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Hans R, Sharma SK, Aickelin U. Optimised deep k-nearest neighbour's based diabetic retinopathy diagnosis(ODeep-NN) using retinal images. Health Inf Sci Syst 2024; 12:23. [PMID: 38469456 PMCID: PMC10924814 DOI: 10.1007/s13755-024-00282-x] [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: 07/31/2023] [Accepted: 02/18/2024] [Indexed: 03/13/2024] Open
Abstract
Diabetes mellitus has been regarded as one of the prime health issues in present days, which can often lead to diabetic retinopathy, a complication of the disease that affects the eyes, causing loss of vision. For precisely detecting the condition's existence, clinicians are required to recognise the presence of lesions in colour fundus images, making it an arduous and time-consuming task. To deal with this problem, a lot of work has been undertaken to develop deep learning-based computer-aided diagnosis systems that assist clinicians in making accurate diagnoses of the diseases in medical images. Contrariwise, the basic operations involved in deep learning models lead to the extraction of a bulky set of features, further taking a long period of training to predict the existence of the disease. For effective execution of these models, feature selection becomes an important task that aids in selecting the most appropriate features, with an aim to increase the classification accuracy. This research presents an optimised deep k-nearest neighbours'-based pipeline model in a bid to amalgamate the feature extraction capability of deep learning models with nature-inspired metaheuristic algorithms, further using k-nearest neighbour algorithm for classification. The proposed model attains an accuracy of 97.67 and 98.05% on two different datasets considered, outperforming Resnet50 and AlexNet deep learning models. Additionally, the experimental results also portray an analysis of five different nature-inspired metaheuristic algorithms, considered for feature selection on the basis of various evaluation parameters.
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Affiliation(s)
- Rahul Hans
- Department of Computer Science and Engineering, DAV University, Jalandhar, Punjab India
| | - Sanjeev Kumar Sharma
- Department of Computer Science and Applications, DAV University, Jalandhar, Punjab India
| | - Uwe Aickelin
- School of Computing and Information Systems, University of Melbourne, Melbourne, Australia
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Effeney B, Pullar A, Burbery J, Hargrave C, Brady C. Dose to organs at risk for total body irradiation: Single-institution data using the modulated arc total body irradiation technique. Pediatr Blood Cancer 2024; 71:e31164. [PMID: 38953144 DOI: 10.1002/pbc.31164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 06/12/2024] [Accepted: 06/12/2024] [Indexed: 07/03/2024]
Abstract
BACKGROUND Organs at risk (OAR) dose reporting for total body irradiation (TBI) patients is limited, and standardly reported only as mean doses to the lungs and kidneys. Consequently, dose received and effects on other OAR remain unexplored. To remedy this gap, this study reports dose data on an extensive list of OAR for patients treated at a single institution using the modulated arc total body irradiation (MATBI) technique. METHOD An audit was undertaken of all patients treated with MATBI between January 2015 and March 2021 who had completed their course of treatment. OAR were contoured on MATBI patient treatment plans, with 12 Gy in six fraction prescription. OAR dose statistics and dose volume histogram data are reported for the whole body, lungs, kidneys, bones, brain, lens, heart, liver and bowel bag. RESULTS The OAR dose data for 29 patients are reported. Mean dose results are body 11.77 Gy, lungs 9.86 Gy, kidneys 11.84 Gy, bones 12.03 Gy, brain 12.12 Gy, right lens 12.31 Gy, left lens 12.64 Gy, heart 11.07 Gy, liver 11.81 Gy and bowel bag 12.06 Gy. Dose statistics at 1-Gy intervals of V6-V13 for lungs and V10-V13 for kidneys are also included. CONCLUSION This is the first time an extensive list of OAR data has been reported for any TBI technique. Due to the paucity of reporting, this information could be used by centres implementing the MATBI technique, in addition to aiding comparison between TBI techniques, with the potential for greater understanding of the relationship between dose volume data and toxicity.
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Affiliation(s)
- Beth Effeney
- Radiation Oncology Princess Alexandra Hospital - Raymond Terrace, South Brisbane, Queensland, Australia
| | - Andrew Pullar
- Radiation Oncology Princess Alexandra Hospital - Raymond Terrace, South Brisbane, Queensland, Australia
| | - Julie Burbery
- School of Clinical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Cathy Hargrave
- Radiation Oncology Princess Alexandra Hospital - Raymond Terrace, South Brisbane, Queensland, Australia
- School of Clinical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Carole Brady
- Radiation Oncology Princess Alexandra Hospital - Raymond Terrace, South Brisbane, Queensland, Australia
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Al Fahoum A, Zyout A. Wavelet Transform, Reconstructed Phase Space, and Deep Learning Neural Networks for EEG-Based Schizophrenia Detection. Int J Neural Syst 2024; 34:2450046. [PMID: 39010724 DOI: 10.1142/s0129065724500461] [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] [Indexed: 07/17/2024]
Abstract
This study proposes an innovative expert system that uses exclusively EEG signals to diagnose schizophrenia in its early stages. For diagnosing psychiatric/neurological disorders, electroencephalogram (EEG) testing is considered a financially viable, safe, and reliable alternative. Using the reconstructed phase space (RPS) and the continuous wavelet transform, the researchers maximized the differences between the EEG nonstationary signals of normal and schizophrenia individuals, which cannot be observed in the time, frequency, or time-frequency domains. This reveals significant information, highlighting more distinguishable features. Then, a deep learning network was trained to enhance the accuracy of the resulting image classification. The algorithm's efficacy was confirmed through three distinct methods: employing 70% of the dataset for training, 15% for validation, and the remaining 15% for testing. This was followed by a 5-fold cross-validation technique and a leave-one-out classification approach. Each method was iterated 100 times to ascertain the algorithm's robustness. The performance metrics derived from these tests - accuracy, precision, sensitivity, F1 score, Matthews correlation coefficient, and Kappa - indicated remarkable outcomes. The algorithm demonstrated steady performance across all evaluation strategies, underscoring its relevance and reliability. The outcomes validate the system's accuracy, precision, sensitivity, and robustness by showcasing its capability to autonomously differentiate individuals diagnosed with schizophrenia from those in a state of normal health.
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Affiliation(s)
- Amjed Al Fahoum
- Biomedical Systems and Informatics Engineering Department, Yarmouk University, Irbid 21163, Jordan
| | - Ala'a Zyout
- Biomedical Systems and Informatics Engineering Department, Yarmouk University, Irbid 21163, Jordan
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Wong KFM, Huang W, Ee DYH, Ng EYK. Design and validation of dual-point time-differentiated photoplethysmogram (2PPG) wearable for cuffless blood pressure estimation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 253:108251. [PMID: 38824806 DOI: 10.1016/j.cmpb.2024.108251] [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: 01/23/2024] [Revised: 05/02/2024] [Accepted: 05/24/2024] [Indexed: 06/04/2024]
Abstract
BACKGROUND & OBJECTIVES Measurement of blood pressure (BP) in ambulatory patients is crucial for at high-risk cardiovascular patients. A non-obtrusive, non-occluding device that continuously measures BP via photoplethysmography will enable long-term ambulatory assessment of BP. The aim of this study is to validate the metasense 2PPG cuffless wearable design for continuous BP estimation without ECG. METHODS A customized high-speed electronic optical sensor architecture with laterally spaced reflectance pulse oximetry was designed into a simple unobtrusive low-power wearable in the form of a watch. 78 volunteers with a mean age of 32.72 ± 7.4 years (21 to 64), 51% male, 49% female were recruited with ECG-2PPG signals acquired. The fiducial features of the 2PPG morphologies were then attributed to the estimator. A 9-1 K-fold cross-validation was applied in the ML. RESULTS The correlation for PTT-SBP was 0.971 and for PTT-DBP was 0.954. The mean absolute error was 3.167±1.636 mmHg for SBP and 6.4 ± 3.9 mm Hg for DBP. The ambulatory estimate for SBP and DBP for an individual over 3 days with 8-hour recordings was 0.70-0.81 for SBP and 0.42-0.51 for DBP with a ± 2.65 mmHg for SBP and ±2.02 mmHg for DBP. For SBP, 98% of metasense measurements were within 15 mm Hg and for DBP, 91% of metasense measurements were within 10 mmHg CONCLUSIONS: The metasense device provides continuous, non-invasive BP estimations that are comparable to ambulatory BP meters. The portability and unobtrusiveness of this device, as well as the ability to continuously measure BP could one day enable long-term ambulatory BP measurement for precision cardiovascular therapeutic regimens.
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Affiliation(s)
- Kei Fong Mark Wong
- School of Mechanical and Aerospace Engineering, Nanyang Technological University (NTU), Singapore
| | | | | | - Eddie Yin Kwee Ng
- School of Mechanical and Aerospace Engineering, Nanyang Technological University (NTU), Singapore.
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Hurkmans C, Bibault JE, Brock KK, van Elmpt W, Feng M, David Fuller C, Jereczek-Fossa BA, Korreman S, Landry G, Madesta F, Mayo C, McWilliam A, Moura F, Muren LP, El Naqa I, Seuntjens J, Valentini V, Velec M. A joint ESTRO and AAPM guideline for development, clinical validation and reporting of artificial intelligence models in radiation therapy. Radiother Oncol 2024; 197:110345. [PMID: 38838989 DOI: 10.1016/j.radonc.2024.110345] [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: 05/23/2024] [Accepted: 05/23/2024] [Indexed: 06/07/2024]
Abstract
BACKGROUND AND PURPOSE Artificial Intelligence (AI) models in radiation therapy are being developed with increasing pace. Despite this, the radiation therapy community has not widely adopted these models in clinical practice. A cohesive guideline on how to develop, report and clinically validate AI algorithms might help bridge this gap. METHODS AND MATERIALS A Delphi process with all co-authors was followed to determine which topics should be addressed in this comprehensive guideline. Separate sections of the guideline, including Statements, were written by subgroups of the authors and discussed with the whole group at several meetings. Statements were formulated and scored as highly recommended or recommended. RESULTS The following topics were found most relevant: Decision making, image analysis, volume segmentation, treatment planning, patient specific quality assurance of treatment delivery, adaptive treatment, outcome prediction, training, validation and testing of AI model parameters, model availability for others to verify, model quality assurance/updates and upgrades, ethics. Key references were given together with an outlook on current hurdles and possibilities to overcome these. 19 Statements were formulated. CONCLUSION A cohesive guideline has been written which addresses main topics regarding AI in radiation therapy. It will help to guide development, as well as transparent and consistent reporting and validation of new AI tools and facilitate adoption.
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Affiliation(s)
- Coen Hurkmans
- Department of Radiation Oncology, Catharina Hospital, Eindhoven, the Netherlands; Department of Electrical Engineering, Technical University Eindhoven, Eindhoven, the Netherlands.
| | | | - Kristy K Brock
- Departments of Imaging Physics and Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Wouter van Elmpt
- Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - Mary Feng
- University of California San Francisco, San Francisco, CA, USA
| | - Clifton David Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer, Houston, TX
| | - Barbara A Jereczek-Fossa
- Dept. of Oncology and Hemato-oncology, University of Milan, Milan, Italy; Dept. of Radiation Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Stine Korreman
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
| | - Guillaume Landry
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany; German Cancer Consortium (DKTK), Partner Site Munich, a Partnership between DKFZ and LMU University Hospital Munich, Germany; Bavarian Cancer Research Center (BZKF), Partner Site Munich, Munich, Germany
| | - Frederic Madesta
- Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Institute for Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Center for Biomedical Artificial Intelligence (bAIome), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Chuck Mayo
- Institute for Healthcare Policy and Innovation, University of Michigan, USA
| | - Alan McWilliam
- Division of Cancer Sciences, The University of Manchester, Manchester, UK
| | - Filipe Moura
- CrossI&D Lisbon Research Center, Portuguese Red Cross Higher Health School Lisbon, Portugal
| | - Ludvig P Muren
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
| | - Issam El Naqa
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL 33612, USA
| | - Jan Seuntjens
- Princess Margaret Cancer Centre, Radiation Medicine Program, University Health Network & Departments of Radiation Oncology and Medical Biophysics, University of Toronto, Toronto, Canada
| | - Vincenzo Valentini
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy; Università Cattolica del Sacro Cuore, Rome, Italy
| | - Michael Velec
- Radiation Medicine Program, Princess Margaret Cancer Centre and Department of Radiation Oncology, University of Toronto, Toronto, Canada
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Russo L, Charles-Davies D, Bottazzi S, Sala E, Boldrini L. Radiomics for clinical decision support in radiation oncology. Clin Oncol (R Coll Radiol) 2024; 36:e269-e281. [PMID: 38548581 DOI: 10.1016/j.clon.2024.03.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 02/14/2024] [Accepted: 03/08/2024] [Indexed: 07/09/2024]
Abstract
Radiomics is a promising tool for the development of quantitative biomarkers to support clinical decision-making. It has been shown to improve the prediction of response to treatment and outcome in different settings, particularly in the field of radiation oncology by optimising the dose delivery solutions and reducing the rate of radiation-induced side effects, leading to a fully personalised approach. Despite the promising results offered by radiomics at each of these stages, standardised methodologies, reproducibility and interpretability of results are still lacking, limiting the potential clinical impact of these tools. In this review, we briefly describe the principles of radiomics and the most relevant applications of radiomics at each stage of cancer management in the framework of radiation oncology. Furthermore, the integration of radiomics into clinical decision support systems is analysed, defining the challenges and offering possible solutions for translating radiomics into a clinically applicable tool.
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Affiliation(s)
- L Russo
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy; Dipartimento di Scienze Radiologiche ed Ematologiche. Università Cattolica Del Sacro Cuore, Rome, Italy.
| | - D Charles-Davies
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - S Bottazzi
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - E Sala
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy; Dipartimento di Scienze Radiologiche ed Ematologiche. Università Cattolica Del Sacro Cuore, Rome, Italy
| | - L Boldrini
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
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Nowroozi A, Salehi MA, Shobeiri P, Agahi S, Momtazmanesh S, Kaviani P, Kalra MK. Artificial intelligence diagnostic accuracy in fracture detection from plain radiographs and comparing it with clinicians: a systematic review and meta-analysis. Clin Radiol 2024; 79:579-588. [PMID: 38772766 DOI: 10.1016/j.crad.2024.04.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 04/09/2024] [Accepted: 04/15/2024] [Indexed: 05/23/2024]
Abstract
PURPOSE Fracture detection is one of the most commonly used and studied aspects of artificial intelligence (AI) in medicine. In this systematic review and meta-analysis, we aimed to summarize available literature and data regarding AI performance in fracture detection on plain radiographs and various factors affecting it. METHODS We systematically reviewed studies evaluating AI algorithms in detecting bone fractures in plain radiographs, combined their performance using meta-analysis (a bivariate regression approach), and compared it with that of clinicians. We also analyzed the factors potentially affecting algorithm performance using meta-regression. RESULTS Our analysis included 100 studies. In 83 studies with confusion matrices, AI algorithms showed a sensitivity of 91.43% and a specificity of 92.12% (Area under the summary receiver operator curve = 0.968). After adjustment and false discovery rate correction, tibia/fibula (excluding ankle) fractures were associated with higher (7.0%, p=0.004) AI sensitivity, while more recent publications (5.5%, p=0.003) and Xception architecture (6.6%, p<0.001) were associated with higher specificity. Clinicians and AI showed similar specificity in fracture identification, although AI leaned to higher sensitivity (7.6%, p=0.07). Radiologists, on the other hand, were more specific than AI overall and in several subgroups, and more sensitive to hip fractures before FDR correction. CONCLUSIONS Currently available AI aids could result in a significant improvement in care where radiologists are not readily available. Moreover, identifying factors affecting algorithm performance could guide AI development teams in their process of optimizing their products.
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Affiliation(s)
- A Nowroozi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - M A Salehi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - P Shobeiri
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - S Agahi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - S Momtazmanesh
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - P Kaviani
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - M K Kalra
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.
