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Yang L, Peng J, Zhang L, Zhang F, Wu J, Zhang X, Pang J, Jiang Y. Advanced Diffusion Tensor Imaging in White Matter Injury After Subarachnoid Hemorrhage. World Neurosurg 2024; 189:77-88. [PMID: 38789033 DOI: 10.1016/j.wneu.2024.05.107] [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: 04/23/2024] [Accepted: 05/16/2024] [Indexed: 05/26/2024]
Abstract
Subarachnoid hemorrhage (SAH) is recognized as an especially severe stroke variant, notorious for its high mortality and long-term disability rates, in addition to a range of both immediate and enduring neurologic impacts. Over half of the SAH survivors experience varying degrees of neurologic disorders, with many enduring chronic neuropsychiatric conditions. Due to the limitations of traditional imaging techniques in depicting subtle changes within brain tissues posthemorrhage, the accurate detection and diagnosis of white matter (WM) injuries are complicated. Against this backdrop, diffusion tensor imaging (DTI) has emerged as a promising biomarker for structural imaging, renowned for its enhanced sensitivity in identifying axonal damage. This capability positions DTI as an invaluable tool for forming precise and expedient prognoses for SAH survivors. This study synthesizes an assessment of DTI for the diagnosis and prognosis of neurologic dysfunctions in patients with SAH, emphasizing the notable changes observed in DTI metrics and their association with potential pathophysiological processes. Despite challenges associated with scanning technology differences and data processing, DTI demonstrates significant clinical potential for early diagnosis of cognitive impairments following SAH and monitoring therapeutic effects. Future research requires the development of highly standardized imaging paradigms to enhance diagnostic accuracy and devise targeted therapeutic strategies for SAH patients. In sum, DTI technology not only augments our understanding of the impact of SAH but also may offer new avenues for improving patient prognoses.
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Affiliation(s)
- Lei Yang
- Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China; Laboratory of Neurological Diseases and Brain Function, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Jianhua Peng
- Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China; Laboratory of Neurological Diseases and Brain Function, The Affiliated Hospital of Southwest Medical University, Luzhou, China; Academician (Expert) Workstation of Sichuan Province, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Lifang Zhang
- Institute of Brain Science, Southwest Medical University, Luzhou, China; Sichuan Clinical Research Center for Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Fan Zhang
- Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China; Laboratory of Neurological Diseases and Brain Function, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Jinpeng Wu
- Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China; Laboratory of Neurological Diseases and Brain Function, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Xianhui Zhang
- Academician (Expert) Workstation of Sichuan Province, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Jinwei Pang
- Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Yong Jiang
- Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China; Laboratory of Neurological Diseases and Brain Function, The Affiliated Hospital of Southwest Medical University, Luzhou, China; Institute of Brain Science, Southwest Medical University, Luzhou, China; Sichuan Clinical Research Center for Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
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Brabec J, Durmo F, Szczepankiewicz F, Brynolfsson P, Lampinen B, Rydelius A, Knutsson L, Westin CF, Sundgren PC, Nilsson M. Separating Glioma Hyperintensities From White Matter by Diffusion-Weighted Imaging With Spherical Tensor Encoding. Front Neurosci 2022; 16:842242. [PMID: 35527815 PMCID: PMC9069143 DOI: 10.3389/fnins.2022.842242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 03/01/2022] [Indexed: 11/13/2022] Open
Abstract
