22312, USA
- University of Illinois Urbana-Champaign, Champaign, IL, 61820, USA
| | - Anish Suvarna
- Thomas Jefferson High School for Science and Technology, Alexandria, VA, 22312, USA
| | - Ram Vempati
- Thomas Jefferson High School for Science and Technology, Alexandria, VA, 22312, USA
| | - Brock C Christensen
- Department of Dermatology, Geisel School of Medicine at Dartmouth, Hanover, NH, 03756, USA
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, NH, 03756, USA
- Department of Community and Family Medicine, Geisel School of Medicine at Dartmouth, Hanover, NH, 03756, USA
| | - Matthew S Hayden
- Department of Dermatology, Geisel School of Medicine at Dartmouth, Hanover, NH, 03756, USA
| | - Louis J Vaickus
- Emerging Diagnostic and Investigative Technologies, Clinical Genomics and Advanced Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, 03756, USA
| | - Matthew R LeBoeuf
- Department of Dermatology, Geisel School of Medicine at Dartmouth, Hanover, NH, 03756, USA
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Liu L, Du K. A perspective on computer vision in biosensing. BIOMICROFLUIDICS 2024; 18:011301. [PMID: 38223547 PMCID: PMC10787640 DOI: 10.1063/5.0185732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 12/26/2023] [Indexed: 01/16/2024]
Abstract
Computer vision has become a powerful tool in the field of biosensing, aiding in the development of innovative and precise systems for the analysis and interpretation of biological data. This interdisciplinary approach harnesses the capabilities of computer vision algorithms and techniques to extract valuable information from various biosensing applications, including medical diagnostics, environmental monitoring, and food health. Despite years of development, there is still significant room for improvement in this area. In this perspective, we outline how computer vision is applied to raw sensor data in biosensors and its advantages to biosensing applications. We then discuss ongoing research and developments in the field and subsequently explore the challenges and opportunities that computer vision faces in biosensor applications. We also suggest directions for future work, ultimately underscoring the significant impact of computer vision on advancing biosensing technologies and their applications.
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Affiliation(s)
- Li Liu
- Department of Chemical and Environmental Engineering, University of California, Riverside, California 92521, USA
| | - Ke Du
- Department of Chemical and Environmental Engineering, University of California, Riverside, California 92521, USA
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Derbal Y. Adaptive Cancer Therapy in the Age of Generative Artificial Intelligence. Cancer Control 2024; 31:10732748241264704. [PMID: 38897721 PMCID: PMC11189021 DOI: 10.1177/10732748241264704] [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: 04/04/2024] [Revised: 05/17/2024] [Accepted: 06/06/2024] [Indexed: 06/21/2024] Open
Abstract
Therapeutic resistance is a major challenge facing the design of effective cancer treatments. Adaptive cancer therapy is in principle the most viable approach to manage cancer's adaptive dynamics through drug combinations with dose timing and modulation. However, there are numerous open issues facing the clinical success of adaptive therapy. Chief among these issues is the feasibility of real-time predictions of treatment response which represent a bedrock requirement of adaptive therapy. Generative artificial intelligence has the potential to learn prediction models of treatment response from clinical, molecular, and radiomics data about patients and their treatments. The article explores this potential through a proposed integration model of Generative Pre-Trained Transformers (GPTs) in a closed loop with adaptive treatments to predict the trajectories of disease progression. The conceptual model and the challenges facing its realization are discussed in the broader context of artificial intelligence integration in oncology.