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Surendran T, Park LK, Lauber MV, Cha B, Jhun RS, Capellini TD, Kumar D, Felson DT, Kolachalama VB. Survival analysis on subchondral bone length for total knee replacement. Skeletal Radiol 2024; 53:1541-1552. [PMID: 38388702 PMCID: PMC11194148 DOI: 10.1007/s00256-024-04627-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 02/01/2024] [Accepted: 02/15/2024] [Indexed: 02/24/2024]
Abstract
OBJECTIVE Use subchondral bone length (SBL), a new MRI-derived measure that reflects the extent of cartilage loss and bone flattening, to predict the risk of progression to total knee replacement (TKR). METHODS We employed baseline MRI data from the Osteoarthritis Initiative (OAI), focusing on 760 men and 1214 women with bone marrow lesions (BMLs) and joint space narrowing (JSN) scores, to predict the progression to TKR. To minimize bias from analyzing both knees of a participant, only the knee with a higher Kellgren-Lawrence (KL) grade was considered, given its greater potential need for TKR. We utilized the Kaplan-Meier survival curves and Cox proportional hazards models, incorporating raw and normalized values of SBL, JSN, and BML as predictors. The study included subgroup analyses for different demographics and clinical characteristics, using models for raw and normalized SBL (merged, femoral, tibial), BML (merged, femoral, tibial), and JSN (medial and lateral compartments). Model performance was evaluated using the time-dependent area under the curve (AUC), Brier score, and Concordance index to gauge accuracy, calibration, and discriminatory power. Knee joint and region-level analyses were conducted to determine the effectiveness of SBL, JSN, and BML in predicting TKR risk. RESULTS The SBL model, incorporating data from both the femur and tibia, demonstrated a predictive capacity for TKR that closely matched the performance of the BML score and the JSN grade. The Concordance index of the SBL model was 0.764, closely mirroring the BML's 0.759 and slightly below JSN's 0.788. The Brier score for the SBL model stood at 0.069, showing comparability with BML's 0.073 and a minor difference from JSN's 0.067. Regarding the AUC, the SBL model achieved 0.803, nearly identical to BML's 0.802 and slightly lower than JSN's 0.827. CONCLUSION SBL's capacity to predict the risk of progression to TKR highlights its potential as an effective imaging biomarker for knee osteoarthritis.
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Affiliation(s)
- Tejus Surendran
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, 72 E. Concord Street, Evans 636, Boston, MA, 02118, USA
| | - Lisa K Park
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, 72 E. Concord Street, Evans 636, Boston, MA, 02118, USA
| | - Meagan V Lauber
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, 72 E. Concord Street, Evans 636, Boston, MA, 02118, USA
| | - Baekdong Cha
- Sargent College, Boston University, Boston, MA, USA
| | - Ray S Jhun
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, 72 E. Concord Street, Evans 636, Boston, MA, 02118, USA
| | - Terence D Capellini
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Deepak Kumar
- Sargent College, Boston University, Boston, MA, USA
| | - David T Felson
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, 72 E. Concord Street, Evans 636, Boston, MA, 02118, USA
| | - Vijaya B Kolachalama
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, 72 E. Concord Street, Evans 636, Boston, MA, 02118, USA.
- Department of Computer Science and Faculty of Computing & Data Sciences, Boston University, Boston, MA, 02215, USA.
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Umar TP, Jain N, Papageorgakopoulou M, Shaheen RS, Alsamhori JF, Muzzamil M, Kostiks A. Artificial intelligence for screening and diagnosis of amyotrophic lateral sclerosis: a systematic review and meta-analysis. Amyotroph Lateral Scler Frontotemporal Degener 2024; 25:425-436. [PMID: 38563056 DOI: 10.1080/21678421.2024.2334836] [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: 09/07/2023] [Revised: 03/04/2024] [Accepted: 03/18/2024] [Indexed: 04/04/2024]
Abstract
INTRODUCTION Amyotrophic lateral sclerosis (ALS) is a rare and fatal neurological disease that leads to progressive motor function degeneration. Diagnosing ALS is challenging due to the absence of a specific detection test. The use of artificial intelligence (AI) can assist in the investigation and treatment of ALS. METHODS We searched seven databases for literature on the application of AI in the early diagnosis and screening of ALS in humans. The findings were summarized using random-effects summary receiver operating characteristic curve. The risk of bias (RoB) analysis was carried out using QUADAS-2 or QUADAS-C tools. RESULTS In the 34 analyzed studies, a meta-prevalence of 47% for ALS was noted. For ALS detection, the pooled sensitivity of AI models was 94.3% (95% CI - 63.2% to 99.4%) with a pooled specificity of 98.9% (95% CI - 92.4% to 99.9%). For ALS classification, the pooled sensitivity of AI models was 90.9% (95% CI - 86.5% to 93.9%) with a pooled specificity of 92.3% (95% CI - 84.8% to 96.3%). Based on type of input for classification, the pooled sensitivity of AI models for gait, electromyography, and magnetic resonance signals was 91.2%, 92.6%, and 82.2%, respectively. The pooled specificity for gait, electromyography, and magnetic resonance signals was 94.1%, 96.5%, and 77.3%, respectively. CONCLUSIONS Although AI can play a significant role in the screening and diagnosis of ALS due to its high sensitivities and specificities, concerns remain regarding quality of evidence reported in the literature.
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Affiliation(s)
- Tungki Pratama Umar
- Department of Medical Profession, Faculty of Medicine, Universitas Sriwijaya, Palembang, Indonesia
| | - Nityanand Jain
- Faculty of Medicine, Riga Stradinš University, Riga, Latvia
| | | | | | | | - Muhammad Muzzamil
- Department of Public Health, Health Services Academy, Islamabad, Pakistan, and
| | - Andrejs Kostiks
- Department of Neurology, Riga East University Clinical Hospital, Riga, Latvia
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Wang TW, Hong JS, Huang JW, Liao CY, Lu CF, Wu YT. Systematic review and meta-analysis of deep learning applications in computed tomography lung cancer segmentation. Radiother Oncol 2024; 197:110344. [PMID: 38806113 DOI: 10.1016/j.radonc.2024.110344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 05/20/2024] [Accepted: 05/22/2024] [Indexed: 05/30/2024]
Abstract
BACKGROUND Accurate segmentation of lung tumors on chest computed tomography (CT) scans is crucial for effective diagnosis and treatment planning. Deep Learning (DL) has emerged as a promising tool in medical imaging, particularly for lung cancer segmentation. However, its efficacy across different clinical settings and tumor stages remains variable. METHODS We conducted a comprehensive search of PubMed, Embase, and Web of Science until November 7, 2023. We assessed the quality of these studies by using the Checklist for Artificial Intelligence in Medical Imaging and the Quality Assessment of Diagnostic Accuracy Studies-2 tools. This analysis included data from various clinical settings and stages of lung cancer. Key performance metrics, such as the Dice similarity coefficient, were pooled, and factors affecting algorithm performance, such as clinical setting, algorithm type, and image processing techniques, were examined. RESULTS Our analysis of 37 studies revealed a pooled Dice score of 79 % (95 % CI: 76 %-83 %), indicating moderate accuracy. Radiotherapy studies had a slightly lower score of 78 % (95 % CI: 74 %-82 %). A temporal increase was noted, with recent studies (post-2022) showing improvement from 75 % (95 % CI: 70 %-81 %). to 82 % (95 % CI: 81 %-84 %). Key factors affecting performance included algorithm type, resolution adjustment, and image cropping. QUADAS-2 assessments identified ambiguous risks in 78 % of studies due to data interval omissions and concerns about generalizability in 8 % due to nodule size exclusions, and CLAIM criteria highlighted areas for improvement, with an average score of 27.24 out of 42. CONCLUSION This meta-analysis demonstrates DL algorithms' promising but varied efficacy in lung cancer segmentation, particularly higher efficacy noted in early stages. The results highlight the critical need for continued development of tailored DL models to improve segmentation accuracy across diverse clinical settings, especially in advanced cancer stages with greater challenges. As recent studies demonstrate, ongoing advancements in algorithmic approaches are crucial for future applications.
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Affiliation(s)
- Ting-Wei Wang
- Institute of Biophotonics, National Yang-Ming Chiao Tung University, Taipei, Taiwan; School of Medicine, National Yang-Ming Chiao Tung University, Taipei, Taiwan
| | - Jia-Sheng Hong
- Institute of Biophotonics, National Yang-Ming Chiao Tung University, Taipei, Taiwan
| | - Jing-Wen Huang
- Department of Radiation Oncology, Taichung Veterans General Hospital, Taichung 407, Taiwan
| | - Chien-Yi Liao
- Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming Chiao Tung University, Taipei, Taiwan; Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Chia-Feng Lu
- Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming Chiao Tung University, Taipei, Taiwan
| | - Yu-Te Wu
- Institute of Biophotonics, National Yang-Ming Chiao Tung University, Taipei, Taiwan; National Yang Ming Chiao Tung University, Brain Research Center, Taiwan.
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Pellegrino S, Origlia D, Di Donna E, Lamagna M, Della Pepa R, Pane F, Del Vecchio S, Fonti R. Coefficient of variation and texture analysis of 18F-FDG PET/CT images for the prediction of outcome in patients with multiple myeloma. Ann Hematol 2024:10.1007/s00277-024-05905-7. [PMID: 39046513 DOI: 10.1007/s00277-024-05905-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 07/18/2024] [Indexed: 07/25/2024]
Abstract
In multiple myeloma (MM) bone marrow infiltration by monoclonal plasma cells can occur in both focal and diffuse manner, making staging and prognosis rather difficult. The aim of our study was to test whether texture analysis of 18 F-2-deoxy-d-glucose positron emission tomography/computed tomography (18F-FDG PET/CT) images can predict survival in MM patients. Forty-six patients underwent 18 F-FDG-PET/CT before treatment. We used an automated contouring program for segmenting the hottest focal lesion (FL) and a lumbar vertebra for assessing diffuse bone marrow involvement (DI). Maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean) and texture features such as Coefficient of variation (CoV), were obtained from 46 FL and 46 DI. After a mean follow-up of 51 months, 24 patients died of myeloma and were compared to the 22 survivors. At univariate analysis, FL SUVmax (p = 0.0453), FL SUVmean (p = 0.0463), FL CoV (p = 0.0211) and DI SUVmax (p = 0.0538) predicted overall survival (OS). At multivariate analysis only FL CoV and DI SUVmax were retained in the model (p = 0.0154). By Kaplan-Meier method and log-rank testing, patients with FL CoV below the cut-off had significantly better OS than those with FL CoV above the cut-off (p = 0.0003), as well as patients with DI SUVmax below the threshold versus those with DI SUVmax above the threshold (p = 0.0006). Combining FL CoV and DI SUVmax by using their respective cut-off values, a statistically significant difference was found between the resulting four survival curves (p = 0.0001). Indeed, patients with both FL CoV and DI SUVmax below their respective cut-off values showed the best prognosis. Conventional and texture parameters derived from 18F-FDG PET/CT analysis can predict survival in MM patients by assessing the heterogeneity and aggressiveness of both focal and diffuse infiltration.
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Affiliation(s)
- Sara Pellegrino
- Department of Advanced Biomedical Sciences, University Federico II, Via Sergio Pansini 5, Naples, 80131, Italy
| | - Davide Origlia
- Department of Advanced Biomedical Sciences, University Federico II, Via Sergio Pansini 5, Naples, 80131, Italy
| | - Erica Di Donna
- Department of Advanced Biomedical Sciences, University Federico II, Via Sergio Pansini 5, Naples, 80131, Italy
| | - Martina Lamagna
- Department of Clinical Medicine and Surgery, University Federico II, Naples, Italy
| | - Roberta Della Pepa
- Department of Clinical Medicine and Surgery, University Federico II, Naples, Italy
| | - Fabrizio Pane
- Department of Clinical Medicine and Surgery, University Federico II, Naples, Italy
| | - Silvana Del Vecchio
- Department of Advanced Biomedical Sciences, University Federico II, Via Sergio Pansini 5, Naples, 80131, Italy
| | - Rosa Fonti
- Department of Advanced Biomedical Sciences, University Federico II, Via Sergio Pansini 5, Naples, 80131, Italy.
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12
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Gregg KW, Ruff C, Koenig G, Penev KI, Shepard A, Kreissler G, Amatuzio M, Owens C, Nagpal P, Glide-Hurst CK. Development and first implementation of a novel multi-modality cardiac motion and dosimetry phantom for radiotherapy applications. Med Phys 2024. [PMID: 39042362 DOI: 10.1002/mp.17315] [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: 12/18/2023] [Revised: 05/11/2024] [Accepted: 06/19/2024] [Indexed: 07/24/2024] Open
Abstract
BACKGROUND Cardiac applications in radiation therapy are rapidly expanding including magnetic resonance guided radiation therapy (MRgRT) for real-time gating for targeting and avoidance near the heart or treating ventricular tachycardia (VT). PURPOSE This work describes the development and implementation of a novel multi-modality and magnetic resonance (MR)-compatible cardiac phantom. METHODS The patient-informed 3D model was derived from manual contouring of a contrast-enhanced Coronary Computed Tomography Angiography scan, exported as a Stereolithography model, then post-processed to simulate female heart with an average volume. The model was 3D-printed using Elastic50A to provide MR contrast to water background. Two rigid acrylic modules containing cardiac structures were designed and assembled, retrofitting to an MR-safe programmable motor to supply cardiac and respiratory motion in superior-inferior directions. One module contained a cavity for an ion chamber (IC), and the other was equipped with multiple interchangeable cavities for plastic scintillation detectors (PSDs). Images were acquired on a 0.35 T MR-linac for validation of phantom geometry, motion, and simulated online treatment planning and delivery. Three motion profiles were prescribed: patient-derived cardiac (sine waveform, 4.3 mm peak-to-peak, 60 beats/min), respiratory (cos4 waveform, 30 mm peak-to-peak, 12 breaths/min), and a superposition of cardiac (sine waveform, 4 mm peak-to-peak, 70 beats/min) and respiratory (cos4 waveform, 24 mm peak-to-peak, 12 breaths/min). The amplitude of the motion profiles was evaluated from sagittal cine images at eight frames/s with a resolution of 2.4 mm × 2.4 mm. Gated dosimetry experiments were performed using the two module configurations for calculating dose relative to stationary. A CT-based VT treatment plan was delivered twice under cone-beam CT guidance and cumulative stationary doses to multi-point PSDs were evaluated. RESULTS No artifacts were observed on any images acquired during phantom operation. Phantom excursions measured 49.3 ± 25.8%/66.9 ± 14.0%, 97.0 ± 2.2%/96.4 ± 1.7%, and 90.4 ± 4.8%/89.3 ± 3.5% of prescription for cardiac, respiratory, and cardio-respiratory motion profiles for the 2-chamber (PSD) and 12-substructure (IC) phantom modules respectively. In the gated experiments, the cumulative dose was <2% from expected using the IC module. Real-time dose measured for the PSDs at 10 Hz acquisition rate demonstrated the ability to detect the dosimetric consequences of cardiac, respiratory, and cardio-respiratory motion when sampling of different locations during a single delivery, and the stability of our phantom dosimetric results over repeated cycles for the high dose and high gradient regions. For the VT delivery, high dose PSD was <1% from expected (5-6 cGy deviation of 5.9 Gy/fraction) and high gradient/low dose regions had deviations <3.6% (6.3 cGy less than expected 1.73 Gy/fraction). CONCLUSIONS A novel multi-modality modular heart phantom was designed, constructed, and used for gated radiotherapy experiments on a 0.35 T MR-linac. Our phantom was capable of mimicking cardiac, cardio-respiratory, and respiratory motion while performing dosimetric evaluations of gated procedures using IC and PSD configurations. Time-resolved PSDs with small sensitive volumes appear promising for low-amplitude/high-frequency motion and multi-point data acquisition for advanced dosimetric capabilities. Illustrating VT planning and delivery further expands our phantom to address the unmet needs of cardiac applications in radiotherapy.
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Affiliation(s)
- Kenneth W Gregg
- Department of Human Oncology, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Chase Ruff
- Department of Human Oncology, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Grant Koenig
- Modus Medical Devices, Inc. (IBA QUASAR), London, Ontario, Canada
| | - Kalin I Penev
- Modus Medical Devices, Inc. (IBA QUASAR), London, Ontario, Canada
| | - Andrew Shepard
- Department of Human Oncology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Grace Kreissler
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Margo Amatuzio
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Cameron Owens
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Prashant Nagpal
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Carri K Glide-Hurst
- Department of Human Oncology, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
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13
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Salzmann M, Hassan Tarek H, Prill R, Becker R, Schreyer AG, Hable R, Ostojic M, Ramadanov N. Artificial intelligence-based assessment of leg axis parameters shows excellent agreement with human raters: A systematic review and meta-analysis. Knee Surg Sports Traumatol Arthrosc 2024. [PMID: 39033340 DOI: 10.1002/ksa.12362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2024] [Revised: 07/01/2024] [Accepted: 07/02/2024] [Indexed: 07/23/2024]
Abstract
PURPOSE The aim of this study was to conduct a systematic review and meta-analysis on the reliability and applicability of artificial intelligence (AI)-based analysis of leg axis parameters. We hypothesized that AI-based leg axis measurements would be less time-consuming and as accurate as those performed by human raters. METHODS The study protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO). PubMed, Epistemonikos, and Web of Science were searched up to 24 February 2024, using a BOOLEAN search strategy. Titles and abstracts of identified records were screened through a stepwise process. Data extraction and quality assessment of the included papers were followed by a frequentist meta-analysis employing a common effect/random effects model with inverse variance and the Sidik-Jonkman heterogeneity estimator. RESULTS A total of 13 studies encompassing 3192 patients were included in this meta-analysis. All studies compared AI-based leg axis measurements on long-leg radiographs (LLR) with those performed by human raters. The parameters hip knee ankle angle (HKA), mechanical lateral distal femoral angle (mLDFA), mechanical medial proximal tibial angle (mMPTA), and joint-line convergence angle (JLCA) showed excellent agreement between AI and human raters. The AI system was approximately 3 min faster in reading standing long-leg anteroposterior radiographs (LLRs) compared with human raters. CONCLUSION AI-based assessment of leg axis parameters is an efficient, accurate, and time-saving procedure. The quality of AI-based assessment of the investigated parameters does not appear to be affected by the presence of implants or pathological conditions. LEVEL OF EVIDENCE Level I.
