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Bellos T, Manolitsis I, Katsimperis S, Juliebø-Jones P, Feretzakis G, Mitsogiannis I, Varkarakis I, Somani BK, Tzelves L. Artificial Intelligence in Urologic Robotic Oncologic Surgery: A Narrative Review. Cancers (Basel) 2024; 16:1775. [PMID: 38730727 PMCID: PMC11083167 DOI: 10.3390/cancers16091775] [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: 02/26/2024] [Revised: 04/29/2024] [Accepted: 05/02/2024] [Indexed: 05/13/2024] Open
Abstract
With the rapid increase in computer processing capacity over the past two decades, machine learning techniques have been applied in many sectors of daily life. Machine learning in therapeutic settings is also gaining popularity. We analysed current studies on machine learning in robotic urologic surgery. We searched PubMed/Medline and Google Scholar up to December 2023. Search terms included "urologic surgery", "artificial intelligence", "machine learning", "neural network", "automation", and "robotic surgery". Automatic preoperative imaging, intraoperative anatomy matching, and bleeding prediction has been a major focus. Early artificial intelligence (AI) therapeutic outcomes are promising. Robot-assisted surgery provides precise telemetry data and a cutting-edge viewing console to analyse and improve AI integration in surgery. Machine learning enhances surgical skill feedback, procedure effectiveness, surgical guidance, and postoperative prediction. Tension-sensors on robotic arms and augmented reality can improve surgery. This provides real-time organ motion monitoring, improving precision and accuracy. As datasets develop and electronic health records are used more and more, these technologies will become more effective and useful. AI in robotic surgery is intended to improve surgical training and experience. Both seek precision to improve surgical care. AI in ''master-slave'' robotic surgery offers the detailed, step-by-step examination of autonomous robotic treatments.
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Affiliation(s)
- Themistoklis Bellos
- 2nd Department of Urology, Sismanoglio General Hospital of Athens, 15126 Athens, Greece; (T.B.); (I.M.); (S.K.); (I.M.); (I.V.)
| | - Ioannis Manolitsis
- 2nd Department of Urology, Sismanoglio General Hospital of Athens, 15126 Athens, Greece; (T.B.); (I.M.); (S.K.); (I.M.); (I.V.)
| | - Stamatios Katsimperis
- 2nd Department of Urology, Sismanoglio General Hospital of Athens, 15126 Athens, Greece; (T.B.); (I.M.); (S.K.); (I.M.); (I.V.)
| | | | - Georgios Feretzakis
- School of Science and Technology, Hellenic Open University, 26335 Patras, Greece;
| | - Iraklis Mitsogiannis
- 2nd Department of Urology, Sismanoglio General Hospital of Athens, 15126 Athens, Greece; (T.B.); (I.M.); (S.K.); (I.M.); (I.V.)
| | - Ioannis Varkarakis
- 2nd Department of Urology, Sismanoglio General Hospital of Athens, 15126 Athens, Greece; (T.B.); (I.M.); (S.K.); (I.M.); (I.V.)
| | - Bhaskar K. Somani
- Department of Urology, University of Southampton, Southampton SO16 6YD, UK;
| | - Lazaros Tzelves
- 2nd Department of Urology, Sismanoglio General Hospital of Athens, 15126 Athens, Greece; (T.B.); (I.M.); (S.K.); (I.M.); (I.V.)
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Salimi M, Roshanfar M, Tabatabaei N, Mosadegh B. Machine Learning-Assisted Short-Wave InfraRed (SWIR) Techniques for Biomedical Applications: Towards Personalized Medicine. J Pers Med 2023; 14:33. [PMID: 38248734 PMCID: PMC10817559 DOI: 10.3390/jpm14010033] [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: 10/24/2023] [Revised: 12/08/2023] [Accepted: 12/20/2023] [Indexed: 01/23/2024] Open
Abstract
Personalized medicine transforms healthcare by adapting interventions to individuals' unique genetic, molecular, and clinical profiles. To maximize diagnostic and/or therapeutic efficacy, personalized medicine requires advanced imaging devices and sensors for accurate assessment and monitoring of individual patient conditions or responses to therapeutics. In the field of biomedical optics, short-wave infrared (SWIR) techniques offer an array of capabilities that hold promise to significantly enhance diagnostics, imaging, and therapeutic interventions. SWIR techniques provide in vivo information, which was previously inaccessible, by making use of its capacity to penetrate biological tissues with reduced attenuation and enable researchers and clinicians to delve deeper into anatomical structures, physiological processes, and molecular interactions. Combining SWIR techniques with machine learning (ML), which is a powerful tool for analyzing information, holds the potential to provide unprecedented accuracy for disease detection, precision in treatment guidance, and correlations of complex biological features, opening the way for the data-driven personalized medicine field. Despite numerous biomedical demonstrations that utilize cutting-edge SWIR techniques, the clinical potential of this approach has remained significantly underexplored. This paper demonstrates how the synergy between SWIR imaging and ML is reshaping biomedical research and clinical applications. As the paper showcases the growing significance of SWIR imaging techniques that are empowered by ML, it calls for continued collaboration between researchers, engineers, and clinicians to boost the translation of this technology into clinics, ultimately bridging the gap between cutting-edge technology and its potential for personalized medicine.
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Affiliation(s)
| | - Majid Roshanfar
- Department of Mechanical Engineering, Concordia University, Montreal, QC H3G 1M8, Canada;
| | - Nima Tabatabaei
- Department of Mechanical Engineering, York University, Toronto, ON M3J 1P3, Canada;
| | - Bobak Mosadegh
- Dalio Institute of Cardiovascular Imaging, Department of Radiology, Weill Cornell Medicine, New York, NY 10021, USA
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Seth I, Bulloch G, Joseph K, Hunter-Smith DJ, Rozen WM. Use of Artificial Intelligence in the Advancement of Breast Surgery and Implications for Breast Reconstruction: A Narrative Review. J Clin Med 2023; 12:5143. [PMID: 37568545 PMCID: PMC10419723 DOI: 10.3390/jcm12155143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 07/28/2023] [Accepted: 08/04/2023] [Indexed: 08/13/2023] Open
Abstract
BACKGROUND Breast reconstruction is a pivotal part of the recuperation process following a mastectomy and aims to restore both the physical aesthetic and emotional well-being of breast cancer survivors. In recent years, artificial intelligence (AI) has emerged as a revolutionary technology across numerous medical disciplines. This narrative review of the current literature and evidence analysis explores the role of AI in the domain of breast reconstruction, outlining its potential to refine surgical procedures, enhance outcomes, and streamline decision making. METHODS A systematic search on Medline (via PubMed), Cochrane Library, Web of Science, Google Scholar, Clinical Trials, and Embase databases from January 1901 to June 2023 was conducted. RESULTS By meticulously evaluating a selection of recent studies and engaging with inherent challenges and prospective trajectories, this review spotlights the promising role AI plays in advancing the techniques of breast reconstruction. However, issues concerning data quality, privacy, and ethical considerations pose hurdles to the seamless integration of AI in the medical field. CONCLUSION The future research agenda comprises dataset standardization, AI algorithm refinement, and the implementation of prospective clinical trials and fosters cross-disciplinary partnerships. The fusion of AI with other emergent technologies like augmented reality and 3D printing could further propel progress in breast surgery.