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Affiliation(s)
- Youcef Derbal
- Ted Rogers School of Information Technology Management, Toronto Metropolitan University, Toronto, ON, Canada
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38
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Huang J, Gao Y, Chang Y, Peng J, Yu Y, Wang B. Machine Learning in Bioelectrocatalysis. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2306583. [PMID: 37946709 PMCID: PMC10787072 DOI: 10.1002/advs.202306583] [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: 09/12/2023] [Indexed: 11/12/2023]
Abstract
At present, the global energy crisis and environmental pollution coexist, and the demand for sustainable clean energy has been highly concerned. Bioelectrocatalysis that combines the benefits of biocatalysis and electrocatalysis produces high-value chemicals, clean biofuel, and biodegradable new materials. It has been applied in biosensors, biofuel cells, and bioelectrosynthesis. However, there are certain flaws in the application process of bioelectrocatalysis, such as low accuracy/efficiency, poor stability, and limited experimental conditions. These issues can possibly be solved using machine learning (ML) in recent reports although the combination of them is still not mature. To summarize the progress of ML in bioelectrocatalysis, this paper first introduces the modeling process of ML, then focuses on the reports of ML in bioelectrocatalysis, and ultimately makes a summary and outlook about current issues and future directions. It is believed that there is plenty of scope for this interdisciplinary research direction.
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Affiliation(s)
- Jiamin Huang
- Department of Environmental Science and Engineering, University of Science and Technology Beijing, Beijing, 100083, China
- CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, National Center for Nanoscience and Technology, Beijing, 100190, China
| | - Yang Gao
- CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, National Center for Nanoscience and Technology, Beijing, 100190, China
| | - Yanhong Chang
- Department of Environmental Science and Engineering, University of Science and Technology Beijing, Beijing, 100083, China
| | - Jiajie Peng
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Yadong Yu
- College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing, 211816, China
| | - Bin Wang
- CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, National Center for Nanoscience and Technology, Beijing, 100190, China
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39
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Thenuwara G, Curtin J, Tian F. Advances in Diagnostic Tools and Therapeutic Approaches for Gliomas: A Comprehensive Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:9842. [PMID: 38139688 PMCID: PMC10747598 DOI: 10.3390/s23249842] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 12/07/2023] [Accepted: 12/08/2023] [Indexed: 12/24/2023]
Abstract
Gliomas, a prevalent category of primary malignant brain tumors, pose formidable clinical challenges due to their invasive nature and limited treatment options. The current therapeutic landscape for gliomas is constrained by a "one-size-fits-all" paradigm, significantly restricting treatment efficacy. Despite the implementation of multimodal therapeutic strategies, survival rates remain disheartening. The conventional treatment approach, involving surgical resection, radiation, and chemotherapy, grapples with substantial limitations, particularly in addressing the invasive nature of gliomas. Conventional diagnostic tools, including computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET), play pivotal roles in outlining tumor characteristics. However, they face limitations, such as poor biological specificity and challenges in distinguishing active tumor regions. The ongoing development of diagnostic tools and therapeutic approaches represents a multifaceted and promising frontier in the battle against this challenging brain tumor. The aim of this comprehensive review is to address recent advances in diagnostic tools and therapeutic approaches for gliomas. These innovations aim to minimize invasiveness while enabling the precise, multimodal targeting of localized gliomas. Researchers are actively developing new diagnostic tools, such as colorimetric techniques, electrochemical biosensors, optical coherence tomography, reflectometric interference spectroscopy, surface-enhanced Raman spectroscopy, and optical biosensors. These tools aim to regulate tumor progression and develop precise treatment methods for gliomas. Recent technological advancements, coupled with bioelectronic sensors, open avenues for new therapeutic modalities, minimizing invasiveness and enabling multimodal targeting with unprecedented precision. The next generation of multimodal therapeutic strategies holds potential for precision medicine, aiding the early detection and effective management of solid brain tumors. These innovations offer promise in adopting precision medicine methodologies, enabling early disease detection, and improving solid brain tumor management. This review comprehensively recognizes the critical role of pioneering therapeutic interventions, holding significant potential to revolutionize brain tumor therapeutics.
