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Ong WL, Stewart J, Sahgal A, Soliman H, Tseng CL, Detsky J, Chen H, Ho L, Das S, Maralani P, Lipsman N, Stanisz G, Perry J, Lim-Fat MJ, Atenafu EG, Lau A, Ruschin M, Myrehaug S. Predictors of Tumor Dynamics Over a 6-Week Course of Concurrent Chemoradiotherapy for Glioblastoma and the Effect on Survival. Int J Radiat Oncol Biol Phys 2024:S0360-3016(24)00453-X. [PMID: 38561051 DOI: 10.1016/j.ijrobp.2024.03.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 02/09/2024] [Accepted: 03/20/2024] [Indexed: 04/04/2024]
Abstract
PURPOSE We present the final analyses of tumor dynamics and their prognostic significance during a 6-week course of concurrent chemoradiotherapy for glioblastoma in the Glioblastoma Longitudinal Imaging Observational study. METHODS AND MATERIALS This is a prospective serial magnetic resonance imaging study in 129 patients with glioblastoma who had magnetic resonance imaging obtained at radiation therapy (RT) planning (F0), fraction 10 (F10), fraction 20 (F20), and 1-month post-RT. Tumor dynamics assessed included gross tumor volume relative to F0 (Vrel) and tumor migration distance (dmigration). Covariables evaluated included: corpus callosum involvement, extent of surgery, O6-methylguanine-DNA-methyltransferase methylation, and isocitrate dehydrogenase mutation status. RESULTS The median Vrel were 0.85 (range, 0.25-2.29) at F10, 0.79 (range, 0.09-2.22) at F20, and 0.78 (range, 0.13-4.27) at 1 month after completion of RT. The median dmigration were 4.7 mm (range, 1.1-20.4 mm) at F10, 4.7 mm (range, 0.8-20.7 mm) at F20, and 6.1 mm (range, 0.0-45.5 mm) at 1 month after completion of RT. Compared with patients who had corpus callosum involvement (n = 26), those without corpus callosum involvement (n = 103) had significant Vrel reduction at F20 (P = .03) and smaller dmigration at F20 (P = .007). Compared with patients who had biopsy alone (n = 19) and subtotal resection (n = 71), those who had gross total resection (n = 38) had significant Vrel reduction at F10 (P = .001) and F20 (P = .001) and a smaller dmigration at F10 (P = .03) and F20 (P = .002). O6-Methylguanine-DNA-methyltransferase methylation and isocitrate dehydrogenase mutation status were not significantly associated with tumor dynamics. The median progression-free survival and overall survival (OS) were 8.5 months (95% CI, 6.9-9.9) and 20.4 months (95% CI, 17.6-25.2). In multivariable analyses, patients with Vrel ≥ 1.33 at F10 had worse OS (hazard ratio [HR], 4.6; 95% CI, 1.8-11.4; P = .001), and patients with dmigration ≥ 5 mm at 1-month post-RT had worse progression-free survival (HR, 1.76; 95% CI, 1.08-2.87) and OS (HR, 2.2; 95% CI, 1.2-4.0; P = .007). CONCLUSIONS Corpus callosum involvement and extent of surgery are independent predictors of tumor dynamics during RT and can enable patient selection for adaptive RT strategies. Significant tumor enlargement at F10 and tumor migration 1-month post-RT were associated with poorer OS.
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Affiliation(s)
- Wee Loon Ong
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada; Alfred Health Radiation Oncology, Central Clinical School, Monash University, Melbourne, Australia
| | - James Stewart
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Arjun Sahgal
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Hany Soliman
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Chia-Lin Tseng
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Jay Detsky
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Hanbo Chen
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Ling Ho
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Sunit Das
- Division of Neurosurgery, University of Toronto, Toronto, Canada; Division of Neurosurgery and Centre for Ethics, St Michael's Hospital, Toronto, Canada; The Arthur and Sonia Labatt Brain Tumour Research Centre, SickKids Hospital, Toronto, Canada
| | - Pejman Maralani
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Nir Lipsman
- Division of Neurosurgery, University of Toronto, Toronto, Canada; Department of Physical Sciences, Sunnybrook Research Institute, Toronto, Canada; Harquail Centre for Neuromodulation, Sunnybrook Health Sciences Centre, Toronto Canada
| | - Greg Stanisz
- Department of Physical Sciences, Sunnybrook Research Institute, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada; Department of Neurosurgery and Paediatric Neurosurgery, Medical University Lublin, Poland
| | - James Perry
- Division of Neurology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Mary Jane Lim-Fat
- Division of Neurology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Eshetu G Atenafu
- Department of Biostatistics, University Health Network, University of Toronto, Toronto, Canada
| | - Angus Lau
- Department of Physical Sciences, Sunnybrook Research Institute, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Mark Ruschin
- Department of Radiation Oncology, University of Toronto, Toronto, Canada; Department of Medical Physics, Sunnybrook Odette Cancer Centre, Toronto, Canada
| | - Sten Myrehaug
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada.
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2
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Ong WL, Stewart J, Sahgal A, Soliman H, Tseng CL, Detsky J, Ho L, Das S, Maralani P, Lipsman N, Stanisz G, Perry J, Chen H, Atenafu E, Lau A, Ruschin ME, Myrehaug SD. Predictors of Tumor Dynamics during a 6-Week Course of Chemoradiotherapy for Glioblastoma. Int J Radiat Oncol Biol Phys 2023; 117:e142. [PMID: 37784716 DOI: 10.1016/j.ijrobp.2023.06.953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Our prior imaging studies have shown geometrically meaningful inter-fraction tumor dynamics specific to glioblastoma (GBM). We aim to identify predictors associated with tumor dynamics during a 6-week course of concurrent chemoradiotherapy (CRT) for GBM. MATERIALS/METHODS Patients enrolled in a prospective serial magnetic resonance imaging (MRI) study were reviewed. All patients were treated with 54-60 Gy in 30 fractions. The gross tumor volume (GTV) included the surgical cavity and T1c enhanced residual tumor; clinical tumor volume (CTV) included GTV with a 15mm isotropic expansion, respecting anatomical boundaries; planning target volume (PTV) was 4mm expansion. MRIs were obtained at RT planning (F0), fraction 10 (F10), and fraction 20 (F20). Tumor dynamic metrics (relative to F0) assessed included the GTV volume (Vrel), Hausdorff distance (dH) and migration distance (dM). dH is the average distance between two datasets in metric space. dM is the maximum linear displacement of the GTV in any direction. Factors to be determined associated with tumor dynamics included: age, sex, corpus callosum (CC) involvement, extent of surgery (gross total resection (GTR), subtotal resection (STR) or biopsy alone (Bx)), MGMT methylation and IDH mutation status. RESULTS A total of 129 patients were reviewed. Median GTV was 20.9cc at F0, 17.6cc at F10 (Vrel 0.85), and 16.1cc at F20 (Vrel 0.78). Patients without CC involvement had more marked GTV volume reduction: Vrel 0.82 vs 1.02 with CC involvement at F10 (P = 0.05), and Vrel 0.77 vs 0.88 with CC involvement at F20 (P = 0.03). Patients with GTR (vs STR vs Bx) had more marked GTV volume reduction across all time points: Vrel 0.78, 0.85 and 1.07 respectively at F10 (P = 0.001), and Vrel 0.69, 0.80, 1.04 respectively at F20 (P = 0.001). The median dH was 8.1mm at F10 and 9.2mm at F20. Patients with CC involvement (vs without CC involvement) had a larger dH: 54% vs 25% had dH>10mm respectively at F10 (P = 0.03), and 73% vs 28% had dH>10mm respectively at F20 (P<0.005). Patients with a GTR had smaller dH at both F10 (P = 0.02) and F20 (P = 0.006). At F20, 20%, 47% and 37% of patients with GTR, STR and Bx had dH>10mm (P = 0.04). The median dM were 4.7mm at F10 and 4.7mm at F20. Patients with CC involvement (vs without CC involvement) had larger dM: 41% vs 12% had dM >10mm respectively at F10 (P = 0.01), and 45% vs 9% had dM >10mm respectively at F20 (P<0.001). Patients with GTR had smaller dM at F10 (P = 0.03) and F20 (P0.002). At F20, 0%, 25% and 19% of patients with GTR, STR and Bx had dM>10mm (P = 0.002). Age, sex, MGMT methylation and IDH mutation status were not associated with Vrel, dH and dM at F10 and F20. CONCLUSION We identified CC involvement and extent of surgery to be associated with tumor dynamics at F10 and F20 over the course of CRT for GBM. This offers opportunities to better select patients who may benefit from earlier/ more frequent RT replan/ adaptation to ensure adequate tumor coverage, or to reduce RT toxicities.
