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Shahram MA, Azimian H, Abbasi B, Ganji Z, Khadem-Reza ZK, Khakshour E, Zare H. Automated glioblastoma patient classification using hypoxia levels measured through magnetic resonance images. BMC Neurosci 2024; 25:26. [PMID: 38789970 PMCID: PMC11127326 DOI: 10.1186/s12868-024-00871-2] [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] [Accepted: 05/14/2024] [Indexed: 05/26/2024] Open
Abstract
INTRODUCTION The challenge of treating Glioblastoma (GBM) tumors is due to various mechanisms that make the tumor resistant to radiation therapy. One of these mechanisms is hypoxia, and therefore, determining the level of hypoxia can improve treatment planning and initial evaluation of its effectiveness in GBM. This study aimed to design an intelligent system to classify glioblastoma patients based on hypoxia levels obtained from magnetic resonance images with the help of an artificial neural network (ANN). MATERIAL AND METHOD MR images and PET measurements were available for this study. MR images were downloaded from the Cancer Imaging Archive (TCIA) database to classify glioblastoma patients based on hypoxia. The images in this database were prepared from 27 patients with glioblastoma on T1W + Gd, T2W-FLAIR, and T2W. Our designed algorithm includes various parts of pre-processing, tumor segmentation, feature extraction from images, and matching these features with quantitative parameters related to hypoxia in PET images. The system's performance is evaluated by categorizing glioblastoma patients based on hypoxia. RESULTS The results of classification with the artificial neural network (ANN) algorithm were as follows: the highest sensitivity, specificity, and accuracy were obtained at 86.71, 85.99 and 83.17%, respectively. The best specificity was related to the T2W-EDEMA image with the tumor to blood ratio (TBR) as a hypoxia parameter. T1W-NECROSIS image with the TBR parameter also showed the highest sensitivity and accuracy. CONCLUSION The results of the present study can be used in clinical procedures before treating glioblastoma patients. Among these treatment approaches, we can mention the radiotherapy treatment design and the prescription of effective drugs for the treatment of hypoxic tumors.
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Affiliation(s)
- Mohammad Amin Shahram
- Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hosein Azimian
- Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Bita Abbasi
- Department of Radiology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Zohreh Ganji
- Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Zahra Khandan Khadem-Reza
- Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Elham Khakshour
- Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hoda Zare
- Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
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Spagnolo F, Gobbi S, Zsoldos E, Edde M, Weigel M, Granziera C, Descoteaux M, Barakovic M, Magon S. Down-sampling in diffusion MRI: a bundle-specific DTI and NODDI study. FRONTIERS IN NEUROIMAGING 2024; 3:1359589. [PMID: 38606197 PMCID: PMC11007093 DOI: 10.3389/fnimg.2024.1359589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 03/13/2024] [Indexed: 04/13/2024]
Abstract
Introduction Multi-shell diffusion Magnetic Resonance Imaging (dMRI) data has been widely used to characterise white matter microstructure in several neurodegenerative diseases. The lack of standardised dMRI protocols often implies the acquisition of redundant measurements, resulting in prolonged acquisition times. In this study, we investigate the impact of the number of gradient directions on Diffusion Tensor Imaging (DTI) and on Neurite Orientation Dispersion and Density Imaging (NODDI) metrics. Methods Data from 124 healthy controls collected in three different longitudinal studies were included. Using an in-house algorithm, we reduced the number of gradient directions in each data shell. We estimated DTI and NODDI measures on six white matter bundles clinically relevant for neurodegenerative diseases. Results Fractional Anisotropy (FA) measures on bundles where data were sampled at the 30% rate, showed a median L1 distance of up to 3.92% and a 95% CI of (1.74, 8.97)% when compared to those obtained at reference sampling. Mean Diffusivity (MD) reached up to 4.31% and a 95% CI of (1.60, 16.98)% on the same premises. At a sampling rate of 50%, we obtained a median of 3.90% and a 95% CI of (1.99, 16.65)% in FA, and 5.49% with a 95% CI of (2.14, 21.68)% in MD. The Intra-Cellular volume fraction (ICvf) median L1 distance was up to 2.83% with a 95% CI of (1.98, 4.82)% at a 30% sampling rate and 3.95% with a 95% CI of (2.39, 7.81)% at a 50% sampling rate. The volume difference of the reconstructed white matter at reference and 50% sampling reached a maximum of (2.09 ± 0.81)%. Discussion In conclusion, DTI and NODDI measures reported at reference sampling were comparable to those obtained when the number of dMRI volumes was reduced by up to 30%. Close to reference DTI and NODDI metrics were estimated with a significant reduction in acquisition time using three shells, respectively with: 4 directions at a b value of 700 s/mm2, 14 at 1000 s/mm2, and 32 at 2000 s/mm2. The study revealed aspects that can be important for large-scale clinical studies on bundle-specific diffusion MRI.