Background Tumor-related hyperintensities in high b-value diffusion-weighted imaging (DWI) are radiologically important in the workup of gliomas. However, the white matter may also appear as hyperintense, which may conflate interpretation. Purpose To investigate whether DWI with spherical b-tensor encoding (STE) can be used to suppress white matter and enhance the conspicuity of glioma hyperintensities unrelated to white matter. Materials and Methods Twenty-five patients with a glioma tumor and at least one pathology-related hyperintensity on DWI underwent conventional MRI at 3 T. The DWI was performed both with linear and spherical tensor encoding (LTE-DWI and STE-DWI). The LTE-DWI here refers to the DWI obtained with conventional diffusion encoding and averaged across diffusion-encoding directions. Retrospectively, the differences in contrast between LTE-DWI and STE-DWI, obtained at a b-value of 2,000 s/mm2, were evaluated by comparing hyperintensities and contralateral normal-appearing white matter (NAWM) both visually and quantitatively in terms of the signal intensity ratio (SIR) and contrast-to-noise ratio efficiency (CNReff). Results The spherical tensor encoding DWI was more effective than LTE-DWI at suppressing signals from white matter and improved conspicuity of pathology-related hyperintensities. The median SIR improved in all cases and on average by 28%. The median (interquartile range) SIR was 1.9 (1.6 – 2.1) for STE and 1.4 (1.3 – 1.7) for LTE, with a significant difference of 0.4 (0.3 –0.5) (p < 10–4, paired U-test). In 40% of the patients, the SIR was above 2 for STE-DWI, but with LTE-DWI, the SIR was below 2 for all patients. The CNReff of STE-DWI was significantly higher than of LTE-DWI: 2.5 (2 – 3.5) vs. 2.3 (1.7 – 3.1), with a significant difference of 0.4 (−0.1 –0.6) (p < 10–3, paired U-test). The STE improved CNReff in 70% of the cases. We illustrate the benefits of STE-DWI in three patients, where STE-DWI may facilitate an improved radiological description of tumor-related hyperintensity, including one case that could have been missed out if only LTE-DWI was inspected. Conclusion The contrast mechanism of high b-value STE-DWI results in a stronger suppression of white matter than conventional LTE-DWI, and may, therefore, be more sensitive and specific for assessment of glioma tumors and DWI-hyperintensities.
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Affiliation(s)
- Jan Brabec
- Medical Radiation Physics, Lund University, Lund, Sweden
- *Correspondence: Jan Brabec,
| | - Faris Durmo
- Diagnostic Radiology, Lund University, Lund, Sweden
| | - Filip Szczepankiewicz
- Diagnostic Radiology, Lund University, Lund, Sweden
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Patrik Brynolfsson
- Division of Medical Radiation Physics, Department of Translational Medicine, Lund University, Lund, Sweden
| | - Björn Lampinen
- Medical Radiation Physics, Lund University, Lund, Sweden
| | - Anna Rydelius
- Department of Neurology, Lund University, Lund, Sweden
| | - Linda Knutsson
- Medical Radiation Physics, Lund University, Lund, Sweden
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Carl-Fredrik Westin
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Pia C. Sundgren
- Diagnostic Radiology, Lund University, Lund, Sweden
- Lund University Bioimaging Center, Lund University, Lund, Sweden
- Department of Imaging and Physiology, Skåne University Hospital, Lund University, Lund, Sweden
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Wong AMC, Siow TY, Wei KC, Chen PY, Toh CH, Castillo M. Prediction of Malignant Transformation of WHO II Astrocytoma Using Mathematical Models Incorporating Apparent Diffusion Coefficient and Contrast Enhancement. Front Oncol 2021; 11:744827. [PMID: 34660309 PMCID: PMC8511697 DOI: 10.3389/fonc.2021.744827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 09/14/2021] [Indexed: 11/30/2022] Open
Abstract
Using only increasing contrast enhancement as a marker of malignant transformation (MT) in gliomas has low specificity and may affect interpretation of clinical outcomes. Therefore we developed a mathematical model to predict MT of low-grade gliomas (LGGs) by considering areas of reduced apparent diffusion coefficient (ADC) with increased contrast enhancement. Patients with contrast-enhancing LGGs who had contemporaneous ADC and histopathology were retrospectively analyzed. Multiple clinical factors and imaging factors (contrast-enhancement size, whole-tumor size, and ADC) were assessed for association with MT. Patients were split into training and validation groups for the development of a predictive model using logistic regression which was assessed with receiver operating characteristic analysis. Among 132 patients, (median age 46.5 years), 106 patients (64 MT) were assigned to the training group and 26 (20 MT) to the validation group. The predictive model comprised age (P = 0.110), radiotherapy (P = 0.168), contrast-enhancement size (P = 0.015), and ADC (P < 0.001). The predictive model (area-under-the-curve [AUC] 0.87) outperformed ADC (AUC 0.85) and contrast-enhancement size (AUC 0.67). The model had an accuracy of 84% for the training group and 85% respectively for the validation group. Our model incorporating ADC and contrast-enhancement size predicted MT in contrast-enhancing LGGs.
