1
|
Oshima S, Kim A, Sun XR, Rifi Z, Cross KA, Fu KA, Salamon N, Ellingson BM, Bari AA, Yao J. Predicting Post-Operative Side Effects in VIM MRgFUS Based on THalamus Optimized Multi Atlas Segmentation (THOMAS) on White-Matter-Nulled MRI: A Retrospective Study. AJNR Am J Neuroradiol 2025; 46:330-340. [PMID: 39730158 PMCID: PMC11878955 DOI: 10.3174/ajnr.a8448] [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: 04/19/2024] [Accepted: 08/01/2024] [Indexed: 12/29/2024]
Abstract
BACKGROUND AND PURPOSE Precise and individualized targeting of the ventral intermediate thalamic nucleus for the MR-guided focused ultrasound is crucial for enhancing treatment efficacy and avoiding undesirable side effects. In this study, we tested the hypothesis that the spatial relationships between Thalamus Optimized Multi Atlas Segmentation derived segmentations and the post-focused ultrasound lesion can predict post-operative side effects in patients treated with MR-guided focused ultrasound. MATERIALS AND METHODS We retrospectively analyzed 30 patients (essential tremor, n = 26; tremor-dominant Parkinson's disease, n = 4) who underwent unilateral ventral intermediate thalamic nucleus focused ultrasound treatment. We created ROIs of coordinate-based indirect treatment target, focused ultrasound-induced lesion, and thalamus and ventral intermediate thalamic nucleus segmentations. We extracted imaging features including 1) focused ultrasound-induced lesion volumes, 2) overlap between lesions and thalamus and ventral intermediate thalamic nucleus segmentations, 3) distance between lesions and ventral intermediate thalamic nucleus segmentation and 4) distance between lesions and the indirect standard target. These imaging features were compared between patients with and without post-operative gait/balance side effects using Wilcoxon rank-sum test. Multivariate prediction models of side effects based on the imaging features were evaluated using the receiver operating characteristic analyses. RESULTS Patients with self-reported gait/balance side effects had a significantly larger extent of focused ultrasound-induced edema, a smaller fraction of the lesion within the ventral intermediate thalamic nucleus segmentation, a larger fraction of the off-target lesion outside the thalamus segmentation, a more inferior centroid of the lesion from the ventral intermediate thalamic nucleus segmentation, and a larger distance between the centroid of the lesion and ventral intermediate thalamic nucleus segmentation (p < 0.05). Similar results were found for exam-based side effects. Multivariate regression models based on the imaging features achieved areas under the curve of 0.99 (95% CI: 0.88 to 1.00) and 0.96 (95% CI: 0.73 to 1.00) for predicting self-reported and exam-based side effects, respectively. CONCLUSIONS Thalamus Optimized Multi Atlas Segmentation-based patient-specific segmentation of the ventral intermediate thalamic nucleus can predict post-operative side effects, which has implications for aiding the direct targeting of MR-guided focused ultrasound and reducing side effects.
