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Sanvito F, Castellano A, Cloughesy TF, Wen PY, Ellingson BM. RANO 2.0 criteria: concepts applicable to the neuroradiologist's clinical practice. Curr Opin Oncol 2024; 36:536-544. [PMID: 39011735 PMCID: PMC11493521 DOI: 10.1097/cco.0000000000001077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/17/2024]
Abstract
PURPOSE OF REVIEW The Response Assessment in Neuro-Oncology (RANO) 2.0 criteria aim at improving the standardization and reliability of treatment response assessment in clinical trials studying central nervous system (CNS) gliomas. This review presents the evidence supporting RANO 2.0 updates and discusses which concepts can be applicable to the clinical practice, particularly in the clinical radiographic reads. RECENT FINDINGS Updates in RANO 2.0 were supported by recent retrospective analyses of multicenter data from recent clinical trials. As proposed in RANO 2.0, in tumors receiving radiation therapy, the post-RT MRI scan should be used as a reference baseline for the following scans, as opposed to the pre-RT scan, and radiographic findings suggesting progression within three months after radiation therapy completion should be verified with confirmatory scans. Volumetric assessments should be considered, when available, especially for low-grade gliomas, and the evaluation of nonenhancing disease should have a marginal role in glioblastoma. However, the radiographic reads in the clinical setting also benefit from aspects that lie outside RANO 2.0 criteria, such as qualitative evaluations, patient-specific clinical considerations, and advanced imaging. SUMMARY While RANO 2.0 criteria are meant for the standardization of the response assessment in clinical trials, some concepts have the potential to improve patients' management in the clinical practice.
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Affiliation(s)
- Francesco Sanvito
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, CA, USA
| | - Antonella Castellano
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele and Vita-Salute San Raffaele University, Milan, Italy
| | - Timothy F Cloughesy
- UCLA Brain Tumor Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
| | - Patrick Y Wen
- Center For Neuro-Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA
| | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, CA, USA
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Yu X, Lai M, Li J, Wang L, Ye K, Zhang D, Hu Q, Li S, Hu X, Wang Q, Ma M, Xiao Z, Zhou J, Shi C, Luo L, Cai L. The relationship between imaging features, therapeutic response, and overall survival in pediatric diffuse intrinsic pontine glioma. Neurosurg Rev 2024; 47:212. [PMID: 38727935 PMCID: PMC11087318 DOI: 10.1007/s10143-024-02435-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 04/24/2024] [Accepted: 04/27/2024] [Indexed: 05/13/2024]
Abstract
We aimed to evaluate the relationship between imaging features, therapeutic responses (comparative cross-product and volumetric measurements), and overall survival (OS) in pediatric diffuse intrinsic pontine glioma (DIPG). A total of 134 patients (≤ 18 years) diagnosed with DIPG were included. Univariate and multivariate analyses were performed to evaluate correlations of clinical and imaging features and therapeutic responses with OS. The correlation between cross-product (CP) and volume thresholds in partial response (PR) was evaluated by linear regression. The log-rank test was used to compare OS patients with discordant therapeutic response classifications and those with concordant classifications. In univariate analysis, characteristics related to worse OS included lower Karnofsky, larger extrapontine extension, ring-enhancement, necrosis, non-PR, and increased ring enhancement post-radiotherapy. In the multivariate analysis, Karnofsky, necrosis, extrapontine extension, and therapeutic response can predict OS. A 25% CP reduction (PR) correlated with a 32% volume reduction (R2 = 0.888). Eight patients had discordant therapeutic response classifications according to CP (25%) and volume (32%). This eight patients' median survival time was 13.0 months, significantly higher than that in the non-PR group (8.9 months), in which responses were consistently classified as non-PR based on CP (25%) and volume (32%). We identified correlations between imaging features, therapeutic responses, and OS; this information is crucial for future clinical trials. Tumor volume may represent the DIPG growth pattern more accurately than CP measurement and can be used to evaluate therapeutic response.
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Affiliation(s)
- Xiaojun Yu
- Department of Medical Imaging Center, Jinan University First Affiliated Hospital, No. 613, Huangpu Road West, Tianhe District, Guangzhou, 510630, Guangdong Province, China
| | - Mingyao Lai
- Department of Oncology, Guangdong sanjiu Brain Hospital, No. 578, Shatai South Road, Baiyun District, Guangzhou, 510510, Guangdong Province, China
| | - Juan Li
- Department of Oncology, Guangdong sanjiu Brain Hospital, No. 578, Shatai South Road, Baiyun District, Guangzhou, 510510, Guangdong Province, China
| | - Lichao Wang
- Department of Oncology, Guangdong sanjiu Brain Hospital, No. 578, Shatai South Road, Baiyun District, Guangzhou, 510510, Guangdong Province, China
| | - Kunlin Ye
- Department of Medical Imaging Center, Jinan University First Affiliated Hospital, No. 613, Huangpu Road West, Tianhe District, Guangzhou, 510630, Guangdong Province, China
| | - Dong Zhang
- Department of Medical Imaging Center, Jinan University First Affiliated Hospital, No. 613, Huangpu Road West, Tianhe District, Guangzhou, 510630, Guangdong Province, China
| | - Qingjun Hu
- Department of Oncology, Guangdong sanjiu Brain Hospital, No. 578, Shatai South Road, Baiyun District, Guangzhou, 510510, Guangdong Province, China
| | - Shaoqun Li
- Department of Oncology, Guangdong sanjiu Brain Hospital, No. 578, Shatai South Road, Baiyun District, Guangzhou, 510510, Guangdong Province, China
| | - Xinpeng Hu
- Department of Medical Imaging Center, Jinan University First Affiliated Hospital, No. 613, Huangpu Road West, Tianhe District, Guangzhou, 510630, Guangdong Province, China
| | - Qiong Wang
- Department of Medical Imaging Center, Jinan University First Affiliated Hospital, No. 613, Huangpu Road West, Tianhe District, Guangzhou, 510630, Guangdong Province, China
| | - Mengjie Ma
- Department of Medical Imaging Center, Jinan University First Affiliated Hospital, No. 613, Huangpu Road West, Tianhe District, Guangzhou, 510630, Guangdong Province, China
| | - Zeyu Xiao
- Department of Medical Imaging Center, Jinan University First Affiliated Hospital, No. 613, Huangpu Road West, Tianhe District, Guangzhou, 510630, Guangdong Province, China
| | - Jiangfen Zhou
- Department of Oncology, Guangdong sanjiu Brain Hospital, No. 578, Shatai South Road, Baiyun District, Guangzhou, 510510, Guangdong Province, China
| | - Changzheng Shi
- Department of Medical Imaging Center, Jinan University First Affiliated Hospital, No. 613, Huangpu Road West, Tianhe District, Guangzhou, 510630, Guangdong Province, China.
| | - Liangping Luo
- Department of Medical Imaging Center, Jinan University First Affiliated Hospital, No. 613, Huangpu Road West, Tianhe District, Guangzhou, 510630, Guangdong Province, China.
- Department of Medical Imaging Center, The Fifth Affiliated Hospital of Jinan University, Yingke Avenue, Heyuan City, 517000, China.
| | - Linbo Cai
- Department of Oncology, Guangdong sanjiu Brain Hospital, No. 578, Shatai South Road, Baiyun District, Guangzhou, 510510, Guangdong Province, China.