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Affiliation(s)
- W L Ong
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada; Alfred Health Radiation Oncology, Monash University Central Clinical School, Melbourne, Australia
| | - J Stewart
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - A Sahgal
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - H Soliman
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - C L Tseng
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - J Detsky
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - L Ho
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - S Das
- Division of Neurosurgery, St. Michael's Hospital, Unity Health Toronto, University of Toronto, Toronto, ON, Canada
| | - P Maralani
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - N Lipsman
- Division of Neurosurgery, University of Toronto, Toronto, ON, Canada
| | - G Stanisz
- Department of Physical Sciences, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada; Department of Neurosurgery and Pediatric Neurosurgery, Medical University, Lublin, Poland
| | - J Perry
- Department of Neurooncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - H Chen
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - E Atenafu
- Department of Biostatistics, University Health Network, University of Toronto, Toronto, ON, Canada
| | - A Lau
- Department of Physical Sciences, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - M E Ruschin
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - S D Myrehaug
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
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Dasgupta A, Saifuddin M, McNabb E, Ho L, Lu L, Vesprini D, Karam I, Soliman H, Chow E, Gandhi S, Trudeau M, Tran W, Curpen B, Stanisz G, Sahgal A, Kolios M, Czarnota GJ. Novel MRI-guided focussed ultrasound stimulated microbubble radiation enhancement treatment for breast cancer. Sci Rep 2023; 13:13566. [PMID: 37604988 PMCID: PMC10442356 DOI: 10.1038/s41598-023-40551-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 08/12/2023] [Indexed: 08/23/2023] Open
Abstract
Preclinical studies have demonstrated focused ultrasound (FUS) stimulated microbubble (MB) rupture leads to the activation of acid sphingomyelinase-ceramide pathway in the endothelial cells. When radiotherapy (RT) is delivered concurrently with FUS-MB, apoptotic pathway leads to increased cell death resulting in potent radiosensitization. Here we report the first human trial of using magnetic resonance imaging (MRI) guided FUS-MB treatment in the treatment of breast malignancies. In the phase 1 prospective interventional study, patients with breast cancer were treated with fractionated RT (5 or 10 fractions) to the disease involving breast or chest wall. FUS-MB treatment was delivered before 1st and 5th fractions of RT (within 1 h). Eight patients with 9 tumours were treated. All 7 evaluable patients with at least 3 months follow-up treated for 8 tumours had a complete response in the treated site. The maximum acute toxicity observed was grade 2 dermatitis in 1 site, and grade 1 in 8 treated sites, at one month post RT, which recovered at 3 months. No RT-related late effect or FUS-MB related toxicity was noted. This study demonstrated safety of combined FUS-MB and RT treatment. Promising response rates suggest potential strong radiosensitization effects of the investigational modality.Trial registration: clinicaltrials.gov, identifier NCT04431674.
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Affiliation(s)
- Archya Dasgupta
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, T2, Toronto, ON, M4N3M5, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | | | - Evan McNabb
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Ling Ho
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, T2, Toronto, ON, M4N3M5, Canada
| | - Lin Lu
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, T2, Toronto, ON, M4N3M5, Canada
| | - Danny Vesprini
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, T2, Toronto, ON, M4N3M5, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Irene Karam
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, T2, Toronto, ON, M4N3M5, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Hany Soliman
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, T2, Toronto, ON, M4N3M5, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Edward Chow
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, T2, Toronto, ON, M4N3M5, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Sonal Gandhi
- Department of Medical Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Medicine, University of Toronto, Toronto, Canada
| | - Maureen Trudeau
- Department of Medical Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Medicine, University of Toronto, Toronto, Canada
| | - William Tran
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, T2, Toronto, ON, M4N3M5, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Belinda Curpen
- Department of Medical Imaging, Sunnybrook Health Sciences, Toronto, Canada
- Department of Medical Imaging, University of Toronto, Toronto, Canada
| | - Greg Stanisz
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
- Department of Biophysics, University of Toronto, Toronto, Canada
- Canada Research Chair in Cancer Imaging, Canadian Institutes of Health Research, Toronto, Canada
| | - Arjun Sahgal
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, T2, Toronto, ON, M4N3M5, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | | | - Gregory J Czarnota
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, T2, Toronto, ON, M4N3M5, Canada.
- Department of Radiation Oncology, University of Toronto, Toronto, Canada.
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada.
- Department of Biophysics, University of Toronto, Toronto, Canada.
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4
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Le TN, Oakden W, Mukherjee S, Ferdous Z, Kuroiwa M, Liu VM, Zhang Z, Situ Y, Paul B, Stanisz G. Magnetic Targeting of Gadolinium Contrast to Enhance MRI of the Inner Ear in Endolymphatic Hydrops. Laryngoscope 2023; 133:914-923. [PMID: 35766261 DOI: 10.1002/lary.30267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 05/06/2022] [Accepted: 06/06/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVES 1. Determine the feasibility and efficiency of local magnetic targeting delivery of gadolinium (Gad) contrast to the inner ear in rodents. 2. Assess any potential ototoxicity of magnetic targeting delivery of Gad in the inner ear. 3. Study the utility of magnetic targeting delivery of Gad to visualize and quantify endolymphatic hydrops (EH) in a transgenic mouse model. STUDY DESIGN Controlled in vivo animal model study. METHODS Paramagnetic Gad was locally delivered to the inner ear using the magnetic targeting technique in both rat and mouse models. Efficiency of contrast delivery was assessed using magnetic resonance imaging (MRI). Ototoxicity of Gad was examined with histology of the cochlea and functional audiological tests. The Phex mouse model was used to study EH, hearing loss, and balance dysfunction. Magnetic targeting delivery of Gad contrast was used in the Phex mouse model to visualize the effects of EH using MRI. RESULTS Magnetic targeting improved the delivery of Gad to the inner ear and the technique was reproducible in both rat and mouse models. The delivery method did not result in microstructural damage or any significant hearing loss in a normal animal. Magnetic targeting of Gad in the Phex mouse model allowed detailed visualization and quantification of EH. CONCLUSION This study provided the first evidence of the effectiveness and efficiency of the local magnetic targeting delivery of gadolinium contrast to the inner ear and its application to the visualization and quantification of EH. Laryngoscope, 133:914-923, 2023.