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Affiliation(s)
- Mikhail Salzmann
- Center of Orthopaedics and Traumatology, Brandenburg Medical School, University Hospital Brandenburg an der Havel, Brandenburg an der Havel, Germany
- Faculty of Health Science Brandenburg, Brandenburg Medical School Theodor Fontane, Brandenburg an der Havel, Germany
| | - Hakam Hassan Tarek
- Center of Orthopaedics and Traumatology, Brandenburg Medical School, University Hospital Brandenburg an der Havel, Brandenburg an der Havel, Germany
- Faculty of Health Science Brandenburg, Brandenburg Medical School Theodor Fontane, Brandenburg an der Havel, Germany
| | - Robert Prill
- Center of Orthopaedics and Traumatology, Brandenburg Medical School, University Hospital Brandenburg an der Havel, Brandenburg an der Havel, Germany
- Faculty of Health Science Brandenburg, Brandenburg Medical School Theodor Fontane, Brandenburg an der Havel, Germany
| | - Roland Becker
- Center of Orthopaedics and Traumatology, Brandenburg Medical School, University Hospital Brandenburg an der Havel, Brandenburg an der Havel, Germany
- Faculty of Health Science Brandenburg, Brandenburg Medical School Theodor Fontane, Brandenburg an der Havel, Germany
| | - Andreas G Schreyer
- Institute for Diagnostic and Interventional Radiology, Brandenburg Medical School Theodor Fontane, Brandenburg an der Havel, Germany
| | - Robert Hable
- Faculty of Applied Computer Science, Deggendorf Institute of Technology, Deggendorf, Germany
| | - Marko Ostojic
- Department of Orthopedics, University Hospital Mostar, Mostar, Bosnia and Herzegovina
| | - Nikolai Ramadanov
- Center of Orthopaedics and Traumatology, Brandenburg Medical School, University Hospital Brandenburg an der Havel, Brandenburg an der Havel, Germany
- Faculty of Health Science Brandenburg, Brandenburg Medical School Theodor Fontane, Brandenburg an der Havel, Germany
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14
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Yao Y, Jia Y, Wu M, Wang S, Song H, Fang X, Liao X, Li D, Zhao Q. Detection of atrial fibrillation using a nonlinear Lorenz Scattergram and deep learning in primary care. BMC PRIMARY CARE 2024; 25:267. [PMID: 39033295 PMCID: PMC11265054 DOI: 10.1186/s12875-024-02407-3] [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: 11/07/2023] [Accepted: 04/24/2024] [Indexed: 07/23/2024]
Abstract
BACKGROUND Atrial fibrillation (AF) is highly correlated with heart failure, stroke and death. Screening increases AF detection and facilitates the early adoption of comprehensive intervention. Long-term wearable devices have become increasingly popular for AF screening in primary care. However, interpreting data obtained by long-term wearable ECG devices is a problem in primary care. To diagnose the disease quickly and accurately, we aimed to build AF episode detection model based on a nonlinear Lorenz scattergram (LS) and deep learning. METHODS The MIT-BIH Normal Sinus Rhythm Database, MIT-BIH Arrhythmia Database and the Long-Term AF Database were extracted to construct the MIT-BIH Ambulatory Electrocardiograph (MIT-BIH AE) dataset. We converted the long-term ECG into a two-dimensional LSs. The LSs from MIT-BIH AE dataset was randomly divided into training and internal validation sets in a 9:1 ratio, which was used to develop and internally validated model. We built a MOBILE-SCREEN-AF (MS-AF) dataset from a single-lead wearable ECG device in primary care for external validation. Performance was quantified using a confusion matrix and standard classification metrics. RESULTS During the evaluation of model performance based on the LS, the sensitivity, specificity and accuracy of the model in diagnosing AF were 0.992, 0.973, and 0.983 in the internal validation set respectively. In the external validation set, these metrics were 0.989, 0.956, and 0.967, respectively. Furthermore, when evaluating the model's performance based on ECG records in the MS-AF dataset, the sensitivity, specificity and accuracy of model diagnosis paroxysmal AF were 1.000, 0.870 and 0.876 respectively, and 0.927, 1.000 and 0.973 for the persistent AF. CONCLUSIONS The model based on the nonlinear LS and deep learning has high accuracy, making it promising for AF screening in primary care. It has potential for generalization and practical application.
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Grants
- 2023YFS0027, 2023YFS0240, 2023YFS0074, 2023NSFSC1652, 2022YFS0279, 2021YFQ0062, 2022JDRC0148 Sichuan Province Science and Technology Support Program
- 2023YFS0027, 2023YFS0240, 2023YFS0074, 2023NSFSC1652, 2022YFS0279, 2021YFQ0062, 2022JDRC0148 Sichuan Province Science and Technology Support Program
- ZH2022-101 Sichuan Provincial Health Commission
- HXHL21016 Sichuan University West China Nursing Discipline Development Special Fund Project
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Affiliation(s)
- Yi Yao
- General Practice Ward/International Medical Center Ward, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
- Teaching&Research Section, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
- General Practice Medical Center and General Practice Research Institute, West China Hospital, Sichuan University, Chengdu, China
| | - Yu Jia
- General Practice Ward/International Medical Center Ward, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
- Teaching&Research Section, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
- General Practice Medical Center and General Practice Research Institute, West China Hospital, Sichuan University, Chengdu, China
| | - Miaomiao Wu
- General Practice Ward/International Medical Center Ward, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
- Teaching&Research Section, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
- General Practice Medical Center and General Practice Research Institute, West China Hospital, Sichuan University, Chengdu, China
| | - Songzhu Wang
- General Practice Ward/International Medical Center Ward, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
- Teaching&Research Section, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
- General Practice Medical Center and General Practice Research Institute, West China Hospital, Sichuan University, Chengdu, China
| | - Haiqi Song
- General Practice Ward/International Medical Center Ward, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
- Teaching&Research Section, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
- General Practice Medical Center and General Practice Research Institute, West China Hospital, Sichuan University, Chengdu, China
| | - Xiang Fang
- General Practice Ward/International Medical Center Ward, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
- Teaching&Research Section, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
- General Practice Medical Center and General Practice Research Institute, West China Hospital, Sichuan University, Chengdu, China
| | - Xiaoyang Liao
- General Practice Ward/International Medical Center Ward, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
- Teaching&Research Section, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
- General Practice Medical Center and General Practice Research Institute, West China Hospital, Sichuan University, Chengdu, China
| | - Dongze Li
- Department of Emergency Medicine and Laboratory of Emergency Medicine, West China Hospital, Sichuan University, Chengdu, China.
| | - Qian Zhao
- General Practice Ward/International Medical Center Ward, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China.
- Teaching&Research Section, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China.
- General Practice Medical Center and General Practice Research Institute, West China Hospital, Sichuan University, Chengdu, China.
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15
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Iramina H, Tsuneda M, Okamoto H, Kadoya N, Mukumoto N, Toyota M, Fukunaga J, Fujita Y, Tohyama N, Onishi H, Nakamura M. Multi-institutional questionnaire-based survey on online adaptive radiotherapy performed using commercial systems in Japan in 2023. Radiol Phys Technol 2024:10.1007/s12194-024-00828-4. [PMID: 39028438 DOI: 10.1007/s12194-024-00828-4] [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: 05/27/2024] [Revised: 07/12/2024] [Accepted: 07/13/2024] [Indexed: 07/20/2024]
Abstract
In this study, we aimed to conduct a survey on the current clinical practice of, staffing for, commissioning of, and staff training for online adaptive radiotherapy (oART) in the institutions that installed commercial oART systems in Japan, and to share the information with institutions that will implement oART systems in future. A web-based questionnaire, containing 107 questions, was distributed to nine institutions in Japan. Data were collected from November to December 2023. Three institutions each with the MRIdian (ViewRay, Oakwood Village, OH, USA), Unity (Elekta AB, Stockholm, Sweden), and Ethos (Varian Medical Systems, Palo Alto, CA, USA) systems completed the questionnaire. One institution (MRIdian) had not performed oART by the response deadline. Each institution had installed only one oART system. Hypofractionation, and moderate hypofractionation or conventional fractionation were employed in the MRIdian/Unity and Ethos systems, respectively. The elapsed time for the oART process was faster with the Ethos than with the other systems. All institutions added additional staff for oART. Commissioning periods differed among the oART systems owing to provision of beam data from the vendors. Chambers used during commissioning measurements differed among the institutions. Institutional training was provided by all nine institutions. To the best of our knowledge, this was the first survey about oART performed using commercial systems in Japan. We believe that this study will provide useful information to institutions that installed, are installing, or are planning to install oART systems.
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Affiliation(s)
- Hiraku Iramina
- Adaptive Radiotherapy Working Group (ART-WG), QA/QC Committee, Japan Society of Medical Physics, Tokyo, Japan
- Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto-Shi, Kyoto, 606-8507, Japan
| | - Masato Tsuneda
- Adaptive Radiotherapy Working Group (ART-WG), QA/QC Committee, Japan Society of Medical Physics, Tokyo, Japan
- Department of Radiation Oncology, MR Linac ART Division, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-Ku, Chiba-Shi, Chiba, 260-8670, Japan
| | - Hiroyuki Okamoto
- Adaptive Radiotherapy Working Group (ART-WG), QA/QC Committee, Japan Society of Medical Physics, Tokyo, Japan
- Radiation Safety and Quality Assurance Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan
| | - Noriyuki Kadoya
- Adaptive Radiotherapy Working Group (ART-WG), QA/QC Committee, Japan Society of Medical Physics, Tokyo, Japan
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-Machi, Aoba-Ku, Sendai-Shi, Miyagi, 980-8574, Japan
| | - Nobutaka Mukumoto
- Adaptive Radiotherapy Working Group (ART-WG), QA/QC Committee, Japan Society of Medical Physics, Tokyo, Japan
- Department of Radiation Oncology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-Machi, Abeno-Ku, Osaka-Shi, Osaka, 545-8585, Japan
| | - Masahiko Toyota
- Adaptive Radiotherapy Working Group (ART-WG), QA/QC Committee, Japan Society of Medical Physics, Tokyo, Japan
- Division of Radiology, Department of Clinical Technology, Kagoshima University Hospital, 8-35-1 Sakuragaoka, Kagoshima-Shi, Kagoshima, 890-8520, Japan
| | - Junichi Fukunaga
- Adaptive Radiotherapy Working Group (ART-WG), QA/QC Committee, Japan Society of Medical Physics, Tokyo, Japan
- Division of Radiology, Department of Medical Technology, Kyushu University Hospital, 3-1-1 Maidashi, Higashi-Ku, Fukuoka-Shi, Fukuoka, 812-8582, Japan
| | - Yukio Fujita
- Adaptive Radiotherapy Working Group (ART-WG), QA/QC Committee, Japan Society of Medical Physics, Tokyo, Japan
- Department of Radiation Oncology, MR Linac ART Division, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-Ku, Chiba-Shi, Chiba, 260-8670, Japan
- Department of Radiological Sciences, Komazawa University, 1-23-1 Komazawa, Setagaya-Ku, Tokyo, 154-8525, Japan
| | - Naoki Tohyama
- Department of Radiological Sciences, Komazawa University, 1-23-1 Komazawa, Setagaya-Ku, Tokyo, 154-8525, Japan
| | - Hiroshi Onishi
- Department of Radiology, University of Yamanashi, 1110 Shimokato, Chuo-Shi, Yamanashi, 409-3898, Japan
| | - Mitsuhiro Nakamura
- Adaptive Radiotherapy Working Group (ART-WG), QA/QC Committee, Japan Society of Medical Physics, Tokyo, Japan.
- Department of Advanced Medical Physics, Graduate School of Medicine, Kyoto University, 53 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto-Shi, Kyoto, 606-8507, Japan.
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16
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Yaqub M, Jinchao F, Aijaz N, Ahmed S, Mehmood A, Jiang H, He L. Intelligent breast cancer diagnosis with two-stage using mammogram images. Sci Rep 2024; 14:16672. [PMID: 39030248 DOI: 10.1038/s41598-024-65926-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 06/25/2024] [Indexed: 07/21/2024] Open
Abstract
Breast cancer (BC) significantly contributes to cancer-related mortality in women, underscoring the criticality of early detection for optimal patient outcomes. Mammography is a key tool for identifying and diagnosing breast abnormalities; however, accurately distinguishing malignant mass lesions remains challenging. To address this issue, we propose a novel deep learning approach for BC screening utilizing mammography images. Our proposed model comprises three distinct stages: data collection from established benchmark sources, image segmentation employing an Atrous Convolution-based Attentive and Adaptive Trans-Res-UNet (ACA-ATRUNet) architecture, and BC identification via an Atrous Convolution-based Attentive and Adaptive Multi-scale DenseNet (ACA-AMDN) model. The hyperparameters within the ACA-ATRUNet and ACA-AMDN models are optimized using the Modified Mussel Length-based Eurasian Oystercatcher Optimization (MML-EOO) algorithm. The performance is evaluated using a variety of metrics, and a comparative analysis against conventional methods is presented. Our experimental results reveal that the proposed BC detection framework attains superior precision rates in early disease detection, demonstrating its potential to enhance mammography-based screening methodologies.
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Affiliation(s)
- Muhammad Yaqub
- School of Biomedical Sciences, Hunan University, Changsha, People's Republic of China.
| | - Feng Jinchao
- Faculty of Information Technology, Beijing University of Technology, Beijing, People's Republic of China
| | - Nazish Aijaz
- School of Biomedical Sciences, Hunan University, Changsha, People's Republic of China
| | - Shahzad Ahmed
- Faculty of Information Technology, Beijing University of Technology, Beijing, People's Republic of China
| | - Atif Mehmood
- Department of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321002, People's Republic of China
| | - Hao Jiang
- Department of Biomedical Informatics School of Life Sciences, Central South University, Changsha, 410013, Hunan, People's Republic of China.
| | - Lan He
- School of Biomedical Sciences, Hunan University, Changsha, People's Republic of China.
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17
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Nelson G, Sarkar V, Szegedi M, Molineu A, Olch AJ, Kunz JN, Zhao H, Huang YJ, Pillai S, Rassiah P. Validation of Acuros for total body irradiation at extended distance. J Appl Clin Med Phys 2024:e14468. [PMID: 39023298 DOI: 10.1002/acm2.14468] [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: 04/02/2024] [Revised: 05/23/2024] [Accepted: 06/20/2024] [Indexed: 07/20/2024] Open
Abstract
PURPOSE Standardized and accurately reported doses are essential in conventional total body irradiation (TBI), especially lung doses. This study evaluates the accuracy of the Acuros algorithm in predicting doses for extended-distance TBI. METHODS Measurements and calculations were done with both 6 and 18 MV. Tissue Maximum Ratio (TMR), output and off axis ratios (OAR) were measured at 200 and 500 cm source to detector distance and compared to Acuros calculated values. Two end-to-end tests were carried out, one with an in-house phantom (solid water and Styrofoam) with inserted ion chambers and the other was with the Imaging and Radiation Oncology Core (IROC) TBI anthropomorphic phantom equipped with TLDs. The end-to-end test was done for 6 and 18 MV both with and without lung blocks. The source to midplane distance for both phantoms were at 518 and 508 cm respectively. Lung blocks were placed at the phantom surface and a beam spoiler was positioned 30 cm from the surface of the phantoms as per our clinical set up. RESULTS The agreement between measured and calculated TMR, output and off axis ratios for both 6 and 18 MV were within 2%. Ion chamber measurements in both the Styrofoam and solid water for both energies carried out with and without lung blocks were within 2% of calculated values. TLD measured doses for both 6 and 18 MV in the IROC phantom were within 5% of calculated doses which is within the uncertainty of the TLD measurement. CONCLUSIONS The results indicate that the clinical beam model for Acuros 16.1 commissioned at standard clinical distances is capable of calculating doses accurately at extended distances up to 500 cm.