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Affiliation(s)
- Ishith Seth
- Department of Plastic Surgery, Peninsula Health, Melbourne, VIC 3199, Australia
- Faculty of Medicine, The University of Melbourne, Melbourne, VIC 3053, Australia
| | - Gabriella Bulloch
- Faculty of Medicine, The University of Melbourne, Melbourne, VIC 3053, Australia
| | - Konrad Joseph
- Faculty of Medicine, The University of Wollongong, Wollongon, NSW 2500, Australia
| | | | - Warren Matthew Rozen
- Department of Plastic Surgery, Peninsula Health, Melbourne, VIC 3199, Australia
- Faculty of Medicine, The University of Melbourne, Melbourne, VIC 3053, Australia
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Sun Y, Dumont AP, Arefin MS, Patil CA. Model-based characterization platform of fiber optic extended-wavelength diffuse reflectance spectroscopy for identification of neurovascular bundles. JOURNAL OF BIOMEDICAL OPTICS 2022; 27:095002. [PMID: 36088529 PMCID: PMC9463544 DOI: 10.1117/1.jbo.27.9.095002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 08/22/2022] [Indexed: 06/15/2023]
Abstract
SIGNIFICANCE Fiber-optic extended-wavelength diffuse reflectance spectroscopy (EWDRS) using both visible/near-infrared and shortwave-infrared detectors enables improved detection of spectral absorbances arising from lipids, water, and collagen and has demonstrated promise in a variety of applications, including detection of nerves and neurovascular bundles (NVB). Development of future applications of EWDRS for nerve detection could benefit from the use of model-based analyses including Monte Carlo (MC) simulations and evaluation of agreement between model systems and empirical measurements. AIM The aim of this work is to characterize agreement between EWDRS measurements and simulations and inform future applications of model-based studies of nerve-detecting applications. APPROACH A model-based platform consisting of an ex vivo microsurgical nerve dissection model, unique two-layer optical phantoms, and MC model simulations of fiber-optic EWDRS spectroscopic measurements were used to characterize EWDRS and compare agreement across models. In addition, MC simulations of an EWDRS measurement scenario are performed to provide a representative example of future analyses. RESULTS EWDRS studies performed in the common chicken thigh femoral nerve microsurgical dissection model indicate similar spectral features for classification of NVB versus adjacent tissues as reported in porcine models and human subjects. A comparison of measurements from unique EWDRS issue mimicking optical phantoms and MC simulations indicates high agreement between the two in homogeneous and two-layer optical phantoms, as well as in dissected tissues. Finally, MC simulations of measurement over a simulated NVB indicate the potential of future applications for measurement of nerve plexus. CONCLUSIONS Characterization of agreement between fiber-optic EWDRS measurements and MC simulations demonstrates strong agreement across a variety of tissues and optical phantoms, offering promise for further use to guide the continued development of EWDRS for translational applications.
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Affiliation(s)
- Yu Sun
- Temple University, Department of Bioengineering, Philadelphia, Pennsylvania, United States
| | - Alexander P. Dumont
- Temple University, Department of Bioengineering, Philadelphia, Pennsylvania, United States
| | | | - Chetan A. Patil
- Temple University, Department of Bioengineering, Philadelphia, Pennsylvania, United States
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郝 哲, 岳 蜀, 周 利. [Application of Raman-based technologies in the detection of urological tumors]. BEIJING DA XUE XUE BAO. YI XUE BAN = JOURNAL OF PEKING UNIVERSITY. HEALTH SCIENCES 2022; 54:779-784. [PMID: 35950408 PMCID: PMC9385527 DOI: 10.19723/j.issn.1671-167x.2022.04.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Indexed: 06/15/2023]
Abstract
Urinary system tumors affect a huge number of individuals, and are frequently recurrent and progressing following surgery, necessitating lifelong surveillance. As a result, early and precise diagnosis of urinary system cancers is important for prevention and therapy. Histopathology is now the golden stan-dard for the diagnosis, but it is invasive, time-consuming, and inconvenient for initial diagnosis and re-gular follow-up assessment. Endoscopy can directly witness the tumor's structure, but intrusive detection is likely to cause harm to the patient's organs, and it is apt to create other hazards in frequently examined patients. Imaging is a valuable non-invasive and quick assessment tool; however, it can be difficult to define the type of lesions and has limited sensitivity for early tumor detection. The conventional approaches for detecting tumors have their own set of limitations. Thus, detection methods that combine non-invasive detection, label-free detection, high sensitivity and high specificity are urgently needed to aid clinical diagnosis. Optical diagnostics and imaging are increasingly being employed in healthcare settings in a variety of sectors. Raman scattering can assess changes in molecular signatures in cancer cells or tissues based on the interaction with vibrational modes of common molecular bonds. Due to the advantages of label-free, strong chemical selectivity, and high sensitivity, Raman scattering, especially coherent Raman scattering microscopy imaging with high spatial resolution, has been widely used in biomedical research. And quantity studies have shown that it has a good application in the detection and diagnosis of bladder can-cer, renal clear cell carcinoma, prostate cancer, and other cancers. In this paper, several nonlinear imaging techniques based on Raman scattering technology are briefly described, including Raman spectroscopy, coherent anti-Stokes Raman scattering, stimulated Raman scattering, and surface-enhanced Raman spectroscopy. And we will discuss the application of these techniques for detecting urologic malignancy. Future research directions are predicted using the advantages and limitations of the aforesaid methodologies in the research. For clinical practice, Raman scattering technology is intended to enable more accurate, rapid, and non-invasive in early diagnosis, intraoperative margins, and pathological grading basis for clinical practice.