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Ramakrishnan D, Brüningk SC, von Reppert M, Memon F, Maleki N, Aneja S, Kazerooni AF, Nabavizadeh A, Lin M, Bousabarah K, Molinaro A, Nicolaides T, Prados M, Mueller S, Aboian MS. Comparison of Volumetric and 2D Measurements and Longitudinal Trajectories in the Response Assessment of BRAF V600E-Mutant Pediatric Gliomas in the Pacific Pediatric Neuro-Oncology Consortium Clinical Trial. AJNR Am J Neuroradiol 2024; 45:475-482. [PMID: 38453411 PMCID: PMC11288571 DOI: 10.3174/ajnr.a8189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 12/03/2023] [Indexed: 03/09/2024]
Abstract
BACKGROUND AND PURPOSE Response on imaging is widely used to evaluate treatment efficacy in clinical trials of pediatric gliomas. While conventional criteria rely on 2D measurements, volumetric analysis may provide a more comprehensive response assessment. There is sparse research on the role of volumetrics in pediatric gliomas. Our purpose was to compare 2D and volumetric analysis with the assessment of neuroradiologists using the Brain Tumor Reporting and Data System (BT-RADS) in BRAF V600E-mutant pediatric gliomas. MATERIALS AND METHODS Manual volumetric segmentations of whole and solid tumors were compared with 2D measurements in 31 participants (292 follow-up studies) in the Pacific Pediatric Neuro-Oncology Consortium 002 trial (NCT01748149). Two neuroradiologists evaluated responses using BT-RADS. Receiver operating characteristic analysis compared classification performance of 2D and volumetrics for partial response. Agreement between volumetric and 2D mathematically modeled longitudinal trajectories for 25 participants was determined using the model-estimated time to best response. RESULTS Of 31 participants, 20 had partial responses according to BT-RADS criteria. Receiver operating characteristic curves for the classification of partial responders at the time of first detection (median = 2 months) yielded an area under the curve of 0.84 (95% CI, 0.69-0.99) for 2D area, 0.91 (95% CI, 0.80-1.00) for whole-volume, and 0.92 (95% CI, 0.82-1.00) for solid volume change. There was no significant difference in the area under the curve between 2D and solid (P = .34) or whole volume (P = .39). There was no significant correlation in model-estimated time to best response (ρ = 0.39, P >.05) between 2D and whole-volume trajectories. Eight of the 25 participants had a difference of ≥90 days in transition from partial response to stable disease between their 2D and whole-volume modeled trajectories. CONCLUSIONS Although there was no overall difference between volumetrics and 2D in classifying partial response assessment using BT-RADS, further prospective studies will be critical to elucidate how the observed differences in tumor 2D and volumetric trajectories affect clinical decision-making and outcomes in some individuals.
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Affiliation(s)
- Divya Ramakrishnan
- From the Department of Radiology and Biomedical Imaging (D.R., M.v.R., F.M., N.M., M.L., M.S.A.), Yale School of Medicine, New Haven, Connecticut
| | - Sarah C Brüningk
- Department of Biosystems Science and Engineering (S.C.B.), ETH Zürich, Basel, Switzerland
- Swiss Institute for Bioinformatics (S.C.B.), Lausanne, Switzerland
| | - Marc von Reppert
- From the Department of Radiology and Biomedical Imaging (D.R., M.v.R., F.M., N.M., M.L., M.S.A.), Yale School of Medicine, New Haven, Connecticut
- Department of Neuroradiology (M.v.R.), Leipzig University Hospital, Leipzig, Germany
| | - Fatima Memon
- From the Department of Radiology and Biomedical Imaging (D.R., M.v.R., F.M., N.M., M.L., M.S.A.), Yale School of Medicine, New Haven, Connecticut
| | - Nazanin Maleki
- From the Department of Radiology and Biomedical Imaging (D.R., M.v.R., F.M., N.M., M.L., M.S.A.), Yale School of Medicine, New Haven, Connecticut
| | - Sanjay Aneja
- Department of Therapeutic Radiology (S.A.), Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation (S.A.), Yale School of Medicine, New Haven, Connecticut
| | - Anahita Fathi Kazerooni
- Center for Biomedical Image Computing and Analytics (A.F.K.), University of Pennsylvania, Philadelphia, Pennsylvania
| | - Ali Nabavizadeh
- Center for Data-Driven Discovery in Biomedicine (A.N.), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - MingDe Lin
- From the Department of Radiology and Biomedical Imaging (D.R., M.v.R., F.M., N.M., M.L., M.S.A.), Yale School of Medicine, New Haven, Connecticut
- Visage Imaging (M.L.), San Diego, Calfornia
| | | | - Annette Molinaro
- Department of Neurological Surgery (A.M.), University of California San Francisco, San Francisco, Calfornia
| | | | - Michael Prados
- Department of Neurology (M.P., S.M.), Neurosurgery, and Pediatrics, University of California San Francisco, San Francisco, Calfornia
| | - Sabine Mueller
- Department of Neurology (M.P., S.M.), Neurosurgery, and Pediatrics, University of California San Francisco, San Francisco, Calfornia
- Children's University Hospital Zürich (S.M.), Zürich, Switzerland
| | - Mariam S Aboian
- From the Department of Radiology and Biomedical Imaging (D.R., M.v.R., F.M., N.M., M.L., M.S.A.), Yale School of Medicine, New Haven, Connecticut
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Haas-Kogan DA, Aboian MS, Minturn JE, Leary SE, Abdelbaki MS, Goldman S, Elster JD, Kraya A, Lueder MR, Ramakrishnan D, von Reppert M, Liu KX, Rokita JL, Resnick AC, Solomon DA, Phillips JJ, Prados M, Molinaro AM, Waszak SM, Mueller S. Everolimus for Children With Recurrent or Progressive Low-Grade Glioma: Results From the Phase II PNOC001 Trial. J Clin Oncol 2024; 42:441-451. [PMID: 37978951 PMCID: PMC10824388 DOI: 10.1200/jco.23.01838] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 10/09/2023] [Accepted: 10/17/2023] [Indexed: 11/19/2023] Open
Abstract
PURPOSE The PNOC001 phase II single-arm trial sought to estimate progression-free survival (PFS) associated with everolimus therapy for progressive/recurrent pediatric low-grade glioma (pLGG) on the basis of phosphatidylinositol 3-kinase (PI3K)/AKT/mammalian target of rapamycin (mTOR) pathway activation as measured by phosphorylated-ribosomal protein S6 and to identify prognostic and predictive biomarkers. PATIENTS AND METHODS Patients, age 3-21 years, with progressive/recurrent pLGG received everolimus orally, 5 mg/m2 once daily. Frequency of driver gene alterations was compared among independent pLGG cohorts of newly diagnosed and progressive/recurrent patients. PFS at 6 months (primary end point) and median PFS (secondary end point) were estimated for association with everolimus therapy. RESULTS Between 2012 and 2019, 65 subjects with progressive/recurrent pLGG (median age, 9.6 years; range, 3.0-19.9; 46% female) were enrolled, with a median follow-up of 57.5 months. The 6-month PFS was 67.4% (95% CI, 60.0 to 80.0) and median PFS was 11.1 months (95% CI, 7.6 to 19.8). Hypertriglyceridemia was the most common grade ≥3 adverse event. PI3K/AKT/mTOR pathway activation did not correlate with clinical outcomes (6-month PFS, active 68.4% v nonactive 63.3%; median PFS, active 11.2 months v nonactive 11.1 months; P = .80). Rare/novel KIAA1549::BRAF fusion breakpoints were most frequent in supratentorial midline pilocytic astrocytomas, in patients with progressive/recurrent disease, and correlated with poor clinical outcomes (median PFS, rare/novel KIAA1549::BRAF fusion breakpoints 6.1 months v common KIAA1549::BRAF fusion breakpoints 16.7 months; P < .05). Multivariate analysis confirmed their independent risk factor status for disease progression in PNOC001 and other, independent cohorts. Additionally, rare pathogenic germline variants in homologous recombination genes were identified in 6.8% of PNOC001 patients. CONCLUSION Everolimus is a well-tolerated therapy for progressive/recurrent pLGGs. Rare/novel KIAA1549::BRAF fusion breakpoints may define biomarkers for progressive disease and should be assessed in future clinical trials.