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Affiliation(s)
- Geoffrey Nelson
- Department of Radiation Oncology, University of Utah, Salt Lake City, Utah, USA
| | - Vikren Sarkar
- Department of Radiation Oncology, University of Utah, Salt Lake City, Utah, USA
| | - Martin Szegedi
- Department of Radiation Oncology, University of Utah, Salt Lake City, Utah, USA
| | - Andrea Molineu
- Imaging and Radiation Oncology Core, Houston QA Center, MD Anderson Cancer Center, Houston, Texas, USA
| | - Arthur J Olch
- Department of Radiation Oncology, University of Southern California and Children's Hospital of Los Angeles, Los Angeles, California, USA
| | - Jeremy N Kunz
- Department of Radiation Oncology, University of Utah, Salt Lake City, Utah, USA
| | - Hui Zhao
- Department of Radiation Oncology, University of Utah, Salt Lake City, Utah, USA
| | - Y Jessica Huang
- Department of Radiation Oncology, University of Utah, Salt Lake City, Utah, USA
| | | | - Prema Rassiah
- Department of Radiation Oncology, University of Utah, Salt Lake City, Utah, USA
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18
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Rovaris IB, de Carvalho AL, Silva GA, Gerardi DG, Alievi MM. Thermographic analysis of perforations in polyurethane blocks performed with experimental conical drill bit in comparison to conventional orthopedic drill bit: a preliminary study. BMC Res Notes 2024; 17:197. [PMID: 39020384 PMCID: PMC11256403 DOI: 10.1186/s13104-024-06862-0] [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: 02/21/2024] [Accepted: 07/10/2024] [Indexed: 07/19/2024] Open
Abstract
OBJECTIVE Conical orthopedic drill bits may have the potential to improve the stabilization of orthopedic screws. During perforations, heat energy is released, and elevated temperatures could be related to thermal osteonecrosis. This study was designed to evaluate the thermal behavior of an experimental conical drill bit, when compared to the conventional cylindrical drill, using polyurethane blocks perforations. RESULTS The sample was divided into two groups, according to the method of drilling, including 25 polyurethane blocks in each: In Group 1, perforations were performed with a conventional orthopedic cylindrical drill; while in Group 2, an experimental conical drill was used. No statistically significant difference was observed in relation to the maximum temperature (MT) during the entire drilling in the groups, however the perforation time (PT) was slightly longer in Group 2. Each drill bit perforated five times and number of perforations was not correlated with a temperature increase, when evaluated universally or isolated by groups. The PT had no correlation with an increase in temperature when evaluating the perforations universally (n = 50) and in Group 1 alone; however, Group 2 showed an inversely proportional correlation for these variables, indicating that, for the conical drill bit, drillings with longer PT had lower MT.
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Affiliation(s)
- Inácio Bernhardt Rovaris
- Department of Animal Medicine, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil.
| | | | | | - Daniel Guimarães Gerardi
- Department of Animal Medicine, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Marcelo Meller Alievi
- Department of Animal Medicine, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
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Xin Z, Qin L, Tang Y, Guo S, Li F, Fang Y, Li G, Yao Y, Zheng B, Zhang B, Wu D, Xiao J, Ni C, Wei Q, Zhang T. Immune mediated support of metastasis: Implication for bone invasion. Cancer Commun (Lond) 2024. [PMID: 39003618 DOI: 10.1002/cac2.12584] [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: 12/28/2023] [Revised: 06/05/2024] [Accepted: 06/18/2024] [Indexed: 07/15/2024] Open
Abstract
Bone is a common organ affected by metastasis in various advanced cancers, including lung, breast, prostate, colorectal, and melanoma. Once a patient is diagnosed with bone metastasis, the patient's quality of life and overall survival are significantly reduced owing to a wide range of morbidities and the increasing difficulty of treatment. Many studies have shown that bone metastasis is closely related to bone microenvironment, especially bone immune microenvironment. However, the effects of various immune cells in the bone microenvironment on bone metastasis remain unclear. Here, we described the changes in various immune cells during bone metastasis and discussed their related mechanisms. Osteoblasts, adipocytes, and other non-immune cells closely related to bone metastasis were also included. This review also summarized the existing treatment methods and potential therapeutic targets, and provided insights for future studies of cancer bone metastasis.
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Affiliation(s)
- Zengfeng Xin
- Department of Orthopedic Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, P. R. China
| | - Luying Qin
- Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, National Ministry of Education), Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, P. R. China
| | - Yang Tang
- Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, National Ministry of Education), Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, P. R. China
| | - Siyu Guo
- Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, National Ministry of Education), Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, P. R. China
- Department of Radiation Oncology, Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, P. R. China
| | - Fangfang Li
- Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, National Ministry of Education), Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, P. R. China
| | - Yuan Fang
- Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, National Ministry of Education), Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, P. R. China
| | - Gege Li
- Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, National Ministry of Education), Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, P. R. China
| | - Yihan Yao
- Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, National Ministry of Education), Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, P. R. China
| | - Binbin Zheng
- Department of Respiratory Medicine, Ningbo Hangzhou Bay Hospital, Ningbo, Zhejiang, P. R. China
| | - Bicheng Zhang
- Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, National Ministry of Education), Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, P. R. China
- Department of Radiation Oncology, Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, P. R. China
| | - Dang Wu
- Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, National Ministry of Education), Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, P. R. China
- Department of Radiation Oncology, Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, P. R. China
| | - Jie Xiao
- Department of Orthopedic Surgery, Second Affiliated Hospital (Jiande Branch), Zhejiang University School of Medicine, Hangzhou, Zhejiang, P. R. China
| | - Chao Ni
- Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, National Ministry of Education), Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, P. R. China
- Department of Breast Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, P. R. China
| | - Qichun Wei
- Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, National Ministry of Education), Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, P. R. China
- Department of Radiation Oncology, Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, P. R. China
| | - Ting Zhang
- Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, National Ministry of Education), Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, P. R. China
- Department of Radiation Oncology, Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, P. R. China
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20
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Zhao H, Ou L, Zhang Z, Zhang L, Liu K, Kuang J. The value of deep learning-based X-ray techniques in detecting and classifying K-L grades of knee osteoarthritis: a systematic review and meta-analysis. Eur Radiol 2024:10.1007/s00330-024-10928-9. [PMID: 38997539 DOI: 10.1007/s00330-024-10928-9] [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: 02/02/2024] [Revised: 04/25/2024] [Accepted: 05/29/2024] [Indexed: 07/14/2024]
Abstract
OBJECTIVES Knee osteoarthritis (KOA), a prevalent degenerative joint disease, is primarily diagnosed through X-ray imaging. The Kellgren-Lawrence grading system (K-L) is the gold standard for evaluating KOA severity through X-ray analysis. However, this method is highly subjective and non-quantifiable, limiting its effectiveness in detecting subtle joint changes on X-rays. Recent researchers have been directed towards developing deep-learning (DL) techniques for a more accurate diagnosis of KOA using X-ray images. Despite advancements in these intelligent methods, the debate over their diagnostic sensitivity continues. Hence, we conducted the current meta-analysis. METHODS A comprehensive search was conducted in PubMed, Cochrane, Embase, Web of Science, and IEEE up to July 11, 2023. The QUADAS-2 tool was employed to assess the risk of bias in the included studies. Given the multi-classification nature of DL tasks, the sensitivity of DL across different K-L grades was meta-analyzed. RESULTS A total of 19 studies were included, encompassing 62,158 images. These images consisted of 22,388 for K-L0, 13,415 for K-L1, 15,597 for K-L2, 7768 for K-L3, and 2990 for K-L4. The meta-analysis demonstrated that the sensitivity of DL was 86.74% for K-L0 (95% CI: 80.01%-92.28%), 64.00% for K-L1 (95% CI: 51.81%-75.35%), 75.03% for K-L2 (95% CI: 66.00%-83.09%), 84.76% for K-L3 (95% CI: 78.34%-90.25%), and 90.32% for K-L4 (95% CI: 85.39%-94.40%). CONCLUSIONS The DL multi-classification methods based on X-ray imaging generally demonstrate a favorable sensitivity rate (over 50%) in distinguishing between K-L0-K-L4. Specifically, for K-L4, the sensitivity is highly satisfactory at 90.32%. In contrast, the sensitivity rates for K-L1-2 still need improvement. CLINICAL RELEVANCE STATEMENT Deep-learning methods have been useful to some extent in assessing the effectiveness of X-rays for osteoarthritis of the knee. However, this requires further research and reliable data to provide specific recommendations for clinical practice. KEY POINTS X-ray deep-learning (DL) methods are debatable for evaluating knee osteoarthritis (KOA) under The Kellgren-Lawrence system (K-L). Multi-classification deep-learning methods are more clinically relevant for assessing K-L grading than dichotomous results. For K-L3 and K-L4, X-ray-based DL has high diagnostic performance; early KOA needs to be further improved.
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Affiliation(s)
- Haoming Zhao
- Hunan University of Chinese Medicine, 300 Xueshi Road Hanpu Science & Education Park, Yuelu District, Changsha, Hunan, 410208, China
| | - Liang Ou
- Hunan Academy of Chinese Medicine No. 142 Yuehua Road, Yuelu District, Changsha, Hunan, 410013, China
| | - Ziming Zhang
- Hunan University of Chinese Medicine, 300 Xueshi Road Hanpu Science & Education Park, Yuelu District, Changsha, Hunan, 410208, China
| | - Le Zhang
- Hunan University of Chinese Medicine, 300 Xueshi Road Hanpu Science & Education Park, Yuelu District, Changsha, Hunan, 410208, China
| | - Ke Liu
- Hunan University of Chinese Medicine, 300 Xueshi Road Hanpu Science & Education Park, Yuelu District, Changsha, Hunan, 410208, China
| | - Jianjun Kuang
- Hunan Academy of Chinese Medicine No. 142 Yuehua Road, Yuelu District, Changsha, Hunan, 410013, China.
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21
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Latreche I, Slatnia S, Kazar O, Harous S, Khelili MA. Identification and diagnosis of schizophrenia based on multichannel EEG and CNN deep learning model. Schizophr Res 2024; 271:28-35. [PMID: 39002527 DOI: 10.1016/j.schres.2024.07.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 07/06/2024] [Accepted: 07/07/2024] [Indexed: 07/15/2024]
Abstract
This paper proposes a high-accuracy EEG-based schizophrenia (SZ) detection approach. Unlike comparable literature studies employing conventional machine learning algorithms, our method autonomously extracts the necessary features for network training from EEG recordings. The proposed model is a ten-layered CNN that contains a max pooling layer, a Global Average Pooling layer, four convolution layers, two dropout layers for overfitting prevention, and two fully connected layers. The efficiency of the suggested method was assessed using the ten-fold-cross validation technique and the EEG records of 14 healthy subjects and 14 SZ patients. The obtained mean accuracy score was 99.18 %. To confirm the high mean accuracy attained, we tested the model on unseen data with a near-perfect accuracy score (almost 100 %). In addition, the results we obtained outperform numerous other comparable works.
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Affiliation(s)
- Imene Latreche
- Department of Computer Science, University of Biskra, Biskra, Algeria.
| | - Sihem Slatnia
- Department of Computer Science, University of Biskra, Biskra, Algeria.
| | - Okba Kazar
- College of Arts, Sciences & Information Technology, University of Kalba, Sharjah, United Arab Emirates
| | - Saad Harous
- College of Computing and Informatics, Department of Computer Science, University of Sharjah, Sharjah, United Arab Emirates.
| | - Mohamed Akram Khelili
- Department of Computer Science, University of Biskra, Biskra, Algeria; Numidia Institute of Technology, Algiers, Algeria.
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22
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He Z, Soullié P, Lefebvre P, Ambarki K, Felblinger J, Odille F. Changes of in vivo electrical conductivity in the brain and torso related to age, fat fraction and sex using MRI. Sci Rep 2024; 14:16109. [PMID: 38997324 PMCID: PMC11245625 DOI: 10.1038/s41598-024-67014-9] [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: 04/15/2024] [Accepted: 07/08/2024] [Indexed: 07/14/2024] Open
Abstract
This work was inspired by the observation that a majority of MR-electrical properties tomography studies are based on direct comparisons with ex vivo measurements carried out on post-mortem samples in the 90's. As a result, the in vivo conductivity values obtained from MRI in the megahertz range in different types of tissues (brain, liver, tumors, muscles, etc.) found in the literature may not correspond to their ex vivo equivalent, which still serves as a reference for electromagnetic modelling. This study aims to pave the way for improving current databases since the definition of personalized electromagnetic models (e.g. for Specific Absorption Rate estimation) would benefit from better estimation. Seventeen healthy volunteers underwent MRI of both brain and thorax/abdomen using a three-dimensional ultrashort echo-time (UTE) sequence. We estimated conductivity (S/m) in several classes of macroscopic tissue using a customized reconstruction method from complex UTE images, and give general statistics for each of these regions (mean-median-standard deviation). These values are used to find possible correlations with biological parameters such as age, sex, body mass index and/or fat volume fraction, using linear regression analysis. In short, the collected in vivo values show significant deviations from the ex vivo values in conventional databases, and we show significant relationships with the latter parameters in certain organs for the first time, e.g. a decrease in brain conductivity with age.
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Affiliation(s)
- Zhongzheng He
- IADI U1254, INSERM and Université de Lorraine, Nancy, France
| | - Paul Soullié
- IADI U1254, INSERM and Université de Lorraine, Nancy, France.
| | | | | | - Jacques Felblinger
- IADI U1254, INSERM and Université de Lorraine, Nancy, France
- CIC-IT 1433, INSERM, Université de Lorraine and CHRU Nancy, Nancy, France
| | - Freddy Odille
- IADI U1254, INSERM and Université de Lorraine, Nancy, France
- CIC-IT 1433, INSERM, Université de Lorraine and CHRU Nancy, Nancy, France
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23
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Maruyama S, Watanabe H, Shimosegawa M. An image quality assessment index based on image features and keypoints for X-ray CT images. PLoS One 2024; 19:e0304860. [PMID: 38990930 PMCID: PMC11238976 DOI: 10.1371/journal.pone.0304860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 05/21/2024] [Indexed: 07/13/2024] Open
Abstract
Optimization tasks in diagnostic radiological imaging require objective quantitative metrics that correlate with the subjective perception of observers. However, although one such metric, the structural similarity index (SSIM), is popular, it has limitations across various aspects in its application to medical images. In this study, we introduce a novel image quality evaluation approach based on keypoints and their associated unique image feature values, focusing on developing a framework to address the need for robustness and interpretability that are lacking in conventional methodologies. The proposed index quantifies and visualizes the distance between feature vectors associated with keypoints, which varies depending on changes in the image quality. This metric was validated on images with varying noise levels and resolution characteristics, and its applicability and effectiveness were examined by evaluating images subjected to various affine transformations. In the verification of X-ray computed tomography imaging using a head phantom, the distances between feature descriptors for each keypoint increased as the image quality degraded, exhibiting a strong correlation with the changes in the SSIM. Notably, the proposed index outperformed conventional full-reference metrics in terms of robustness to various transformations which are without changes in the image quality. Overall, the results suggested that image analysis performed using the proposed framework could effectively visualize the corresponding feature points, potentially harnessing lost feature information owing to changes in the image quality. These findings demonstrate the feasibility of applying the novel index to analyze changes in the image quality. This method may overcome limitations inherent in conventional evaluation methodologies and contribute to medical image analysis in the broader domain.
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Affiliation(s)
- Sho Maruyama
- Department of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Gunma, Japan
| | - Haruyuki Watanabe
- Department of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Gunma, Japan
| | - Masayuki Shimosegawa
- Department of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Gunma, Japan
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24
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Jahng JWS, Little MP, No HJ, Loo BW, Wu JC. Consequences of ionizing radiation exposure to the cardiovascular system. Nat Rev Cardiol 2024:10.1038/s41569-024-01056-4. [PMID: 38987578 DOI: 10.1038/s41569-024-01056-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/11/2024] [Indexed: 07/12/2024]
Abstract
Ionizing radiation is widely used in various industrial and medical applications, resulting in increased exposure for certain populations. Lessons from radiation accidents and occupational exposure have highlighted the cardiovascular and cerebrovascular risks associated with radiation exposure. In addition, radiation therapy for cancer has been linked to numerous cardiovascular complications, depending on the distribution of the dose by volume in the heart and other relevant target tissues in the circulatory system. The manifestation of symptoms is influenced by numerous factors, and distinct cardiac complications have previously been observed in different groups of patients with cancer undergoing radiation therapy. However, in contemporary radiation therapy, advances in treatment planning with conformal radiation delivery have markedly reduced the mean heart dose and volume of exposure, and these variables are therefore no longer sole surrogates for predicting the risk of specific types of heart disease. Nevertheless, certain cardiac substructures remain vulnerable to radiation exposure, necessitating close monitoring. In this Review, we provide a comprehensive overview of the consequences of radiation exposure on the cardiovascular system, drawing insights from various cohorts exposed to uniform, whole-body radiation or to partial-body irradiation, and identify potential risk modifiers in the development of radiation-associated cardiovascular disease.
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Affiliation(s)
- James W S Jahng
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA.