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Affiliation(s)
- 哲 郝
- 北京航空航天大学生物与医学工程学院,北京市生物医学工程高精尖创新中心,生物力学与力生物学教育部重点实验室,医用光子学研究所,北京 100083School of Biological and Medical Engineering, Beihang University, Beijing Advanced Innovation Center for Biomedical Engineering, Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Institute of Medical Photonics, Beijing 100083, China
| | - 蜀华 岳
- 北京航空航天大学生物与医学工程学院,北京市生物医学工程高精尖创新中心,生物力学与力生物学教育部重点实验室,医用光子学研究所,北京 100083School of Biological and Medical Engineering, Beihang University, Beijing Advanced Innovation Center for Biomedical Engineering, Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Institute of Medical Photonics, Beijing 100083, China
| | - 利群 周
- 北京航空航天大学生物与医学工程学院,北京市生物医学工程高精尖创新中心,生物力学与力生物学教育部重点实验室,医用光子学研究所,北京 100083School of Biological and Medical Engineering, Beihang University, Beijing Advanced Innovation Center for Biomedical Engineering, Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Institute of Medical Photonics, Beijing 100083, China
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Taieb A, Berkovic G, Haifler M, Cheshnovsky O, Shaked NT. Classification of tissue biopsies by Raman spectroscopy guided by quantitative phase imaging and its application to bladder cancer. JOURNAL OF BIOPHOTONICS 2022; 15:e202200009. [PMID: 35488750 DOI: 10.1002/jbio.202200009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 03/25/2022] [Accepted: 04/11/2022] [Indexed: 06/14/2023]
Abstract
We present a multimodal label-free optical measurement approach for analyzing sliced tissue biopsies by a unique combination of quantitative phase imaging and localized Raman spectroscopy. First, label-free quantitative phase imaging of the entire unstained tissue slice is performed using automated scanning. Then, pixel-wise segmentation of the tissue layers is performed by a kernelled structural support vector machine based on Haralick texture features, which are extracted from the quantitative phase profile, and used to find the best locations for performing the label-free localized Raman measurements. We use this multimodal label-free measurement approach for segmenting the urothelium in benign and malignant bladder cancer tissues by quantitative phase imaging, followed by location-guided Raman spectroscopy measurements. We then use sparse multinomial logistic regression (SMLR) on the Raman spectroscopy measurements to classify the tissue types, demonstrating that the prior segmentation of the urothelium done by label-free quantitative phase imaging improves the Raman spectra classification accuracy from 85.7% to 94.7%.
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Affiliation(s)
- Almog Taieb
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Garry Berkovic
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
- Soreq Nuclear Research Center, Yavne, Israel
| | - Miki Haifler
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
- Department of Urology, Chaim Sheba Medical Center, Tel Hashomer, Israel, Affiliated to Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Ori Cheshnovsky
- School of Chemistry, Faculty of Exact Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Natan T Shaked
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
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Artificial intelligence for renal cancer: From imaging to histology and beyond. Asian J Urol 2022; 9:243-252. [PMID: 36035341 PMCID: PMC9399557 DOI: 10.1016/j.ajur.2022.05.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 04/07/2022] [Accepted: 05/07/2022] [Indexed: 12/24/2022] Open
Abstract
Artificial intelligence (AI) has made considerable progress within the last decade and is the subject of contemporary literature. This trend is driven by improved computational abilities and increasing amounts of complex data that allow for new approaches in analysis and interpretation. Renal cell carcinoma (RCC) has a rising incidence since most tumors are now detected at an earlier stage due to improved imaging. This creates considerable challenges as approximately 10%–17% of kidney tumors are designated as benign in histopathological evaluation; however, certain co-morbid populations (the obese and elderly) have an increased peri-interventional risk. AI offers an alternative solution by helping to optimize precision and guidance for diagnostic and therapeutic decisions. The narrative review introduced basic principles and provide a comprehensive overview of current AI techniques for RCC. Currently, AI applications can be found in any aspect of RCC management including diagnostics, perioperative care, pathology, and follow-up. Most commonly applied models include neural networks, random forest, support vector machines, and regression. However, for implementation in daily practice, health care providers need to develop a basic understanding and establish interdisciplinary collaborations in order to standardize datasets, define meaningful endpoints, and unify interpretation.
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Chen C, Chen F, Yang B, Zhang K, Lv X, Chen C. A novel diagnostic method: FT-IR, Raman and derivative spectroscopy fusion technology for the rapid diagnosis of renal cell carcinoma serum. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 269:120684. [PMID: 34929625 DOI: 10.1016/j.saa.2021.120684] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Revised: 11/23/2021] [Accepted: 11/26/2021] [Indexed: 06/14/2023]
Abstract
This research innovatively combines FT-IR, Raman spectroscopy and their first-derivative spectroscopy to develop a rapid diagnosis method for renal cell carcinoma (RCC). After measuring the Raman spectra and FT-IR spectra of 45 cases of control subjects and 28 cases of RCC, the first derivative of the infrared spectra and the Raman spectra were calculated respectively. Principal component analysis (PCA) was used to extract the features of the infrared spectra, first-derivative infrared spectra, Raman spectra and first-derivative Raman spectra. Then the four feature matrices were merged as fused spectral feature matrices. The fused matrices were used as the input of AlexNet and MCNN. The fused spectral feature matrices were used as the input of AlexNet and MCNN. The adjusted AlexNet model performed better, and the classification accuracy of the fused spectral data is 93%. Compared with the classification results of infrared spectra (74%), Raman spectra (75%) and the fusion of infrared and Raman spectra (79%) combined with the adjusted AlexNet model, the classification result of the fusion of infrared spectra, Raman spectra and their first-derivative was significantly improved. The experimental results show that infrared spectroscopy, Raman spectroscopy and their first-derivative fusion technology combined with deep learning algorithms has great potential in the diagnosis of RCC.
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Affiliation(s)
- Cheng Chen
- College of Software, Xinjiang University, Urumqi 830046, Xinjiang, China
| | - Fangfang Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Bo Yang
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Kai Zhang
- College of Software, Xinjiang University, Urumqi 830046, Xinjiang, China
| | - Xiaoyi Lv
- College of Software, Xinjiang University, Urumqi 830046, Xinjiang, China; Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi 830046, Xinjiang, China.
| | - Chen Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.
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Lauwerends LJ, Abbasi H, Bakker Schut TC, Van Driel PBAA, Hardillo JAU, Santos IP, Barroso EM, Koljenović S, Vahrmeijer AL, Baatenburg de Jong RJ, Puppels GJ, Keereweer S. The complementary value of intraoperative fluorescence imaging and Raman spectroscopy for cancer surgery: combining the incompatibles. Eur J Nucl Med Mol Imaging 2022; 49:2364-2376. [PMID: 35102436 PMCID: PMC9165240 DOI: 10.1007/s00259-022-05705-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 01/23/2022] [Indexed: 01/09/2023]
Abstract
A clear margin is an important prognostic factor for most solid tumours treated by surgery. Intraoperative fluorescence imaging using exogenous tumour-specific
fluorescent agents has shown particular benefit in improving complete resection of tumour tissue. However, signal processing for fluorescence imaging is complex, and fluorescence signal intensity does not always perfectly correlate with tumour location. Raman spectroscopy has the capacity to accurately differentiate between malignant and healthy tissue based on their molecular composition. In Raman spectroscopy, specificity is uniquely high, but signal intensity is weak and Raman measurements are mainly performed in a point-wise manner on microscopic tissue volumes, making whole-field assessment temporally unfeasible. In this review, we describe the state-of-the-art of both optical techniques, paying special attention to the combined intraoperative application of fluorescence imaging and Raman spectroscopy in current clinical research. We demonstrate how these techniques are complementary and address the technical challenges that have traditionally led them to be considered mutually exclusive for clinical implementation. Finally, we present a novel strategy that exploits the optimal characteristics of both modalities to facilitate resection with clear surgical margins.