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Affiliation(s)
- Federico Spagnolo
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center, Basel, Switzerland
| | - Susanna Gobbi
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center, Basel, Switzerland
| | - Enikő Zsoldos
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center, Basel, Switzerland
| | - Manon Edde
- Sherbrooke Connectivity Imaging Lab (SCIL), Université de Sherbrooke, Sherbrooke, QC, Canada
- Imeka Solutions Inc, Sherbrooke, QC, Canada
| | - Matthias Weigel
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
- Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland
| | - Cristina Granziera
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab (SCIL), Université de Sherbrooke, Sherbrooke, QC, Canada
- Imeka Solutions Inc, Sherbrooke, QC, Canada
| | - Muhamed Barakovic
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center, Basel, Switzerland
| | - Stefano Magon
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center, Basel, Switzerland
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Graf S, Wohlgemuth WA, Deistung A. Incorporating a-priori information in deep learning models for quantitative susceptibility mapping via adaptive convolution. Front Neurosci 2024; 18:1366165. [PMID: 38529264 PMCID: PMC10962327 DOI: 10.3389/fnins.2024.1366165] [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: 01/05/2024] [Accepted: 02/20/2024] [Indexed: 03/27/2024] Open
Abstract
Quantitative susceptibility mapping (QSM) has attracted considerable interest for tissue characterization (e.g., iron and calcium accumulation, myelination, venous vasculature) in the human brain and relies on extensive data processing of gradient-echo MRI phase images. While deep learning-based field-to-susceptibility inversion has shown great potential, the acquisition parameters applied in clinical settings such as image resolution or image orientation with respect to the magnetic field have not been fully accounted for. Furthermore, the lack of comprehensive training data covering a wide range of acquisition parameters further limits the current QSM deep learning approaches. Here, we propose the integration of a priori information of imaging parameters into convolutional neural networks with our approach, adaptive convolution, that learns the mapping between the additional presented information (acquisition parameters) and the changes in the phase images associated with these varying acquisition parameters. By associating a-priori information with the network parameters itself, the optimal set of convolution weights is selected based on data-specific attributes, leading to generalizability towards changes in acquisition parameters. Moreover, we demonstrate the feasibility of pre-training on synthetic data and transfer learning to clinical brain data to achieve substantial improvements in the computation of susceptibility maps. The adaptive convolution 3D U-Net demonstrated generalizability in acquisition parameters on synthetic and in-vivo data and outperformed models lacking adaptive convolution or transfer learning. Further experiments demonstrate the impact of the side information on the adaptive model and assessed susceptibility map computation on simulated pathologic data sets and measured phase data.
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Affiliation(s)
- Simon Graf
- University Clinic and Polyclinic for Radiology, University Hospital Halle (Saale), Halle, Germany
- Halle MR Imaging Core Facility, Medical Faculty, Martin-Luther-University Halle-Wittenberg, Halle, Germany
| | - Walter A. Wohlgemuth
- University Clinic and Polyclinic for Radiology, University Hospital Halle (Saale), Halle, Germany
- Halle MR Imaging Core Facility, Medical Faculty, Martin-Luther-University Halle-Wittenberg, Halle, Germany
| | - Andreas Deistung
- University Clinic and Polyclinic for Radiology, University Hospital Halle (Saale), Halle, Germany
- Halle MR Imaging Core Facility, Medical Faculty, Martin-Luther-University Halle-Wittenberg, Halle, Germany
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Posselt C, Avci MY, Yigitsoy M, Schuenke P, Kolbitsch C, Schaeffter T, Remmele S. Simulation of acquisition shifts in T2 weighted fluid-attenuated inversion recovery magnetic resonance images to stress test artificial intelligence segmentation networks. J Med Imaging (Bellingham) 2024; 11:024013. [PMID: 38666039 PMCID: PMC11042016 DOI: 10.1117/1.jmi.11.2.024013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 03/01/2024] [Accepted: 03/29/2024] [Indexed: 04/28/2024] Open
Abstract
Purpose To provide a simulation framework for routine neuroimaging test data, which allows for "stress testing" of deep segmentation networks against acquisition shifts that commonly occur in clinical practice for T2 weighted (T2w) fluid-attenuated inversion recovery magnetic resonance imaging protocols. Approach The approach simulates "acquisition shift derivatives" of MR images based on MR signal equations. Experiments comprise the validation of the simulated images by real MR scans and example stress tests on state-of-the-art multiple sclerosis lesion segmentation networks to explore a generic model function to describe the F1 score in dependence of the contrast-affecting sequence parameters echo time (TE) and inversion time (TI). Results The differences between real and simulated images range up to 19% in gray and white matter for extreme parameter settings. For the segmentation networks under test, the F1 score dependency on TE and TI can be well described by quadratic model functions (R 2 > 0.9 ). The coefficients of the model functions indicate that changes of TE have more influence on the model performance than TI. Conclusions We show that these deviations are in the range of values as may be caused by erroneous or individual differences in relaxation times as described by literature. The coefficients of the F1 model function allow for a quantitative comparison of the influences of TE and TI. Limitations arise mainly from tissues with a low baseline signal (like cerebrospinal fluid) and when the protocol contains contrast-affecting measures that cannot be modeled due to missing information in the DICOM header.