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Affiliation(s)
- Trung N Le
- Department of Otolaryngology - Head and Neck Surgery, University of Toronto, Toronto, Ontario, Canada.,Biological Sciences Platform, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Wendy Oakden
- Physical Sciences Platform, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Subhendu Mukherjee
- Biological Sciences Platform, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Zannatul Ferdous
- Biological Sciences Platform, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Maya Kuroiwa
- Department of Otolaryngology - Head and Neck Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Violet M Liu
- Biological Sciences Platform, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Zhifen Zhang
- Biological Sciences Platform, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Yumai Situ
- Biological Sciences Platform, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Brandon Paul
- Department of Psychology, Ryerson University, Toronto, Ontario, Canada
| | - Greg Stanisz
- Physical Sciences Platform, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Canada.,Department of Neurosurgery & Pediatric Neurosurgery, Medical University, Lublin, Poland
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5
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Moore-Palhares D, Ho L, Lu L, Chugh B, Vesprini D, Karam I, Soliman H, Symons S, Leung E, Loblaw A, Myrehaug S, Stanisz G, Sahgal A, Czarnota GJ. Clinical implementation of magnetic resonance imaging simulation for radiation oncology planning: 5 year experience. Radiat Oncol 2023; 18:27. [PMID: 36750891 PMCID: PMC9903411 DOI: 10.1186/s13014-023-02209-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Accepted: 01/16/2023] [Indexed: 02/09/2023] Open
Abstract
PURPOSE Integrating magnetic resonance (MR) into radiotherapy planning has several advantages. This report details the clinical implementation of an MR simulation (MR-planning) program for external beam radiotherapy (EBRT) in one of North America's largest radiotherapy programs. METHODS AND MATERIALS An MR radiotherapy planning program was developed and implemented at Sunnybrook Health Sciences Center in 2016 with two dedicated wide-bore MR platforms (1.5 and 3.0 Tesla). Planning MR was sequentially implemented every 3 months for separate treatment sites, including the central nervous system (CNS), gynecologic (GYN), head and neck (HN), genitourinary (GU), gastrointestinal (GI), breast, and brachial plexus. Essential protocols and processes were detailed in this report, including clinical workflow, optimized MR-image acquisition protocols, MR-adapted patient setup, strategies to overcome risks and challenges, and an MR-planning quality assurance program. This study retrospectively reviewed simulation site data for all MR-planning sessions performed for EBRT over the past 5 years. RESULTS From July 2016 to December 2021, 8798 MR-planning sessions were carried out, which corresponds to 25% of all computer tomography (CT) simulations (CT-planning) performed during the same period at our institution. There was a progressive rise from 80 MR-planning sessions in 2016 to 1126 in 2017, 1492 in 2018, 1824 in 2019, 2040 in 2020, and 2236 in 2021. As a result, the relative number of planning MR/CT increased from 3% of all planning sessions in 2016 to 36% in 2021. The most common site of MR-planning was CNS (49%), HN (13%), GYN (12%), GU (12%), and others (8%). CONCLUSION Detailed clinical processes and protocols of our MR-planning program were presented, which have been improved over more than 5 years of robust experience. Strategies to overcome risks and challenges in the implementation process are highlighted. Our work provides details that can be used by institutions interested in implementing an MR-planning program.
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Affiliation(s)
- Daniel Moore-Palhares
- grid.413104.30000 0000 9743 1587Department of Radiation Oncology, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, T2, Toronto, ON M4N3M5 Canada ,grid.17063.330000 0001 2157 2938Department of Radiation Oncology, University of Toronto, Toronto, Canada ,grid.17063.330000 0001 2157 2938Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Ling Ho
- grid.413104.30000 0000 9743 1587Department of Radiation Oncology, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, T2, Toronto, ON M4N3M5 Canada
| | - Lin Lu
- grid.413104.30000 0000 9743 1587Department of Radiation Oncology, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, T2, Toronto, ON M4N3M5 Canada
| | - Brige Chugh
- grid.413104.30000 0000 9743 1587Department of Radiation Oncology, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, T2, Toronto, ON M4N3M5 Canada ,grid.17063.330000 0001 2157 2938Department of Radiation Oncology, University of Toronto, Toronto, Canada ,grid.17063.330000 0001 2157 2938Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Danny Vesprini
- grid.413104.30000 0000 9743 1587Department of Radiation Oncology, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, T2, Toronto, ON M4N3M5 Canada ,grid.17063.330000 0001 2157 2938Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Irene Karam
- grid.413104.30000 0000 9743 1587Department of Radiation Oncology, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, T2, Toronto, ON M4N3M5 Canada ,grid.17063.330000 0001 2157 2938Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Hany Soliman
- grid.413104.30000 0000 9743 1587Department of Radiation Oncology, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, T2, Toronto, ON M4N3M5 Canada ,grid.17063.330000 0001 2157 2938Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Sean Symons
- grid.17063.330000 0001 2157 2938Physical Sciences, Sunnybrook Research Institute, Toronto, Canada ,grid.413104.30000 0000 9743 1587Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Eric Leung
- grid.413104.30000 0000 9743 1587Department of Radiation Oncology, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, T2, Toronto, ON M4N3M5 Canada ,grid.17063.330000 0001 2157 2938Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Andrew Loblaw
- grid.413104.30000 0000 9743 1587Department of Radiation Oncology, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, T2, Toronto, ON M4N3M5 Canada ,grid.17063.330000 0001 2157 2938Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Sten Myrehaug
- grid.413104.30000 0000 9743 1587Department of Radiation Oncology, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, T2, Toronto, ON M4N3M5 Canada ,grid.17063.330000 0001 2157 2938Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Greg Stanisz
- grid.17063.330000 0001 2157 2938Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Arjun Sahgal
- grid.413104.30000 0000 9743 1587Department of Radiation Oncology, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, T2, Toronto, ON M4N3M5 Canada ,grid.17063.330000 0001 2157 2938Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Gregory J. Czarnota
- grid.413104.30000 0000 9743 1587Department of Radiation Oncology, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, T2, Toronto, ON M4N3M5 Canada ,grid.17063.330000 0001 2157 2938Department of Radiation Oncology, University of Toronto, Toronto, Canada ,grid.17063.330000 0001 2157 2938Physical Sciences, Sunnybrook Research Institute, Toronto, Canada ,grid.17063.330000 0001 2157 2938Department of Medical Biophysics, University of Toronto, Toronto, Canada
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6
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Martin L, Saha S, Linton L, Taylor M, Zhu J, Chavez S, Stanisz G, Minkin S, Boyd N. Dietary Fiber, Insulin and Breast Tissue Composition at Age 15-18: A Cross-Sectional Study. Nutr Cancer 2022; 74:2946-2954. [PMID: 35243935 DOI: 10.1080/01635581.2022.2047738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
BACKGROUND Risk of breast cancer in adult life is influenced by body size and height in childhood, but the mechanisms responsible for these associations are currently unknown. We carried out research to determine if, at age 15-18, measures of dietary intake were associated with body size, hormones, and with variations in breast tissue composition that in adult life are associated with risk of breast cancer. METHODS In a cross-sectional study of 766 healthy Caucasian women aged 15-18, we measured percent breast water (PBW), total breast water and fat by magnetic resonance (MR), and assessed dietary intake using a validated food frequency questionnaire. We also measured height, weight, skin-fold thicknesses and waist-to-hip ratio, and in fasting blood assayed glucose and insulin. RESULTS After adjustment for age, measures of body size, and energy intake, dietary fiber (insoluble and total fiber) and insulin were associated positively and significantly with PBW. CONCLUSIONS Dietary fiber and fasting insulin were associated with breast tissue measures. These data suggest a potential approach to breast cancer prevention.