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Affiliation(s)
- L J Lauwerends
- Department of Otorhinolaryngology, Head and Neck Surgery, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - H Abbasi
- Department of Otorhinolaryngology, Head and Neck Surgery, Erasmus MC Cancer Institute, Rotterdam, Netherlands.,Center for Optical Diagnostics and Therapy, Department of Dermatology, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - T C Bakker Schut
- Center for Optical Diagnostics and Therapy, Department of Dermatology, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - P B A A Van Driel
- Department of Orthopedic Surgery, Isala Hospital, Zwolle, Netherlands
| | - J A U Hardillo
- Department of Otorhinolaryngology, Head and Neck Surgery, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - I P Santos
- Molecular Physical-Chemistry R&D Unit, Department of Chemistry, University of Coimbra, Coimbra, Portugal
| | | | - S Koljenović
- Department of Pathology, Antwerp University Hospital/Antwerp University, Antwerp, Belgium
| | - A L Vahrmeijer
- Department of Surgery, Leiden University Medical Center, Leiden, Netherlands
| | - R J Baatenburg de Jong
- Department of Otorhinolaryngology, Head and Neck Surgery, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - G J Puppels
- Center for Optical Diagnostics and Therapy, Department of Dermatology, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - S Keereweer
- Department of Otorhinolaryngology, Head and Neck Surgery, Erasmus MC Cancer Institute, Rotterdam, Netherlands.
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Artificial Intelligence in Urology. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Zhou H, Piñeiro Llanes J, Sarntinoranont M, Subhash G, Simmons CS. Label-free quantification of soft tissue alignment by polarized Raman spectroscopy. Acta Biomater 2021; 136:363-374. [PMID: 34537413 DOI: 10.1016/j.actbio.2021.09.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 08/24/2021] [Accepted: 09/09/2021] [Indexed: 11/29/2022]
Abstract
The organization of proteins is an important determinant of functionality in soft tissues. However, such organization is difficult to monitor over time in soft tissue with complex compositions. Here, we establish a method to determine the alignment of proteins in soft tissues of varying composition by polarized Raman spectroscopy (PRS). Unlike most conventional microscopy methods, PRS leverages non-destructive, label-free sample preparation. PRS data from highly aligned muscle layers were utilized to derive a weighting function for aligned proteins via principal component analysis (PCA). This trained weighting function was used as a master loading function to calculate a principal component score (PC1 Score) as a function of polarized angle for tendon, dermis, hypodermis, and fabricated collagen gels. Since the PC1 Score calculated at arbitrary angles was insufficient to determine level of alignment, we developed an Amplitude Alignment Metric by fitting a sine function to PC1 Score with respect to polarized angle. We found that our PRS-based Amplitude Alignment Metric can be used as an indicator of level of protein alignment in soft tissues in a non-destructive manner with label-free preparation and has similar discriminatory capacity among isotropic and anisotropic samples compared to microscopy-based image processing method. This PRS method does not require a priori knowledge of sample orientation nor composition and appears insensitive to changes in protein composition among different tissues. The Amplitude Alignment Metric introduced here could enable convenient and adaptable evaluation of protein alignment in soft tissues of varying protein and cell composition. STATEMENT OF SIGNIFICANCE: Polarized Raman spectroscopy (PRS) has been used to characterize the of organization of soft tissues. However, most of the reported applications of PRS have been on collagen-rich tissues and reliant on intensities of collagen-related vibrations. This work describes a PRS method via a multivariate analysis to characterize alignment in soft tissues composed of varying proteins. Of note, the highly aligned muscle layer of mouse skin was used to train a master function then applied to other soft tissue samples, and the degree of anisotropy in the PRS response was evaluated to obtain the level of alignment in tissues. We have demonstrated that this method supports convenient and adaptable evaluation of protein alignment in soft tissues of varying protein and cell composition.
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Affiliation(s)
- Hui Zhou
- Department of Mechanical and Aerospace Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, Florida, USA
| | - Janny Piñeiro Llanes
- J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, Florida, USA
| | - Malisa Sarntinoranont
- Department of Mechanical and Aerospace Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, Florida, USA
| | - Ghatu Subhash
- Department of Mechanical and Aerospace Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, Florida, USA
| | - Chelsey S Simmons
- J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, Florida, USA.
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Dumont AP, Fang Q, Patil CA. A computationally efficient Monte-Carlo model for biomedical Raman spectroscopy. JOURNAL OF BIOPHOTONICS 2021; 14:e202000377. [PMID: 33733621 PMCID: PMC10069992 DOI: 10.1002/jbio.202000377] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 02/22/2021] [Accepted: 02/23/2021] [Indexed: 05/29/2023]
Abstract
Monte Carlo (MC) modeling is a valuable tool to gain fundamental understanding of light-tissue interactions, provide guidance and assessment to optical instrument designs, and help analyze experimental data. It has been a major challenge to efficiently extend MC towards modeling of bulk-tissue Raman spectroscopy (RS) due to the wide spectral range, relatively sharp spectral features, and presence of background autofluorescence. Here, we report a computationally efficient MC approach for RS by adapting the massively-parallel Monte Carlo eXtreme (MCX) simulator. Simulation efficiency is achieved through "isoweight," a novel approach that combines the statistical generation of Raman scattered and Fluorescence emission with a lookup-table-based technique well-suited for parallelization. The MC model uses a graphics processor to produce dense Raman and fluorescence spectra over a range of 800 - 2000 cm-1 with an approximately 100× increase in speed over prior RS Monte Carlo methods. The simulated RS signals are compared against experimentally collected spectra from gelatin phantoms, showing a strong correlation.
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Affiliation(s)
- Alexander P. Dumont
- Department of Bioengineering, Temple University, Philadelphia, Pennsylvania, USA
| | - Qianqian Fang
- Department of Bioengineering, Northeastern University, Boston, Massachusetts, USA
| | - Chetan A. Patil
- Department of Bioengineering, Temple University, Philadelphia, Pennsylvania, USA
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13
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He C, Wu X, Zhou J, Chen Y, Ye J. Raman optical identification of renal cell carcinoma via machine learning. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 252:119520. [PMID: 33582436 DOI: 10.1016/j.saa.2021.119520] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2020] [Revised: 01/18/2021] [Accepted: 01/19/2021] [Indexed: 05/06/2023]
Abstract
High pathologic tumor-node-metastasis (pTNM) stage grade or Fuhrman grade indicates poor oncological outcome in renal cell carcinoma (RCC). Early diagnosis and screening of these RCCs and adjust surgical planning accordingly are greatly beneficial to patients. Raman spectroscopy is a highly specific fingerprint spectrum on molecular level, pretty appropriate for label-free and noninvasive cancer diagnosis. In this work we established a Raman spectrum-based supporting vector machine (SVM) model to accurately ex vivo distinguish human renal tumor from normal tissues and fat with an accuracy of 92.89%. The model can also be used to determine tumor boundary, showing consistent results to pathological staining analysis. This method can be additionally used to accomplish the classification purposes of renal tumor subtypes and grades with an accuracy of 86.79% and 89.53%, respectively. Therefore, we prove that Raman spectroscopy has great potential in the rapid and accurate clinical diagnosis of renal cancers.