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Affiliation(s)
- Gayathree Thenuwara
- School of Food Science and Environmental Health, Technological University Dublin, Grangegorman Lower, D07 H6K8 Dublin, Ireland;
- Institute of Biochemistry, Molecular Biology, and Biotechnology, University of Colombo, Colombo 00300, Sri Lanka
| | - James Curtin
- Faculty of Engineering and Built Environment, Technological University Dublin, Bolton Street, D01 K822 Dublin, Ireland;
| | - Furong Tian
- School of Food Science and Environmental Health, Technological University Dublin, Grangegorman Lower, D07 H6K8 Dublin, Ireland;
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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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Wu S, Cao Y, Li X, Liu Q, Ye Y, Liu X, Zeng L, Tian M. Attention-guided multi-scale context aggregation network for multi-modal brain glioma segmentation. Med Phys 2023; 50:7629-7640. [PMID: 37151131 DOI: 10.1002/mp.16452] [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: 06/01/2022] [Revised: 03/17/2023] [Accepted: 03/20/2023] [Indexed: 05/09/2023] Open
Abstract
BACKGROUND Accurate segmentation of brain glioma is a critical prerequisite for clinical diagnosis, surgical planning and treatment evaluation. In current clinical workflow, physicians typically perform delineation of brain tumor subregions slice-by-slice, which is more susceptible to variabilities in raters and also time-consuming. Besides, even though convolutional neural networks (CNNs) are driving progress, the performance of standard models still have some room for further improvement. PURPOSE To deal with these issues, this paper proposes an attention-guided multi-scale context aggregation network (AMCA-Net) for the accurate segmentation of brain glioma in the magnetic resonance imaging (MRI) images with multi-modalities. METHODS AMCA-Net extracts the multi-scale features from the MRI images and fuses the extracted discriminative features via a self-attention mechanism for brain glioma segmentation. The extraction is performed via a series of down-sampling, convolution layers, and the global context information guidance (GCIG) modules are developed to fuse the features extracted for contextual features. At the end of the down-sampling, a multi-scale fusion (MSF) module is designed to exploit and combine all the extracted multi-scale features. Each of the GCIG and MSF modules contain a channel attention (CA) module that can adaptively calibrate feature responses and emphasize the most relevant features. Finally, multiple predictions with different resolutions are fused through different weightings given by a multi-resolution adaptation (MRA) module instead of the use of averaging or max-pooling to improve the final segmentation results. RESULTS Datasets used in this paper are publicly accessible, that is, the Multimodal Brain Tumor Segmentation Challenges 2018 (BraTS2018) and 2019 (BraTS2019). BraTS2018 contains 285 patient cases and BraTS2019 contains 335 cases. Simulations show that the AMCA-Net has better or comparable performance against that of the other state-of-the-art models. In terms of the Dice score and Hausdorff 95 for the BraTS2018 dataset, 90.4% and 10.2 mm for the whole tumor region (WT), 83.9% and 7.4 mm for the tumor core region (TC), 80.2% and 4.3 mm for the enhancing tumor region (ET), whereas the Dice score and Hausdorff 95 for the BraTS2019 dataset, 91.0% and 10.7 mm for the WT, 84.2% and 8.4 mm for the TC, 80.1% and 4.8 mm for the ET. CONCLUSIONS The proposed AMCA-Net performs comparably well in comparison to several state-of-the-art neural net models in identifying the areas involving the peritumoral edema, enhancing tumor, and necrotic and non-enhancing tumor core of brain glioma, which has great potential for clinical practice. In future research, we will further explore the feasibility of applying AMCA-Net to other similar segmentation tasks.