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Affiliation(s)
- Daphne A. Haas-Kogan
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, MA
| | - Mariam S. Aboian
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT
| | - Jane E. Minturn
- Division of Oncology, Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA
- Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Sarah E.S. Leary
- Cancer and Blood Disorders Center, Seattle Children's Hospital, Seattle, WA
- Department of Pediatrics, University of Washington, Seattle, WA
- Ben Towne Center for Childhood Cancer Research, Seattle Children's Research Institute, Seattle, WA
| | - Mohamed S. Abdelbaki
- Department of Pediatrics, Washington University School of Medicine, St Louis, MO
| | - Stewart Goldman
- Phoenix Children's Hospital, Phoenix, AZ
- University of Arizona College of Medicine, Phoenix, AZ
| | - Jennifer D. Elster
- Division of Hematology Oncology, Department of Pediatrics, Rady Children's Hospital, University of California, San Diego, San Diego, CA
| | - Adam Kraya
- Division of Neurosurgery, Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA
| | - Matthew R. Lueder
- Division of Neurosurgery, Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA
- Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA
| | - Divya Ramakrishnan
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT
| | - Marc von Reppert
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT
- University of Leipzig, Leipzig, Germany
| | - Kevin X. Liu
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Boston Children's Hospital, Harvard Medical School, Boston, MA
| | - Jo Lynne Rokita
- Division of Neurosurgery, Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA
| | - Adam C. Resnick
- Division of Neurosurgery, Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA
| | - David A. Solomon
- Department of Pathology, University of California, San Francisco, San Francisco, CA
| | - Joanna J. Phillips
- Department of Pathology, University of California, San Francisco, San Francisco, CA
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
| | - Michael Prados
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
- Department of Pediatrics, University of California, San Francisco, San Francisco, CA
| | - Annette M. Molinaro
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
| | - Sebastian M. Waszak
- Laboratory of Computational Neuro-Oncology, Swiss Institute for Experimental Cancer Research, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Department of Neurology, University of California, San Francisco, San Francisco, CA
- Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Sabine Mueller
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
- Department of Pediatrics, University of California, San Francisco, San Francisco, CA
- Department of Neurology, University of California, San Francisco, San Francisco, CA
- Department of Pediatrics, University of Zurich, Zurich, Switzerland
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5
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von Reppert M, Ramakrishnan D, Brüningk SC, Memon F, Abi Fadel S, Maleki N, Bahar R, Avesta AE, Jekel L, Sala M, Lost J, Tillmanns N, Kaur M, Aneja S, Fathi Kazerooni A, Nabavizadeh A, Lin M, Hoffmann KT, Bousabarah K, Swanson KR, Haas-Kogan D, Mueller S, Aboian MS. Comparison of volumetric and 2D-based response methods in the PNOC-001 pediatric low-grade glioma clinical trial. Neurooncol Adv 2024; 6:vdad172. [PMID: 38221978 PMCID: PMC10785766 DOI: 10.1093/noajnl/vdad172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2024] Open
Abstract
Background Although response in pediatric low-grade glioma (pLGG) includes volumetric assessment, more simplified 2D-based methods are often used in clinical trials. The study's purpose was to compare volumetric to 2D methods. Methods An expert neuroradiologist performed solid and whole tumor (including cyst and edema) volumetric measurements on MR images using a PACS-based manual segmentation tool in 43 pLGG participants (213 total follow-up images) from the Pacific Pediatric Neuro-Oncology Consortium (PNOC-001) trial. Classification based on changes in volumetric and 2D measurements of solid tumor were compared to neuroradiologist visual response assessment using the Brain Tumor Reporting and Data System (BT-RADS) criteria for a subset of 65 images using receiver operating characteristic (ROC) analysis. Longitudinal modeling of solid tumor volume was used to predict BT-RADS classification in 54 of the 65 images. Results There was a significant difference in ROC area under the curve between 3D solid tumor volume and 2D area (0.96 vs 0.78, P = .005) and between 3D solid and 3D whole volume (0.96 vs 0.84, P = .006) when classifying BT-RADS progressive disease (PD). Thresholds of 15-25% increase in 3D solid tumor volume had an 80% sensitivity in classifying BT-RADS PD included in their 95% confidence intervals. The longitudinal model of solid volume response had a sensitivity of 82% and a positive predictive value of 67% for detecting BT-RADS PD. Conclusions Volumetric analysis of solid tumor was significantly better than 2D measurements in classifying tumor progression as determined by BT-RADS criteria and will enable more comprehensive clinical management.
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Affiliation(s)
- Marc von Reppert
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
- Department of Neuroradiology, Leipzig University Hospital, Leipzig, Germany
| | - Divya Ramakrishnan
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Sarah C Brüningk
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
- Swiss Institute for Bioinformatics (SIB), Lausanne, Switzerland
| | - Fatima Memon
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Sandra Abi Fadel
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Nazanin Maleki
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Ryan Bahar
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Arman E Avesta
- Department of Therapeutic Radiology, Yale School of Medicine, New Haven, Connecticut, USA
- Center for Outcomes Research and Evaluation (CORE), Yale School of Medicine, New Haven, Connecticut, USA
- Department of Neuroradiology, Harvard Medical School—Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Leon Jekel
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
- University of Duisburg-Essen, Essen, Germany
| | - Matthew Sala
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
- Tulane School of Medicine, New Orleans, Louisiana, USA
| | - Jan Lost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
- Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany
| | - Niklas Tillmanns
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
- Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany
| | - Manpreet Kaur
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
- Ludwig Maximilian University, Munich, Germany
| | - Sanjay Aneja
- Department of Therapeutic Radiology, Yale School of Medicine, New Haven, Connecticut, USA
- Center for Outcomes Research and Evaluation (CORE), Yale School of Medicine, New Haven, Connecticut, USA
| | - Anahita Fathi Kazerooni
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ali Nabavizadeh
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - MingDe Lin
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
- Visage Imaging, Inc., San Diego, California, USA
| | | | | | - Kristin R Swanson
- Mathematical Neuro-Oncology Lab, Department of Neurological Surgery, Mayo Clinic, Phoenix, Arizona, USA
| | - Daphne Haas-Kogan
- Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Sabine Mueller
- Department of Neurology, Neurosurgery, and Pediatrics, UCSF, San Francisco, California, USA
- Children’s University Hospital Zürich, Zürich, Switzerland
| | - Mariam S Aboian
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
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6
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Sanvito F, Kaufmann TJ, Cloughesy TF, Wen PY, Ellingson BM. Standardized brain tumor imaging protocols for clinical trials: current recommendations and tips for integration. FRONTIERS IN RADIOLOGY 2023; 3:1267615. [PMID: 38152383 PMCID: PMC10751345 DOI: 10.3389/fradi.2023.1267615] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 11/24/2023] [Indexed: 12/29/2023]
Abstract
Standardized MRI acquisition protocols are crucial for reducing the measurement and interpretation variability associated with response assessment in brain tumor clinical trials. The main challenge is that standardized protocols should ensure high image quality while maximizing the number of institutions meeting the acquisition requirements. In recent years, extensive effort has been made by consensus groups to propose different "ideal" and "minimum requirements" brain tumor imaging protocols (BTIPs) for gliomas, brain metastases (BM), and primary central nervous system lymphomas (PCSNL). In clinical practice, BTIPs for clinical trials can be easily integrated with additional MRI sequences that may be desired for clinical patient management at individual sites. In this review, we summarize the general concepts behind the choice and timing of sequences included in the current recommended BTIPs, we provide a comparative overview, and discuss tips and caveats to integrate additional clinical or research sequences while preserving the recommended BTIPs. Finally, we also reflect on potential future directions for brain tumor imaging in clinical trials.