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Affiliation(s)
- Chang He
- State Key Laboratory of Oncogenes and Related Genes, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, PR China
| | - Xiaorong Wu
- Department of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PR China
| | - Jiale Zhou
- Department of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PR China
| | - Yonghui Chen
- Department of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PR China.
| | - Jian Ye
- State Key Laboratory of Oncogenes and Related Genes, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, PR China; Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PR China.
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14
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Artificial Intelligence and Its Impact on Urological Diseases and Management: A Comprehensive Review of the Literature. J Clin Med 2021; 10:jcm10091864. [PMID: 33925767 PMCID: PMC8123407 DOI: 10.3390/jcm10091864] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 04/04/2021] [Accepted: 04/08/2021] [Indexed: 12/22/2022] Open
Abstract
Recent advances in artificial intelligence (AI) have certainly had a significant impact on the healthcare industry. In urology, AI has been widely adopted to deal with numerous disorders, irrespective of their severity, extending from conditions such as benign prostate hyperplasia to critical illnesses such as urothelial and prostate cancer. In this article, we aim to discuss how algorithms and techniques of artificial intelligence are equipped in the field of urology to detect, treat, and estimate the outcomes of urological diseases. Furthermore, we explain the advantages that come from using AI over any existing traditional methods.
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15
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Müller-Deile J, Sarau G, Kotb AM, Jaremenko C, Rolle-Kampczyk UE, Daniel C, Kalkhof S, Christiansen SH, Schiffer M. Novel diagnostic and therapeutic techniques reveal changed metabolic profiles in recurrent focal segmental glomerulosclerosis. Sci Rep 2021; 11:4577. [PMID: 33633212 PMCID: PMC7907124 DOI: 10.1038/s41598-021-83883-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 02/09/2021] [Indexed: 12/19/2022] Open
Abstract
Idiopathic forms of Focal Segmental Glomerulosclerosis (FSGS) are caused by circulating permeability factors, which can lead to early recurrence of FSGS and kidney failure after kidney transplantation. In the past three decades, many research endeavors were undertaken to identify these unknown factors. Even though some potential candidates have been recently discussed in the literature, "the" actual factor remains elusive. Therefore, there is an increased demand in FSGS research for the use of novel technologies that allow us to study FSGS from a yet unexplored angle. Here, we report the successful treatment of recurrent FSGS in a patient after living-related kidney transplantation by removal of circulating factors with CytoSorb apheresis. Interestingly, the classical published circulating factors were all in normal range in this patient but early disease recurrence in the transplant kidney and immediate response to CytoSorb apheresis were still suggestive for pathogenic circulating factors. To proof the functional effects of the patient's serum on podocytes and the glomerular filtration barrier we used a podocyte cell culture model and a proteinuria model in zebrafish to detect pathogenic effects on the podocytes actin cytoskeleton inducing a functional phenotype and podocyte effacement. We then performed Raman spectroscopy in the < 50 kDa serum fraction, on cultured podocytes treated with the FSGS serum and in kidney biopsies of the same patient at the time of transplantation and at the time of disease recurrence. The analysis revealed changes in podocyte metabolome induced by the FSGS serum as well as in focal glomerular and parietal epithelial cell regions in the FSGS biopsy. Several altered Raman spectra were identified in the fractionated serum and metabolome analysis by mass spectrometry detected lipid profiles in the FSGS serum, which were supported by disturbances in the Raman spectra. Our novel innovative analysis reveals changed lipid metabolome profiles associated with idiopathic FSGS that might reflect a new subtype of the disease.
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Affiliation(s)
- Janina Müller-Deile
- Department of Nephrology and Hypertension, Friedrich-Alexander-University (FAU) Erlangen-Nuremberg, Erlangen, Germany.
| | - George Sarau
- Fraunhofer Institute for Ceramic Technologies and Systems IKTS, Dresden, Germany.,Leuchs Emeritus Group, Max Planck Institute for the Science of Light, Erlangen, Germany.,Institute for Nanotechnology and Correlative Microscopy eV INAM, Forchheim, Germany
| | - Ahmed M Kotb
- Department of Nephrology and Hypertension, Friedrich-Alexander-University (FAU) Erlangen-Nuremberg, Erlangen, Germany.,Department of Anatomy and Histology, Faculty of Veterinary Medicine, Assiut University, Asyût, Egypt
| | - Christian Jaremenko
- Institute for Nanotechnology and Correlative Microscopy eV INAM, Forchheim, Germany.,Institute of Optics, Information and Photonics, Friedrich-Alexander-University (FAU) Erlangen-Nuremberg, Erlangen, Germany
| | - Ulrike E Rolle-Kampczyk
- Department Molecular Systems Biology, Helmholtz Centre for Environmental Research, Leipzig, Germany
| | - Christoph Daniel
- Department of Nephropathology, Friedrich-Alexander-University (FAU) Erlangen-Nuremberg, Erlangen, Germany
| | - Stefan Kalkhof
- Institute for Bioanalysis, University of Applied Sciences Coburg, Coburg, Germany.,Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research-UFZ, Leipzig, Germany
| | - Silke H Christiansen
- Fraunhofer Institute for Ceramic Technologies and Systems IKTS, Dresden, Germany.,Leuchs Emeritus Group, Max Planck Institute for the Science of Light, Erlangen, Germany.,Institute for Nanotechnology and Correlative Microscopy eV INAM, Forchheim, Germany.,Physics Department, Freie Universität Berlin, Berlin, Germany
| | - Mario Schiffer
- Department of Nephrology and Hypertension, Friedrich-Alexander-University (FAU) Erlangen-Nuremberg, Erlangen, Germany
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16
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Chaichi A, Hasan SMA, Mehta N, Donnarumma F, Ebenezer P, Murray KK, Francis J, Gartia MR. Label-free lipidome study of paraventricular thalamic nucleus (PVT) of rat brain with post-traumatic stress injury by Raman imaging. Analyst 2021; 146:170-183. [PMID: 33135036 DOI: 10.1039/d0an01615b] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Post-traumatic stress disorder (PTSD) is a widespread psychiatric injury that develops serious life-threatening symptoms like substance abuse, severe depression, cognitive impairments, and persistent anxiety. However, the mechanisms of post-traumatic stress injury in brain are poorly understood due to the lack of practical methods to reveal biochemical alterations in various brain regions affected by this type of injury. Here, we introduce a novel method that provides quantitative results from Raman maps in the paraventricular nucleus of the thalamus (PVT) region. By means of this approach, we have shown a lipidome comparison in PVT regions of control and PTSD rat brains. Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry was also employed for validation of the Raman results. Lipid alterations can reveal invaluable information regarding the PTSD mechanisms in affected regions of brain. We have showed that the concentration of cholesterol, cholesteryl palmitate, phosphatidylinositol, phosphatidylserine, phosphatidylethanolamine, sphingomyelin, ganglioside, glyceryl tripalmitate and sulfatide changes in the PVT region of PTSD compared to control rats. A higher concentration of cholesterol suggests a higher level of corticosterone in the brain. Moreover, concentration changes of phospholipids and sphingolipids suggest the alteration of phospholipase A2 (PLA2) which is associated with inflammatory processes in the brain. Our results have broadened the understanding of biomolecular mechanisms for PTSD in the PVT region of the brain. This is the first report regarding the application of Raman spectroscopy for PTSD studies. This method has a wide spectrum of applications and can be applied to various other brain related disorders or other regions of the brain.