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Affiliation(s)
- Shaozhi Wu
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Yunjian Cao
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Xinke Li
- West China School of Medicine, Sichuan University, Chengdu, China
| | - Qiyu Liu
- Radiology Department, Mianyang Central Hospital, Mianyang, China
| | - Yuyun Ye
- Department of Electrical and Computer Engineering, University of Tulsa, Tulsa, Oklahoma, USA
| | - Xingang Liu
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Liaoyuan Zeng
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Miao Tian
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
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Xu X, Jia Q, Yuan H, Qiu H, Dong Y, Xie W, Yao Z, Zhang J, Nie Z, Li X, Shi Y, Zou JY, Huang M, Zhuang J. A clinically applicable AI system for diagnosis of congenital heart diseases based on computed tomography images. Med Image Anal 2023; 90:102953. [PMID: 37734140 DOI: 10.1016/j.media.2023.102953] [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: 12/19/2022] [Revised: 08/22/2023] [Accepted: 09/01/2023] [Indexed: 09/23/2023]
Abstract
Congenital heart disease (CHD) is the most common type of birth defect. Without timely detection and treatment, approximately one-third of children with CHD would die in the infant period. However, due to the complicated heart structures, early diagnosis of CHD and its types is quite challenging, even for experienced radiologists. Here, we present an artificial intelligence (AI) system that achieves a comparable performance of human experts in the critical task of classifying 17 categories of CHD types. We collected the first-large CT dataset from three different CT machines, including more than 3750 CHD patients over 14 years. Experimental results demonstrate that it can achieve diagnosis accuracy (86.03%) comparable with junior cardiovascular radiologists (86.27%) in a World Health Organization appointed research and cooperation center in China on most types of CHD, and obtains a higher sensitivity (82.91%) than junior cardiovascular radiologists (76.18%). The accuracy of the combination of our AI system (97.20%) and senior radiologists achieves comparable results to that of junior radiologists and senior radiologists (97.16%) which is the current clinical routine. Our AI system can further provide 3D visualization of hearts to senior radiologists for interpretation and flexible review, surgeons for precise intuition of heart structures, and clinicians for more precise outcome prediction. We demonstrate the potential of our model to be integrated into current clinic practice to improve the diagnosis of CHD globally, especially in regions where experienced radiologists can be scarce.
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Affiliation(s)
- Xiaowei Xu
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Qianjun Jia
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Department of Catheterization Lab, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Haiyun Yuan
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Department of Cardiovascular Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Hailong Qiu
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Department of Cardiovascular Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Yuhao Dong
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Department of Catheterization Lab, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Wen Xie
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Department of Cardiovascular Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Zeyang Yao
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Department of Cardiovascular Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Jiawei Zhang
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Zhiqaing Nie
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Xiaomeng Li
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong Special Administrative Region
| | - Yiyu Shi
- Computer Science and Engineering, University of Notre Dame, IN, 46656, USA
| | - James Y Zou
- Department of Computer Science, Stanford University, Stanford, CA, 94305, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA.
| | - Meiping Huang
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Department of Catheterization Lab, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
| | - Jian Zhuang
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Department of Cardiovascular Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
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Zaidan AM. The leading global health challenges in the artificial intelligence era. Front Public Health 2023; 11:1328918. [PMID: 38089037 PMCID: PMC10711066 DOI: 10.3389/fpubh.2023.1328918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 11/13/2023] [Indexed: 12/18/2023] Open
Abstract
Millions of people's health is at risk because of several factors and multiple overlapping crises, all of which hit the vulnerable the most. These challenges are dynamic and evolve in response to emerging health challenges and concerns, which need effective collaboration among countries working toward achieving Sustainable Development Goals (SDGs) and securing global health. Mental Health, the Impact of climate change, cardiovascular diseases (CVDs), diabetes, Infectious diseases, health system, and population aging are examples of challenges known to pose a vast burden worldwide. We are at a point known as the "digital revolution," characterized by the expansion of artificial intelligence (AI) and a fusion of technology types. AI has emerged as a powerful tool for addressing various health challenges, and the last ten years have been influential due to the rapid expansion in the production and accessibility of health-related data. The computational models and algorithms can understand complicated health and medical data to perform various functions and deep-learning strategies. This narrative mini-review summarizes the most current AI applications to address the leading global health challenges. Harnessing its capabilities can ultimately mitigate the Impact of these challenges and revolutionize the field. It has the ability to strengthen global health through personalized health care and improved preparedness and response to future challenges. However, ethical and legal concerns about individual or community privacy and autonomy must be addressed for effective implementation.