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
| | - Mark P Little
- Radiation Epidemiology Branch, National Cancer Institute, Bethesda, MD, USA
- Faculty of Health and Life Sciences, Oxford Brookes University, Headington Campus, Oxford, UK
| | - Hyunsoo J No
- Department of Radiation Oncology, Southern California Permanente Medical Group, Los Angeles, CA, USA
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Billy W Loo
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Joseph C Wu
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA.
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
- Greenstone Biosciences, Palo Alto, CA, USA.
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25
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Zhang SC, Nikolova AP, Kamrava M, Mak RH, Atkins KM. A roadmap for modelling radiation-induced cardiac disease. J Med Imaging Radiat Oncol 2024. [PMID: 38985978 DOI: 10.1111/1754-9485.13716] [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: 01/31/2024] [Accepted: 05/21/2024] [Indexed: 07/12/2024]
Abstract
Cardiac risk mitigation is a major priority in improving outcomes for cancer survivors as advances in cancer screening and treatments continue to decrease cancer mortality. More than half of adult cancer patients will be treated with radiotherapy (RT); therefore it is crucial to develop a framework for how to assess and predict radiation-induced cardiac disease (RICD). Historically, RICD was modelled solely using whole heart metrics such as mean heart dose. However, data over the past decade has identified cardiac substructures which outperform whole heart metrics in predicting for significant cardiac events. Additionally, non-RT factors such as pre-existing cardiovascular risk factors and toxicity from other therapies contribute to risk of future cardiac events. In this review, we aim to discuss the current evidence and knowledge gaps in predicting RICD and provide a roadmap for the development of comprehensive models based on three interrelated components, (1) baseline CV risk assessment, (2) cardiac substructure radiation dosimetry linked with cardiac-specific outcomes and (3) novel biomarker development.
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Affiliation(s)
- Samuel C Zhang
- Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Andriana P Nikolova
- Department of Cardiology, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Mitchell Kamrava
- Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Raymond H Mak
- Department of Radiation Oncology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Katelyn M Atkins
- Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Cardiology, Cedars-Sinai Medical Center, Los Angeles, California, USA
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26
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Bantan H, Yasuda H. Reading of gafchromic EBT-3 film using an overhead scanner. Biomed Phys Eng Express 2024; 10:055004. [PMID: 38941982 DOI: 10.1088/2057-1976/ad5cf8] [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: 02/28/2024] [Accepted: 06/28/2024] [Indexed: 06/30/2024]
Abstract
Gafchromic film, a commercially available radiochromic film, has been developed and widely used as an effective tool for radiation dose verification and quality assurance in radiotherapy. However, the orientation effect in scanning a film remains a concern for practical application in beam profile monitoring. To resolve this issue, the authors introduced a novel method using an overhead scanner (OHS) coupled with a tracing light board instead of a conventional flatbed scanner (FBS) to read Gafchromic EBT3 films. We investigated the orientation effect of the EBT3 film with a regular hexagonal shape after irradiation with 5 Gy x-rays (160 kV, 6.3 mA) and compared the digitized images acquired using a commercially available OHS (CZUR Aura) and a conventional FBS (EPSON GT-X980). As a result, RGB color intensities acquired from the OHS showed significantly lower orientation effect of the color intensities of RGB components than those from FBS. This finding indicates the high potential of the proposed method for achieving more precise two-dimensional dosimetry. Further studies are required to confirm the effectiveness of this method under different irradiation conditions over a wider dose range.
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Affiliation(s)
- H Bantan
- Department of Radiation Biophysics, Research Institute for Radiation Biology and Medicine (RIRBM), Hiroshima University, Kasumi 1-2-3, Minami-ku, Hiroshima 734-8551, Japan
- Phoenix Leader Education Program (Hiroshima Initiative) for Renaissance from Radiation Disaster, Hiroshima University, Kasumi 1-2-3 Minami-ku, Hiroshima 734-8553, Japan
- Graduate School of Biomedical and Health Sciences, Hiroshima University, Kasumi 1-2-3 Minami-ku, Hiroshima, 734-8553, Japan
| | - H Yasuda
- Department of Radiation Biophysics, Research Institute for Radiation Biology and Medicine (RIRBM), Hiroshima University, Kasumi 1-2-3, Minami-ku, Hiroshima 734-8551, Japan
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27
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Oude Nijhuis KD, Dankelman LHM, Wiersma JP, Barvelink B, IJpma FFA, Verhofstad MHJ, Doornberg JN, Colaris JW, Wijffels MME. AI for detection, classification and prediction of loss of alignment of distal radius fractures; a systematic review. Eur J Trauma Emerg Surg 2024:10.1007/s00068-024-02557-0. [PMID: 38981869 DOI: 10.1007/s00068-024-02557-0] [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: 03/05/2024] [Accepted: 05/14/2024] [Indexed: 07/11/2024]
Abstract
PURPOSE Early and accurate assessment of distal radius fractures (DRFs) is crucial for optimal prognosis. Identifying fractures likely to lose threshold alignment (instability) in a cast is vital for treatment decisions, yet prediction tools' accuracy and reliability remain challenging. Artificial intelligence (AI), particularly Convolutional Neural Networks (CNNs), can evaluate radiographic images with high performance. This systematic review aims to summarize studies utilizing CNNs to detect, classify, or predict loss of threshold alignment of DRFs. METHODS A literature search was performed according to the PRISMA. Studies were eligible when the use of AI for the detection, classification, or prediction of loss of threshold alignment was analyzed. Quality assessment was done with a modified version of the methodologic index for non-randomized studies (MINORS). RESULTS Of the 576 identified studies, 15 were included. On fracture detection, studies reported sensitivity and specificity ranging from 80 to 99% and 73-100%, respectively; the AUC ranged from 0.87 to 0.99; the accuracy varied from 82 to 99%. The accuracy of fracture classification ranged from 60 to 81% and the AUC from 0.59 to 0.84. No studies focused on predicting loss of thresholds alignement of DRFs. CONCLUSION AI models for DRF detection show promising performance, indicating the potential of algorithms to assist clinicians in the assessment of radiographs. In addition, AI models showed similar performance compared to clinicians. No algorithms for predicting the loss of threshold alignment were identified in our literature search despite the clinical relevance of such algorithms.
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Affiliation(s)
- Koen D Oude Nijhuis
- Department of Orthopedic Surgery, Groningen, Groningen University Medical Centre, Groningen, The Netherlands.
- Department of Surgery, Groningen, University Medical Centre, Groningen, The Netherlands.
| | - Lente H M Dankelman
- Trauma Research Unit Department of Surgery, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, Rotterdam, 3000 CA, The Netherlands.
- Department of Orthopedic Surgery, Hand and Arm Center, Massachusetts General Hospital, Boston MA, Harvard Medical School, Boston MA, The Netherlands.
| | - Jort P Wiersma
- Department of Orthopedic Surgery, Groningen, Groningen University Medical Centre, Groningen, The Netherlands
- University Medical Center, Utrecht, The Netherlands
| | - Britt Barvelink
- Department of Orthopedics and Sports Medicine, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Frank F A IJpma
- Department of Surgery, Groningen, University Medical Centre, Groningen, The Netherlands
| | - Michael H J Verhofstad
- Trauma Research Unit Department of Surgery, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, Rotterdam, 3000 CA, The Netherlands
| | - Job N Doornberg
- Department of Orthopedic Surgery, Groningen, Groningen University Medical Centre, Groningen, The Netherlands
- Department of Surgery, Groningen, University Medical Centre, Groningen, The Netherlands
- Department of Orthopaedic and Trauma Surgery, Flinders University and Flinders Medical Centre, Adelaide, Australia
| | - Joost W Colaris
- Department of Orthopedics and Sports Medicine, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Mathieu M E Wijffels
- Trauma Research Unit Department of Surgery, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, Rotterdam, 3000 CA, The Netherlands
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Deshmukh M, Khemchandani M, Thakur PM. Contributions of brain regions to machine learning-based classifications of attention deficit hyperactivity disorder (ADHD) utilizing EEG signals. APPLIED NEUROPSYCHOLOGY. ADULT 2024:1-15. [PMID: 38976722 DOI: 10.1080/23279095.2024.2368655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
OBJECTIVE The study presented focuses on the creation of a machine learning (ML) model that uses electrophysiological (EEG) data to identify kids with attention deficit hyperactivity disorder (ADHD) from healthy controls. The EEG signals are acquired during cognitive tasks to distinguish children with ADHD from their counterparts. METHODOLOGY The EEG data recorded in cognitive exercises was filtered using low pass Bessel filter and notch filters to remove artifacts, by the data set owners. To identify unique EEG patterns, we used many well-known classifiers, including Naïve Bayes (NB), Random Forest, Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), AdaBoost and Linear Discriminant Analysis (LDA), to identify distinct EEG patterns. Input features comprised EEG data from nineteen channels, individually and in combination. FINDINGS Study indicates that EEG-based categorization can differentiate between individuals with ADHD and healthy individuals with accuracy of 84%. The RF classifier achieved a maximum accuracy of 0.84 when particular region combinations were used. Evaluation of classification performance utilizing hemisphere-specific EEG data yielded promising outcomes, particularly in the right hemisphere channels. NOVELTY The study goes beyond traditional methodologies by investigating the effect of regional data on categorization results. The contributions of various brain regions to these classifications are being extensively researched. Understanding the role of different brain regions in ADHD can lead to better diagnosis and treatment options for individuals with ADHD. The study of categorization ability, utilizing EEG data specific to each hemisphere, particularly channels in the right hemisphere region, provides further granularity to the findings.
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Affiliation(s)
- Manjusha Deshmukh
- Computer Engineering Department, Saraswati College of Engineering, Navi Mumbai, India
| | - Mahi Khemchandani
- Information Technology, Saraswati College of Engineering, Navi Mumbai, India
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Islam N, Mohsin ASM, Choudhury SH, Shaer TP, Islam MA, Sadat O, Taz NH. COVID-19 and Pneumonia detection and web deployment from CT scan and X-ray images using deep learning. PLoS One 2024; 19:e0302413. [PMID: 38976703 PMCID: PMC11230556 DOI: 10.1371/journal.pone.0302413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 04/03/2024] [Indexed: 07/10/2024] Open
Abstract
During the COVID-19 pandemic, pneumonia was the leading cause of respiratory failure and death. In addition to SARS-COV-2, it can be caused by several other bacterial and viral agents. Even today, variants of SARS-COV-2 are endemic and COVID-19 cases are common in many places. The symptoms of COVID-19 are highly diverse and robust, ranging from invisible to severe respiratory failure. Current detection methods for the disease are time-consuming and expensive with low accuracy and precision. To address such situations, we have designed a framework for COVID-19 and Pneumonia detection using multiple deep learning algorithms further accompanied by a deployment scheme. In this study, we have utilized four prominent deep learning models, which are VGG-19, ResNet-50, Inception V3 and Xception, on two separate datasets of CT scan and X-ray images (COVID/Non-COVID) to identify the best models for the detection of COVID-19. We achieved accuracies ranging from 86% to 99% depending on the model and dataset. To further validate our findings, we have applied the four distinct models on two more supplementary datasets of X-ray images of bacterial pneumonia and viral pneumonia. Additionally, we have implemented a flask app to visualize the outcome of our framework to show the identified COVID and Non-COVID images. The findings of this study will be helpful to develop an AI-driven automated tool for the cost effective and faster detection and better management of COVID-19 patients.
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Affiliation(s)
- Nahid Islam
- Department of Electrical and Electronics Engineering, Nanotechnology, IoT and Machine Learning Research Group, BRAC University, Dhaka, Bangladesh
| | - Abu S M Mohsin
- Department of Electrical and Electronics Engineering, Nanotechnology, IoT and Machine Learning Research Group, BRAC University, Dhaka, Bangladesh
| | - Shadab Hafiz Choudhury
- Department of Electrical and Electronics Engineering, Nanotechnology, IoT and Machine Learning Research Group, BRAC University, Dhaka, Bangladesh
| | - Tazwar Prodhan Shaer
- Department of Electrical and Electronics Engineering, Nanotechnology, IoT and Machine Learning Research Group, BRAC University, Dhaka, Bangladesh
| | - Md Adnan Islam
- Department of Electrical and Electronics Engineering, Nanotechnology, IoT and Machine Learning Research Group, BRAC University, Dhaka, Bangladesh
| | - Omar Sadat
- Department of Electrical and Electronics Engineering, Nanotechnology, IoT and Machine Learning Research Group, BRAC University, Dhaka, Bangladesh
| | - Nahid Hossain Taz
- Department of Electrical and Electronics Engineering, Nanotechnology, IoT and Machine Learning Research Group, BRAC University, Dhaka, Bangladesh
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Soliveri L, Bruneau D, Ring J, Bozzetto M, Remuzzi A, Valen-Sendstad K. Toward a physiological model of vascular wall vibrations in the arteriovenous fistula. Biomech Model Mechanobiol 2024:10.1007/s10237-024-01865-z. [PMID: 38977647 DOI: 10.1007/s10237-024-01865-z] [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: 12/09/2023] [Accepted: 06/05/2024] [Indexed: 07/10/2024]
Abstract
The mechanism behind hemodialysis arteriovenous fistula (AVF) failure remains poorly understood, despite previous efforts to correlate altered hemodynamics with vascular remodeling. We have recently demonstrated that transitional flow induces high-frequency vibrations in the AVF wall, albeit with a simplified model. This study addresses the key limitations of our original fluid-structure interaction (FSI) approach, aiming to evaluate the vibration response using a more realistic model. A 3D AVF geometry was generated from contrast-free MRI and high-fidelity FSI simulations were performed. Patient-specific inflow and pressure were incorporated, and a three-term Mooney-Rivlin model was fitted using experimental data. The viscoelastic effect of perivascular tissue was modeled with Robin boundary conditions. Prescribing pulsatile inflow and pressure resulted in a substantial increase in vein displacement ( + 400 %) and strain ( + 317 %), with a higher maximum spectral frequency becoming visible above -42 dB (from 200 to 500 Hz). Transitioning from Saint Venant-Kirchhoff to Mooney-Rivlin model led to displacement amplitudes exceeding 10 micrometers and had a substantial impact on strain ( + 116 %). Robin boundary conditions significantly damped high-frequency displacement ( - 60 %). Incorporating venous tissue properties increased vibrations by 91%, extending up to 700 Hz, with a maximum strain of 0.158. Notably, our results show localized, high levels of vibration at the inner curvature of the vein, a site known for experiencing pronounced remodeling. Our findings, consistent with experimental and clinical reports of bruits and thrills, underscore the significance of incorporating physiologically plausible modeling approaches to investigate the role of wall vibrations in AVF remodeling and failure.
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Affiliation(s)
- Luca Soliveri
- Department of Bioengineering, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Bergamo, Italy
| | - David Bruneau
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Canada
| | - Johannes Ring
- Department of Computational Physiology, Simula Research Laboratory, Oslo, Norway
| | - Michela Bozzetto
- Department of Bioengineering, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Bergamo, Italy
| | - Andrea Remuzzi
- Department of Management, Information and Production Engineering, University of Bergamo, Bergamo, Italy
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Garcia JR, Jover R, Mila M, Muxi A, Vallejos V, Garcia A, Caballero E. Unveiling the speciality future: challenges and opportunities in nuclear medicine. Rev Esp Med Nucl Imagen Mol 2024:500036. [PMID: 38986813 DOI: 10.1016/j.remnie.2024.500036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2024] [Revised: 06/17/2024] [Accepted: 06/17/2024] [Indexed: 07/12/2024]
Affiliation(s)
- J R Garcia
- Grupo Trabajo Gestión y Calidad, Sociedad Española Medicina Nuclear e Imagen Molecular.
| | - R Jover
- Grupo Trabajo Gestión y Calidad, Sociedad Española Medicina Nuclear e Imagen Molecular
| | - M Mila
- Grupo Trabajo Gestión y Calidad, Sociedad Española Medicina Nuclear e Imagen Molecular
| | - A Muxi
- Grupo Trabajo Gestión y Calidad, Sociedad Española Medicina Nuclear e Imagen Molecular
| | - V Vallejos
- Grupo Trabajo Gestión y Calidad, Sociedad Española Medicina Nuclear e Imagen Molecular
| | - A Garcia
- Grupo Trabajo Gestión y Calidad, Sociedad Española Medicina Nuclear e Imagen Molecular
| | - E Caballero
- Grupo Trabajo Gestión y Calidad, Sociedad Española Medicina Nuclear e Imagen Molecular
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Deng J, Yang J, Wang X, Zhang X. A Novel Instruction Driven 1-D CNN Processor for ECG Classification. SENSORS (BASEL, SWITZERLAND) 2024; 24:4376. [PMID: 39001155 PMCID: PMC11244409 DOI: 10.3390/s24134376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/12/2024] [Revised: 07/01/2024] [Accepted: 07/04/2024] [Indexed: 07/16/2024]
Abstract
Electrocardiography (ECG) has emerged as a ubiquitous diagnostic tool for the identification and characterization of diverse cardiovascular pathologies. Wearable health monitoring devices, equipped with on-device biomedical artificial intelligence (AI) processors, have revolutionized the acquisition, analysis, and interpretation of ECG data. However, these systems necessitate AI processors that exhibit flexible configuration, facilitate portability, and demonstrate optimal performance in terms of power consumption and latency for the realization of various functionalities. To address these challenges, this study proposes an instruction-driven convolutional neural network (CNN) processor. This processor incorporates three key features: (1) An instruction-driven CNN processor to support versatile ECG-based application. (2) A Processing element (PE) array design that simultaneously considers parallelism and data reuse. (3) An activation unit based on the CORDIC algorithm, supporting both Tanh and Sigmoid computations. The design has been implemented using 110 nm CMOS process technology, occupying a die area of 1.35 mm2 with 12.94 µW power consumption. It has been demonstrated with two typical ECG AI applications, including two-class (i.e., normal/abnormal) classification and five-class classification. The proposed 1-D CNN algorithm performs with a 97.95% accuracy for the two-class classification and 97.9% for the five-class classification, respectively.