Collapse
Affiliation(s)
- Sonoko Oshima
- From the UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers (S.O., A.K., B.M.E., J.Y.), University of California, Los Angeles, Los Angeles, California
- Department of Radiological Sciences (S.O., N.S., B.M.E., J.Y.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Asher Kim
- From the UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers (S.O., A.K., B.M.E., J.Y.), University of California, Los Angeles, Los Angeles, California
- Department of Bioengineering (A.K., B.M.E., J.Y.), Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, California
| | - Xiaonan R Sun
- Department of Neurosurgery (X.R.S., Z.R., B.M.E., A.A.B.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Ziad Rifi
- Department of Neurosurgery (X.R.S., Z.R., B.M.E., A.A.B.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Katy A Cross
- Department of Neurology (K.A.C., K.A.F.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Katherine A Fu
- Department of Neurology (K.A.C., K.A.F.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Noriko Salamon
- Department of Radiological Sciences (S.O., N.S., B.M.E., J.Y.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Benjamin M Ellingson
- From the UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers (S.O., A.K., B.M.E., J.Y.), University of California, Los Angeles, Los Angeles, California
- Department of Radiological Sciences (S.O., N.S., B.M.E., J.Y.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
- Department of Bioengineering (A.K., B.M.E., J.Y.), Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, California
- Department of Neurosurgery (X.R.S., Z.R., B.M.E., A.A.B.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
- Department of Psychiatry and Biobehavioral Sciences (B.M.E.), David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
| | - Ausaf A Bari
- Department of Neurosurgery (X.R.S., Z.R., B.M.E., A.A.B.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Jingwen Yao
- From the UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers (S.O., A.K., B.M.E., J.Y.), University of California, Los Angeles, Los Angeles, California
- Department of Radiological Sciences (S.O., N.S., B.M.E., J.Y.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
- Department of Bioengineering (A.K., B.M.E., J.Y.), Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, California
| |
Collapse
|
2
|
Abulibdeh R, Tu K, Butt DA, Train A, Crampton N, Sejdić E. Assessing the capture of sociodemographic information in electronic medical records to inform clinical decision making. PLoS One 2025; 20:e0317599. [PMID: 39823404 PMCID: PMC11741650 DOI: 10.1371/journal.pone.0317599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Accepted: 01/01/2025] [Indexed: 01/19/2025] Open
Abstract
There is a growing need to document sociodemographic factors in electronic medical records to produce representative cohorts for medical research and to perform focused research for potentially vulnerable populations. The objective of this work was to assess the content of family physicians' electronic medical records and characterize the quality of the documentation of sociodemographic characteristics. Descriptive statistics were reported for each sociodemographic characteristic. The association between the completeness rates of the sociodemographic data and the various clinics, electronic medical record vendors, and physician characteristics was analyzed. Supervised machine learning models were used to determine the absence or presence of each characteristic for all adult patients over the age of 18 in the database. Documentation of marital status (51.0%) and occupation (47.2%) were significantly higher compared to the rest of the variables. Race (1.4%), sexual orientation (2.5%), and gender identity (0.8%) had the lowest documentation rates with a 97.5% missingness rate or higher. The correlation analysis for vendor type demonstrated that there was significant variation in the availability of marital and occupation information between vendors (χ2 > 6.0, P < 0.05). Variability in documentation between clinics indicated that the majority of characteristics exhibited high variation in completeness rates with the highest variation for occupation (median: 47.2, interquartile range: 60.6%) and marital status (median: 45.6, interquartile: 59.7%). Finally, physician sex, years since a physician graduated, and whether a physician was a foreign vs a Canadian medical graduate were significantly associated with documentation rates of place of birth, citizenship status, occupation, and education in the electronic medical records. Our findings suggest a crucial need to implement better documentation strategies for sociodemographic information in the healthcare setting. To improve completeness rates, healthcare systems should monitor, encourage, enforce, or incentivize sociodemographic data collection standards.
Collapse
Affiliation(s)
- Rawan Abulibdeh
- Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Karen Tu
- Department of Family and Community Medicine, University of Toronto, Toronto, Ontario, Canada
- North York General Hospital, Toronto, Ontario, Canada
- Toronto Western Hospital Family Health Team, University Health Network, Toronto, Ontario, Canada
| | - Debra A. Butt
- Department of Family and Community Medicine, University of Toronto, Toronto, Ontario, Canada
- Department of Family and Community Medicine, Scarborough Health Network, Scarborough, Ontario, Canada
| | - Anthony Train
- Department of Family Medicine, Queen’s University, Kingston, Ontario, Canada
| | - Noah Crampton
- Department of Family and Community Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada
- North York General Hospital, Toronto, Ontario, Canada
| |
Collapse
|
3
|
Mohamed AA, Faragalla S, Khan A, Flynn G, Rainone G, Johansen PM, Lucke-Wold B. Neurosurgical and pharmacological management of dystonia. World J Psychiatry 2024; 14:624-634. [PMID: 38808085 PMCID: PMC11129150 DOI: 10.5498/wjp.v14.i5.624] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 04/20/2024] [Accepted: 04/26/2024] [Indexed: 05/16/2024] Open
Abstract
Dystonia characterizes a group of neurological movement disorders characterized by abnormal muscle movements, often with repetitive or sustained contraction resulting in abnormal posturing. Different types of dystonia present based on the affected body regions and play a prominent role in determining the potential efficacy of a given intervention. For most patients afflicted with these disorders, an exact cause is rarely identified, so treatment mainly focuses on symptomatic alleviation. Pharmacological agents, such as oral anticholinergic administration and botulinum toxin injection, play a major role in the initial treatment of patients. In more severe and/or refractory cases, focal areas for neurosurgical intervention are identified and targeted to improve quality of life. Deep brain stimulation (DBS) targets these anatomical locations to minimize dystonia symptoms. Surgical ablation procedures and peripheral denervation surgeries also offer potential treatment to patients who do not respond to DBS. These management options grant providers and patients the ability to weigh the benefits and risks for each individual patient profile. This review article explores these pharmacological and neurosurgical management modalities for dystonia, providing a comprehensive assessment of each of their benefits and shortcomings.