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Affiliation(s)
- Amal Mousa Zaidan
- King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- King Abdullah International Medical Research Center (KAIMRC), Riyadh, Saudi Arabia
- Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
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44
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Liu Y. methylClass: an R package to construct DNA methylation-based classification models. Brief Bioinform 2023; 25:bbad485. [PMID: 38205965 PMCID: PMC10782803 DOI: 10.1093/bib/bbad485] [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: 08/03/2023] [Revised: 12/02/2023] [Accepted: 12/05/2023] [Indexed: 01/12/2024] Open
Abstract
DNA methylation profiling is a useful tool to increase the accuracy of a cancer diagnosis. However, a comprehensive R package specially for it is lacking. Hence, we developed the R package methylClass for methylation-based classification. Within it, we provide the eSVM (ensemble-based support vector machine) model to achieve much higher accuracy in methylation data classification than the popular random forest model and overcome the time-consuming problem of the traditional SVM. In addition, some novel feature selection methods are included in the package to improve the classification. Furthermore, because methylation data can be converted to other omics, such as copy number variation data, we also provide functions for multi-omics studies. The testing of this package on four datasets shows the accurate performance of our package, especially eSVM, which can be used in both methylation and multi-omics models and outperforms other methods in both cases. methylClass is available at: https://github.com/yuabrahamliu/methylClass.
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Affiliation(s)
- Yu Liu
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
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Kourounis G, Elmahmudi AA, Thomson B, Hunter J, Ugail H, Wilson C. Computer image analysis with artificial intelligence: a practical introduction to convolutional neural networks for medical professionals. Postgrad Med J 2023; 99:1287-1294. [PMID: 37794609 PMCID: PMC10658730 DOI: 10.1093/postmj/qgad095] [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: 07/26/2023] [Revised: 09/06/2023] [Accepted: 09/13/2023] [Indexed: 10/06/2023]
Abstract
Artificial intelligence tools, particularly convolutional neural networks (CNNs), are transforming healthcare by enhancing predictive, diagnostic, and decision-making capabilities. This review provides an accessible and practical explanation of CNNs for clinicians and highlights their relevance in medical image analysis. CNNs have shown themselves to be exceptionally useful in computer vision, a field that enables machines to 'see' and interpret visual data. Understanding how these models work can help clinicians leverage their full potential, especially as artificial intelligence continues to evolve and integrate into healthcare. CNNs have already demonstrated their efficacy in diverse medical fields, including radiology, histopathology, and medical photography. In radiology, CNNs have been used to automate the assessment of conditions such as pneumonia, pulmonary embolism, and rectal cancer. In histopathology, CNNs have been used to assess and classify colorectal polyps, gastric epithelial tumours, as well as assist in the assessment of multiple malignancies. In medical photography, CNNs have been used to assess retinal diseases and skin conditions, and to detect gastric and colorectal polyps during endoscopic procedures. In surgical laparoscopy, they may provide intraoperative assistance to surgeons, helping interpret surgical anatomy and demonstrate safe dissection zones. The integration of CNNs into medical image analysis promises to enhance diagnostic accuracy, streamline workflow efficiency, and expand access to expert-level image analysis, contributing to the ultimate goal of delivering further improvements in patient and healthcare outcomes.