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Affiliation(s)
- Francesco Sanvito
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | | | - Timothy F. Cloughesy
- UCLA Neuro-Oncology Program, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Patrick Y. Wen
- Center for Neuro-Oncology, Dana-Farber/Brigham and Women’s Cancer Center, Harvard Medical School, Boston, MA, United States
| | - Benjamin M. Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
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7
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Lombardi S, Tortora D, Picariello S, Sudhakar S, De Vita E, Mankad K, Varadkar S, Consales A, Nobili L, Cooper J, Tisdall MM, D'Arco F. Intraoperative MRI Assessment of the Tissue Damage during Laser Ablation of Hypothalamic Hamartoma. Diagnostics (Basel) 2023; 13:2331. [PMID: 37510075 PMCID: PMC10378573 DOI: 10.3390/diagnostics13142331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 06/29/2023] [Accepted: 07/03/2023] [Indexed: 07/30/2023] Open
Abstract
Laser ablation for treatment of hypothalamic hamartoma (HH) is a minimally invasive and effective technique used to destroy hamartomatous tissue and disconnect it from the functioning brain. Currently, the gold standard to evaluate the amount of tissue being "burned" is the use of heat maps during the ablation procedure. However, these maps have low spatial resolution and can be misleading in terms of extension of the tissue damage. The aim of this study is to use different MRI sequences immediately after each laser ablation and correlate the extension of signal changes with the volume of malacic changes in a long-term follow-up scan. During the laser ablation procedure, we imaged the hypothalamic region with high-resolution axial diffusion-weighted images (DWI) and T2-weighted images (T2WI) after each ablation. At the end of the procedure, we also added a post-contrast T1-weighted image (T1WI) of the same region. We then correlated the product of the maximum diameters on axial showing signal changes (acute oedema on T2WI, DWI restriction rim, DWI hypointense core and post-contrast T1WI rim) with the product of the maximum diameters on axial T2WI of the malacic changes in the follow-up scan, both as a fraction of the total area of the hamartoma. The area of the hypointense core on DWI acquired immediately after the laser ablation statistically correlated better with the final area of encephalomalacia, while the T2WI, hyperintense oedema, DWI rim and T1WI rim of enhancement tended to overestimate the encephalomalacic damage. In conclusion, the use of intraoperative sequences (in particular DWI) during laser ablation can give surgeons valuable information in real time about the effective heating damage on the hamartomatous tissue, with better spatial resolution in comparison to the thermal maps.
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Affiliation(s)
- Sophie Lombardi
- Radiodiagnostic Department, Fondazione IRCCS Policlinico San Matteo, 27100 Pavia, Italy
| | - Domenico Tortora
- Neuroradiology Unit, IRCCS Istituto Giannina Gaslini, 16147 Genoa, Italy
| | - Stefania Picariello
- Neuro-Oncology Unit, Department of Paediatric Oncology, Santobono-Pausilipon Children's Hospital, 80123 Naples, Italy
| | - Sniya Sudhakar
- Great Ormond Street Hospital for Children NHS Foundation Trust, London WC1N 3JH, UK
| | - Enrico De Vita
- Great Ormond Street Hospital for Children NHS Foundation Trust, London WC1N 3JH, UK
| | - Kshitij Mankad
- Great Ormond Street Hospital for Children NHS Foundation Trust, London WC1N 3JH, UK
| | - Sophia Varadkar
- Great Ormond Street Hospital for Children NHS Foundation Trust, London WC1N 3JH, UK
| | - Alessandro Consales
- Department of Surgical Sciences, Division of Neurosurgery, IRCCS Istituto Giannina Gaslini, 16147 Genoa, Italy
| | - Lino Nobili
- Child Neuropsychiatry Unit, IRCCS Istituto Giannina Gaslini, 16147 Genoa, Italy
| | - Jessica Cooper
- Great Ormond Street Hospital for Children NHS Foundation Trust, London WC1N 3JH, UK
| | - Martin M Tisdall
- Great Ormond Street Hospital for Children NHS Foundation Trust, London WC1N 3JH, UK
| | - Felice D'Arco
- Great Ormond Street Hospital for Children NHS Foundation Trust, London WC1N 3JH, UK
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8
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Raman F, Mullen A, Byrd M, Bae S, Kim J, Sotoudeh H, Morón FE, Fathallah-Shaykh HM. Evaluation of RANO Criteria for the Assessment of Tumor Progression for Lower-Grade Gliomas. Cancers (Basel) 2023; 15:3274. [PMID: 37444384 DOI: 10.3390/cancers15133274] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 06/05/2023] [Accepted: 06/08/2023] [Indexed: 07/15/2023] Open
Abstract
PURPOSE The Response Assessment in Neuro-Oncology (RANO) criteria for lower-grade gliomas (LGGs) define tumor progression as ≥25% change in the T2/FLAIR signal area based on an operator's discretion of the perpendicular diameter of the largest tumor cross-section. Potential sources of error include acquisition inconsistency of 2D slices, operator selection variabilities in both representative tumor cross-section and measurement line locations, and the inability to quantify infiltrative tumor margins and satellite lesions. Our goal was to assess the accuracy and reproducibility of RANO in LG. MATERIALS AND METHODS A total of 651 FLAIR MRIs from 63 participants with LGGs were retrospectively analyzed by three blinded attending physicians and three blinded resident trainees using RANO criteria, 2D visual assessment, and computer-assisted 3D volumetric assessment. RESULTS RANO product measurements had poor-to-moderate inter-operator reproducibility (r2 = 0.28-0.82; coefficient of variance (CV) = 44-110%; mean percent difference (diff) = 0.4-46.8%) and moderate-to-excellent intra-operator reproducibility (r2 = 0.71-0.88; CV = 31-58%; diff = 0.3-23.9%). When compared to 2D visual ground truth, the accuracy of RANO compared to previous and baseline scans was 66.7% and 65.1%, with an area under the ROC curve (AUC) of 0.67 and 0.66, respectively. When comparing to volumetric ground truth, the accuracy of RANO compared to previous and baseline scans was 21.0% and 56.5%, with an AUC of 0.39 and 0.55, respectively. The median time delay at diagnosis was greater for false negative cases than for false positive cases for the RANO assessment compared to previous (2.05 > 0.50 years, p = 0.003) and baseline scans (1.08 > 0.50 years, p = 0.02). CONCLUSION RANO-based assessment of LGGs has moderate reproducibility and poor accuracy when compared to either visual or volumetric ground truths.
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Affiliation(s)
- Fabio Raman
- Department of Radiology, Johns Hopkins Hospital, 600 N Wolfe St., Baltimore, MD 21287, USA
- Department of Radiology, The University of Alabama at Birmingham, Birmingham, AL 35233, USA
| | - Alexander Mullen
- Department of Radiology, The University of Alabama at Birmingham, Birmingham, AL 35233, USA
| | - Matthew Byrd
- Department of Neurology, The University of Alabama at Birmingham, Birmingham, AL 35233, USA
| | - Sejong Bae
- Department of Medicine, O'Neal Comprehensive Cancer Center, The University of Alabama at Birmingham, Birmingham, AL 35233, USA
| | - Jinsuh Kim
- Department of Radiology, Emory University, Atlanta, GA 30329, USA
| | - Houman Sotoudeh
- Department of Radiology, The University of Alabama at Birmingham, Birmingham, AL 35233, USA
| | - Fanny E Morón
- Department of Radiology, Baylor College of Medicine, Houston, TX 77030, USA
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9
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Peira E, Sensi F, Rei L, Gianeri R, Tortora D, Fiz F, Piccardo A, Bottoni G, Morana G, Chincarini A. Towards an Automated Approach to the Semi-Quantification of [ 18F]F-DOPA PET in Pediatric-Type Diffuse Gliomas. J Clin Med 2023; 12:jcm12082765. [PMID: 37109101 PMCID: PMC10142802 DOI: 10.3390/jcm12082765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 03/28/2023] [Accepted: 04/04/2023] [Indexed: 04/29/2023] Open
Abstract
BACKGROUND This study aims to evaluate the use of a computer-aided, semi-quantification approach to [18F]F-DOPA positron emission tomography (PET) in pediatric-type diffuse gliomas (PDGs) to calculate the tumor-to-background ratio. METHODS A total of 18 pediatric patients with PDGs underwent magnetic resonance imaging and [18F]F-DOPA PET, which were analyzed using both manual and automated procedures. The former provided a tumor-to-normal-tissue ratio (TN) and tumor-to-striatal-tissue ratio (TS), while the latter provided analogous scores (tn, ts). We tested the correlation, consistency, and ability to stratify grading and survival between these methods. RESULTS High Pearson correlation coefficients resulted between the ratios calculated with the two approaches: ρ = 0.93 (p < 10-4) and ρ = 0.814 (p < 10-4). The analysis of the residuals suggested that tn and ts were more consistent than TN and TS. Similarly to TN and TS, the automatically computed scores showed significant differences between low- and high-grade gliomas (p ≤ 10-4, t-test) and the overall survival was significantly shorter in patients with higher values when compared to those with lower ones (p < 10-3, log-rank test). CONCLUSIONS This study suggested that the proposed computer-aided approach could yield similar results to the manual procedure in terms of diagnostic and prognostic information.