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Affiliation(s)
- Ardalan Chaichi
- Department of Mechanical and Industrial Engineering, Louisiana State University, Baton Rouge, LA 70803, USA.
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17
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Chu KY, Tradewell MB. Artificial Intelligence in Urology. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_172-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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18
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Ma R, Vanstrum EB, Lee R, Chen J, Hung AJ. Machine learning in the optimization of robotics in the operative field. Curr Opin Urol 2020; 30:808-816. [PMID: 32925312 PMCID: PMC7735438 DOI: 10.1097/mou.0000000000000816] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
PURPOSE OF REVIEW The increasing use of robotics in urologic surgery facilitates collection of 'big data'. Machine learning enables computers to infer patterns from large datasets. This review aims to highlight recent findings and applications of machine learning in robotic-assisted urologic surgery. RECENT FINDINGS Machine learning has been used in surgical performance assessment and skill training, surgical candidate selection, and autonomous surgery. Autonomous segmentation and classification of surgical data have been explored, which serves as the stepping-stone for providing real-time surgical assessment and ultimately, improve surgical safety and quality. Predictive machine learning models have been created to guide appropriate surgical candidate selection, whereas intraoperative machine learning algorithms have been designed to provide 3-D augmented reality and real-time surgical margin checks. Reinforcement-learning strategies have been utilized in autonomous robotic surgery, and the combination of expert demonstrations and trial-and-error learning by the robot itself is a promising approach towards autonomy. SUMMARY Robot-assisted urologic surgery coupled with machine learning is a burgeoning area of study that demonstrates exciting potential. However, further validation and clinical trials are required to ensure the safety and efficacy of incorporating machine learning into surgical practice.
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Affiliation(s)
- Runzhuo Ma
- Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, California, USA
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19
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Kirchberger-Tolstik T, Pradhan P, Vieth M, Grunert P, Popp J, Bocklitz TW, Stallmach A. Towards an Interpretable Classifier for Characterization of Endoscopic Mayo Scores in Ulcerative Colitis Using Raman Spectroscopy. Anal Chem 2020; 92:13776-13784. [PMID: 32965101 DOI: 10.1021/acs.analchem.0c02163] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Ulcerative colitis (UC) is one of the main types of chronic inflammatory diseases that affect the bowel, but its pathogenesis is yet to be completely defined. Assessing the disease activity of UC is vital for developing a personalized treatment. Conventionally, the assessment of UC is performed by colonoscopy and histopathology. However, conventional methods fail to retain biomolecular information associated to the severity of UC and are solely based on morphological characteristics of the inflamed colon. Furthermore, assessing endoscopic disease severity is limited by the requirement for experienced human reviewers. Therefore, this work presents a nondestructive biospectroscopic technique, for example, Raman spectroscopy, for assessing endoscopic disease severity according to the four-level Mayo subscore. This contribution utilizes multidimensional Raman spectroscopic data to generate a predictive model for identifying colonic inflammation. The predictive modeling of the Raman spectroscopic data is performed using a one-dimensional deep convolutional neural network (1D-CNN). The classification results of 1D-CNN achieved a mean sensitivity of 78% and a mean specificity of 93% for the four Mayo endoscopic scores. Furthermore, the results of the 1D-CNN are interpreted by a first-order Taylor expansion, which extracts the Raman bands important for classification. Additionally, a regression model of the 1D-CNN model is constructed to study the extent of misclassification and border-line patients. The overall results of Raman spectroscopy with 1D-CNN as a classification and regression model show a good performance, and such a method can serve as a complementary method for UC analysis.
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Affiliation(s)
- Tatiana Kirchberger-Tolstik
- Leibniz Institute of Photonic Technology, Member of Leibniz Health Technology, 07745 Jena, Germany.,Department of Internal Medicine IV (Gastroenterology, Hepatology, Infectious Disease), Jena University Hospital, 07747 Jena, Germany
| | - Pranita Pradhan
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich-Schiller University, 07743 Jena, Germany.,Leibniz Institute of Photonic Technology, Member of Leibniz Health Technology, 07745 Jena, Germany
| | - Michael Vieth
- Klinikum Bayreuth GmbH, Preuschwitzer Str. 101, 95445 Bayreuth, Germany
| | - Philip Grunert
- Department of Internal Medicine IV (Gastroenterology, Hepatology, Infectious Disease), Jena University Hospital, 07747 Jena, Germany
| | - Juergen Popp
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich-Schiller University, 07743 Jena, Germany.,Leibniz Institute of Photonic Technology, Member of Leibniz Health Technology, 07745 Jena, Germany
| | - Thomas Wilhelm Bocklitz
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich-Schiller University, 07743 Jena, Germany.,Leibniz Institute of Photonic Technology, Member of Leibniz Health Technology, 07745 Jena, Germany
| | - Andreas Stallmach
- Department of Internal Medicine IV (Gastroenterology, Hepatology, Infectious Disease), Jena University Hospital, 07747 Jena, Germany
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20
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Jin H, He X, Zhou H, Zhang M, Tang Q, Lin L, Hao J, Zeng R. Efficacy of raman spectroscopy in the diagnosis of kidney cancer: A systematic review and meta-analysis. Medicine (Baltimore) 2020; 99:e20933. [PMID: 32629694 PMCID: PMC7337610 DOI: 10.1097/md.0000000000020933] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2019] [Revised: 05/03/2020] [Accepted: 05/25/2020] [Indexed: 02/05/2023] Open
Abstract
OBJECTIVE To comprehensively analyze the relative effectiveness of Raman spectroscopy (RS) in the diagnosis of suspected kidney cancer. PATIENTS AND METHODS We performed a complete systematic review based on studies from PubMed/Medline, EMBASE, Web of Science, Ovid, Web of Knowledge, Cochrane Library and China National Knowledge Infrastructure. We identified 2413 spectra with strict criteria in 6 individual studies published between January 2008 and November 2018 in accordance to Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. We summarized the test performance using random effects models. RESULTS General pooled diagnostic sensitivity and specificity of RS to kidney cancer were 0.96 (95% confidence interval [CI] 0.95-0.97) and 0.91 (95% CI 0.89-0.92). The pooled positive likelihood ratio (LR) was 9.57 (95% CI 5.73-15.46) while the negative LR was 0.04 (95% CI 0.02-0.11). The pooled diagnostic odds ratio was 238.06 (95% CI 77.79-728.54). The area under curve of summary receiver operator characteristics was 0.9466. CONCLUSION Through this meta-analysis, we found a promisingly high sensitivity and specificity of RS in the diagnosis of suspected kidney masses and tumors. Other parameters like positive LR, negative LR, diagnostic odds ratio and area under curve of the summary receiver operator characteristics curve all helped to illustrate the high efficacy of RS in the diagnosis of kidney cancer.