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Affiliation(s)
- Lisa Martin
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Sudipta Saha
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Linda Linton
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Monica Taylor
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Jie Zhu
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Sofia Chavez
- Imaging Research, Sunnybrook Hospital, Toronto, ON, Canada
| | - Greg Stanisz
- Imaging Research, Sunnybrook Hospital, Toronto, ON, Canada
| | - Salomon Minkin
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Norman Boyd
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
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7
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Mukherjee S, Kuroiwa M, Oakden W, Paul BT, Noman A, Chen J, Lin V, Dimitrijevic A, Stanisz G, Le TN. Local magnetic delivery of adeno-associated virus AAV2(quad Y-F)-mediated BDNF gene therapy restores hearing after noise injury. Mol Ther 2022; 30:519-533. [PMID: 34298130 PMCID: PMC8821893 DOI: 10.1016/j.ymthe.2021.07.013] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Revised: 05/11/2021] [Accepted: 07/14/2021] [Indexed: 02/04/2023] Open
Abstract
Moderate noise exposure may cause acute loss of cochlear synapses without affecting the cochlear hair cells and hearing threshold; thus, it remains "hidden" to standard clinical tests. This cochlear synaptopathy is one of the main pathologies of noise-induced hearing loss (NIHL). There is no effective treatment for NIHL, mainly because of the lack of a proper drug-delivery technique. We hypothesized that local magnetic delivery of gene therapy into the inner ear could be beneficial for NIHL. In this study, we used superparamagnetic iron oxide nanoparticles (SPIONs) and a recombinant adeno-associated virus (AAV) vector (AAV2(quad Y-F)) to deliver brain-derived neurotrophic factor (BDNF) gene therapy into the rat inner ear via minimally invasive magnetic targeting. We found that the magnetic targeting effectively accumulates and distributes the SPION-tagged AAV2(quad Y-F)-BDNF vector into the inner ear. We also found that AAV2(quad Y-F) efficiently transfects cochlear hair cells and enhances BDNF gene expression. Enhanced BDNF gene expression substantially recovers noise-induced BDNF gene downregulation, auditory brainstem response (ABR) wave I amplitude reduction, and synapse loss. These results suggest that magnetic targeting of AAV2(quad Y-F)-mediated BDNF gene therapy could reverse cochlear synaptopathy after NIHL.
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Affiliation(s)
- Subhendu Mukherjee
- Biological Sciences Platform, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada
| | - Maya Kuroiwa
- Department of Otolaryngology Head & Neck Surgery, Faculty of Medicine, University of Toronto, ON M5S 1A1, Canada
| | - Wendy Oakden
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada
| | - Brandon T. Paul
- Evaluative Clinical Sciences Platform, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada
| | - Ayesha Noman
- Biological Sciences Platform, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada
| | - Joseph Chen
- Department of Otolaryngology Head & Neck Surgery, Faculty of Medicine, University of Toronto, ON M5S 1A1, Canada
| | - Vincent Lin
- Biological Sciences Platform, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada,Department of Otolaryngology Head & Neck Surgery, Faculty of Medicine, University of Toronto, ON M5S 1A1, Canada
| | - Andrew Dimitrijevic
- Evaluative Clinical Sciences Platform, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada,Department of Otolaryngology Head & Neck Surgery, Faculty of Medicine, University of Toronto, ON M5S 1A1, Canada
| | - Greg Stanisz
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada
| | - Trung N. Le
- Biological Sciences Platform, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada,Department of Otolaryngology Head & Neck Surgery, Faculty of Medicine, University of Toronto, ON M5S 1A1, Canada,Corresponding author: Trung N. Le, Biological Sciences Platform, Hurvitz Brain Sciences Program, Sunnybrook Health Sciences Centre, 2075 Bayview Ave., Room M1 102, Toronto, ON M4N 3M5, Canada.
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8
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Jabehdar Maralani P, Myrehaug S, Mehrabian H, Chan AKM, Wintermark M, Heyn C, Conklin J, Ellingson BM, Rahimi S, Lau AZ, Tseng CL, Soliman H, Detsky J, Daghighi S, Keith J, Munoz DG, Das S, Atenafu EG, Lipsman N, Perry J, Stanisz G, Sahgal A. Intravoxel incoherent motion (IVIM) modeling of diffusion MRI during chemoradiation predicts therapeutic response in IDH wildtype glioblastoma. Radiother Oncol 2021; 156:258-265. [PMID: 33418005 PMCID: PMC8186561 DOI: 10.1016/j.radonc.2020.12.037] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 12/14/2020] [Accepted: 12/22/2020] [Indexed: 12/12/2022]
Abstract
Background: Prediction of early progression in glioblastoma may provide an opportunity to personalize treatment. Simplified intravoxel incoherent motion (IVIM) MRI offers quantitative estimates of diffusion and perfusion metrics. We investigated whether these metrics, during chemoradiation, could predict treatment outcome. Methods: 38 patients with newly diagnosed IDH-wildtype glioblastoma undergoing 6-week/30-fraction chemoradiation had standardized post-operative MRIs at baseline (radiation planning), and at the 10th and 20th fractions. Non-overlapping T1-enhancing (T1C) and non-enhancing T2-FLAIR hyperintense regions were independently segmented. Apparent diffusion coefficient (ADCT1C, ADCT2-FLAIR) and perfusion fraction (fT1C, fT2-FLAIR) maps were generated with simplified IVIM modelling. Parameters associated with progression before or after 6.9 months (early vs late progression, respectively), overall survival (OS) and progression-free survival (PFS) were investigated. Results: Higher ADCT2-FLAIR at baseline [Odds Ratio (OR) = 1.06, 95% CI 1.01–1.15, p = 0.025], lower fT2-FLAIR at fraction 10 (OR = 2.11, 95% CI 1.04–4.27, p = 0.018), and lack of increase in ADCT2-FLAIR at fraction 20 compared to baseline (OR = 1.12, 95% CI 1.02–1.22, p = 0.02) were associated with early progression. Combining ADCT2-FLAIR at baseline, fT2-FLAIR at fraction 10, ECOG and MGMT promoter methylation status significantly improved AUC to 90.3% compared to a model with only ECOG and MGMT promoter methylation status (p = 0.001). Using multivariable analysis, neither IVIM metrics were associated with OS but higher fT2-FLAIR at fraction 10 (HR = 0.72, 95% CI 0.56–0.95, p = 0.018) was associated with longer PFS. Conclusion: ADCT2-FLAIR at baseline, its lack of increase from baseline to fraction 20, or fT2-FLAIR at fraction 10 significantly predicted early progression. fT2-FLAIR at fraction 10 was associated with PFS.