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Affiliation(s)
- Christiane Posselt
- University of Applied Sciences, Faculty of Electrical and Industrial Engineering, Landshut, Germany
| | | | | | - Patrick Schuenke
- Physikalisch‐Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Christoph Kolbitsch
- Physikalisch‐Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Tobias Schaeffter
- Physikalisch‐Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
- Technical University of Berlin, Department of Medical Engineering, Berlin, Germany
| | - Stefanie Remmele
- University of Applied Sciences, Faculty of Electrical and Industrial Engineering, Landshut, Germany
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Liu X, Jiang Z, Roth HR, Anwar SM, Bonner ER, Mahtabfar A, Packer RJ, Kazerooni AF, Bornhorst M, Linguraru MG. Early prognostication of overall survival for pediatric diffuse midline gliomas using MRI radiomics and machine learning: a two-center study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.11.01.23297935. [PMID: 37961086 PMCID: PMC10635257 DOI: 10.1101/2023.11.01.23297935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Background Diffuse midline gliomas (DMG) are aggressive pediatric brain tumors that are diagnosed and monitored through MRI. We developed an automatic pipeline to segment subregions of DMG and select radiomic features that predict patient overall survival (OS). Methods We acquired diagnostic and post-radiation therapy (RT) multisequence MRI (T1, T1ce, T2, T2 FLAIR) and manual segmentations from two centers of 53 (internal cohort) and 16 (external cohort) DMG patients. We pretrained a deep learning model on a public adult brain tumor dataset, and finetuned it to automatically segment tumor core (TC) and whole tumor (WT) volumes. PyRadiomics and sequential feature selection were used for feature extraction and selection based on the segmented volumes. Two machine learning models were trained on our internal cohort to predict patient 1-year survival from diagnosis. One model used only diagnostic tumor features and the other used both diagnostic and post-RT features. Results For segmentation, Dice score (mean [median]±SD) was 0.91 (0.94)±0.12 and 0.74 (0.83)±0.32 for TC, and 0.88 (0.91)±0.07 and 0.86 (0.89)±0.06 for WT for internal and external cohorts, respectively. For OS prediction, accuracy was 77% and 81% at time of diagnosis, and 85% and 78% post-RT for internal and external cohorts, respectively. Homogeneous WT intensity in baseline T2 FLAIR and larger post-RT TC/WT volume ratio indicate shorter OS. Conclusions Machine learning analysis of MRI radiomics has potential to accurately and non-invasively predict which pediatric patients with DMG will survive less than one year from the time of diagnosis to provide patient stratification and guide therapy.
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Affiliation(s)
- Xinyang Liu
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital
| | - Zhifan Jiang
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital
| | | | - Syed Muhammad Anwar
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital
- School of Medicine and Health Sciences, George Washington University
| | - Erin R Bonner
- Brain Tumor Institute, Children's National Hospital
- School of Medicine and Health Sciences, George Washington University
| | - Aria Mahtabfar
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia
| | | | - Anahita Fathi Kazerooni
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia
- Department of Neurosurgery, University of Pennsylvania
- Center for AI & Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania
| | - Miriam Bornhorst
- Brain Tumor Institute, Children's National Hospital
- School of Medicine and Health Sciences, George Washington University
| | - Marius George Linguraru
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital
- School of Medicine and Health Sciences, George Washington University
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Taoka T. In reply: the improvement technique for reproducibility of diffusion tensor image analysis along the perivascular space (DTI-ALPS) for evaluating interstitial fluid diffusivity and glymphatic function. Jpn J Radiol 2023; 41:1031-1032. [PMID: 37079170 PMCID: PMC10468732 DOI: 10.1007/s11604-023-01431-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 04/04/2023] [Indexed: 04/21/2023]
Affiliation(s)
- Toshiaki Taoka
- Department of Innovative Biomedical Visualization (iBMV), Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, Aichi, 466-8550, Japan.