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Affiliation(s)
- Georgios Kourounis
- NIHR Blood and Transplant Research Unit, Newcastle University and Cambridge University, Newcastle upon Tyne, NE1 7RU, United Kingdom
- Institute of Transplantation, The Freeman Hospital, Newcastle upon Tyne, NE7 7DN, United Kingdom
| | - Ali Ahmed Elmahmudi
- Faculty of Engineering and Informatics, Bradford University, Bradford, BD7 1DP, United Kingdom
| | - Brian Thomson
- Faculty of Engineering and Informatics, Bradford University, Bradford, BD7 1DP, United Kingdom
| | - James Hunter
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, OX3 9DU, United Kingdom
| | - Hassan Ugail
- Faculty of Engineering and Informatics, Bradford University, Bradford, BD7 1DP, United Kingdom
| | - Colin Wilson
- NIHR Blood and Transplant Research Unit, Newcastle University and Cambridge University, Newcastle upon Tyne, NE1 7RU, United Kingdom
- Institute of Transplantation, The Freeman Hospital, Newcastle upon Tyne, NE7 7DN, United Kingdom
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Wurm LM, Fischer B, Neuschmelting V, Reinecke D, Fischer I, Croner RS, Goldbrunner R, Hacker MC, Dybaś J, Kahlert UD. Rapid, label-free classification of glioblastoma differentiation status combining confocal Raman spectroscopy and machine learning. Analyst 2023; 148:6109-6119. [PMID: 37927114 DOI: 10.1039/d3an01303k] [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: 11/07/2023]
Abstract
Label-free identification of tumor cells using spectroscopic assays has emerged as a technological innovation with a proven ability for rapid implementation in clinical care. Machine learning facilitates the optimization of processing and interpretation of extensive data, such as various spectroscopy data obtained from surgical samples. The here-described preclinical work investigates the potential of machine learning algorithms combining confocal Raman spectroscopy to distinguish non-differentiated glioblastoma cells and their respective isogenic differentiated phenotype by means of confocal ultra-rapid measurements. For this purpose, we measured and correlated modalities of 1146 intracellular single-point measurements and sustainingly clustered cell components to predict tumor stem cell existence. By further narrowing a few selected peaks, we found indicative evidence that using our computational imaging technology is a powerful approach to detect tumor stem cells in vitro with an accuracy of 91.7% in distinct cell compartments, mainly because of greater lipid content and putative different protein structures. We also demonstrate that the presented technology can overcome intra- and intertumoral cellular heterogeneity of our disease models, verifying the elevated physiological relevance of our applied disease modeling technology despite intracellular noise limitations for future translational evaluation.
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Affiliation(s)
- Lennard M Wurm
- Department of Neurosurgery, University Hospital Düsseldorf and Medical Faculty Heinrich-Heine University, Düsseldorf, Germany
- Department of Neurosurgery, University Hospital Cologne, Cologne, Germany
| | - Björn Fischer
- Institute of Pharmaceutics and Biopharmaceutics, University of Düsseldorf, Düsseldorf, Germany
- FISCHER GmbH, Raman Spectroscopic Services, 40667 Meerbusch, Germany
| | | | - David Reinecke
- Department of Neurosurgery, University Hospital Cologne, Cologne, Germany
| | - Igor Fischer
- Department of Neurosurgery, University Hospital Düsseldorf and Medical Faculty Heinrich-Heine University, Düsseldorf, Germany
| | - Roland S Croner
- Clinic of General- Visceral-, Vascular and Transplantation Surgery, Department of Molecular and Experimental Surgery, University Hospital Magdeburg and Medical Faculty Otto-von-Guericke University, Magdeburg, Germany.
| | - Roland Goldbrunner
- Department of Neurosurgery, University Hospital Cologne, Cologne, Germany
| | - Michael C Hacker
- Institute of Pharmaceutics and Biopharmaceutics, University of Düsseldorf, Düsseldorf, Germany
| | - Jakub Dybaś
- Jagiellonian Center for Experimental Therapeutics, Jagiellonian University, Krakow, Poland
| | - Ulf D Kahlert
- Clinic of General- Visceral-, Vascular and Transplantation Surgery, Department of Molecular and Experimental Surgery, University Hospital Magdeburg and Medical Faculty Otto-von-Guericke University, Magdeburg, Germany.