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Affiliation(s)
- Alex Mun-Ching Wong
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Keelung, Keelong, Taiwan.,College of Medicine, Chang Gung University, Tao-Yuan, Taiwan
| | - Tiing Yee Siow
- College of Medicine, Chang Gung University, Tao-Yuan, Taiwan.,Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Tao-Yuan, Taiwan
| | - Kuo-Chen Wei
- College of Medicine, Chang Gung University, Tao-Yuan, Taiwan.,Department of Neurosurgery, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Pin-Yuan Chen
- College of Medicine, Chang Gung University, Tao-Yuan, Taiwan.,Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Tao-Yuan, Taiwan
| | - Cheng Hong Toh
- College of Medicine, Chang Gung University, Tao-Yuan, Taiwan.,Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Tao-Yuan, Taiwan
| | - Mauricio Castillo
- Department of Radiology, University of North Carolina School of Medicine, Chapel Hill, NC, United States
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Hu R, Hoch MJ. Application of Diffusion Weighted Imaging and Diffusion Tensor Imaging in the Pretreatment and Post-treatment of Brain Tumor. Radiol Clin North Am 2021; 59:335-347. [PMID: 33926681 DOI: 10.1016/j.rcl.2021.01.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Diffusion MR imaging exploits the diffusion properties of water to generate contrast between normal tissue and pathology. Diffusion is an essential component of nearly all brain tumor MR imaging examinations. This review covers the important clinical applications of diffusion weighted imaging in the pretreatment diagnosis and grading of brain tumors and assessment of treatment response. Diffusion imaging improves the accuracy of identifying treatment-related effects that may mimic tumor improvement or worsening. Fiber tractography models of eloquent white matter pathways are generated using diffusion tensor imaging. A practical and concise tractography guide is provided for anyone new to preoperative surgical mapping.
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Affiliation(s)
- Ranliang Hu
- Department of Radiology & Imaging Sciences, Emory University, Emory University Hospital, 1364 Clifton Road, BG 20, Atlanta, GA 30322, USA
| | - Michael J Hoch
- Department of Radiology, University of Pennsylvania, Hospital of the University of Pennsylvania, 3400 Spruce Street, Suite 130, Philadelphia, PA 19104, USA.
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Mobark NA, Alharbi M, Alhabeeb L, AlMubarak L, Alaljelaify R, AlSaeed M, Almutairi A, Alqubaishi F, Ahmad M, Al-Banyan A, Alotabi FE, Barakeh D, AlZahrani M, Al-Khalidi H, Ajlan A, Ramkissoon LA, Ramkissoon SH, Abedalthagafi M. Clinical management and genomic profiling of pediatric low-grade gliomas in Saudi Arabia. PLoS One 2020; 15:e0228356. [PMID: 31995621 PMCID: PMC6988947 DOI: 10.1371/journal.pone.0228356] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Accepted: 01/13/2020] [Indexed: 01/22/2023] Open
Abstract
Pediatric Low Grade Gliomas (PLGGs) display heterogeneity regarding morphology, genomic drivers and clinical outcomes. The treatment modality dictates the outcome and optimizing patient management can be challenging. In this study, we profiled a targeted panel of cancer-related genes in 37 Saudi Arabian patients with pLGGs to identify genetic abnormalities that can inform prognostic and therapeutic decision-making. We detected genetic alterations (GAs) in 97% (36/37) of cases, averaging 2.51 single nucleotide variations (SNVs) and 0.91 gene fusions per patient. The KIAA1549-BRAF fusion was the most common alteration (21/37 patients) followed by AFAP1-NTRK2 (2/37) and TBLXR-PI3KCA (2/37) fusions that were observed at much lower frequencies. The most frequently mutated) genes were NOTCH1-3 (7/37), ATM (4/37), RAD51C (3/37), RNF43 (3/37), SLX4 (3/37) and NF1 (3/37). Interestingly, we identified a GOPC-ROS1 fusion in an 8-year-old patient whose tumor lacked BRAF alterations and histologically classified as low grade glioma. The patient underwent gross total resection (GTR). The patient is currently disease free. To our knowledge this is the first report of GOPC-ROS1 fusion in PLGG. Taken together, we reveal the genetic characteristics of pLGG patients can enhance diagnostics and therapeutic decisions. In addition, we identified a GOPC-ROS1 fusion that may be a biomarker for pLGG.