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Nakamori A, Tsuyoshi H, Tsujikawa T, Orisaka M, Kurokawa T, Yoshida Y. Evaluation of calcification distribution by CT-based textural analysis for discrimination of immature teratoma. J Ovarian Res 2023; 16:179. [PMID: 37635241 PMCID: PMC10464244 DOI: 10.1186/s13048-023-01268-1] [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: 04/17/2023] [Accepted: 08/22/2023] [Indexed: 08/29/2023] Open
Abstract
BACKGROUND Mature and immature teratomas are differentiated based on tumor markers and calcification or fat distribution. However, no study has objectively quantified the differences in calcification and fat distributions between these tumors. This study aimed to evaluate the diagnostic potential of CT-based textural analysis in differentiating between mature and immature teratomas in patients aged < 20 years. MATERIALS AND METHODS Thirty-two patients with pathologically proven mature cystic (n = 28) and immature teratomas (n = 4) underwent transabdominal ultrasound and/or abdominal and pelvic CT before surgery. The diagnostic performance of CT for assessing imaging features, including subjective manual measurement and objective textural analysis of fat and calcification distributions in the tumors, was evaluated by two experienced readers. The histopathological results were used as the gold standard. The Mann-Whitney U test was used for statistical analysis. RESULTS We evaluated 32 patients (mean age, 14.5 years; age range, 6-19 years). The mean maximum diameter and number of calcifications of immature teratomas were significantly larger than those of mature cystic teratomas (p < 0.01). The mean number of fats of immature teratomas was significantly larger than that of mature cystic teratomas (p < 0.01); however, no significant difference in the maximum diameter of fats was observed. CT textural features for calcification distribution in the tumors showed that mature cystic teratomas had higher homogeneity and energy than immature teratomas. However, immature teratomas showed higher correlation, entropy, and dissimilarity than mature cystic teratomas among features derived from the gray-level co-occurrence matrix (GLCM) (p < 0.05). No significant differences were observed in the CT features of fats derived from GLCM. CONCLUSION Our results demonstrate that calcification distribution on CT is a potential diagnostic biomarker to discriminate mature from immature teratomas, thus enabling optimal therapeutic selection for patients aged < 20 years.
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Affiliation(s)
- Akari Nakamori
- Department of Obstetrics and Gynecology, Faculty of Medical Sciences, University of Fukui, 23-3 Shimoaizuki, Matsuoka, Eiheiji-Cho, Yoshida-Gun, Fukui, 910-1193, Japan
| | - Hideaki Tsuyoshi
- Department of Obstetrics and Gynecology, Faculty of Medical Sciences, University of Fukui, 23-3 Shimoaizuki, Matsuoka, Eiheiji-Cho, Yoshida-Gun, Fukui, 910-1193, Japan.