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He S, Guan Y, Cheng CH, Moore TL, Luebke JI, Killiany RJ, Rosene DL, Koo BB, Ou Y. Human-to-monkey transfer learning identifies the frontal white matter as a key determinant for predicting monkey brain age. Front Aging Neurosci 2023; 15:1249415. [PMID: 38020785 PMCID: PMC10646581 DOI: 10.3389/fnagi.2023.1249415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 10/10/2023] [Indexed: 12/01/2023] Open
Abstract
The application of artificial intelligence (AI) to summarize a whole-brain magnetic resonance image (MRI) into an effective "brain age" metric can provide a holistic, individualized, and objective view of how the brain interacts with various factors (e.g., genetics and lifestyle) during aging. Brain age predictions using deep learning (DL) have been widely used to quantify the developmental status of human brains, but their wider application to serve biomedical purposes is under criticism for requiring large samples and complicated interpretability. Animal models, i.e., rhesus monkeys, have offered a unique lens to understand the human brain - being a species in which aging patterns are similar, for which environmental and lifestyle factors are more readily controlled. However, applying DL methods in animal models suffers from data insufficiency as the availability of animal brain MRIs is limited compared to many thousands of human MRIs. We showed that transfer learning can mitigate the sample size problem, where transferring the pre-trained AI models from 8,859 human brain MRIs improved monkey brain age estimation accuracy and stability. The highest accuracy and stability occurred when transferring the 3D ResNet [mean absolute error (MAE) = 1.83 years] and the 2D global-local transformer (MAE = 1.92 years) models. Our models identified the frontal white matter as the most important feature for monkey brain age predictions, which is consistent with previous histological findings. This first DL-based, anatomically interpretable, and adaptive brain age estimator could broaden the application of AI techniques to various animal or disease samples and widen opportunities for research in non-human primate brains across the lifespan.
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Affiliation(s)
- Sheng He
- Harvard Medical School, Boston Children's Hospital, Boston, MA, United States
| | - Yi Guan
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Chia Hsin Cheng
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Tara L. Moore
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Jennifer I. Luebke
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Ronald J. Killiany
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Douglas L. Rosene
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Bang-Bon Koo
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Yangming Ou
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
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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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Agadi K, Dominari A, Tebha SS, Mohammadi A, Zahid S. Neurosurgical Management of Cerebrospinal Tumors in the Era of Artificial Intelligence : A Scoping Review. J Korean Neurosurg Soc 2023; 66:632-641. [PMID: 35831137 PMCID: PMC10641423 DOI: 10.3340/jkns.2021.0213] [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: 08/23/2021] [Revised: 10/06/2021] [Accepted: 03/14/2022] [Indexed: 11/27/2022] Open
Abstract
Central nervous system tumors are identified as tumors of the brain and spinal cord. The associated morbidity and mortality of cerebrospinal tumors are disproportionately high compared to other malignancies. While minimally invasive techniques have initiated a revolution in neurosurgery, artificial intelligence (AI) is expediting it. Our study aims to analyze AI's role in the neurosurgical management of cerebrospinal tumors. We conducted a scoping review using the Arksey and O'Malley framework. Upon screening, data extraction and analysis were focused on exploring all potential implications of AI, classification of these implications in the management of cerebrospinal tumors. AI has enhanced the precision of diagnosis of these tumors, enables surgeons to excise the tumor margins completely, thereby reducing the risk of recurrence, and helps to make a more accurate prediction of the patient's prognosis than the conventional methods. AI also offers real-time training to neurosurgeons using virtual and 3D simulation, thereby increasing their confidence and skills during procedures. In addition, robotics is integrated into neurosurgery and identified to increase patient outcomes by making surgery less invasive. AI, including machine learning, is rigorously considered for its applications in the neurosurgical management of cerebrospinal tumors. This field requires further research focused on areas clinically essential in improving the outcome that is also economically feasible for clinical use. The authors suggest that data analysts and neurosurgeons collaborate to explore the full potential of AI.