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Affiliation(s)
- Enrico Peira
- Istituto Nazionale di Fisica Nucleare (INFN), 16146 Genoa, Italy
| | - Francesco Sensi
- Istituto Nazionale di Fisica Nucleare (INFN), 16146 Genoa, Italy
| | - Luca Rei
- Istituto Nazionale di Fisica Nucleare (INFN), 16146 Genoa, Italy
| | - Ruben Gianeri
- Istituto Nazionale di Fisica Nucleare (INFN), 16146 Genoa, Italy
| | - Domenico Tortora
- Neuroradiology Unit, IRCCS Istituto Giannina Gaslini, 16147 Genoa, Italy
| | - Francesco Fiz
- S.C. di Medicina Nucleare, E.O. Ospedali Galliera, 16128 Genoa, Italy
| | - Arnoldo Piccardo
- S.C. di Medicina Nucleare, E.O. Ospedali Galliera, 16128 Genoa, Italy
| | - Gianluca Bottoni
- S.C. di Medicina Nucleare, E.O. Ospedali Galliera, 16128 Genoa, Italy
| | - Giovanni Morana
- Department of Neurosciences, University of Turin, 10124 Turin, Italy
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10
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Tsai JW, Choi JJ, Ouaalam H, Murillo EA, Yeo KK, Vogelzang J, Sousa C, Woods JK, Ligon KL, Warfield SK, Bandopadhayay P, Cooney TM. Integrated response analysis of pediatric low-grade gliomas during and after targeted therapy treatment. Neurooncol Adv 2023; 5:vdac182. [PMID: 36926246 PMCID: PMC10011805 DOI: 10.1093/noajnl/vdac182] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Background Pediatric low-grade gliomas (pLGGs) are the most common central nervous system tumor in children, characterized by RAS/MAPK pathway driver alterations. Genomic advances have facilitated the use of molecular targeted therapies, however, their long-term impact on tumor behavior remains critically unanswered. Methods We performed an IRB-approved, retrospective chart and imaging review of pLGGs treated with off-label targeted therapy at Dana-Farber/Boston Children's from 2010 to 2020. Response analysis was performed for BRAFV600E and BRAF fusion/duplication-driven pLGG subsets. Results Fifty-five patients were identified (dabrafenib n = 15, everolimus n = 26, trametinib n = 11, and vemurafenib n = 3). Median duration of targeted therapy was 9.48 months (0.12-58.44). The 1-year, 3-year, and 5-year EFS from targeted therapy initiation were 62.1%, 38.2%, and 31.8%, respectively. Mean volumetric change for BRAFV600E mutated pLGG on BRAF inhibitors was -54.11%; median time to best volumetric response was 8.28 months with 9 of 12 (75%) objective RAPNO responses. Median time to largest volume post-treatment was 2.86 months (+13.49%); mean volume by the last follow-up was -14.02%. Mean volumetric change for BRAF fusion/duplication pLGG on trametinib was +7.34%; median time to best volumetric response was 6.71 months with 3 of 7 (43%) objective RAPNO responses. Median time to largest volume post-treatment was 2.38 months (+71.86%); mean volume by the last follow-up was +39.41%. Conclusions Our integrated analysis suggests variability in response by pLGG molecular subgroup and targeted therapy, as well as the transience of some tumor growth following targeted therapy cessation.
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Affiliation(s)
- Jessica W Tsai
- Dana-Farber/Boston Children’s Cancer and Blood Disorders Center, Boston, Massachusetts, USA
| | - Jungwhan John Choi
- Department of Radiology, Boston Children’s Hospital, Boston, Massachusetts, USA
| | - Hakim Ouaalam
- Department of Radiology, Boston Children’s Hospital, Boston, Massachusetts, USA
| | - Efrain Aguilar Murillo
- Department of Radiology, Division of Neuroradiology and Neurointervention, Boston, Massachusetts, USA
| | - Kee Kiat Yeo
- Dana-Farber/Boston Children’s Cancer and Blood Disorders Center, Boston, Massachusetts, USA
| | - Jayne Vogelzang
- Department of Pathology, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Cecilia Sousa
- Department of Pathology, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Jared K Woods
- Department of Pathology, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Keith L Ligon
- Department of Pathology, Brigham and Women’s Hospital, Boston, Massachusetts, USA
- Department of Pathology, Boston Children’s Hospital, Boston Massachusetts, USA
- Department of Pathology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Simon K Warfield
- Department of Radiology, Boston Children’s Hospital, Boston, Massachusetts, USA
| | - Pratiti Bandopadhayay
- Dana-Farber/Boston Children’s Cancer and Blood Disorders Center, Boston, Massachusetts, USA
| | - Tabitha M Cooney
- Dana-Farber/Boston Children’s Cancer and Blood Disorders Center, Boston, Massachusetts, USA
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11
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Ramakrishnan D, von Reppert M, Krycia M, Sala M, Mueller S, Aneja S, Nabavizadeh A, Galldiks N, Lohmann P, Raji C, Ikuta I, Memon F, Weinberg BD, Aboian MS. Evolution and implementation of radiographic response criteria in neuro-oncology. Neurooncol Adv 2023; 5:vdad118. [PMID: 37860269 PMCID: PMC10584081 DOI: 10.1093/noajnl/vdad118] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2023] Open
Abstract
Radiographic response assessment in neuro-oncology is critical in clinical practice and trials. Conventional criteria, such as the MacDonald and response assessment in neuro-oncology (RANO) criteria, rely on bidimensional (2D) measurements of a single tumor cross-section. Although RANO criteria are established for response assessment in clinical trials, there is a critical need to address the complexity of brain tumor treatment response with multiple new approaches being proposed. These include volumetric analysis of tumor compartments, structured MRI reporting systems like the Brain Tumor Reporting and Data System, and standardized approaches to advanced imaging techniques to distinguish tumor response from treatment effects. In this review, we discuss the strengths and limitations of different neuro-oncology response criteria and summarize current research findings on the role of novel response methods in neuro-oncology clinical trials and practice.
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Affiliation(s)
- Divya Ramakrishnan
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Marc von Reppert
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Mark Krycia
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Matthew Sala
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
- Tulane University School of Medicine, New Orleans, Louisiana, USA
| | - Sabine Mueller
- Department of Neurology, Neurosurgery, and Pediatrics, University of California San Francisco, San Francisco, California, USA
| | - Sanjay Aneja
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Ali Nabavizadeh
- Department of Radiology, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA
| | - Norbert Galldiks
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Institute of Neuroscience and Medicine (INM-3), Research Center Juelich, Juelich, Germany
- Center for Integrated Oncology (CIO), Universities of Aachen, Bonn, Cologne, and Duesseldorf, Cologne, Germany
| | - Philipp Lohmann
- Institute of Neuroscience and Medicine (INM-4), Research Center Juelich, Juelich, Germany
| | - Cyrus Raji
- Department of Radiology, Washington University in St. Louis School of Medicine, St. Louis, Missouri, USA
| | - Ichiro Ikuta
- Department of Radiology, Mayo Clinic, Phoenix, Arizona, USA
| | - Fatima Memon
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Brent D Weinberg
- Department of Radiology, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Mariam S Aboian
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
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12
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Adela M, Ales V, Petr B, Katerina V, David S, Lucie S, Lucie S, Miroslav K, Josef Z, Martin K, Zuzana H, Petr L, Jakub T, Vladimir B, Ivana P, David JTW, Martin S, Terezia S, Lenka K, Michal Z. Integrated genomic analysis reveals actionable targets in pediatric spinal cord low-grade gliomas. Acta Neuropathol Commun 2022; 10:143. [PMID: 36163281 PMCID: PMC9513869 DOI: 10.1186/s40478-022-01446-0] [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: 07/22/2022] [Accepted: 09/13/2022] [Indexed: 11/29/2022] Open
Abstract
Gliomas are the most common central nervous tumors in children and adolescents. However, spinal cord low-grade gliomas (sLGGs) are rare, with scarce information on tumor genomics and epigenomics. To define the molecular landscape of sLGGs, we integrated clinical data, histology, and multi-level genetic and epigenetic analyses on a consecutive cohort of 26 pediatric patients. Driver molecular alteration was found in 92% of patients (24/26). A novel variant of KIAA1549:BRAF fusion (ex10:ex9) was identified using RNA-seq in four cases. Importantly, only one-third of oncogenic drivers could be revealed using standard diagnostic methods, and two-thirds of pediatric patients with sLGGs required extensive molecular examination. The majority (23/24) of detected alterations were potentially druggable targets. Four patients in our cohort received targeted therapy with MEK or NTRK inhibitors. Three of those exhibited clinical improvement (two with trametinib, one with larotrectinib), and two patients achieved partial response. Methylation profiling was implemented to further refine the diagnosis and revealed intertumoral heterogeneity in sLGGs. Although 55% of tumors clustered with pilocytic astrocytoma, other rare entities were identified in this patient population. In particular, diffuse leptomeningeal glioneuronal tumors (n = 3) and high-grade astrocytoma with piloid features (n = 1) and pleomorphic xanthoastrocytoma (n = 1) were present. A proportion of tumors (14%) had no match with the current version of the classifier. Complex molecular genetic sLGGs characterization was invaluable to refine diagnosis, which has proven to be essential in such a rare tumor entity. Moreover, identifying a high proportion of drugable targets in sLGGs opened an opportunity for new treatment modalities.