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Affiliation(s)
- Hongyu Jin
- Department of Liver Surgery and Liver Transplantation Center, West China Hospital
| | - Xiao He
- West China Clinical Skills Training Center, West China School of Medicine, Sichuan University
| | - Hui Zhou
- Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology
| | | | | | | | | | - Rui Zeng
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu, China
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21
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22
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Ralbovsky NM, Lednev IK. Towards development of a novel universal medical diagnostic method: Raman spectroscopy and machine learning. Chem Soc Rev 2020; 49:7428-7453. [DOI: 10.1039/d0cs01019g] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
This review summarizes recent progress made using Raman spectroscopy and machine learning for potential universal medical diagnostic applications.
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Affiliation(s)
| | - Igor K. Lednev
- Department of Chemistry
- University at Albany
- SUNY
- Albany
- USA
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23
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Féré M, Gobinet C, Liu LH, Beljebbar A, Untereiner V, Gheldof D, Chollat M, Klossa J, Chatelain B, Piot O. Implementation of a classification strategy of Raman data collected in different clinical conditions: application to the diagnosis of chronic lymphocytic leukemia. Anal Bioanal Chem 2019; 412:949-962. [PMID: 31853604 DOI: 10.1007/s00216-019-02321-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Revised: 10/31/2019] [Accepted: 12/03/2019] [Indexed: 02/06/2023]
Abstract
The literature is rich in proof of concept studies demonstrating the potential of Raman spectroscopy for disease diagnosis. However, few studies are conducted in a clinical context to demonstrate its applicability in current clinical practice and workflow. Indeed, this translational research remains far from the patient's bedside for several reasons. First, samples are often cultured cell lines. Second, they are prepared on non-standard substrates for clinical routine. Third, a unique supervised classification model is usually constructed using inadequate cross-validation strategy. Finally, the implemented models maximize classification accuracy without taking into account the clinician's needs. In this paper, we address these issues through a diagnosis problem in real clinical conditions, i.e., the diagnosis of chronic lymphocytic leukemia from fresh unstained blood smears spread on glass slides. From Raman data acquired in different experimental conditions, a repeated double cross-validation strategy was combined with different cross-validation approaches, a consensus label strategy and adaptive thresholds able to adapt to the clinician's needs. Combined with validation at the patient level, classification results were improved compared to traditional strategies.
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Affiliation(s)
- M Féré
- BioSpecT EA 7506, Faculty of Pharmacy, University of Reims Champagne-Ardenne, 51 rue Cognacq-Jay, 51096, Reims, France
| | - C Gobinet
- BioSpecT EA 7506, Faculty of Pharmacy, University of Reims Champagne-Ardenne, 51 rue Cognacq-Jay, 51096, Reims, France.
| | - L H Liu
- BioSpecT EA 7506, Faculty of Pharmacy, University of Reims Champagne-Ardenne, 51 rue Cognacq-Jay, 51096, Reims, France
| | - A Beljebbar
- BioSpecT EA 7506, Faculty of Pharmacy, University of Reims Champagne-Ardenne, 51 rue Cognacq-Jay, 51096, Reims, France
| | - V Untereiner
- Cellular and Tissular Imaging Platform PICT, Faculty of Pharmacy, University of Reims Champagne-Ardenne, 51 rue Cognacq-Jay, 51096, Reims, France
| | - D Gheldof
- CHU UCL Namur, Namur Thrombosis and Hemostasis Center, Hematology Laboratory, Rue Dr Gaston Therasse, Catholic University of Louvain, 5530, Yvoir, Belgium
| | - M Chollat
- TRIBVN, 39 Rue Louveau, 92320, Châtillon, France
| | - J Klossa
- TRIBVN, 39 Rue Louveau, 92320, Châtillon, France
| | - B Chatelain
- CHU UCL Namur, Namur Thrombosis and Hemostasis Center, Hematology Laboratory, Rue Dr Gaston Therasse, Catholic University of Louvain, 5530, Yvoir, Belgium
| | - O Piot
- BioSpecT EA 7506, Faculty of Pharmacy, University of Reims Champagne-Ardenne, 51 rue Cognacq-Jay, 51096, Reims, France.,Cellular and Tissular Imaging Platform PICT, Faculty of Pharmacy, University of Reims Champagne-Ardenne, 51 rue Cognacq-Jay, 51096, Reims, France
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24
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Shen R, Li Y, Yu L, Wu H, Cui R, Liu S, Song Y, Wang D. Ex vivo detection of cadmium-induced renal damage by using confocal Raman spectroscopy. JOURNAL OF BIOPHOTONICS 2019; 12:e201900157. [PMID: 31407491 DOI: 10.1002/jbio.201900157] [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: 04/28/2019] [Revised: 08/08/2019] [Accepted: 08/09/2019] [Indexed: 06/10/2023]
Abstract
Cadmium (Cd) is a toxic heavy metal which is harmful to environment and organisms. The reabsorption of Cd in kidney leads it to be the main damaged organ in animals under the Cd exposure. In this work, we applied confocal Raman spectroscopy to map the pathological changes in situ in normal and Cd-exposed mice kidney. The renal tissue from Cd-exposed group displayed a remarkable decreasing in the intensity of typical peaks related to mitochondria, DNA, proteins and lipids. On the contrary, the peaks of collagen in Cd-exposed group elevated significantly. The components in each tissue were identified and distinguished by principal component analysis. Furthermore, all the biological investigations in this study were consistent with the Raman spectrum detection, which revealed the progression and degree of lesion induced by Cd. The confocal Raman spectroscopy provides a new perspective for in situ monitoring of substances changes in tissues, which exhibits more comprehensive understanding of the pathogenic mechanisms of heavy metals in molecular toxicology.