| | - Tetsuya Tsujikawa
- Department of Radiology, Faculty of Medical Sciences, University of Fukui, 23-3 Shimoaizuki, Matsuoka, Eiheiji-Cho, Yoshida-Gun, Fukui, 910-1193, Japan
| | - Makoto Orisaka
- Department of Obstetrics and Gynecology, Faculty of Medical Sciences, University of Fukui, 23-3 Shimoaizuki, Matsuoka, Eiheiji-Cho, Yoshida-Gun, Fukui, 910-1193, Japan
| | - Tetsuji Kurokawa
- Department of Obstetrics and Gynecology, Faculty of Medical Sciences, University of Fukui, 23-3 Shimoaizuki, Matsuoka, Eiheiji-Cho, Yoshida-Gun, Fukui, 910-1193, Japan
| | - Yoshio Yoshida
- Department of Obstetrics and Gynecology, Faculty of Medical Sciences, University of Fukui, 23-3 Shimoaizuki, Matsuoka, Eiheiji-Cho, Yoshida-Gun, Fukui, 910-1193, Japan
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Priya S, Dhruba DD, Sorensen E, Aher PY, Narayanasamy S, Nagpal P, Jacob M, Carter KD. ComBat Harmonization of Myocardial Radiomic Features Sensitive to Cardiac MRI Acquisition Parameters. Radiol Cardiothorac Imaging 2023; 5:e220312. [PMID: 37693205 PMCID: PMC10483256 DOI: 10.1148/ryct.220312] [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: 12/16/2022] [Revised: 05/09/2023] [Accepted: 05/31/2023] [Indexed: 09/12/2023]
Abstract
Purpose To investigate the effect of ComBat harmonization methods on the robustness of cardiac MRI-derived radiomic features to variations in imaging parameters. Materials and Methods This Health Insurance Portability and Accountability Act-compliant retrospective study used a publicly available data set of 11 healthy controls (mean age, 33 years ± 16 [SD]; six men) and five patients (mean age, 52 years ± 16; four men). A single midventricular short-axis section was acquired with 3-T MRI using cine balanced steady-state free precision, T1-weighted, T2-weighted, T1 mapping, and T2 mapping imaging sequences. Each sequence was acquired using baseline parameters and after variations in flip angle, spatial resolution, section thickness, and parallel imaging. Image registration was performed for all sequences at a per-individual level. Manual myocardial contouring was performed, and 1652 radiomic features per sequence were extracted using baseline and variations in imaging parameters. Radiomic feature stability to change in imaging parameters was assessed using Cohen d sensitivity. The stability of radiomic features was assessed both without and after ComBat harmonization of radiomic features. Three ComBat methods were studied: parametric, nonparametric, and Gaussian mixture model (GMM). Results For all sequences combined, 51.4% of features were robust to changes in imaging parameters when no ComBat method was applied. ComBat harmonization substantially increased the number of stable features to 95.1% (95% CI: 94.9, 95.3) when parametric ComBat was used and 90.9% (95% CI: 90.6, 91.2) when nonparametric ComBat was used. GMM combat resulted in only 52.6% stable features. Conclusion ComBat harmonization improved the stability of radiomic features to changes in imaging parameters across all cardiac MRI sequences.Keywords: Cardiac MRI, Radiomics, ComBat, Harmonization Supplemental material is available for this article. © RSNA, 2023.
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Affiliation(s)
| | | | - Eldon Sorensen
- From the Department of Radiology, University of Iowa Carver College
of Medicine, 200 Hawkins Dr, Iowa City, IA 52242 (S.P., S.N.); Department of
Electrical and Computer Engineering (D.D.D., M.J.) and Department of
Biostatistics (E.S., K.D.C.), University of Iowa, Iowa City, Iowa; Department of
Radiology, University of Miami, Miller School of Medicine, Miami, Fla (P.Y.A.);
and Department of Radiology, University of Wisconsin School of Medicine and
Public Health, Madison, Wis (P.N.)
| | - Pritish Y. Aher
- From the Department of Radiology, University of Iowa Carver College
of Medicine, 200 Hawkins Dr, Iowa City, IA 52242 (S.P., S.N.); Department of
Electrical and Computer Engineering (D.D.D., M.J.) and Department of
Biostatistics (E.S., K.D.C.), University of Iowa, Iowa City, Iowa; Department of
Radiology, University of Miami, Miller School of Medicine, Miami, Fla (P.Y.A.);
and Department of Radiology, University of Wisconsin School of Medicine and
Public Health, Madison, Wis (P.N.)
| | - Sabarish Narayanasamy
- From the Department of Radiology, University of Iowa Carver College
of Medicine, 200 Hawkins Dr, Iowa City, IA 52242 (S.P., S.N.); Department of
Electrical and Computer Engineering (D.D.D., M.J.) and Department of
Biostatistics (E.S., K.D.C.), University of Iowa, Iowa City, Iowa; Department of
Radiology, University of Miami, Miller School of Medicine, Miami, Fla (P.Y.A.);
and Department of Radiology, University of Wisconsin School of Medicine and
Public Health, Madison, Wis (P.N.)
| | - Prashant Nagpal
- From the Department of Radiology, University of Iowa Carver College
of Medicine, 200 Hawkins Dr, Iowa City, IA 52242 (S.P., S.N.); Department of
Electrical and Computer Engineering (D.D.D., M.J.) and Department of
Biostatistics (E.S., K.D.C.), University of Iowa, Iowa City, Iowa; Department of
Radiology, University of Miami, Miller School of Medicine, Miami, Fla (P.Y.A.);
and Department of Radiology, University of Wisconsin School of Medicine and
Public Health, Madison, Wis (P.N.)