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Affiliation(s)
- Nahla A. Mobark
- Department of Paediatric Oncology Comprehensive Cancer Centre, King Fahad Medical City, Riyadh, Kingdom of Saudi Arabia
| | - Musa Alharbi
- Department of Paediatric Oncology Comprehensive Cancer Centre, King Fahad Medical City, Riyadh, Kingdom of Saudi Arabia
| | - Lamees Alhabeeb
- Genomics Research Department, Saudi Human Genome Project, King Fahad Medical City and King Abdulaziz City for Science and Technology, Riyadh, Saudi Arabia
| | - Latifa AlMubarak
- Genomics Research Department, Saudi Human Genome Project, King Fahad Medical City and King Abdulaziz City for Science and Technology, Riyadh, Saudi Arabia
| | - Rasha Alaljelaify
- Genomics Research Department, Saudi Human Genome Project, King Fahad Medical City and King Abdulaziz City for Science and Technology, Riyadh, Saudi Arabia
| | - Mariam AlSaeed
- Genomics Research Department, Saudi Human Genome Project, King Fahad Medical City and King Abdulaziz City for Science and Technology, Riyadh, Saudi Arabia
| | - Amal Almutairi
- Genomics Research Department, Saudi Human Genome Project, King Fahad Medical City and King Abdulaziz City for Science and Technology, Riyadh, Saudi Arabia
| | - Fatmah Alqubaishi
- Genomics Research Department, Saudi Human Genome Project, King Fahad Medical City and King Abdulaziz City for Science and Technology, Riyadh, Saudi Arabia
| | - Maqsood Ahmad
- Department of Neuroscience, King Fahad Medical City, Riyadh, Kingdom of Saudi Arabia
| | - Ayman Al-Banyan
- Department of Neuroscience, King Fahad Medical City, Riyadh, Kingdom of Saudi Arabia
| | - Fahad E. Alotabi
- Department of Neuroscience, King Fahad Medical City, Riyadh, Kingdom of Saudi Arabia
| | - Duna Barakeh
- Department of Pathology, King Khalid Hospital, King Saud University, Riyadh, Saudi Arabia
| | - Malak AlZahrani
- Department of Pathology, King Khalid Hospital, King Saud University, Riyadh, Saudi Arabia
| | - Hisham Al-Khalidi
- Department of Pathology, King Khalid Hospital, King Saud University, Riyadh, Saudi Arabia
| | - Abdulrazag Ajlan
- Department of Pathology, King Khalid Hospital, King Saud University, Riyadh, Saudi Arabia
| | - Lori A. Ramkissoon
- Department of Neurosurgery, University of North Carolina School of Medicine, Chapel Hill, NC, United States of America
| | - Shakti H. Ramkissoon
- Wake Forest Comprehensive Cancer Center and Department of Pathology, Wake Forest School of Medicine, Winston-Salem, NC, United States of America
- Foundation Medicine Inc., Morrisville, NC, United States of America
| | - Malak Abedalthagafi
- Genomics Research Department, Saudi Human Genome Project, King Fahad Medical City and King Abdulaziz City for Science and Technology, Riyadh, Saudi Arabia
- * E-mail:
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Witulla B, Goerig N, Putz F, Frey B, Engelhorn T, Dörfler A, Uder M, Fietkau R, Bert C, Laun FB. On PTV definition for glioblastoma based on fiber tracking of diffusion tensor imaging data. PLoS One 2020; 15:e0227146. [PMID: 31905221 PMCID: PMC6944332 DOI: 10.1371/journal.pone.0227146] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Accepted: 12/11/2019] [Indexed: 01/20/2023] Open
Abstract
Radiotherapy (RT) is commonly applied for the treatment of glioblastoma multiforme (GBM). Following the planning target volume (PTV) definition procedure standardized in guidelines, a 20% risk of missing non-local recurrences is present. Purpose of this study was to evaluate whether diffusion tensor imaging (DTI)-based fiber tracking may be beneficial for PTV definition taking into account the prediction of distant recurrences. 56 GBM patients were examined with magnetic resonance imaging (MRI) including DTI performed before RT after resection of the primary tumor. Follow-up MRIs were acquired in three month intervals. For the seven patients with a distant recurrence, fiber tracking was performed with three algorithms and it was evaluated whether connections existed from the primary tumor region to the distant recurrence. It depended strongly on the used tracking algorithm and the used tracking parameters whether a connection was observed. Most of the connections were weak and thus not usable for PTV definition. Only in one of the seven patients with a recurring tumor, a clear connection was present. It seems unlikely that DTI-based fiber tracking can be beneficial for predicting distant recurrences in the planning of PTVs for glioblastoma multiforme.