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Affiliation(s)
- Kuchalambal Agadi
- Division of Research and Academic Affairs, Larkin Health System, South Miami, FL, USA
| | - Asimina Dominari
- Division of Research and Academic Affairs, Larkin Health System, South Miami, FL, USA
- Aristotle University of Thessaloniki School of Medicine, Thessaloniki, Greece
| | - Sameer Saleem Tebha
- Division of Research and Academic Affairs, Larkin Health System, South Miami, FL, USA
- Department of Neurosurgery and Neurology, Jinnah Medical and Dental College, Karachi, Pakistan
| | - Asma Mohammadi
- Division of Research and Academic Affairs, Larkin Health System, South Miami, FL, USA
| | - Samina Zahid
- Division of Research and Academic Affairs, Larkin Health System, South Miami, FL, USA
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50
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Soteriou D, Kubánková M, Schweitzer C, López-Posadas R, Pradhan R, Thoma OM, Györfi AH, Matei AE, Waldner M, Distler JHW, Scheuermann S, Langejürgen J, Eckstein M, Schneider-Stock R, Atreya R, Neurath MF, Hartmann A, Guck J. Rapid single-cell physical phenotyping of mechanically dissociated tissue biopsies. Nat Biomed Eng 2023; 7:1392-1403. [PMID: 37024677 PMCID: PMC10651479 DOI: 10.1038/s41551-023-01015-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 02/22/2023] [Indexed: 04/08/2023]
Abstract
During surgery, rapid and accurate histopathological diagnosis is essential for clinical decision making. Yet the prevalent method of intra-operative consultation pathology is intensive in time, labour and costs, and requires the expertise of trained pathologists. Here we show that biopsy samples can be analysed within 30 min by sequentially assessing the physical phenotypes of singularized suspended cells dissociated from the tissues. The diagnostic method combines the enzyme-free mechanical dissociation of tissues, real-time deformability cytometry at rates of 100-1,000 cells s-1 and data analysis by unsupervised dimensionality reduction and logistic regression. Physical phenotype parameters extracted from brightfield images of single cells distinguished cell subpopulations in various tissues, enhancing or even substituting measurements of molecular markers. We used the method to quantify the degree of colon inflammation and to accurately discriminate healthy and tumorous tissue in biopsy samples of mouse and human colons. This fast and label-free approach may aid the intra-operative detection of pathological changes in solid biopsies.
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Affiliation(s)
- Despina Soteriou
- Max Planck Institute for the Science of Light and Max-Planck-Zentrum für Physik und Medizin, Erlangen, Germany
| | - Markéta Kubánková
- Max Planck Institute for the Science of Light and Max-Planck-Zentrum für Physik und Medizin, Erlangen, Germany
| | - Christine Schweitzer
- Max Planck Institute for the Science of Light and Max-Planck-Zentrum für Physik und Medizin, Erlangen, Germany
| | - Rocío López-Posadas
- Department of Medicine 1-Gastroenterology, Pneumology and Endocrinology, Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and University Hospital Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and University Hospital Erlangen, Erlangen, Germany
| | - Rashmita Pradhan
- Department of Medicine 1-Gastroenterology, Pneumology and Endocrinology, Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and University Hospital Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and University Hospital Erlangen, Erlangen, Germany
| | - Oana-Maria Thoma
- Department of Medicine 1-Gastroenterology, Pneumology and Endocrinology, Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and University Hospital Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and University Hospital Erlangen, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Andrea-Hermina Györfi
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and University Hospital Erlangen, Erlangen, Germany
- Department of Internal Medicine 3-Rheumatology and Immunology, Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and University Hospital Erlangen, Erlangen, Germany
| | - Alexandru-Emil Matei
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and University Hospital Erlangen, Erlangen, Germany
- Department of Internal Medicine 3-Rheumatology and Immunology, Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and University Hospital Erlangen, Erlangen, Germany
| | - Maximilian Waldner
- Department of Medicine 1-Gastroenterology, Pneumology and Endocrinology, Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and University Hospital Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and University Hospital Erlangen, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Jörg H W Distler
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and University Hospital Erlangen, Erlangen, Germany
- Department of Internal Medicine 3-Rheumatology and Immunology, Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and University Hospital Erlangen, Erlangen, Germany
| | | | | | - Markus Eckstein
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
- Institute of Pathology, University Hospital, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Regine Schneider-Stock
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
- Institute of Pathology, University Hospital, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Raja Atreya
- Department of Medicine 1-Gastroenterology, Pneumology and Endocrinology, Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and University Hospital Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and University Hospital Erlangen, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Markus F Neurath
- Department of Medicine 1-Gastroenterology, Pneumology and Endocrinology, Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and University Hospital Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and University Hospital Erlangen, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Arndt Hartmann
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
- Institute of Pathology, University Hospital, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Jochen Guck
- Max Planck Institute for the Science of Light and Max-Planck-Zentrum für Physik und Medizin, Erlangen, Germany.
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