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Affiliation(s)
- Pejman Jabehdar Maralani
- Department of Medical Imaging, Sunnybrook Health Sciences Center, University of Toronto, Canada.
| | - Sten Myrehaug
- Department of Radiation Oncology, Sunnybrook Health Sciences Center, University of Toronto, Canada
| | - Hatef Mehrabian
- Department of Radiation Oncology, Sunnybrook Health Sciences Center, University of Toronto, Canada
| | - Aimee K M Chan
- Department of Medical Imaging, Sunnybrook Health Sciences Center, University of Toronto, Canada
| | - Max Wintermark
- Department of Radiology, Stanford University, United States
| | - Chris Heyn
- Department of Medical Imaging, Sunnybrook Health Sciences Center, University of Toronto, Canada
| | - John Conklin
- Department of Radiology, Massachusetts General Hospital, United States
| | - Benjamin M Ellingson
- Department of Radiological Sciences and Psychiatry, University of California Los Angeles, United States
| | - Saba Rahimi
- Department of Biomedical Engineering, University of Toronto, Canada
| | - Angus Z Lau
- Department of Medical Biophysics, Sunnybrook Research Institute, University of Toronto, Canada
| | - Chia-Lin Tseng
- Department of Radiation Oncology, Sunnybrook Health Sciences Center, University of Toronto, Canada
| | - Hany Soliman
- Department of Radiation Oncology, Sunnybrook Health Sciences Center, University of Toronto, Canada
| | - Jay Detsky
- Department of Radiation Oncology, Sunnybrook Health Sciences Center, University of Toronto, Canada
| | - Shadi Daghighi
- Department of Medical Imaging, Sunnybrook Health Sciences Center, University of Toronto, Canada
| | - Julia Keith
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Canada
| | - David G Munoz
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Canada
| | - Sunit Das
- Department of Surgery, Division of Neurosurgery, University of Toronto, Canada
| | | | - Nir Lipsman
- Department of Surgery, Division of Neurosurgery, University of Toronto, Canada
| | - James Perry
- Department of Medicine, Division of Neurology, University of Toronto, Canada
| | - Greg Stanisz
- Department of Medical Biophysics, Sunnybrook Research Institute, University of Toronto, Canada
| | - Arjun Sahgal
- Department of Radiation Oncology, Sunnybrook Health Sciences Center, University of Toronto, Canada
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9
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Mehrabian H, Lam W, Myrehaug S, Ruschin M, Tseng C, Detsky J, Husain Z, Stanisz G, Sahgal A, Soliman H. Quantitative MRI Metrics Differentiating Radioresistant from Radiosensitive Brain Metastases. Int J Radiat Oncol Biol Phys 2020. [DOI: 10.1016/j.ijrobp.2020.07.2192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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10
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Mehrabian H, Lam W, Myrehaug S, Ruschin M, Tseng C, Detsky J, Husain Z, Stanisz G, Sahgal A, Soliman H. Quantitative MRI (qMRI) Metrics of Response to Stereotactic Radiosurgery for Brain Metastases. Int J Radiat Oncol Biol Phys 2020. [DOI: 10.1016/j.ijrobp.2020.07.2084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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11
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Stewart J, Sahgal A, Lee Y, Soliman H, Tseng CL, Detsky J, Husain Z, Ho L, Das S, Maralani PJ, Lipsman N, Stanisz G, Perry J, Chen H, Atenafu EG, Campbell M, Lau AZ, Ruschin M, Myrehaug S. Quantitating Interfraction Target Dynamics During Concurrent Chemoradiation for Glioblastoma: A Prospective Serial Imaging Study. Int J Radiat Oncol Biol Phys 2020; 109:736-746. [PMID: 33068687 DOI: 10.1016/j.ijrobp.2020.10.002] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 09/18/2020] [Accepted: 10/05/2020] [Indexed: 12/15/2022]
Abstract
PURPOSE Magnetic resonance image (MRI) guided radiation therapy has the potential to improve outcomes for glioblastoma by adapting to tumor changes during radiation therapy. This study quantifies interfraction dynamics (tumor size, position, and geometry) based on sequential magnetic resonance imaging scans obtained during standard 6-week chemoradiation. METHODS AND MATERIALS Sixty-one patients were prospectively imaged with gadolinium-enhanced T1 (T1c) and T2/FLAIR axial sequences at planning (Fx0), fraction 10 (Fx10), fraction 20 (Fx20), and 1 month after the final fraction of chemoradiation therapy (P1M). Gross tumor volumes (GTVs) and clinical target volumes (CTVs) were contoured at all time points. Target dynamics were quantified by absolute volume (V), volume relative to Fx0 (Vrel), and the migration distance (dmigrate; the linear displacement of the GTV or CTV relative to Fx0). Temporal changes were assessed using a linear mixed-effects model. RESULTS Median volumes at Fx0, Fx10, Fx20, and P1M for the GTV were 18.4 cm3 (range, 1.1-110.5 cm3), 14.7 cm3 (range, 0.9-115.1 cm3), 13.7 cm3 (range, 0.6-174.2 cm3), and 13.0 cm3 (range, 0.9-76.3 cm3), respectively, with corresponding median Vrel of 0.88 at Fx10, 0.77 at Fx20, and 0.71 at P1M relative to Fx0 (P < .001 for all). The GTV (CTV) migration distances were greater than 5 mm in 46% (54%) of patients at Fx10, 50% (58%) of patients at Fx20, and 52% (57%) of patients at P1M. Dynamic tumor morphologic changes were observed, with 40% of patients exhibiting a decreased GTV (Vrel ≤1) with a dmigrate >5 mm during chemoradiation therapy. CONCLUSIONS Clinically meaningful tumor dynamics were observed during chemoradiation therapy for glioblastoma, supporting evaluation of daily MRI guided radiation therapy and treatment plan adaptation.