| | - Mathews Jacob
- From the Department of Radiology, University of Iowa Carver College
of Medicine, 200 Hawkins Dr, Iowa City, IA 52242 (S.P., S.N.); Department of
Electrical and Computer Engineering (D.D.D., M.J.) and Department of
Biostatistics (E.S., K.D.C.), University of Iowa, Iowa City, Iowa; Department of
Radiology, University of Miami, Miller School of Medicine, Miami, Fla (P.Y.A.);
and Department of Radiology, University of Wisconsin School of Medicine and
Public Health, Madison, Wis (P.N.)
| | - Knute D. Carter
- From the Department of Radiology, University of Iowa Carver College
of Medicine, 200 Hawkins Dr, Iowa City, IA 52242 (S.P., S.N.); Department of
Electrical and Computer Engineering (D.D.D., M.J.) and Department of
Biostatistics (E.S., K.D.C.), University of Iowa, Iowa City, Iowa; Department of
Radiology, University of Miami, Miller School of Medicine, Miami, Fla (P.Y.A.);
and Department of Radiology, University of Wisconsin School of Medicine and
Public Health, Madison, Wis (P.N.)
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Waldenberg C, Brisby H, Hebelka H, Lagerstrand KM. Associations between Vertebral Localized Contrast Changes and Adjacent Annular Fissures in Patients with Low Back Pain: A Radiomics Approach. J Clin Med 2023; 12:4891. [PMID: 37568293 PMCID: PMC10420134 DOI: 10.3390/jcm12154891] [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: 07/06/2023] [Revised: 07/21/2023] [Accepted: 07/22/2023] [Indexed: 08/13/2023] Open
Abstract
Low back pain (LBP) is multifactorial and associated with various spinal tissue changes, including intervertebral disc fissures, vertebral pathology, and damaged endplates. However, current radiological markers lack specificity and individualized diagnostic capability, and the interactions between the various markers are not fully clear. Radiomics, a data-driven analysis of radiological images, offers a promising approach to improve evaluation and deepen the understanding of spinal changes related to LBP. This study investigated possible associations between vertebral changes and annular fissures using radiomics. A dataset of 61 LBP patients who underwent conventional magnetic resonance imaging followed by discography was analyzed. Radiomics features were extracted from segmented vertebrae and carefully reduced to identify the most relevant features associated with annular fissures. The results revealed three important texture features that display concentrated high-intensity gray levels, extensive regions with elevated gray levels, and localized areas with reduced gray levels within the vertebrae. These features highlight patterns within vertebrae that conventional classification systems cannot reflect on distinguishing between vertebrae adjacent to an intervertebral disc with or without an annular fissure. As such, the present study reveals associations that contribute to the understanding of pathophysiology and may provide improved diagnostics of LBP.
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Affiliation(s)
- Christian Waldenberg
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 413 45 Gothenburg, Sweden;
- Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 405 30 Gothenburg, Sweden; (H.B.); (H.H.)
- Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, 413 45 Gothenburg, Sweden
| | - Helena Brisby
- Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 405 30 Gothenburg, Sweden; (H.B.); (H.H.)
- Department of Orthopaedics, Sahlgrenska University Hospital, 413 45 Gothenburg, Sweden
| | - Hanna Hebelka
- Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 405 30 Gothenburg, Sweden; (H.B.); (H.H.)
- Department of Radiology, Sahlgrenska University Hospital, 413 45 Gothenburg, Sweden
| | - Kerstin Magdalena Lagerstrand
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 413 45 Gothenburg, Sweden;
- Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 405 30 Gothenburg, Sweden; (H.B.); (H.H.)
- Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, 413 45 Gothenburg, Sweden
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Hu F, Chen AA, Horng H, Bashyam V, Davatzikos C, Alexander-Bloch A, Li M, Shou H, Satterthwaite TD, Yu M, Shinohara RT. Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization. Neuroimage 2023; 274:120125. [PMID: 37084926 PMCID: PMC10257347 DOI: 10.1016/j.neuroimage.2023.120125] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 04/12/2023] [Accepted: 04/19/2023] [Indexed: 04/23/2023] Open
Abstract
Magnetic resonance imaging and computed tomography from multiple batches (e.g. sites, scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to obtain new insights into the human brain. However, significant confounding due to batch-related technical variation, called batch effects, is present in this data; direct application of downstream analyses to the data may lead to biased results. Image harmonization methods seek to remove these batch effects and enable increased generalizability and reproducibility of downstream results. In this review, we describe and categorize current approaches in statistical and deep learning harmonization methods. We also describe current evaluation metrics used to assess harmonization methods and provide a standardized framework to evaluate newly-proposed methods for effective harmonization and preservation of biological information. Finally, we provide recommendations to end-users to advocate for more effective use of current methods and to methodologists to direct future efforts and accelerate development of the field.