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Affiliation(s)
- Misove Adela
- Prague Brain Tumor Research Group, Second Faculty of Medicine, Charles University and University Hospital Motol, Prague, Czech Republic.,Department of Pediatric Haematology and Oncology, Second Faculty of Medicine, Charles University and University Hospital Motol, Prague, Czech Republic
| | - Vicha Ales
- Prague Brain Tumor Research Group, Second Faculty of Medicine, Charles University and University Hospital Motol, Prague, Czech Republic.,Department of Pediatric Haematology and Oncology, Second Faculty of Medicine, Charles University and University Hospital Motol, Prague, Czech Republic
| | - Broz Petr
- Department of Pediatric Haematology and Oncology, Second Faculty of Medicine, Charles University and University Hospital Motol, Prague, Czech Republic.,Department of Pathology and Molecular Medicine, Second Faculty of Medicine, Charles University Prague and Faculty Hospital Motol, Prague, Czech Republic
| | - Vanova Katerina
- Prague Brain Tumor Research Group, Second Faculty of Medicine, Charles University and University Hospital Motol, Prague, Czech Republic.,Department of Pediatric Haematology and Oncology, Second Faculty of Medicine, Charles University and University Hospital Motol, Prague, Czech Republic
| | - Sumerauer David
- Prague Brain Tumor Research Group, Second Faculty of Medicine, Charles University and University Hospital Motol, Prague, Czech Republic.,Department of Pediatric Haematology and Oncology, Second Faculty of Medicine, Charles University and University Hospital Motol, Prague, Czech Republic
| | - Stolova Lucie
- Department of Pediatric Haematology and Oncology, Second Faculty of Medicine, Charles University and University Hospital Motol, Prague, Czech Republic
| | - Sramkova Lucie
- Department of Pediatric Haematology and Oncology, Second Faculty of Medicine, Charles University and University Hospital Motol, Prague, Czech Republic
| | - Koblizek Miroslav
- Prague Brain Tumor Research Group, Second Faculty of Medicine, Charles University and University Hospital Motol, Prague, Czech Republic.,Department of Pathology and Molecular Medicine, Second Faculty of Medicine, Charles University Prague and Faculty Hospital Motol, Prague, Czech Republic
| | - Zamecnik Josef
- Prague Brain Tumor Research Group, Second Faculty of Medicine, Charles University and University Hospital Motol, Prague, Czech Republic.,Department of Pathology and Molecular Medicine, Second Faculty of Medicine, Charles University Prague and Faculty Hospital Motol, Prague, Czech Republic
| | - Kyncl Martin
- Department of Radiology, Second Faculty of Medicine, Charles University in Prague and Motol University Hospital, Prague, Czech Republic
| | - Holubova Zuzana
- Department of Radiology, Second Faculty of Medicine, Charles University in Prague and Motol University Hospital, Prague, Czech Republic
| | - Liby Petr
- Department of Neurosurgery, Second Faculty of Medicine, Charles University and University Hospital Motol, Prague, Czech Republic
| | - Taborsky Jakub
- Department of Neurosurgery, Second Faculty of Medicine, Charles University and University Hospital Motol, Prague, Czech Republic
| | - Benes Vladimir
- Department of Neurosurgery, Second Faculty of Medicine, Charles University and University Hospital Motol, Prague, Czech Republic
| | - Pernikova Ivana
- Department of Neurology, Second Faculty of Medicine, Charles University and University Hospital Motol, Prague, Czech Republic
| | - Jones T W David
- Hopp Children's Cancer Center Heidelberg (KiTZ), Heidelberg, Germany.,Pediatric Glioma Research Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Sill Martin
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Stancokova Terezia
- Department of Pediatric Oncology and Hematology, Children's University Hospital, Banska Bystrica, Slovakia
| | - Krskova Lenka
- Prague Brain Tumor Research Group, Second Faculty of Medicine, Charles University and University Hospital Motol, Prague, Czech Republic.,Department of Pathology and Molecular Medicine, Second Faculty of Medicine, Charles University Prague and Faculty Hospital Motol, Prague, Czech Republic
| | - Zapotocky Michal
- Prague Brain Tumor Research Group, Second Faculty of Medicine, Charles University and University Hospital Motol, Prague, Czech Republic. .,Department of Pediatric Haematology and Oncology, Second Faculty of Medicine, Charles University and University Hospital Motol, Prague, Czech Republic.
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13
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Lazow MA, Nievelstein MT, Lane A, Bandopadhayhay P, DeWire-Schottmiller M, Fouladi M, Glod JW, Greiner RJ, Hoffman LM, Hummel TR, Kilburn L, Leary S, Minturn JE, Packer R, Ziegler DS, Chaney B, Black K, de Blank P, Leach JL. Volumetric endpoints in diffuse intrinsic pontine glioma: comparison to cross-sectional measures and outcome correlations in the International DIPG/DMG Registry. Neuro Oncol 2022; 24:1598-1608. [PMID: 35148393 PMCID: PMC9435485 DOI: 10.1093/neuonc/noac037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Cross-sectional tumor measures are traditional clinical trial endpoints; however volumetric measures may better assess tumor growth. We determined the correlation and compared the prognostic impact of cross-sectional and volumetric measures of progressive disease (PD) among patients with DIPG. METHODS Imaging and clinical data were abstracted from the International DIPG Registry. Tumor volume and cross-sectional product (CP) were measured with mint Lesion™ software using manual contouring. Correlation between CP and volume (segmented and mathematical [ellipsoid] model) thresholds of PD were assessed by linear regression. Landmark analyses determined differences in survival (via log-rank) between patients classified as PD versus non-PD by CP and volumetric measurements at 1, 3, 5, 7, and 9 months postradiotherapy (RT). Hazard ratios (HR) for survival after these time points were calculated by Cox regression. RESULTS A total of 312 MRIs (46 patients) were analyzed. Comparing change from the previous smallest measure, CP increase of 25% (PD) correlated with a segmented volume increase of 30% (R2 = 0.710), rather than 40% (spherical model extrapolation). CP-determined PD predicted survival at 1 month post-RT (HR = 2.77), but not other time points. Segmented volumetric-determined PD (40% threshold) predicted survival at all imaging timepoints (HRs = 2.57, 2.62, 3.35, 2.71, 16.29), and 30% volumetric PD threshold predicted survival at 1, 3, 5, and 9 month timepoints (HRs = 2.57, 2.62, 4.65, 5.54). Compared to ellipsoid volume, segmented volume demonstrated superior survival associations. CONCLUSIONS Segmented volumetric assessments of PD correlated better with survival than CP or ellipsoid volume at most time points. Semiautomated tumor volume likely represents a more accurate, prognostically-relevant measure of disease burden in DIPG.