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Affiliation(s)
- James Stewart
- Department of Radiation Oncology, Sunnybrook Odette Cancer Centre, Toronto, Canada
| | - Arjun Sahgal
- Department of Radiation Oncology, Sunnybrook Odette Cancer Centre, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Young Lee
- Department of Radiation Oncology, University of Toronto, Toronto, Canada; Department of Medical Physics, Sunnybrook Odette Cancer Centre, Toronto, Ontario, Canada
| | - Hany Soliman
- Department of Radiation Oncology, Sunnybrook Odette Cancer Centre, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Chia-Lin Tseng
- Department of Radiation Oncology, Sunnybrook Odette Cancer Centre, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Jay Detsky
- Department of Radiation Oncology, Sunnybrook Odette Cancer Centre, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Zain Husain
- Department of Radiation Oncology, Sunnybrook Odette Cancer Centre, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Ling Ho
- Department of Radiation Oncology, Sunnybrook Odette Cancer Centre, Toronto, Canada
| | - Sunit Das
- Division of Neurosurgery and Centre for Ethics, St. Michael's Hospital, Toronto, Canada; The Arthur and Sonia Labatt Brain Tumour Research Centre, SickKids Hospital, Toronto, Canada; Division of Neurosurgery, University of Toronto, Toronto, Canada
| | - Pejman Jabehdar Maralani
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Nir Lipsman
- Division of Neurosurgery, University of Toronto, Toronto, Canada; Department of Physical Sciences, Sunnybrook Research Institute, Toronto, Canada; Harquail Centre for Neuromodulation, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Greg Stanisz
- Department of Physical Sciences, Sunnybrook Research Institute, Toronto, Canada; Department of Medical Biophysics University of Toronto, Toronto, Canada; Department of Neurosurgery and Pediatric Neurosurgery, Medical University, Lublin, Poland
| | - James Perry
- Division of Neurology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Hanbo Chen
- Department of Radiation Oncology, Sunnybrook Odette Cancer Centre, Toronto, Canada
| | - Eshetu G Atenafu
- Department of Biostatistics, University Health Network, University of Toronto, Toronto, Canada
| | - Mikki Campbell
- Department of Radiation Oncology, Sunnybrook Odette Cancer Centre, Toronto, Canada
| | - Angus Z Lau
- Department of Physical Sciences, Sunnybrook Research Institute, Toronto, Canada; Department of Medical Biophysics University of Toronto, Toronto, Canada
| | - Mark Ruschin
- Department of Radiation Oncology, University of Toronto, Toronto, Canada; Department of Medical Physics, Sunnybrook Odette Cancer Centre, Toronto, Ontario, Canada
| | - Sten Myrehaug
- Department of Radiation Oncology, Sunnybrook Odette Cancer Centre, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada.
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12
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Quiaoit K, DiCenzo D, Fatima K, Bhardwaj D, Sannachi L, Gangeh M, Sadeghi-Naini A, Dasgupta A, Kolios MC, Trudeau M, Gandhi S, Eisen A, Wright F, Look-Hong N, Sahgal A, Stanisz G, Brezden C, Dinniwell R, Tran WT, Yang W, Curpen B, Czarnota GJ. Quantitative ultrasound radiomics for therapy response monitoring in patients with locally advanced breast cancer: Multi-institutional study results. PLoS One 2020; 15:e0236182. [PMID: 32716959 PMCID: PMC7384762 DOI: 10.1371/journal.pone.0236182] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Accepted: 06/30/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Neoadjuvant chemotherapy (NAC) is the standard of care for patients with locally advanced breast cancer (LABC). The study was conducted to investigate the utility of quantitative ultrasound (QUS) carried out during NAC to predict the final tumour response in a multi-institutional setting. METHODS Fifty-nine patients with LABC were enrolled from three institutions in North America (Sunnybrook Health Sciences Centre (Toronto, Canada), MD Anderson Cancer Centre (Texas, USA), and Princess Margaret Cancer Centre (Toronto, Canada)). QUS data were collected before starting NAC and subsequently at weeks 1 and 4 during chemotherapy. Spectral tumour parametric maps were generated, and textural features determined using grey-level co-occurrence matrices. Patients were divided into two groups based on their pathological outcomes following surgery: responders and non-responders. Machine learning algorithms using Fisher's linear discriminant (FLD), K-nearest neighbour (K-NN), and support vector machine (SVM-RBF) were used to generate response classification models. RESULTS Thirty-six patients were classified as responders and twenty-three as non-responders. Among all the models, SVM-RBF had the highest accuracy of 81% at both weeks 1 and week 4 with area under curve (AUC) values of 0.87 each. The inclusion of week 1 and 4 features led to an improvement of the classifier models, with the accuracy and AUC from baseline features only being 76% and 0.68, respectively. CONCLUSION QUS data obtained during NAC reflect the ongoing treatment-related changes during chemotherapy and can lead to better classifier performances in predicting the ultimate pathologic response to treatment compared to baseline features alone.
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Affiliation(s)
- Karina Quiaoit
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Daniel DiCenzo
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Kashuf Fatima
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Divya Bhardwaj
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Lakshmanan Sannachi
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Mehrdad Gangeh
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Ali Sadeghi-Naini
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
- Department of Electrical Engineering and Computer Sciences, Lassonde School of Engineering, York University, Toronto, Canada
| | - Archya Dasgupta
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | | | - Maureen Trudeau
- Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Medicine, University of Toronto, Toronto, Canada
| | - Sonal Gandhi
- Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Medicine, University of Toronto, Toronto, Canada
| | - Andrea Eisen
- Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Medicine, University of Toronto, Toronto, Canada
| | - Frances Wright
- Surgical Oncology, Department of Surgery, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Surgery, University of Toronto, Toronto, Canada
| | - Nicole Look-Hong
- Surgical Oncology, Department of Surgery, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Surgery, University of Toronto, Toronto, Canada
| | - Arjun Sahgal
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Greg Stanisz
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Christine Brezden
- Department of Medical Oncology, Saint Michael's Hospital, University of Toronto, Toronto, Canada
| | - Robert Dinniwell
- Department of Radiation Oncology, Princess Margaret Hospital, University Health Network, Toronto, Canada
- Department of Radiation Oncology, London Health Sciences Centre, London, Canada
- Department of Oncology, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - William T. Tran
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Evaluative Clinical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Wei Yang
- Department of Diagnostic Radiology, University of Texas, M.D. Anderson Cancer Center, Houston, Texas, United States of America
| | - Belinda Curpen
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Medical Imaging, University of Toronto, Toronto, Canada
| | - Gregory J. Czarnota
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
- Department of Electrical Engineering and Computer Sciences, Lassonde School of Engineering, York University, Toronto, Canada
- Department of Physics, Ryerson University, Toronto, Canada
- * E-mail:
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13
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DiCenzo D, Quiaoit K, Fatima K, Bhardwaj D, Sannachi L, Gangeh M, Sadeghi-Naini A, Dasgupta A, Kolios MC, Trudeau M, Gandhi S, Eisen A, Wright F, Look Hong N, Sahgal A, Stanisz G, Brezden C, Dinniwell R, Tran WT, Yang W, Curpen B, Czarnota GJ. Quantitative ultrasound radiomics in predicting response to neoadjuvant chemotherapy in patients with locally advanced breast cancer: Results from multi-institutional study. Cancer Med 2020; 9:5798-5806. [PMID: 32602222 PMCID: PMC7433820 DOI: 10.1002/cam4.3255] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Revised: 05/02/2020] [Accepted: 06/04/2020] [Indexed: 12/21/2022] Open
Abstract
Background This study was conducted in order to develop a model for predicting response to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer (LABC) using pretreatment quantitative ultrasound (QUS) radiomics. Methods This was a multicenter study involving four sites across North America, and appropriate approval was obtained from the individual ethics committees. Eighty‐two patients with LABC were included for final analysis. Primary tumors were scanned using a clinical ultrasound system before NAC was started. The tumors were contoured, and radiofrequency data were acquired and processed from whole tumor regions of interest. QUS spectral parameters were derived from the normalized power spectrum, and texture analysis was performed based on six QUS features using a gray level co‐occurrence matrix. Patients were divided into responder or nonresponder classes based on their clinical‐pathological response. Classification analysis was performed using machine learning algorithms, which were trained to optimize classification accuracy. Cross‐validation was performed using a leave‐one‐out cross‐validation method. Results Based on the clinical outcomes of NAC treatment, there were 48 responders and 34 nonresponders. A K‐nearest neighbors (K‐NN) approach resulted in the best classifier performance, with a sensitivity of 91%, a specificity of 83%, and an accuracy of 87%. Conclusion QUS‐based radiomics can predict response to NAC based on pretreatment features with acceptable accuracy.