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Affiliation(s)
- Rong Shen
- School of Basic Medical Sciences, Lanzhou University, Lanzhou, Gansu, China
| | - Yuee Li
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu, China
| | - Linghui Yu
- School of Basic Medical Sciences, Lanzhou University, Lanzhou, Gansu, China
| | - Haining Wu
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu, China
| | - Rong Cui
- School of Basic Medical Sciences, Lanzhou University, Lanzhou, Gansu, China
| | - Sha Liu
- School of Basic Medical Sciences, Lanzhou University, Lanzhou, Gansu, China
| | - Yanfeng Song
- School of Basic Medical Sciences, Lanzhou University, Lanzhou, Gansu, China
| | - Degui Wang
- School of Basic Medical Sciences, Lanzhou University, Lanzhou, Gansu, China
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25
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Chen J, Remulla D, Nguyen JH, Dua A, Liu Y, Dasgupta P, Hung AJ. Current status of artificial intelligence applications in urology and their potential to influence clinical practice. BJU Int 2019; 124:567-577. [PMID: 31219658 DOI: 10.1111/bju.14852] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
OBJECTIVE To investigate the applications of artificial intelligence (AI) in diagnosis, treatment and outcome predictionin urologic diseases and evaluate its advantages over traditional models and methods. MATERIALS AND METHODS A literature search was performed after PROSPERO registration (CRD42018103701) and in compliance with Preferred Reported Items for Systematic Reviews and Meta-Analyses (PRISMA) methods. Articles between 1994 and 2018 using the search terms "urology", "artificial intelligence", "machine learning" were included and categorized by the application of AI in urology. Review articles, editorial comments, articles with no full-text access, and nonurologic studies were excluded. RESULTS Initial search yielded 231 articles, but after excluding duplicates and following full-text review and examination of article references, only 111 articles were included in the final analysis. AI applications in urology include: utilizing radiomic imaging or ultrasonic echo data to improve or automate cancer detection or outcome prediction, utilizing digitized tissue specimen images to automate detection of cancer on pathology slides, and combining patient clinical data, biomarkers, or gene expression to assist disease diagnosis or outcome prediction. Some studies employed AI to plan brachytherapy and radiation treatments while others used video based or robotic automated performance metrics to objectively evaluate surgical skill. Compared to conventional statistical analysis, 71.8% of studies concluded that AI is superior in diagnosis and outcome prediction. CONCLUSION AI has been widely adopted in urology. Compared to conventional statistics AI approaches are more accurate in prediction and more explorative for analyzing large data cohorts. With an increasing library of patient data accessible to clinicians, AI may help facilitate evidence-based and individualized patient care.
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Affiliation(s)
- Jian Chen
- Catherine & Joseph Aresty Department of Urology, Center for Robotic Simulation & Education, University of Southern California Institute of Urology, Los Angeles, CA, USA
| | - Daphne Remulla
- Catherine & Joseph Aresty Department of Urology, Center for Robotic Simulation & Education, University of Southern California Institute of Urology, Los Angeles, CA, USA
| | - Jessica H Nguyen
- Catherine & Joseph Aresty Department of Urology, Center for Robotic Simulation & Education, University of Southern California Institute of Urology, Los Angeles, CA, USA
| | - Aastha Dua
- Computer Science Department, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
| | - Yan Liu
- Computer Science Department, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
| | - Prokar Dasgupta
- Division of Transplantation Immunology and Mucosal Biology, Faculty of Life Sciences and Medicine, Kings College London, London, UK
| | - Andrew J Hung
- Catherine & Joseph Aresty Department of Urology, Center for Robotic Simulation & Education, University of Southern California Institute of Urology, Los Angeles, CA, USA
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26
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Jang E, Jeong J, Yim JH, Kim Y, Lee CH, Choi D, Chung H. Improved infrared spectroscopic discrimination between gall bladder (GB) polyps and GB cancer using component-descriptive spectral features of separated phases from bile. Analyst 2019; 144:4826-4834. [PMID: 31290490 DOI: 10.1039/c9an00878k] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
This study demonstrates a unique strategy for enhancing infrared (IR) spectroscopic discrimination between gall bladder (GB) polyps and cancer. This strategy includes the separation of raw bile juice into three sections of organic, aqueous, and amphiphilic phases and a cooperative combination of all IR spectral features of each separated phase for the discrimination. Raw bile juice is viscous and complex in composition because it contains fatty acids, cholesterol, proteins, phospholipids, bilirubin, and other components; therefore, the acquisition of IR spectra providing more component-discernible information is fundamental for improving discrimination. For this purpose, raw bile juice was separated into an aqueous phase, mostly containing bile salts, an organic phase with isolated lipids, and an amphiphilic phase, mainly containing proteins. The subsequent IR spectra of each separated phase were mutually characteristic and complementary to each other. When all the IR spectral features were combined, the discrimination was improved compared to that using the spectra of raw bile juice with no separation. The cooperative integration of more component-specific spectra obtained from each separated phase enhanced the discrimination. In addition, the IR spectra of the major constituents in bile juice, such as bile acids, conjugated bile salts, lecithin, and cholesterol, were recorded to explain the IR features of each separated phase.
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Affiliation(s)
- Eunjin Jang
- Department of Chemistry and Research Institute for Convergence of Basic Science, Hanyang University, Seoul 04763, Republic of Korea.
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27
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Feng X, Muzikansky A, Ross AH, Hamblin MR, Jermain PR, Yaroslavsky AN. Multimodal quantitative imaging of brain cancer in cultured cells. BIOMEDICAL OPTICS EXPRESS 2019; 10:4237-4248. [PMID: 31453007 PMCID: PMC6701554 DOI: 10.1364/boe.10.004237] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 06/12/2019] [Accepted: 06/17/2019] [Indexed: 05/30/2023]
Abstract
Fluorescence emission, polarization and subcellular localization of methylene blue (MB) were studied in four cancerous and two normal human brain cell lines. Fluorescence emission and polarization images were acquired and analyzed. The co-localization of MB with mitochondria, lysosomes and nuclei of the cells was evaluated. Glioblastoma cells exhibited significantly higher MB fluorescence polarization compared to normal astrocytes. Preferential accumulation of MB in mitochondria of glioblastoma cells may explain higher fluorescence polarization values in cancer cells as compared to normal. These findings may lead to the development of a quantitative method for the detection of brain cancer in single cells.
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Affiliation(s)
- Xin Feng
- Advanced Biophotonics Laboratory, UMASS Lowell, Lowell, MA 01851, USA
| | - Alona Muzikansky
- Massachusetts General Hospital Biostatistics Center, Boston, MA 02114, USA
| | - Alonzo H. Ross
- Department of Biochemistry and Molecular Pharmacology, UMASS Medical School, Worcester, MA 01605, USA
| | - Michael R. Hamblin
- Wellman Center for Photomedicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Peter R. Jermain
- Advanced Biophotonics Laboratory, UMASS Lowell, Lowell, MA 01851, USA
| | - Anna N. Yaroslavsky
- Advanced Biophotonics Laboratory, UMASS Lowell, Lowell, MA 01851, USA
- Wellman Center for Photomedicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
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Brito da Silva H, Sekhar LN. In Reply: Cranial Chordoma: A New Preoperative Grading System. Neurosurgery 2018; 83:E52-E53. [DOI: 10.1093/neuros/nyy130] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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