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Affiliation(s)
- Barbara Witulla
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Nicole Goerig
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Florian Putz
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Benjamin Frey
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Tobias Engelhorn
- Department of Neuroradiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Arnd Dörfler
- Department of Neuroradiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Michael Uder
- Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Rainer Fietkau
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Christoph Bert
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- * E-mail:
| | - Frederik Bernd Laun
- Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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Nygaard C, Jensen H, Christensen J, Vedsted P. Health care use before a diagnosis of primary intracranial tumor: a Danish nationwide register study. Clin Epidemiol 2018; 10:809-829. [PMID: 30038522 PMCID: PMC6049603 DOI: 10.2147/clep.s147865] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
Introduction Detailed knowledge of prediagnostic health care use among patients with primary intracranial tumors is sparse. We aimed to investigate the health care use among adults during the 2 years preceding a diagnosis of a benign or malignant primary intracranial tumor in Denmark. Methods We conducted a population-based matched cohort study using historical data from Danish nationwide registries, including all patients aged 30–90 years diagnosed with a first-time primary intracranial tumor from January 1, 2009 to December 31, 2014, and with no prior cancer diagnosis (n=5,926). For each patient, ten comparison subjects were identified using density sampling. We analyzed differences in the frequency and timing of health care use within general practice, physiotherapy, radiology, ear –nose –throat, ophthalmology, neurology, and psychiatry. Odds ratios of having multiple contacts were calculated using a conditional logistical regression model. Monthly incidence rate ratios were estimated using a negative binomial regression model. Results Of all patients, 62% had a benign tumor. Patients with benign tumors were more likely to have multiple consultations with health care providers in the period 2–12 months prior to diagnosis and to have increased rates of consultations 1–24 months prior to diagnosis, depending on health service. Conclusion Patients diagnosed with a benign or a malignant primary intracranial tumor use the health care services differently. Increased health care use is seen within relatively close proximity to the diagnosis for patients with malignant tumors. However, patients with benign tumors have increased health care use from up to 2 years prior to diagnosis; this suggests a window of opportunity for earlier diagnosis.
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Affiliation(s)
- Charlotte Nygaard
- Research Centre for Cancer Diagnosis in Primary Care (CaP), Research Unit for General Practice & Section for General Medicine, Department of Public Health, Aarhus University, Aarhus, Denmark,
| | - Henry Jensen
- Research Centre for Cancer Diagnosis in Primary Care (CaP), Research Unit for General Practice & Section for General Medicine, Department of Public Health, Aarhus University, Aarhus, Denmark,
| | - Jakob Christensen
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.,Department of Neurology, Aarhus University Hospital, Aarhus, Denmark
| | - Peter Vedsted
- Research Centre for Cancer Diagnosis in Primary Care (CaP), Research Unit for General Practice & Section for General Medicine, Department of Public Health, Aarhus University, Aarhus, Denmark, .,Department of Clinical Medicine, Aarhus University & Diagnostic Center, University Research Clinic for Innovative Patient Pathways, Silkeborg Regional Hospital, Silkeborg, Denmark
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