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Affiliation(s)
- Daniel DiCenzo
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada.,Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Karina Quiaoit
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada.,Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Kashuf Fatima
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada.,Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Divya Bhardwaj
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada.,Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Lakshmanan Sannachi
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada.,Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Mehrdad Gangeh
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada.,Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Ali Sadeghi-Naini
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.,Department of Electrical Engineering and Computer Sciences, Lassonde School of Engineering, York University, Toronto, ON, Canada
| | - Archya Dasgupta
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada.,Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
| | | | - Maureen Trudeau
- Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Sonal Gandhi
- Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Andrea Eisen
- Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Frances Wright
- Surgical Oncology, Department of Surgery, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Nicole Look Hong
- Surgical Oncology, Department of Surgery, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Arjun Sahgal
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada.,Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Greg Stanisz
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Christine Brezden
- Medical Oncology, Saint Michael's Hospital, University of Toronto, Toronto, ON, Canada
| | - Robert Dinniwell
- Department of Radiation Oncology, Princess Margaret Hospital, University Health Network, Toronto, ON, Canada.,Radiation Oncology, London Health Sciences Centre, London, ON, Canada.,Department of Oncology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - William T Tran
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada.,Evaluative Clinical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Wei Yang
- Department of Diagnostic Radiology, University of Texas, Houston, TX, USA
| | - Belinda Curpen
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - Gregory J Czarnota
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada.,Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.,Department of Electrical Engineering and Computer Sciences, Lassonde School of Engineering, York University, Toronto, ON, Canada.,Department of Physics, Ryerson University, Toronto, ON, Canada
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14
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Nosrati R, Pejović-Milić A, Chung H, Stanisz G, Morton G. MRI-Only Post Implant Dosimetry Process for Prostate LDR Brachytherapy. Brachytherapy 2019. [DOI: 10.1016/j.brachy.2019.04.147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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15
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Nosrati R, Soliman A, Safigholi H, Hashemi M, Wronski M, Morton G, Pejović-Milić A, Stanisz G, Song WY. MRI-based automated detection of implanted low dose rate (LDR) brachytherapy seeds using quantitative susceptibility mapping (QSM) and unsupervised machine learning (ML). Radiother Oncol 2018; 129:540-547. [DOI: 10.1016/j.radonc.2018.09.003] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Revised: 09/05/2018] [Accepted: 09/05/2018] [Indexed: 12/19/2022]
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Nosrati R, Wronski M, Ravi A, Safigholi H, Pejović-Milić A, Stanisz G, Morton G. MRI-Based Post-Implant Dosimetry of Prostate Brachytherapy Seeds. Brachytherapy 2018. [DOI: 10.1016/j.brachy.2018.04.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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17
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Boyd N, Berman H, Zhu J, Martin LJ, Yaffe MJ, Chavez S, Stanisz G, Hislop G, Chiarelli AM, Minkin S, Paterson AD. The origins of breast cancer associated with mammographic density: a testable biological hypothesis. Breast Cancer Res 2018. [PMID: 29514672 PMCID: PMC5842598 DOI: 10.1186/s13058-018-0941-y] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
Background Our purpose is to develop a testable biological hypothesis to explain the known increased risk of breast cancer associated with extensive percent mammographic density (PMD), and to reconcile the apparent paradox that although PMD decreases with increasing age, breast cancer incidence increases. Methods We used the Moolgavkar model of carcinogenesis as a framework to examine the known biological properties of the breast tissue components associated with PMD that includes epithelium and stroma, in relation to the development of breast cancer. In this model, normal epithelial cells undergo a mutation to become intermediate cells, which, after further mutation, become malignant cells. A clone of such cells grows to become a tumor. The model also incorporates changes with age in the number of susceptible epithelial cells associated with menarche, parity, and menopause. We used measurements of the radiological properties of breast tissue in 4454 healthy subjects aged from 15 to 80+ years to estimate cumulative exposure to PMD (CBD) in the population, and we examined the association of CBD with the age-incidence curve of breast cancer in the population. Results Extensive PMD is associated with a greater number of breast epithelial cells, lobules, and fibroblasts, and greater amounts of collagen and extracellular matrix. The known biological properties of these tissue components may, singly or in combination, promote the acquisition of mutations by breast epithelial cells specified by the Moolgavkar model, and the subsequent growth of a clone of malignant cells to form a tumor. We also show that estimated CBD in the population from ages 15 to 80+ years is closely associated with the age-incidence curve of breast cancer in the population. Conclusions These findings are consistent with the hypothesis that the biological properties of the breast tissue components associated with PMD increase the probability of the transition of normal epithelium to malignant cells, and that the accumulation of mutations with CBD may influence the age-incidence curve of breast cancer. This hypothesis gives rise to several testable predictions. Electronic supplementary material The online version of this article (10.1186/s13058-018-0941-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Norman Boyd
- Princess Margaret Cancer Centre, 610 University Avenue, Room 9-502, Toronto, ON, M5G 2M9, Canada.
| | - Hal Berman
- Princess Margaret Cancer Centre, 610 University Avenue, Room 9-502, Toronto, ON, M5G 2M9, Canada
| | - Jie Zhu
- Princess Margaret Cancer Centre, 610 University Avenue, Room 9-502, Toronto, ON, M5G 2M9, Canada
| | - Lisa J Martin
- Princess Margaret Cancer Centre, 610 University Avenue, Room 9-502, Toronto, ON, M5G 2M9, Canada
| | - Martin J Yaffe
- Imaging Research, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Sofia Chavez
- Imaging Research, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Greg Stanisz
- Imaging Research, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | | | | | - Salomon Minkin
- Princess Margaret Cancer Centre, 610 University Avenue, Room 9-502, Toronto, ON, M5G 2M9, Canada
| | - Andrew D Paterson
- Genetics and Genome Biology, Hospital for Sick Children Research Institute, Toronto, ON, Canada.,Divisions of Epidemiology and Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
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18
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Soliman AS, Burns L, Owrangi A, Lee Y, Song WY, Stanisz G, Chugh BP. A realistic phantom for validating MRI-based synthetic CT images of the human skull. Med Phys 2017. [PMID: 28644905 DOI: 10.1002/mp.12428] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
PURPOSE To introduce a new realistic human skull phantom for the validation of synthetic CT images of cortical bone from ultra-short echo-time (UTE) sequences. METHODS A human skull of an adult female was utilized as a realistic representation of skull cortical bone. The skull was stabilized in a special acrylic container and was filled with contrast agents that have T1 and T2 relaxation times similar to human brain. The phantom was MR scanned at 3T with UTE and T2 -weighted sequences, followed by CT. A clustering approach was developed to extract the cortical bone signal from MR images. T2∗ maps of the skull were calculated. Synthetic CT images of the bone were compared to cortical bone signal extracted from CT images and confounding factors, such as registration errors, were analyzed. RESULTS Dice similarity coefficient (DSC) of UTE-detected cortical bone was 0.84 and gradually decreased with decreasing number of spokes. DSC did not significantly depend on echo-time. Registration errors were found to be significant confounding factors, with 25% decrease in DSC for consistent 2 mm error at each axis. CONCLUSION This work introduced a new realistic human skull phantom, specifically for the evaluation and analysis of synthetic CT images of cortical bone.