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Affiliation(s)
- Jiawen Deng
- The Key Laboratory of Integrated Microsystems, Peking University Shenzhen Graduate School, Shenzhen 518000, China; (J.D.); (J.Y.)
| | - Jie Yang
- The Key Laboratory of Integrated Microsystems, Peking University Shenzhen Graduate School, Shenzhen 518000, China; (J.D.); (J.Y.)
| | - Xin’an Wang
- The Key Laboratory of Integrated Microsystems, Peking University Shenzhen Graduate School, Shenzhen 518000, China; (J.D.); (J.Y.)
| | - Xing Zhang
- School of Integrated Circuits, Peking University, Beijing 100871, China
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Korkmaz E, Aerts S, Coesoij R, Bhatt CR, Velghe M, Colussi L, Land D, Petroulakis N, Spirito M, Bolte J. A comprehensive review of 5G NR RF-EMF exposure assessment technologies: fundamentals, advancements, challenges, niches, and implications. ENVIRONMENTAL RESEARCH 2024:119524. [PMID: 38972338 DOI: 10.1016/j.envres.2024.119524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 06/16/2024] [Accepted: 06/30/2024] [Indexed: 07/09/2024]
Abstract
This review offers a detailed examination of the current landscape of radio frequency (RF) electromagnetic field (EMF) assessment tools, ranging from spectrum analyzers and broadband field meters to area monitors and custom-built devices. The discussion encompasses both standardized and non-standardized measurement protocols, shedding light on the various methods employed in this domain. Furthermore, the review highlights the prevalent use of mobile apps for characterizing 5G-NR radio network data. A growing need for low-cost measurement devices is observed, commonly referred to as "sensors" or "sensor nodes," that are capable of enduring diverse environmental conditions. These sensors play a crucial role in both microenvironmental surveys and individual exposures, enabling stationary, mobile, and personal exposure assessments based on body-worn sensors, across wider geographical areas. This review revealed a notable need for cost-effective and long-lasting sensors, whether for individual exposure assessments, mobile (vehicle-integrated) measurements, or incorporation into distributed sensor networks. However, there is a lack of comprehensive information on existing custom-developed RF-EMF measurement tools, especially in terms of measuring uncertainty. Additionally, there is a need for real-time, fast-sampling solutions to understand the highly irregular temporal variations EMF distribution in next-generation networks. Given the diversity of tools and methods, a comprehensive comparison is crucial to determine the necessary statistical tools for aggregating the available measurement data.
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Affiliation(s)
- Erdal Korkmaz
- The Hague University of Applied Sciences, Research Group Smart Sensor Systems, 2627 AL, Delft, The Netherlands.
| | - Sam Aerts
- The Hague University of Applied Sciences, Research Group Smart Sensor Systems, 2627 AL, Delft, The Netherlands
| | - Richard Coesoij
- Delft University of Technology, Department of Microelectronics, 2628 CN, Delft, The Netherlands
| | - Chhavi Raj Bhatt
- Australian Radiation Protection and Nuclear Safety Agency, VIC 3085, Yallambie, Australia
| | - Maarten Velghe
- National Institute for Public Health and the Environment, Centre for Sustainability, Environment and Health, 3720 BA, Bilthoven, The Netherlands
| | - Loek Colussi
- Dutch Authority for Digital Infrastructure, 9700 AL, Groningen, The Netherlands
| | - Derek Land
- The Hague University of Applied Sciences, Research Group Smart Sensor Systems, 2627 AL, Delft, The Netherlands
| | - Nikolaos Petroulakis
- Institute of Computer Science, Foundation for Research and Technology-Hellas, 70013, Heraklion, Greece
| | - Marco Spirito
- Delft University of Technology, Department of Microelectronics, 2628 CN, Delft, The Netherlands
| | - John Bolte
- The Hague University of Applied Sciences, Research Group Smart Sensor Systems, 2627 AL, Delft, The Netherlands; National Institute for Public Health and the Environment, Centre for Sustainability, Environment and Health, 3720 BA, Bilthoven, The Netherlands
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Decoodt P, Sierra-Sosa D, Anghel L, Cuminetti G, De Keyzer E, Morissens M. Transfer Learning Video Classification of Preserved, Mid-Range, and Reduced Left Ventricular Ejection Fraction in Echocardiography. Diagnostics (Basel) 2024; 14:1439. [PMID: 39001328 PMCID: PMC11241427 DOI: 10.3390/diagnostics14131439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2024] [Revised: 07/01/2024] [Accepted: 07/02/2024] [Indexed: 07/16/2024] Open
Abstract
Identifying patients with left ventricular ejection fraction (EF), either reduced [EF < 40% (rEF)], mid-range [EF 40-50% (mEF)], or preserved [EF > 50% (pEF)], is considered of primary clinical importance. An end-to-end video classification using AutoML in Google Vertex AI was applied to echocardiographic recordings. Datasets balanced by majority undersampling, each corresponding to one out of three possible classifications, were obtained from the Standford EchoNet-Dynamic repository. A train-test split of 75/25 was applied. A binary video classification of rEF vs. not rEF demonstrated good performance (test dataset: ROC AUC score 0.939, accuracy 0.863, sensitivity 0.894, specificity 0.831, positive predicting value 0.842). A second binary classification of not pEF vs. pEF was slightly less performing (test dataset: ROC AUC score 0.917, accuracy 0.829, sensitivity 0.761, specificity 0.891, positive predicting value 0.888). A ternary classification was also explored, and lower performance was observed, mainly for the mEF class. A non-AutoML PyTorch implementation in open access confirmed the feasibility of our approach. With this proof of concept, end-to-end video classification based on transfer learning to categorize EF merits consideration for further evaluation in prospective clinical studies.
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Affiliation(s)
- Pierre Decoodt
- Cardiologie, Centre Hospitalier Universitaire Brugmann, Faculté de Médecine, Université Libre de Bruxelles, Place Van Gehuchten 4, 1020 Brussels, Belgium; (L.A.); (G.C.); (E.D.K.); (M.M.)
| | - Daniel Sierra-Sosa
- Computer Science and Information Technologies Department, Hood College, 401 Rosemont Ave., Frederick, MD 21702, USA;
| | - Laura Anghel
- Cardiologie, Centre Hospitalier Universitaire Brugmann, Faculté de Médecine, Université Libre de Bruxelles, Place Van Gehuchten 4, 1020 Brussels, Belgium; (L.A.); (G.C.); (E.D.K.); (M.M.)
| | - Giovanni Cuminetti
- Cardiologie, Centre Hospitalier Universitaire Brugmann, Faculté de Médecine, Université Libre de Bruxelles, Place Van Gehuchten 4, 1020 Brussels, Belgium; (L.A.); (G.C.); (E.D.K.); (M.M.)
| | - Eva De Keyzer
- Cardiologie, Centre Hospitalier Universitaire Brugmann, Faculté de Médecine, Université Libre de Bruxelles, Place Van Gehuchten 4, 1020 Brussels, Belgium; (L.A.); (G.C.); (E.D.K.); (M.M.)
| | - Marielle Morissens
- Cardiologie, Centre Hospitalier Universitaire Brugmann, Faculté de Médecine, Université Libre de Bruxelles, Place Van Gehuchten 4, 1020 Brussels, Belgium; (L.A.); (G.C.); (E.D.K.); (M.M.)
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Zandbagleh A, Miltiadous A, Sanei S, Azami H. Beta-to-Theta Entropy Ratio of EEG in Aging, Frontotemporal Dementia, and Alzheimer's Dementia. Am J Geriatr Psychiatry 2024:S1064-7481(24)00380-4. [PMID: 39004533 DOI: 10.1016/j.jagp.2024.06.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Revised: 06/25/2024] [Accepted: 06/26/2024] [Indexed: 07/16/2024]
Abstract
BACKGROUND Aging, frontotemporal dementia (FTD), and Alzheimer's dementia (AD) manifest electroencephalography (EEG) alterations, particularly in the beta-to-theta power ratio derived from linear power spectral density (PSD). Given the brain's nonlinear nature, the EEG nonlinear features could provide valuable physiological indicators of aging and cognitive impairment. Multiscale dispersion entropy (MDE) serves as a sensitive nonlinear metric for assessing the information content in EEGs across biologically relevant time scales. OBJECTIVE To compare the MDE-derived beta-to-theta entropy ratio with its PSD-based counterpart to detect differences between healthy young and elderly subjects and between different dementia subtypes. METHODS Scalp EEG recordings were obtained from two datasets: 1) Aging dataset: 133 healthy young and 65 healthy older adult individuals; and 2) Dementia dataset: 29 age-matched healthy controls (HC), 23 FTD, and 36 AD participants. The beta-to-theta ratios based on MDE vs. PSD were analyzed for both datasets. Finally, the relationships between cognitive performance and the beta-to-theta ratios were explored in HC, FTD, and AD. RESULTS In the Aging dataset, older adults had significantly higher beta-to-theta entropy ratios than young adults. In the Dementia dataset, this ratio outperformed the beta-to-theta PSD approach in distinguishing between HC, FTD, and AD. The AD participants had a significantly lower beta-to-theta entropy ratio than FTD, especially in the temporal region, unlike its corresponding PSD-based ratio. The beta-to-theta entropy ratio correlated significantly with cognitive performance. CONCLUSION Our study introduces the beta-to-theta entropy ratio using nonlinear MDE for EEG analysis, highlighting its potential as a sensitive biomarker for aging and cognitive impairment.
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Affiliation(s)
- Ahmad Zandbagleh
- School of Electrical Engineering (AZ), Iran University of Science and Technology, Tehran, Iran
| | - Andreas Miltiadous
- Department of Informatics and Telecommunications (AM), University of Ioannina, Arta, Greece
| | - Saeid Sanei
- Electrical and Electronic Engineering Department (SS), Imperial College London, London, UK
| | - Hamed Azami
- Centre for Addiction and Mental Health (HA), University of Toronto, Toronto, ON, Canada.
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Haghayegh F, Norouziazad A, Haghani E, Feygin AA, Rahimi RH, Ghavamabadi HA, Sadighbayan D, Madhoun F, Papagelis M, Felfeli T, Salahandish R. Revolutionary Point-of-Care Wearable Diagnostics for Early Disease Detection and Biomarker Discovery through Intelligent Technologies. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2400595. [PMID: 38958517 DOI: 10.1002/advs.202400595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 06/19/2024] [Indexed: 07/04/2024]
Abstract
Early-stage disease detection, particularly in Point-Of-Care (POC) wearable formats, assumes pivotal role in advancing healthcare services and precision-medicine. Public benefits of early detection extend beyond cost-effectively promoting healthcare outcomes, to also include reducing the risk of comorbid diseases. Technological advancements enabling POC biomarker recognition empower discovery of new markers for various health conditions. Integration of POC wearables for biomarker detection with intelligent frameworks represents ground-breaking innovations enabling automation of operations, conducting advanced large-scale data analysis, generating predictive models, and facilitating remote and guided clinical decision-making. These advancements substantially alleviate socioeconomic burdens, creating a paradigm shift in diagnostics, and revolutionizing medical assessments and technology development. This review explores critical topics and recent progress in development of 1) POC systems and wearable solutions for early disease detection and physiological monitoring, as well as 2) discussing current trends in adoption of smart technologies within clinical settings and in developing biological assays, and ultimately 3) exploring utilities of POC systems and smart platforms for biomarker discovery. Additionally, the review explores technology translation from research labs to broader applications. It also addresses associated risks, biases, and challenges of widespread Artificial Intelligence (AI) integration in diagnostics systems, while systematically outlining potential prospects, current challenges, and opportunities.
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Affiliation(s)
- Fatemeh Haghayegh
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Biomedical Engineering Program, Lassonde School of Engineering, York University, Toronto, M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Alireza Norouziazad
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Biomedical Engineering Program, Lassonde School of Engineering, York University, Toronto, M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Elnaz Haghani
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Biomedical Engineering Program, Lassonde School of Engineering, York University, Toronto, M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Ariel Avraham Feygin
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Biomedical Engineering Program, Lassonde School of Engineering, York University, Toronto, M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Reza Hamed Rahimi
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Biomedical Engineering Program, Lassonde School of Engineering, York University, Toronto, M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Hamidreza Akbari Ghavamabadi
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Biomedical Engineering Program, Lassonde School of Engineering, York University, Toronto, M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Deniz Sadighbayan
- Department of Biology, Faculty of Science, York University, Toronto, ON, M3J 1P3, Canada
| | - Faress Madhoun
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Biomedical Engineering Program, Lassonde School of Engineering, York University, Toronto, M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Manos Papagelis
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Tina Felfeli
- Department of Ophthalmology and Vision Sciences, University of Toronto, Ontario, M5T 3A9, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Ontario, M5T 3M6, Canada
| | - Razieh Salahandish
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Biomedical Engineering Program, Lassonde School of Engineering, York University, Toronto, M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science (EECS), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
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Riis HL, Engstrøm KH, Slama L, Dass J, Ebert MA, Rowshanfarzad P. Assessing focal spot alignment in clinical linear accelerators: a comprehensive evaluation with triplet phantoms. Phys Eng Sci Med 2024:10.1007/s13246-024-01450-9. [PMID: 38954381 DOI: 10.1007/s13246-024-01450-9] [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: 03/04/2024] [Accepted: 05/21/2024] [Indexed: 07/04/2024]
Abstract
A fundamental parameter to evaluate the beam delivery precision and stability on a clinical linear accelerator (linac) is the focal spot position (FSP) measured relative to the collimator axis of the radiation head. The aims of this work were to evaluate comprehensive data on FSP acquired on linacs in clinical use and to establish the ability of alternative phantoms to detect effects on patient plan delivery related to FSP. FSP measurements were conducted using a rigid phantom holding two ball-bearings at two different distances from the radiation source. Images of these ball-bearings were acquired using the electronic portal imaging device (EPID) integrated with each linac. Machine QA was assessed using a radiation head-mounted PTW STARCHECK phantom. Patient plan QA was investigated using the SNC ArcCHECK phantom positioned on the treatment couch, irradiated with VMAT plans across a complete 360° gantry rotation and three X-ray energies. This study covered eight Elekta linacs, including those with 6 MV, 18 MV, and 6 MV flattening-filter-free (FFF) beams. The largest range in the FSP was found for 6 MV FFF. The FSP of one linac, retrofitted with 6 MV FFF, displayed substantial differences in FSP compared to 6 MV FFF beams on other linacs, which all had FSP ranges less than 0.50 mm and 0.25 mm in the lateral and longitudinal directions, respectively. The PTW STARCHECK phantom proved effective in characterising the FSP, while the SNC ArcCHECK measurements could not discern FSP-related features. Minor variations in FSP may be attributed to adjustments in linac parameters, component replacements necessary for beam delivery, and the wear and tear of various linac components, including the magnetron and gun filament. Consideration should be given to the ability of any particular phantom to detect a subsequent impact on the accuracy of patient plan delivery.
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Affiliation(s)
- Hans L Riis
- Department of Oncology, Odense University Hospital, Odense, Denmark.
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark.