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Affiliation(s)
| | | | - Adam Lane
- Brain Tumor Center, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | | | | | - Maryam Fouladi
- Pediatric Neuro-Oncology Program, Nationwide Children’s Hospital, Columbus, Ohio, USA,Department of Pediatrics, The Ohio State University College of Medicine, Columbus, Ohio, USA
| | - John W Glod
- Cancer for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA
| | - Robert J Greiner
- Division of Oncology, Penn State Health Children’s Hospital, Hershey, Pennsylvania, USA
| | - Lindsey M Hoffman
- Division of Oncology, Phoenix Children’s Hospital, Phoenix, Arizona, USA
| | - Trent R Hummel
- Brain Tumor Center, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Lindsay Kilburn
- Division of Oncology, Children’s National Medical Center, Washington, DC, USA
| | - Sarah Leary
- Cancer and Blood Disorders Center, Seattle Children’s Hospital, Seattle, Washington, USA
| | - Jane E Minturn
- Division of Oncology, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Roger Packer
- Division of Oncology, Children’s National Medical Center, Washington, DC, USA
| | - David S Ziegler
- Kids Cancer Centre, Sydney Children’s Hospital, Sydney, NSW, Australia,School of Women’s and Children’s Health, University of New South Wales, Sydney, NSW, Australia
| | - Brooklyn Chaney
- Brain Tumor Center, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA
| | - Katie Black
- Brain Tumor Center, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA
| | | | - James L Leach
- Corresponding Author: James L. Leach, MD, Department of Radiology, Cincinnati Children’s Hospital Medical Center, University of Cincinnati College of Medicine, 3333 Burnet Avenue Cincinnati, OH 45229, USA ()
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14
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Clinical and molecular characteristics of pediatric low-grade glioma complicated with ventriculo-peritoneal shunt related ascites. J Neurooncol 2022; 157:147-156. [PMID: 35122583 DOI: 10.1007/s11060-022-03956-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 01/25/2022] [Indexed: 10/19/2022]
Abstract
INTRODUCTION Ventriculo-peritoneal shunt (VPS) related ascites is a rare complication of pediatric low grade gliomas (pLGG). Physiopathology of this complication is not fully understood and there is paucity of data regarding the molecular profile of pLGG gliomas complicating with ascites and the optimal management of this unusual event. METHODS International multi-institutional retrospective analysis of patients diagnosed with BRAF altered pLGG and ascites arising as a complication of VPS. Demographics, tumor characteristics, therapeutic approaches and outcomes were recorded. RESULTS Nineteen patients were identified. Median age at diagnosis was 14 months (R: 2-144). Most patients (17; 89.4%) presented with lesions involving the optic pathway. Mean tumor standard volume was 34.8 cm2 (R: 12.5-85.4). Pilocytic Astrocytoma was the most frequent histological diagnosis (14;7 3.7%). Eight (42.1%) tumors harbored BRAF V600-E mutation and seven (36.8%) KIAA1549 fusion. The onset of ascites was documented at a median time of 5 months following VPS insertion. Four (21%) patients were managed with paracentesis only, 7(36.8%) required both paracentesis and shunt diversion, 7(36.8%) required only a shunt diversion and 1 (5.2%) patient was managed conservatively. Chemotherapy regimen was changed in 10 patients following ascites. Eight patients received targeted therapy (4 dabrafenib/4 trametinib) and 5 were radiated. There were eleven survivors with a median OS of 69 months (R: 3-144). CONCLUSIONS Ascites is an early feature in the clinical course of young patients with midline BRAF altered pLGG, with high mortality rate observed in our cohort. The hypothesis of ascites as an adverse prognostic factor in pLGG warrants further prospective research.
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Lotan E, Zhang B, Dogra S, Wang W, Carbone D, Fatterpekar G, Oermann E, Lui Y. Development and Practical Implementation of a Deep Learning-Based Pipeline for Automated Pre- and Postoperative Glioma Segmentation. AJNR Am J Neuroradiol 2022; 43:24-32. [PMID: 34857514 PMCID: PMC8757542 DOI: 10.3174/ajnr.a7363] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 09/22/2021] [Indexed: 01/03/2023]
Abstract
BACKGROUND AND PURPOSE Quantitative volumetric segmentation of gliomas has important implications for diagnosis, treatment, and prognosis. We present a deep-learning model that accommodates automated preoperative and postoperative glioma segmentation with a pipeline for clinical implementation. Developed and engineered in concert, the work seeks to accelerate clinical realization of such tools. MATERIALS AND METHODS A deep learning model, autoencoder regularization-cascaded anisotropic, was developed, trained, and tested fusing key elements of autoencoder regularization with a cascaded anisotropic convolutional neural network. We constructed a dataset consisting of 437 cases with 40 cases reserved as a held-out test and the remainder split 80:20 for training and validation. We performed data augmentation and hyperparameter optimization and used a mean Dice score to evaluate against baseline models. To facilitate clinical adoption, we developed the model with an end-to-end pipeline including routing, preprocessing, and end-user interaction. RESULTS The autoencoder regularization-cascaded anisotropic model achieved median and mean Dice scores of 0.88/0.83 (SD, 0.09), 0.89/0.84 (SD, 0.08), and 0.81/0.72 (SD, 0.1) for whole-tumor, tumor core/resection cavity, and enhancing tumor subregions, respectively, including both preoperative and postoperative follow-up cases. The overall total processing time per case was ∼10 minutes, including data routing (∼1 minute), preprocessing (∼6 minute), segmentation (∼1-2 minute), and postprocessing (∼1 minute). Implementation challenges were discussed. CONCLUSIONS We show the feasibility and advantages of building a coordinated model with a clinical pipeline for the rapid and accurate deep learning segmentation of both preoperative and postoperative gliomas. The ability of the model to accommodate cases of postoperative glioma is clinically important for follow-up. An end-to-end approach, such as used here, may lead us toward successful clinical translation of tools for quantitative volume measures for glioma.
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Affiliation(s)
- E. Lotan
- From the Department of Radiology (E.L., B.Z., S.D., D.C., G.F., E.K.O., Y.W.L.)
| | - B. Zhang
- From the Department of Radiology (E.L., B.Z., S.D., D.C., G.F., E.K.O., Y.W.L.)
| | - S. Dogra
- From the Department of Radiology (E.L., B.Z., S.D., D.C., G.F., E.K.O., Y.W.L.)
| | | | - D. Carbone
- From the Department of Radiology (E.L., B.Z., S.D., D.C., G.F., E.K.O., Y.W.L.)
| | - G. Fatterpekar
- From the Department of Radiology (E.L., B.Z., S.D., D.C., G.F., E.K.O., Y.W.L.)
| | - E.K. Oermann
- From the Department of Radiology (E.L., B.Z., S.D., D.C., G.F., E.K.O., Y.W.L.),Neurosurgery, School of Medicine (E.K.O.), NYU Langone Health, New York, New York
| | - Y.W. Lui
- From the Department of Radiology (E.L., B.Z., S.D., D.C., G.F., E.K.O., Y.W.L.)