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Affiliation(s)
- Fengling Hu
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States.
| | - Andrew A Chen
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States
| | - Hannah Horng
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States
| | - Vishnu Bashyam
- Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| | - Aaron Alexander-Bloch
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States; Penn-CHOP Lifespan Brain Institute, United States; Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, United States
| | - Mingyao Li
- Statistical Center for Single-Cell and Spatial Genomics, Perelman School of Medicine, University of Pennsylvania, United States
| | - Haochang Shou
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States; Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States; Penn-CHOP Lifespan Brain Institute, United States; The Penn Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States
| | - Meichen Yu
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, United States
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States; Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
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11
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Reijonen M, Holopainen E, Arponen O, Könönen M, Vanninen R, Anttila M, Sallinen H, Rinta-Kiikka I, Lindgren A. Neoadjuvant chemotherapy induces an elevation of tumour apparent diffusion coefficient values in patients with ovarian cancer. BMC Cancer 2023; 23:299. [PMID: 37005578 PMCID: PMC10068179 DOI: 10.1186/s12885-023-10760-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 03/21/2023] [Indexed: 04/04/2023] Open
Abstract
OBJECTIVES Multiparametric magnetic resonance imaging (mMRI) is the modality of choice in the imaging of ovarian cancer (OC). We aimed to investigate the feasibility of different types of regions of interest (ROIs) in the measurement of apparent diffusion coefficient (ADC) values of diffusion-weighted imaging in OC patients treated with neoadjuvant chemotherapy (NACT). METHODS We retrospectively enrolled 23 consecutive patients with advanced OC who had undergone NACT and mMRI. Seventeen of them had been imaged before and after NACT. Two observers independently measured the ADC values in both ovaries and in the metastatic mass by drawing on a single slice of (1) freehand large ROIs (L-ROIs) covering the solid parts of the whole tumour and (2) three small round ROIs (S-ROIs). The side of the primary ovarian tumour was defined. We evaluated the interobserver reproducibility and statistical significance of the change in tumoural pre- and post-NACT ADC values. Each patient's disease was defined as platinum-sensitive, semi-sensitive, or resistant. The patients were deemed either responders or non-responders. RESULTS The interobserver reproducibility of the L-ROI and S-ROI measurements ranged from good to excellent (ICC range: 0.71-0.99). The mean ADC values were significantly higher after NACT in the primary tumour (L-ROI p < 0.001, S-ROIs p < 0.01), and the increase after NACT was associated with sensitivity to platinum-based chemotherapy. The changes in the ADC values of the omental mass were associated with a response to NACT. CONCLUSION The mean ADC values of the primary tumour increased significantly after NACT in the OC patients, and the amount of increase in omental mass was associated with the response to platinum-based NACT. Our study indicates that quantitative analysis of ADC values with a single slice and a whole tumour ROI placement is a reproducible method that has a potential role in the evaluation of NACT response in patients with OC. TRIAL REGISTRATION Retrospectively registered (institutional permission code: 5302501; date of the permission: 31.7.2020).
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Affiliation(s)
- Milja Reijonen
- Department of Radiology, Tampere University Hospital, Tampere, Finland.
- Faculty of Medicine and Health Technology, University of Tampere, Tampere, Finland.