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Affiliation(s)
- Abraam S Soliman
- Medical Physics, Sunnybrook Health Sciences Centre, Toronto, ON, M4N 3M5, Canada.,Physical Sciences, Sunnybrook Research Institute, Toronto, ON, M4N 3M5, Canada
| | - Levi Burns
- Medical Physics, Sunnybrook Health Sciences Centre, Toronto, ON, M4N 3M5, Canada.,Physics and Astronomy, University of British Columbia, Vancouver, BC, V6T 1Z1, Canada
| | - Amir Owrangi
- Medical Physics, Sunnybrook Health Sciences Centre, Toronto, ON, M4N 3M5, Canada.,Radiation Oncology, University of Toronto, Toronto, ON, M5S 3E2, Canada
| | - Young Lee
- Medical Physics, Sunnybrook Health Sciences Centre, Toronto, ON, M4N 3M5, Canada.,Radiation Oncology, University of Toronto, Toronto, ON, M5S 3E2, Canada
| | - William Y Song
- Medical Physics, Sunnybrook Health Sciences Centre, Toronto, ON, M4N 3M5, Canada.,Physical Sciences, Sunnybrook Research Institute, Toronto, ON, M4N 3M5, Canada.,Radiation Oncology, University of Toronto, Toronto, ON, M5S 3E2, Canada
| | - Greg Stanisz
- Medical Physics, Sunnybrook Health Sciences Centre, Toronto, ON, M4N 3M5, Canada.,Physical Sciences, Sunnybrook Research Institute, Toronto, ON, M4N 3M5, Canada.,Medical Biophysics, University of Toronto, Toronto, ON, M4N 3M5, Canada
| | - Brige P Chugh
- Medical Physics, Sunnybrook Health Sciences Centre, Toronto, ON, M4N 3M5, Canada.,Radiation Oncology, University of Toronto, Toronto, ON, M5S 3E2, Canada
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Klein J, Czarnota G, Lam W, Tarapacki C, Stanisz G. In Vivo Measurements of CEST Magnetic Resonance Imaging Signal in Breast Cancer Xenografts at 7T. Int J Radiat Oncol Biol Phys 2016. [DOI: 10.1016/j.ijrobp.2016.06.2251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Fatemi-Ardekani A, Wronski M, Kim A, Stanisz G, Sarfehnia A, Keller B. SU-E-J-209: Geometric Distortion at 3T in a Commercial 4D MRI-Compatible Phantom. Med Phys 2015. [DOI: 10.1118/1.4924295] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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21
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Chavez S, Stanisz G. Comparing average breast fat content results from two different protocols at 1.5T and 3T: can the data be pooled? J Magn Reson Imaging 2013; 40:890-8. [PMID: 24989130 DOI: 10.1002/jmri.24452] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2013] [Accepted: 09/08/2013] [Indexed: 11/07/2022] Open
Abstract
PURPOSE To compare the total breast fat content computed from two separate studies, performed on different scanners and with different protocols, with the goal of defining a relationship to allow pooling the data. MATERIALS AND METHODS Twelve healthy volunteer women were scanned with two different protocols on the same day. The protocols differed in four important aspects: vendors (GE vs. Philips), scanner main magnetic field strengths (1.5T vs. 3T), pulse sequences (2D fast spin-echo vs. 3D spoiled gradient-echo), and water/fat separation techniques. The resulting water and fat maps were processed with in-house software to extract breast tissue slice-wise. Percent fat content was calculated for each breast, per subject. RESULTS Total percent fat contents (averaged across both breasts) resulting from both protocols were plotted against each other, on a subject-by-subject basis, revealing a strong correlation (R(2) > 0.99), with an overestimation of the fat content from Protocol 1 relative to Protocol 2. The proposed T2 TE-correction for Protocol 1 improves the correlation while decreasing the discrepancy between protocols. CONCLUSION Total breast fat content of healthy women resulting from the two protocols can be pooled using a linear relationship. The proposed T2 TE-corrected Protocol 1 is expected to yield accurate fat content.
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Affiliation(s)
- Sofia Chavez
- Centre for Addiction and Mental Health, Research Imaging Centre, Toronto, ON, Canada; University of Toronto, Psychiatry, Toronto, ON, Canada
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Pop M, Ghugre NR, Ramanan V, Morikawa L, Stanisz G, Dick AJ, Wright GA. Quantification of fibrosis in infarcted swine hearts byex vivolate gadolinium-enhancement and diffusion-weighted MRI methods. Phys Med Biol 2013; 58:5009-28. [DOI: 10.1088/0031-9155/58/15/5009] [Citation(s) in RCA: 77] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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Vlad R, Samavati N, Moseley J, Tadayyon H, Iradji S, Stanisz G, Czarnota G, Brock K. WE-C-BRA-03: Best in Physics (Joint Imaging-Therapy) - Registration of Magnetic Resonance, Reconstructed 3D Ultrasound Imaging and Whole-Mount Breast Pathology for Therapy Assessment of Breast Cancer. Med Phys 2012. [DOI: 10.1118/1.4736108] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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Tisserand DJ, Stanisz G, Lobaugh N, Gibson E, Li T, Black SE, Levine B. Diffusion tensor imaging for the evaluation of white matter pathology in traumatic brain injury. Brain Cogn 2006; 60:216-7. [PMID: 16646129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Affiliation(s)
- D J Tisserand
- Rotman Research Institute, Baycrest Centre for Geriatric Care, Toronto, Canada
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Morrison C, Stanisz G, Henkelman RM. Modeling magnetization transfer for biological-like systems using a semi-solid pool with a super-Lorentzian lineshape and dipolar reservoir. J Magn Reson B 1995; 108:103-13. [PMID: 7648009 DOI: 10.1006/jmrb.1995.1111] [Citation(s) in RCA: 129] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Magnetization transfer is modeled as an exchange between a liquid pool and a semi-solid pool where the semi-solid pool has, in addition to the Zeeman reservoir, a dipolar reservoir. The lineshape for the liquid pool is characterized by the usual Lorentzian, whereas the semi-solid pool is characterized as a super-Lorentzian. Three systems were investigated: (1) a membrane mixture of a phosphatidylcholine lipid (POPC) and cholesterol; (2) lyophilized white matter at eight solid concentrations ranging from 1 to 24%; and (3) fresh white matter. In all systems, this model fitted the experimental data well with the effect of the dipolar reservoir being most important for the membrane mixture. For the tissue, the dipolar reservoir is not required and the two-pool model with a single Zeeman reservoir for the semi-solid spins characterizes the experimental data equally well.
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Affiliation(s)
- C Morrison
- Sunnybrook Health Science Center, University of Toronto, Ontario, Canada
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