Collapse
Affiliation(s)
- Ali Ahmed Mohamed
- Charles E Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL 33431, United States
| | - Steven Faragalla
- Charles E Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL 33431, United States
| | - Asad Khan
- Charles E Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL 33431, United States
| | - Garrett Flynn
- Charles E Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL 33431, United States
| | - Gersham Rainone
- Department of Neurosurgery and Brain Repair, University of South Florida, Tampa, FL 33606, United States
| | - Phillip Mitchell Johansen
- Department of Neurosurgery and Brain Repair, University of South Florida, Tampa, FL 33606, United States
| | - Brandon Lucke-Wold
- Department of Neurosurgery, University of Florida, Gainesville, FL 32611, United States
| |
Collapse
|
4
|
Elias GJB, Germann J, Joel SE, Li N, Horn A, Boutet A, Lozano AM. A large normative connectome for exploring the tractographic correlates of focal brain interventions. Sci Data 2024; 11:353. [PMID: 38589407 PMCID: PMC11002007 DOI: 10.1038/s41597-024-03197-0] [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: 09/25/2023] [Accepted: 03/28/2024] [Indexed: 04/10/2024] Open
Abstract
Diffusion-weighted MRI (dMRI) is a widely used neuroimaging modality that permits the in vivo exploration of white matter connections in the human brain. Normative structural connectomics - the application of large-scale, group-derived dMRI datasets to out-of-sample cohorts - have increasingly been leveraged to study the network correlates of focal brain interventions, insults, and other regions-of-interest (ROIs). Here, we provide a normative, whole-brain connectome in MNI space that enables researchers to interrogate fiber streamlines that are likely perturbed by given ROIs, even in the absence of subject-specific dMRI data. Assembled from multi-shell dMRI data of 985 healthy Human Connectome Project subjects using generalized Q-sampling imaging and multispectral normalization techniques, this connectome comprises ~12 million unique streamlines, the largest to date. It has already been utilized in at least 18 peer-reviewed publications, most frequently in the context of neuromodulatory interventions like deep brain stimulation and focused ultrasound. Now publicly available, this connectome will constitute a useful tool for understanding the wider impact of focal brain perturbations on white matter architecture going forward.
Collapse
Affiliation(s)
- Gavin J B Elias
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, Canada
- Krembil Research Institute, University of Toronto, Toronto, Canada
| | - Jürgen Germann
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, Canada
- Krembil Research Institute, University of Toronto, Toronto, Canada
- Center for Advancing Neurotechnological Innovation to Application (CRANIA), University Health Network, Toronto, Canada
| | | | - Ningfei Li
- Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Andreas Horn
- Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham & Women's Hospital, Harvard Medical School, Boston, USA
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Alexandre Boutet
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, Canada
- Krembil Research Institute, University of Toronto, Toronto, Canada
- Joint Department of Medical Imaging, University of Toronto, Toronto, Canada
| | - Andres M Lozano
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, Canada.
- Krembil Research Institute, University of Toronto, Toronto, Canada.
| |
Collapse
|