| | - Erikka Holopainen
- Department of Radiology, Kuopio University Hospital, Kuopio, Finland
- Institute of Clinical Medicine, School of Medicine, Clinical Radiology, University of Eastern Finland, Kuopio, Finland
| | - Otso Arponen
- Department of Radiology, Tampere University Hospital, Tampere, Finland
- Faculty of Medicine and Health Technology, University of Tampere, Tampere, Finland
| | - Mervi Könönen
- Department of Radiology, Kuopio University Hospital, Kuopio, Finland
- Department of Clinical Neurophysiology, Kuopio University Hospital, Kuopio, Finland
| | - Ritva Vanninen
- Department of Radiology, Kuopio University Hospital, Kuopio, Finland
- Institute of Clinical Medicine, School of Medicine, Clinical Radiology, University of Eastern Finland, Kuopio, Finland
| | - Maarit Anttila
- Department of Gynaecology and Obstetrics, Kuopio University Hospital, Kuopio, Finland
- Institute of Clinical Medicine, School of Medicine, Obstetrics and Gynaecology, University of Eastern Finland, Kuopio, Finland
| | - Hanna Sallinen
- Department of Gynaecology and Obstetrics, Kuopio University Hospital, Kuopio, Finland
| | - Irina Rinta-Kiikka
- Department of Radiology, Tampere University Hospital, Tampere, Finland
- Faculty of Medicine and Health Technology, University of Tampere, Tampere, Finland
| | - Auni Lindgren
- Department of Gynaecology and Obstetrics, Kuopio University Hospital, Kuopio, Finland
- Institute of Clinical Medicine, School of Medicine, Obstetrics and Gynaecology, University of Eastern Finland, Kuopio, Finland
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Wen G, Shim V, Holdsworth SJ, Fernandez J, Qiao M, Kasabov N, Wang A. Machine Learning for Brain MRI Data Harmonisation: A Systematic Review. Bioengineering (Basel) 2023; 10:bioengineering10040397. [PMID: 37106584 PMCID: PMC10135601 DOI: 10.3390/bioengineering10040397] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 03/16/2023] [Accepted: 03/21/2023] [Indexed: 04/29/2023] Open
Abstract
BACKGROUND Magnetic Resonance Imaging (MRI) data collected from multiple centres can be heterogeneous due to factors such as the scanner used and the site location. To reduce this heterogeneity, the data needs to be harmonised. In recent years, machine learning (ML) has been used to solve different types of problems related to MRI data, showing great promise. OBJECTIVE This study explores how well various ML algorithms perform in harmonising MRI data, both implicitly and explicitly, by summarising the findings in relevant peer-reviewed articles. Furthermore, it provides guidelines for the use of current methods and identifies potential future research directions. METHOD This review covers articles published through PubMed, Web of Science, and IEEE databases through June 2022. Data from studies were analysed based on the criteria of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Quality assessment questions were derived to assess the quality of the included publications. RESULTS a total of 41 articles published between 2015 and 2022 were identified and analysed. In the review, MRI data has been found to be harmonised either in an implicit (n = 21) or an explicit (n = 20) way. Three MRI modalities were identified: structural MRI (n = 28), diffusion MRI (n = 7) and functional MRI (n = 6). CONCLUSION Various ML techniques have been employed to harmonise different types of MRI data. There is currently a lack of consistent evaluation methods and metrics used across studies, and it is recommended that the issue be addressed in future studies. Harmonisation of MRI data using ML shows promises in improving performance for ML downstream tasks, while caution should be exercised when using ML-harmonised data for direct interpretation.
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Affiliation(s)
- Grace Wen
- Auckland Bioengineering Institute, University of Auckland, Auckland 1142, New Zealand
| | - Vickie Shim
- Auckland Bioengineering Institute, University of Auckland, Auckland 1142, New Zealand
- Centre for Brain Research, University of Auckland, Auckland 1142, New Zealand
| | - Samantha Jane Holdsworth
- Centre for Brain Research, University of Auckland, Auckland 1142, New Zealand
- Mātai Medical Research Institute, Tairāwhiti-Gisborne 4010, New Zealand
- Department of Anatomy & Medical Imaging, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1142, New Zealand
| | - Justin Fernandez
- Auckland Bioengineering Institute, University of Auckland, Auckland 1142, New Zealand
| | - Miao Qiao
- Department of Computer Science, University of Auckland, Auckland 1142, New Zealand
| | - Nikola Kasabov
- Auckland Bioengineering Institute, University of Auckland, Auckland 1142, New Zealand
- Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland 1010, New Zealand
- Intelligent Systems Research Centre, Ulster University, Londonderry BT52 1SA, UK
- Institute for Information and Communication Technologies, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria
| | - Alan Wang
- Auckland Bioengineering Institute, University of Auckland, Auckland 1142, New Zealand
- Centre for Brain Research, University of Auckland, Auckland 1142, New Zealand
- Department of Anatomy & Medical Imaging, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1142, New Zealand
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Hirahara D. [The Fundamentals of Diffusion Weighted Imaging (DWI) in the Mammary Region and Its Application to Artificial Intelligence (AI)]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2023; 79:1310-1317. [PMID: 37981314 DOI: 10.6009/jjrt.2023-2269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2023]
Affiliation(s)
- Daisuke Hirahara
- Department of AI Research Lab, Harada Academy
- Department of Advanced Biomedical Imaging Informatics, St. Marianna University School of Medicine
- Center for Data-Driven Science and Artificial Intelligence, Tohoku University
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