- Radiofysisk Laboratorium, Odense University Hospital, Kløvervænget 19, DK-5000 Odense C, Odense, Denmark.
| | - Kenni H Engstrøm
- Department of Oncology, Odense University Hospital, Odense, Denmark
| | - Luke Slama
- Department of Radiation Oncology, Sir Charles Gairdner Hospital, Nedlands, WA, 6009, Australia
| | - Joshua Dass
- Department of Radiation Oncology, Sir Charles Gairdner Hospital, Nedlands, WA, 6009, Australia
- Centre for Advanced Technologies in Cancer Research (CATCR), Perth, WA, 6000, Australia
| | - Martin A Ebert
- Department of Radiation Oncology, Sir Charles Gairdner Hospital, Nedlands, WA, 6009, Australia
- Centre for Advanced Technologies in Cancer Research (CATCR), Perth, WA, 6000, Australia
- School of Physics, Mathematics, and Computing, The University of Western Australia, Crawley, WA, 6009, Australia
| | - Pejman Rowshanfarzad
- Centre for Advanced Technologies in Cancer Research (CATCR), Perth, WA, 6000, Australia
- School of Physics, Mathematics, and Computing, The University of Western Australia, Crawley, WA, 6009, Australia
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Xie L, Zhao J, Li Y, Bai J. PET brain imaging in neurological disorders. Phys Life Rev 2024; 49:100-111. [PMID: 38574584 DOI: 10.1016/j.plrev.2024.03.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 03/20/2024] [Indexed: 04/06/2024]
Abstract
Brain disorders are a series of conditions with damage or loss of neurons, such as Parkinson's disease (PD), Alzheimer's disease (AD), or drug dependence. These individuals have gradual deterioration of cognitive, motor, and other central nervous system functions affected. This degenerative trajectory is intricately associated with dysregulations in neurotransmitter systems. Positron Emission Tomography (PET) imaging, employing radiopharmaceuticals and molecular imaging techniques, emerges as a crucial tool for detecting brain biomarkers. It offers invaluable insights for early diagnosis and distinguishing brain disorders. This article comprehensively reviews the application and progress of conventional and novel PET imaging agents in diagnosing brain disorders. Furthermore, it conducts a thorough analysis on merits and limitations. The article also provides a forward-looking perspective in the future development directions of PET imaging agents for diagnosing brain disorders and proposes potential innovative strategies. It aims to furnish clinicians and researchers with an all-encompassing overview of the latest advancements and forthcoming trends in the utilization of PET imaging for diagnosing brain disorders.
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Affiliation(s)
- Lijun Xie
- Faculty of Life science and Technology, Kunming University of Science and Technology, Kunming 650500, PR China; Laboratory of Molecular Neurobiology, Medical school, Kunming University of Science and Technology, Kunming 650500, PR China; Department of Nuclear Medicine, First Affiliated Hospital of Kunming Medical University, Kunming 650032, PR China
| | - Jihua Zhao
- Department of Nuclear Medicine, First Affiliated Hospital of Kunming Medical University, Kunming 650032, PR China
| | - Ye Li
- Laboratory of Molecular Neurobiology, Medical school, Kunming University of Science and Technology, Kunming 650500, PR China.
| | - Jie Bai
- Laboratory of Molecular Neurobiology, Medical school, Kunming University of Science and Technology, Kunming 650500, PR China.
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Cai H, Jiang H, Xie D, Lai Z, Wu J, Chen M, Yang Z, Xu R, Zeng S, Ma H. Enhancing image quality in computed tomography angiography follow-ups after endovascular aneurysm repair: a comparative study of reconstruction techniques. BMC Med Imaging 2024; 24:162. [PMID: 38956470 PMCID: PMC11218285 DOI: 10.1186/s12880-024-01343-z] [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: 04/27/2024] [Accepted: 06/20/2024] [Indexed: 07/04/2024] Open
Abstract
BACKGROUND The image quality of computed tomography angiography (CTA) images following endovascular aneurysm repair (EVAR) is not satisfactory, since artifacts resulting from metallic implants obstruct the clear depiction of stent and isolation lumens, and also adjacent soft tissues. However, current techniques to reduce these artifacts still need further advancements due to higher radiation doses, longer processing times and so on. Thus, the aim of this study is to assess the impact of utilizing Single-Energy Metal Artifact Reduction (SEMAR) alongside a novel deep learning image reconstruction technique, known as the Advanced Intelligent Clear-IQ Engine (AiCE), on image quality of CTA follow-ups conducted after EVAR. MATERIALS This retrospective study included 47 patients (mean age ± standard deviation: 68.6 ± 7.8 years; 37 males) who underwent CTA examinations following EVAR. Images were reconstructed using four different methods: hybrid iterative reconstruction (HIR), AiCE, the combination of HIR and SEMAR (HIR + SEMAR), and the combination of AiCE and SEMAR (AiCE + SEMAR). Two radiologists, blinded to the reconstruction techniques, independently evaluated the images. Quantitative assessments included measurements of image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), the longest length of artifacts (AL), and artifact index (AI). These parameters were subsequently compared across different reconstruction methods. RESULTS The subjective results indicated that AiCE + SEMAR performed the best in terms of image quality. The mean image noise intensity was significantly lower in the AiCE + SEMAR group (25.35 ± 6.51 HU) than in the HIR (47.77 ± 8.76 HU), AiCE (42.93 ± 10.61 HU), and HIR + SEMAR (30.34 ± 4.87 HU) groups (p < 0.001). Additionally, AiCE + SEMAR exhibited the highest SNRs and CNRs, as well as the lowest AIs and ALs. Importantly, endoleaks and thrombi were most clearly visualized using AiCE + SEMAR. CONCLUSIONS In comparison to other reconstruction methods, the combination of AiCE + SEMAR demonstrates superior image quality, thereby enhancing the detection capabilities and diagnostic confidence of potential complications such as early minor endleaks and thrombi following EVAR. This improvement in image quality could lead to more accurate diagnoses and better patient outcomes.
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Affiliation(s)
- Huasong Cai
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, No. 58, Zhongshan Er Road, Guangzhou, Guangdong Province, 510080, People's Republic of China
| | - Hairong Jiang
- Department of Radiology, Foresea Life Insurance Guangzhou General Hospital, No. 703, Xincheng Avenue, Zengcheng District, Guangzhou, Guangdong, 511300, China
| | - Dingxiang Xie
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, No. 58, Zhongshan Er Road, Guangzhou, Guangdong Province, 510080, People's Republic of China
| | - Zhiman Lai
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, No. 58, Zhongshan Er Road, Guangzhou, Guangdong Province, 510080, People's Republic of China
| | - Jiale Wu
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, No. 58, Zhongshan Er Road, Guangzhou, Guangdong Province, 510080, People's Republic of China
| | - Mingjie Chen
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, No. 58, Zhongshan Er Road, Guangzhou, Guangdong Province, 510080, People's Republic of China
| | - Zhiyun Yang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, No. 58, Zhongshan Er Road, Guangzhou, Guangdong Province, 510080, People's Republic of China
| | - Rulin Xu
- Research Collaboration, Canon Medical Systems, No.10 Huaxia Road, Guangzhou, Guangdong, 510623, China
| | - Shanmei Zeng
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, No. 58, Zhongshan Er Road, Guangzhou, Guangdong Province, 510080, People's Republic of China.
| | - Hui Ma
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, No. 58, Zhongshan Er Road, Guangzhou, Guangdong Province, 510080, People's Republic of China.
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Liu C, Chen W, Li M. A hybrid EEG classification model using layered cascade deep learning architecture. Med Biol Eng Comput 2024; 62:2213-2229. [PMID: 38507121 DOI: 10.1007/s11517-024-03072-5] [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: 09/19/2023] [Accepted: 03/11/2024] [Indexed: 03/22/2024]
Abstract
The problem of multi-class classification is always a challenge in the field of EEG (electroencephalogram)-based seizure detection. The traditional studies focus on computing or learning a set of features from EEG to distinguish between different patterns. However, the extraction of characteristic information becomes increasingly difficult as the number of EEG types increases. To address this issue, a creative EEG classification technique is proposed by employing a principal component analysis network (PCANet) coupled with phase space reconstruction (PSR) and power spectrum density (PSD). We have introduced the PSR and PSD to prepare the inputs, where dynamic and frequency information are exposed from deep within PCANet. It is remarkable that a layered cascade strategy is designed to make a powerful deep learner according to the rule of one network vs one task (OVO). The proposed method has achieved greater effects than the individual models and shown superior performance in comparison with state-of-the-art algorithms, which present 98.0% of sensitivity, 99.90% of specificity, and 99.07% of accuracy. Our ensemble PCANet model works in an assembly line-like manner, obviating the need for hand-craft features. Results demonstrate that the proposed scheme can greatly enhances the accuracy and robustness of seizure detection from EEG signals.
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Affiliation(s)
- Chang Liu
- College of Communication Engineering, Jilin University, Ren Min Street 5988, Changchun, China
| | - Wanzhong Chen
- College of Communication Engineering, Jilin University, Ren Min Street 5988, Changchun, China
| | - Mingyang Li
- College of Communication Engineering, Jilin University, Ren Min Street 5988, Changchun, China.
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Renjini A, Swapna MNS, Sankararaman SI. Graph features based classification of bronchial and pleural rub sound signals: the potential of complex network unwrapped. Phys Eng Sci Med 2024:10.1007/s13246-024-01455-4. [PMID: 38954378 DOI: 10.1007/s13246-024-01455-4] [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: 02/27/2023] [Accepted: 06/04/2024] [Indexed: 07/04/2024]
Abstract
The study presents a novel technique for lung auscultation based on graph theory, emphasizing the potential of graph parameters in distinguishing lung sounds and supporting earlier detection of various respiratory pathologies. The frequency spread and the component magnitudes are revealed from the analysis of eighty-five bronchial (BS) and pleural rub (PS) lung sounds employing the power spectral density (PSD) plot and wavelet scalogram. The low-frequency spread, and persistence of the high-intensity frequency components are visible in BS sounds emanating from the uniform cross-sectional area of the trachea. The frictional rub between the pleurae causes a higher frequency spread of low-intensity intermittent frequency components in PS signals. From the complex networks of BS and PS, the extracted graph features are - graph density ([Formula: see text], transitivity ([Formula: see text], degree centrality ([Formula: see text]), betweenness centrality ([Formula: see text], eigenvector centrality ([Formula: see text]), and graph entropy (En). The high values of [Formula: see text] and [Formula: see text] show a strong correlation between distinct segments of the BS signal originating from a consistent cross-sectional tracheal diameter and, hence, the generation of high-intense low-spread frequency components. An intermittent low-intense and a relatively greater frequency spread in PS signal appear as high [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] values. With these complex network parameters as input attributes, the supervised machine learning techniques- discriminant analyses, support vector machines, k-nearest neighbors, and neural network pattern recognition (PRNN)- classify the signals with more than 90% accuracy, with PRNN having 25 neurons in the hidden layer achieving the highest (98.82%).
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Affiliation(s)
- Ammini Renjini
- Department of Optoelectronics, University of Kerala, Trivandrum, Kerala, 695581, India
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Ibáñez P, Villa-Abaunza A, Udías JM. Impact on the estimated dose of different tissue assignment strategies during partial breast irradiations with INTRABEAM. Brachytherapy 2024; 23:470-477. [PMID: 38705803 DOI: 10.1016/j.brachy.2024.02.003] [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: 11/24/2023] [Revised: 01/19/2024] [Accepted: 02/12/2024] [Indexed: 05/07/2024]
Abstract
PURPOSE Partial breast irradiations with electronic brachytherapy or kilovoltage intraoperative radiotherapy devices such as Axxent or INTRABEAM are becoming more common every day. Breast is mainly composed of glandular and adipose tissues, which are not always clearly disentangled in planning breast CTs. In these cases, breast tissues are replaced with an average soft tissue, or even water. However, at kilovoltage energies, this may lead to large differences in the delivered dose, due to the dominance of photoelectric effect. Therefore, the aim of this work was to study the effect on the dose prescribed in breast with the INTRABEAM device using different soft tissue assignment strategies that would replace the adipose and glandular tissues that constitute the breast in cases where these tissues cannot be adequately distinguished in a CT scan. METHODS AND MATERIALS Dose was computed with a Monte Carlo code in five patients with a 3 cm diameter INTRABEAM spherical applicator. Tissues within the breast were assigned following six different strategies: one based on the TG-43 recommendations, representing the whole breast as water of unity density, another one also water-based but with CT derived density, and the other four also based on CT-derived densities, using a single tissue resulting from different mixes of glandular and adipose tissues. These were compared against the reference dose computed in an accurately segmented CT, following TG-186 recommendations. Relative differences and dose ratios between the reference and the other tissue assignment strategies were obtained in three regions of interest inside the breast. RESULTS AND CONCLUSIONS Dose planning in water-based tissues was found inaccurate for breast treatment with INTRABEAM, as it would incur in up to 30% under-prescription of dose. If accurate soft tissue assignments in the breast cannot be safely done, a single-tissue composition of 80% adipose and 20% glandular tissue, or even a 100% adipose tissue, would be recommended to avoid dose under-prescription.
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Affiliation(s)
- Paula Ibáñez
- Nuclear Physics Group and IPARCOS, Department of Structure of Matter, Thermal Physics and Electronics, CEI Moncloa, Universidad Complutense de Madrid, Madrid, Spain; Instituto de Investigación Sanitaria del Hospital Clínico San Carlos, Madrid, Spain.
| | - Amaia Villa-Abaunza
- Nuclear Physics Group and IPARCOS, Department of Structure of Matter, Thermal Physics and Electronics, CEI Moncloa, Universidad Complutense de Madrid, Madrid, Spain
| | - José Manuel Udías
- Nuclear Physics Group and IPARCOS, Department of Structure of Matter, Thermal Physics and Electronics, CEI Moncloa, Universidad Complutense de Madrid, Madrid, Spain; Instituto de Investigación Sanitaria del Hospital Clínico San Carlos, Madrid, Spain
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Chin V, Finnegan RN, Chlap P, Holloway L, Thwaites DI, Otton J, Delaney GP, Vinod SK. Dosimetric Impact of Delineation and Motion Uncertainties on the Heart and Substructures in Lung Cancer Radiotherapy. Clin Oncol (R Coll Radiol) 2024; 36:420-429. [PMID: 38649309 DOI: 10.1016/j.clon.2024.04.002] [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: 07/17/2023] [Revised: 03/27/2024] [Accepted: 04/02/2024] [Indexed: 04/25/2024]
Abstract
AIMS Delineation variations and organ motion produce difficult-to-quantify uncertainties in planned radiation doses to targets and organs at risk. Similar to manual contouring, most automatic segmentation tools generate single delineations per structure; however, this does not indicate the range of clinically acceptable delineations. This study develops a method to generate a range of automatic cardiac structure segmentations, incorporating motion and delineation uncertainty, and evaluates the dosimetric impact in lung cancer. MATERIALS AND METHODS Eighteen cardiac structures were delineated using a locally developed auto-segmentation tool. It was applied to lung cancer planning CTs for 27 curative (planned dose ≥50 Gy) cases, and delineation variations were estimated by using ten mapping-atlases to provide separate substructure segmentations. Motion-related cardiac segmentation variations were estimated by auto-contouring structures on ten respiratory phases for 9/27 cases that had 4D-planning CTs. Dose volume histograms (DVHs) incorporating these variations were generated for comparison. RESULTS Variations in mean doses (Dmean), defined as the range in values across ten feasible auto-segmentations, were calculated for each cardiac substructure. Over the study cohort the median variations for delineation uncertainty and motion were 2.20-11.09 Gy and 0.72-4.06 Gy, respectively. As relative values, variations in Dmean were between 18.7%-65.3% and 7.8%-32.5% for delineation uncertainty and motion, respectively. Doses vary depending on the individual planned dose distribution, not simply on segmentation differences, with larger dose variations to cardiac structures lying within areas of steep dose gradient. CONCLUSION Radiotherapy dose uncertainties from delineation variations and respiratory-related heart motion were quantified using a cardiac substructure automatic segmentation tool. This predicts the 'dose range' where doses to structures are most likely to fall, rather than single DVH curves. This enables consideration of these uncertainties in cardiotoxicity research and for future plan optimisation. The tool was designed for cardiac structures, but similar methods are potentially applicable to other OARs.
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Affiliation(s)
- V Chin
- University of New South Wales, South Western Sydney Clinical School, Sydney, Australia; Liverpool and Macarthur Cancer Therapy Centres, Department of Radiation Oncology, Sydney, Australia; Ingham Institute for Applied Medical Research, Sydney, Australia; University of Sydney, Image X Institute, Sydney, Australia.
| | - R N Finnegan
- Ingham Institute for Applied Medical Research, Sydney, Australia; University of Sydney, Institute of Medical Physics, Sydney, Australia; Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, Australia
| | - P Chlap
- University of New South Wales, South Western Sydney Clinical School, Sydney, Australia; Liverpool and Macarthur Cancer Therapy Centres, Department of Radiation Oncology, Sydney, Australia; Ingham Institute for Applied Medical Research, Sydney, Australia
| | - L Holloway
- University of New South Wales, South Western Sydney Clinical School, Sydney, Australia; Liverpool and Macarthur Cancer Therapy Centres, Department of Radiation Oncology, Sydney, Australia; Ingham Institute for Applied Medical Research, Sydney, Australia; University of Sydney, Institute of Medical Physics, Sydney, Australia
| | - D I Thwaites
- University of Sydney, Institute of Medical Physics, Sydney, Australia; St James's Hospital and University of Leeds, Leeds Institute of Medical Research, Radiotherapy Research Group, Leeds, United Kingdom
| | - J Otton
- University of New South Wales, South Western Sydney Clinical School, Sydney, Australia; Liverpool Hospital, Department of Cardiology, Sydney, Australia
| | - G P Delaney
- University of New South Wales, South Western Sydney Clinical School, Sydney, Australia; Liverpool and Macarthur Cancer Therapy Centres, Department of Radiation Oncology, Sydney, Australia; Ingham Institute for Applied Medical Research, Sydney, Australia
| | - S K Vinod
- University of New South Wales, South Western Sydney Clinical School, Sydney, Australia; Liverpool and Macarthur Cancer Therapy Centres, Department of Radiation Oncology, Sydney, Australia; Ingham Institute for Applied Medical Research, Sydney, Australia
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Moreau M, Mao S, Ngwa U, Yasmin-Karim S, China D, Hooshangnejad H, Sforza D, Ding K, Li H, Rezaee M, Narang AK, Ngwa W. Democratizing FLASH Radiotherapy. Semin Radiat Oncol 2024; 34:344-350. [PMID: 38880543 PMCID: PMC11218907 DOI: 10.1016/j.semradonc.2024.05.001] [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] [Indexed: 06/18/2024]
Abstract
FLASH radiotherapy (RT) is emerging as a potentially revolutionary advancement in cancer treatment, offering the potential to deliver RT at ultra-high dose rates (>40 Gy/s) while significantly reducing damage to healthy tissues. Democratizing FLASH RT by making this cutting-edge approach more accessible and affordable for healthcare systems worldwide would have a substantial impact in global health. Here, we review recent developments in FLASH RT and present perspective on further developments that could facilitate the democratizing of FLASH RT. These include upgrading and validating current technologies that can deliver and measure the FLASH radiation dose with high accuracy and precision, establishing a deeper mechanistic understanding of the FLASH effect, and optimizing dose delivery conditions and parameters for different types of tumors and normal tissues, such as the dose rate, dose fractionation, and beam quality for high efficacy. Furthermore, we examine the potential for democratizing FLASH radioimmunotherapy leveraging evidence that FLASH RT can make the tumor microenvironment more immunogenic, and parallel developments in nanomedicine or use of smart radiotherapy biomaterials for combining RT and immunotherapy. We conclude that the democratization of FLASH radiotherapy represents a major opportunity for concerted cross-disciplinary research collaborations with potential for tremendous impact in reducing radiotherapy disparities and extending the cancer moonshot globally.
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Affiliation(s)
- Michele Moreau
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins Hospital, Baltimore, MD..
| | - Serena Mao
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins Hospital, Baltimore, MD
| | - Uriel Ngwa
- Department of Chemistry, University of Florida, Gainesville, Florida
| | - Sayeda Yasmin-Karim
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, and Harvard Medical School, Boston MA
| | - Debarghya China
- Department of Biomedical Engineering, Johns Hopkins Hospital, Baltimore, MD
| | - Hamed Hooshangnejad
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins Hospital, Baltimore, MD
| | - Daniel Sforza
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins Hospital, Baltimore, MD
| | - Kai Ding
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins Hospital, Baltimore, MD
| | - Heng Li
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins Hospital, Baltimore, MD
| | - Mohammad Rezaee
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins Hospital, Baltimore, MD
| | - Amol K Narang
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins Hospital, Baltimore, MD
| | - Wilfred Ngwa
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins Hospital, Baltimore, MD
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Wang ML, Tie CW, Wang JH, Zhu JQ, Chen BH, Li Y, Zhang S, Liu L, Guo L, Yang L, Yang LQ, Wei J, Jiang F, Zhao ZQ, Wang GQ, Zhang W, Zhang QM, Ni XG. Multi-instance learning based artificial intelligence model to assist vocal fold leukoplakia diagnosis: A multicentre diagnostic study. Am J Otolaryngol 2024; 45:104342. [PMID: 38703609 DOI: 10.1016/j.amjoto.2024.104342] [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: 02/28/2024] [Accepted: 04/23/2024] [Indexed: 05/06/2024]
Abstract
OBJECTIVE To develop a multi-instance learning (MIL) based artificial intelligence (AI)-assisted diagnosis models by using laryngoscopic images to differentiate benign and malignant vocal fold leukoplakia (VFL). METHODS The AI system was developed, trained and validated on 5362 images of 551 patients from three hospitals. Automated regions of interest (ROI) segmentation algorithm was utilized to construct image-level features. MIL was used to fusion image level results to patient level features, then the extracted features were modeled by seven machine learning algorithms. Finally, we evaluated the image level and patient level results. Additionally, 50 videos of VFL were prospectively gathered to assess the system's real-time diagnostic capabilities. A human-machine comparison database was also constructed to compare the diagnostic performance of otolaryngologists with and without AI assistance. RESULTS In internal and external validation sets, the maximum area under the curve (AUC) for image level segmentation models was 0.775 (95 % CI 0.740-0.811) and 0.720 (95 % CI 0.684-0.756), respectively. Utilizing a MIL-based fusion strategy, the AUC at the patient level increased to 0.869 (95 % CI 0.798-0.940) and 0.851 (95 % CI 0.756-0.945). For real-time video diagnosis, the maximum AUC at the patient level reached 0.850 (95 % CI, 0.743-0.957). With AI assistance, the AUC improved from 0.720 (95 % CI 0.682-0.755) to 0.808 (95 % CI 0.775-0.839) for senior otolaryngologists and from 0.647 (95 % CI 0.608-0.686) to 0.807 (95 % CI 0.773-0.837) for junior otolaryngologists. CONCLUSIONS The MIL based AI-assisted diagnosis system can significantly improve the diagnostic performance of otolaryngologists for VFL and help to make proper clinical decisions.
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Affiliation(s)
- Mei-Ling Wang
- Department of Endoscopy, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Shenzhen, China
| | - Cheng-Wei Tie
- Department of Endoscopy, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Jian-Hui Wang
- Department of Endoscopy, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China
| | - Ji-Qing Zhu
- Department of Endoscopy, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Bing-Hong Chen
- Department of Endoscopy, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Shenzhen, China
| | - Ying Li
- Department of Endoscopy, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Shenzhen, China
| | - Sen Zhang
- Department of Otolaryngology Head and Neck Surgery, The First Hospital, Shanxi Medical University, Taiyuan, China
| | - Lin Liu
- Department of Otolaryngology Head and Neck Surgery, Dalian Friendship Hospital, Dalian, China
| | - Li Guo
- Department of Otolaryngology Head and Neck Surgery, the First Affiliated Hospital, College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
| | - Long Yang
- Department of Otolaryngology, The Second People's Hospital of Baoshan City, Baoshan, China
| | - Li-Qun Yang
- Department of Otolaryngology, The Second People's Hospital of Baoshan City, Baoshan, China
| | - Jiao Wei
- Department of Otolaryngology, Qujing Second People's Hospital of Yunnan Province, Qujing, China
| | - Feng Jiang
- Department of Otolaryngology, Kunming First People's Hospital, Kunming, China
| | - Zhi-Qiang Zhao
- Department of Otolaryngology, Baoshan People's Hospital, Baoshan, China
| | - Gui-Qi Wang
- Department of Endoscopy, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.
| | - Wei Zhang
- Department of Endoscopy, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Shenzhen, China.
| | - Quan-Mao Zhang
- Department of Endoscopy, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China.
| | - Xiao-Guang Ni
- Department of Endoscopy, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.
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Blomquist MB, Roth JD. Ultrasound-based bone tracking using cross-correlation enables dynamic measurements of knee kinematics during clinical assessments. J Exp Orthop 2024; 11:e12050. [PMID: 38846378 PMCID: PMC11154828 DOI: 10.1002/jeo2.12050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 04/29/2024] [Accepted: 05/15/2024] [Indexed: 06/09/2024] Open
Abstract
Purpose Measuring joint kinematics in the clinic is important for diagnosing injuries, tracking healing and guiding treatments; however, current methods are limited by accuracy and/or feasibility of widespread clinical adoption. Therefore, the purpose of this study was to develop and validate an ultrasound (US)-based method for measuring knee kinematics during clinical assessments. Methods We mimicked four clinical laxity assessments (i.e., anterior, posterior, varus, valgus) on five human cadaver knees using our robotic testing system. We simultaneously collected B-mode cine loops with an US transducer. We computed the errors in kinematics between those measured using our bone-tracking algorithm, which cross-correlated regions of interest across frames of the cine loops, and those measured using optical motion capture with bone pins. Additionally, we conducted studies to determine the effects of loading rate and transducer placement on kinematics measured using our US-based bone tracking. Results Pooling the trials at experimental speeds and those downsampled to replicate clinical laxity assessments, the maximum root-mean-square errors of knee kinematics using our bone-tracking algorithm were 2.2 mm and 1.3° for the anterior-posterior and varus-valgus laxity assessments, respectively. Repeated laxity assessments proved to have good-to-excellent repeatability (intraclass correlation coefficients [ICCs] of 0.81-0.99), but ICCs from repositioning the transducer varied more widely, ranging from poor-to-good reproducibility (0.19-0.89). Conclusion Our results demonstrate that US is capable of tracking knee kinematics during dynamic movement. Because US is a safe and commonly used imaging modality, when paired with our bone-tracking algorithm, US has the potential to assess dynamic knee kinematics across a wide variety of applications in the clinic. Level of Evidence Not applicable.
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Affiliation(s)
- Matthew B. Blomquist
- Department of Biomedical EngineeringUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Joshua D. Roth
- Department of Orthopedics and RehabilitationUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of Mechanical EngineeringUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
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Shams AM, Jabbari S. A deep learning approach for diagnosis of schizophrenia disorder via data augmentation based on convolutional neural network and long short-term memory. Biomed Eng Lett 2024; 14:663-675. [PMID: 38946814 PMCID: PMC11208387 DOI: 10.1007/s13534-024-00360-9] [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: 10/27/2023] [Revised: 01/09/2024] [Accepted: 01/30/2024] [Indexed: 07/02/2024] Open
Abstract
Schizophrenia (SZ) is a severe, chronic mental disorder without specific treatment. Due to the increasing prevalence of SZ in societies and the similarity of the characteristics of this disease with other mental illnesses such as bipolar disorder, most people are not aware of having it in their daily lives. Therefore, early detection of this disease will allow the sufferer to seek treatment or at least control it. Previous SZ detection studies through machine learning methods, require the extraction and selection of features before the classification process. This study attempts to develop a novel, end-to-end approach based on a 15-layers convolutional neural network (CNN) and a 16-layers CNN- long short-term memory (LSTM) to help psychiatrists automatically diagnose SZ from electroencephalogram (EEG) signals. The deep model uses CNN layers to learn the temporal properties of the signals, while LSTM layers provide the sequence learning mechanism. Also, data augmentation method based on generative adversarial networks is employed over the training set to increase the diversity of the data. Results on a large EEG dataset show the high diagnostic potential of both proposed methods, achieving remarkable accuracy of 98% and 99%. This study shows that the proposed framework is able to accurately discriminate SZ from healthy subject and is potentially useful for developing diagnostic tools for SZ disorder.
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Affiliation(s)
- Amin Mashayekhi Shams
- Electrical Engineering Department, Engineering Faculty, University of Zanjan, Zanjan, Iran
| | - Sepideh Jabbari
- Electrical Engineering Department, Engineering Faculty, University of Zanjan, Zanjan, Iran
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Grunz JP, Huflage H. Photon-Counting Computed Tomography: Experience in Musculoskeletal Imaging. Korean J Radiol 2024; 25:662-672. [PMID: 38942460 PMCID: PMC11214923 DOI: 10.3348/kjr.2024.0096] [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: 01/27/2024] [Revised: 03/28/2024] [Accepted: 04/19/2024] [Indexed: 06/30/2024] Open
Abstract
Since the emergence of the first photon-counting computed tomography (PCCT) system in late 2021, its advantages and a wide range of applications in all fields of radiology have been demonstrated. Compared to standard energy-integrating detector-CT, PCCT allows for superior geometric dose efficiency in every examination. While this aspect by itself is groundbreaking, the advantages do not stop there. PCCT facilitates an unprecedented combination of ultra-high-resolution imaging without dose penalty or field-of-view restrictions, detector-based elimination of electronic noise, and ubiquitous multi-energy spectral information. Considering the high demands of orthopedic imaging for the visualization of minuscule details while simultaneously covering large portions of skeletal and soft tissue anatomy, no subspecialty may benefit more from this novel detector technology than musculoskeletal radiology. Deeply rooted in experimental and clinical research, this review article aims to provide an introduction to the cosmos of PCCT, explain its technical basics, and highlight the most promising applications for patient care, while also mentioning current limitations that need to be overcome.
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Affiliation(s)
- Jan-Peter Grunz
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany.
| | - Henner Huflage
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
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Karius A, Kreppner S, Strnad V, Schweizer C, Lotter M, Fietkau R, Bert C. Inter-observer effects in needle reconstruction for temporary prostate brachytherapy: Dosimetric implications and adaptive CBCT-TRUS registration solutions. Brachytherapy 2024; 23:421-432. [PMID: 38845268 DOI: 10.1016/j.brachy.2024.05.002] [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: 12/12/2023] [Revised: 05/08/2024] [Accepted: 05/08/2024] [Indexed: 07/19/2024]
Abstract
PURPOSE To investigate geometric and dosimetric inter-observer variability in needle reconstruction for temporary prostate brachytherapy. To assess the potential of registrations between transrectal ultrasound (TRUS) and cone-beam computed tomography (CBCT) to support implant reconstructions. METHODS AND MATERIALS The needles implanted in 28 patients were reconstructed on TRUS by three physicists. Corresponding geometric deviations and associated dosimetric variations to prostate and organs at risk (urethra, bladder, rectum) were analyzed. To account for the found inter-observer variability, various approaches (template-based, probe-based, marker-based) for registrations of CBCT to TRUS were investigated regarding the respective needle transfer accuracy in a phantom study. Three patient cases were examined to assess registration accuracy in-vivo. RESULTS Geometric inter-observer deviations >1 mm and >3 mm were found for 34.9% and 3.5% of all needles, respectively. Prostate dose coverage (changes up to 7.2%) and urethra dose (partly exceeding given dose constraints) were most affected by associated dosimetric changes. Marker-based and probe-based registrations resulted in the phantom study in high mean needle transfer accuracies of 0.73 mm and 0.12 mm, respectively. In the patient cases, the marker-based approach was the superior technique for CBCT-TRUS fusions. CONCLUSION Inter-observer variability in needle reconstruction can substantially affect dosimetry for individual patients. Especially marker-based CBCT-TRUS registrations can help to ensure accurate reconstructions for improved treatment planning.
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Affiliation(s)
- Andre Karius
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.
| | - Stephan Kreppner
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Vratislav Strnad
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Claudia Schweizer
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Michael Lotter
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Rainer Fietkau
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Christoph Bert
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
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Riis HL, Engstrøm KH, Andersen CE. Recombination and polarity effects of Farmer chambers in a strong magnetic field. Phys Med 2024; 123:103406. [PMID: 38875931 DOI: 10.1016/j.ejmp.2024.103406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 04/08/2024] [Accepted: 06/05/2024] [Indexed: 06/16/2024] Open
Abstract
PURPOSE Ionisation chamber based reference dosimetry in magnetic resonance linear accelerators (MRL) aimed for radiotherapy requires correction for recombination losses. Published studies have found that such corrections can be carried out using the two-voltage method. These studies have, however, not included comparison with recombination corrections based on the Niatel method, which can be seen as a robust reference method due to its clear separation of initial and volume recombination and its explicit account of the pulsed nature of the dose delivery. The primary objective of this work therefore was to carry out such a comparison. MATERIALS AND METHODS Four Farmer-type chambers (PTW-30006 and PTW-30013) were placed in a water phantom in 1.5 T Elekta Unity MRL. The chambers were oriented antiparallel or perpendicular to the static magnetic field B0 and irradiated at a source-to-surface distance of 133.5 cm with a 10 × 10 cm2 field size. RESULTS The two-voltage method gave results in agreement (within 0.1%) with the recombination corrections derived from the Niatel method. The recombination corrections from three Niatel parameter sets (one based on a Varian Truebeam and two obtained directly in the MRL) deviated less than 0.1% from each other. A systematic shift in the recombination correction of less than 0.05% was observed if polarity corrections were not applied. CONCLUSIONS The study supports the use of the two-voltage method in MRLs based on its excellent agreement with the Niatel method. This work, therefore, complements existing knowledge as previous studies have not included a comparison with the Niatel method.
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Affiliation(s)
- Hans Lynggaard Riis
- Odense University Hospital, Department of Oncology, Odense, Denmark; University of Southern Denmark, Department of Clinical Research, Odense, Denmark.
| | | | - Claus E Andersen
- Technical University of Denmark, Department of Health Technology, Roskilde, Denmark
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