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Erker C, Tamrazi B, Poussaint TY, Mueller S, Mata-Mbemba D, Franceschi E, Brandes AA, Rao A, Haworth KB, Wen PY, Goldman S, Vezina G, MacDonald TJ, Dunkel IJ, Morgan PS, Jaspan T, Prados MD, Warren KE. Response assessment in paediatric high-grade glioma: recommendations from the Response Assessment in Pediatric Neuro-Oncology (RAPNO) working group. Lancet Oncol 2020; 21:e317-e329. [PMID: 32502458 DOI: 10.1016/s1470-2045(20)30173-x] [Citation(s) in RCA: 84] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Revised: 03/05/2020] [Accepted: 03/12/2020] [Indexed: 12/27/2022]
Abstract
Response criteria for paediatric high-grade glioma vary historically and across different cooperative groups. The Response Assessment in Neuro-Oncology working group developed response criteria for adult high-grade glioma, but these were not created to meet the unique challenges in children with the disease. The Response Assessment in Pediatric Neuro-Oncology (RAPNO) working group, consisting of an international panel of paediatric and adult neuro-oncologists, clinicians, radiologists, radiation oncologists, and neurosurgeons, was established to address issues and unique challenges in assessing response in children with CNS tumours. We established a subcommittee to develop response assessment criteria for paediatric high-grade glioma. Current practice and literature were reviewed to identify major challenges in assessing the response of paediatric high-grade gliomas to various treatments. For areas in which scientific investigation was scarce, consensus was reached through an iterative process. RAPNO response assessment recommendations include the use of MRI of the brain and the spine, assessment of clinical status, and the use of corticosteroids or antiangiogenics. Imaging standards for brain and spine are defined. Compared with the recommendations for the management of adult high-grade glioma, for paediatrics there is inclusion of diffusion-weighted imaging and a higher reliance on T2-weighted fluid-attenuated inversion recovery. Consensus recommendations and response definitions have been established and, similar to other RAPNO recommendations, prospective validation in clinical trials is warranted.
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Affiliation(s)
- Craig Erker
- Department of Pediatrics, Division of Pediatric Hematology/Oncology, Dalhousie University and IWK Health Centre, Halifax, NS, Canada.
| | - Benita Tamrazi
- Department of Radiology, Keck School of Medicine, University of Southern California and Children's Hospital of Los Angeles, Los Angeles, CA, USA
| | - Tina Y Poussaint
- Department of Radiology, Boston Children's Hospital, Boston, MA, USA
| | - Sabine Mueller
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA; Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA; Department of Neurosurgery, University of California San Francisco, San Francisco, CA, USA
| | - Daddy Mata-Mbemba
- Department of Diagnostic Imaging, Dalhousie University and IWK Health Centre, Halifax, NS, Canada
| | - Enrico Franceschi
- Department of Medical Oncology, Azienda USL, Bologna, Italy; IRCCS Institute of Neurological Sciences, Bologna, Italy
| | - Alba A Brandes
- Department of Medical Oncology, Azienda USL, Bologna, Italy; IRCCS Institute of Neurological Sciences, Bologna, Italy
| | - Arvind Rao
- Departments of Computational Medicine and Bioinformatics and Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Kellie B Haworth
- Division of Neuro-Oncology, Department of Oncology, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Patrick Y Wen
- Center For Neuro-Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Stewart Goldman
- Department of Haematology, Oncology, Neuro-Oncology, and Stem Cell Transplantation, Ann and Robert H Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Gilbert Vezina
- Department of Radiology, Children's National Medical Center, Washington, DC, USA
| | - Tobey J MacDonald
- Department of Pediatrics, Aflac Cancer and Blood Disorders Center, Emory University School of Medicine, Atlanta, GA, USA
| | - Ira J Dunkel
- Department of Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Paul S Morgan
- Department of Medical Physics and Clinical Engineering, Nottingham University Hospitals, Queen's Medical Centre, Nottingham, UK
| | - Tim Jaspan
- Department of Radiology, Nottingham University Hospitals, Queen's Medical Centre, Nottingham, UK
| | - Michael D Prados
- Department of Neurosurgery, University of California San Francisco, San Francisco, CA, USA
| | - Katherine E Warren
- Department of Pediatric Oncology, Dana- Farber/Boston Children's Cancer and Blood Disorders Center, Dana-Farber Cancer Institute, Boston, MA, USA
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Gilligan LA, DeWire-Schottmiller MD, Fouladi M, DeBlank P, Leach JL. Tumor Response Assessment in Diffuse Intrinsic Pontine Glioma: Comparison of Semiautomated Volumetric, Semiautomated Linear, and Manual Linear Tumor Measurement Strategies. AJNR Am J Neuroradiol 2020; 41:866-873. [PMID: 32354716 DOI: 10.3174/ajnr.a6555] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Accepted: 02/26/2020] [Indexed: 01/01/2023]
Abstract
BACKGROUND AND PURPOSE 2D measurements of diffuse intrinsic pontine gliomas are limited by variability, and volumetric response criteria are poorly defined. Semiautomated 2D measurements may improve consistency; however, the impact on tumor response assessments is unknown. The purpose of this study was to compare manual 2D, semiautomated 2D, and volumetric measurement strategies for diffuse intrinsic pontine gliomas. MATERIALS AND METHODS This study evaluated patients with diffuse intrinsic pontine gliomas through a Phase I/II trial (NCT02607124). Clinical 2D cross-product values were derived from manual linear measurements (cross-product = long axis × short axis). By means of dedicated software (mint Lesion), tumor margins were traced and maximum cross-product and tumor volume were automatically derived. Correlation and bias between methods were assessed, and response assessment per measurement strategy was reported. RESULTS Ten patients (median age, 7.6 years) underwent 58 MR imaging examinations. Correlation and mean bias (95% limits) of percentage change in tumor size from prior examinations were the following: clinical and semiautomated cross-product, r = 0.36, -1.5% (-59.9%, 56.8%); clinical cross-product and volume, r = 0.61, -2.1% (-52.0%, 47.8%); and semiautomated cross-product and volume, r = 0.79, 0.6% (-39.3%, 38.1%). Stable disease, progressive disease, and partial response rates per measurement strategy were the following: clinical cross-product, 82%, 18%, 0%; semiautomated cross-product, 54%, 42%, 4%; and volume, 50%, 46%, 4%, respectively. CONCLUSIONS Manual 2D cross-product measurements may underestimate tumor size and disease progression compared with semiautomated 2D and volumetric measurements.
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Affiliation(s)
- L A Gilligan
- From the Departments of Radiology (L.A.G., J.L.L.).,Department of Graduate Medical Education (L.A.G., M.D.D.-S.), Mount Carmel Health System, Columbus, Ohio
| | - M D DeWire-Schottmiller
- and Cancer and Blood Diseases Institute (M.D.D.-S., M.F.), Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.,Department of Graduate Medical Education (L.A.G., M.D.D.-S.), Mount Carmel Health System, Columbus, Ohio
| | - M Fouladi
- and Cancer and Blood Diseases Institute (M.D.D.-S., M.F.), Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.,and Departments of Pediatrics (M.F., P.D.)
| | - P DeBlank
- and Departments of Pediatrics (M.F., P.D.)
| | - J L Leach
- From the Departments of Radiology (L.A.G., J.L.L.) .,Radiology (J.L.L.), University of Cincinnati College of Medicine, Cincinnati, Ohio
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Berntsen EM, Stensjøen AL, Langlo MS, Simonsen SQ, Christensen P, Moholdt VA, Solheim O. Volumetric segmentation of glioblastoma progression compared to bidimensional products and clinical radiological reports. Acta Neurochir (Wien) 2020; 162:379-387. [PMID: 31760532 DOI: 10.1007/s00701-019-04110-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Accepted: 10/14/2019] [Indexed: 11/24/2022]
Abstract
BACKGROUND Detection of progression is clinically important for the management of glioblastoma. We sought to assess the accuracy of clinical radiological reporting and measured bidimensional products to identify radiological glioblastoma progression. The two were compared to volumetric segmentation. METHODS In this retrospective study, we included 106 patients with histopathologically verified glioblastomas and two separate MRI scans obtained before surgery. Bidimensional products based on measurements on the axial slice with the largest tumor area were calculated, and growth estimations from the clinical radiological reports were retrieved. The two growth estimations were compared to manual volumetric segmentations. Inter-observer agreement using the bidimensional product was assessed using Kappa-statistics and by calculating the difference between two neuroradiologist in percentage change of the bidimensional product for each tumor. RESULTS Clinical radiological reports and bidimensional products showed fairly equal accuracy when compared to volumetric segmentation with an accuracy of 67% and 66-68%, respectively. There was a difference in median volume increase of 6.9 mL (2.4 vs 9.3 mL, p < 0.001) between tumors evaluated as stable and progressed based on the clinical radiological reports. This difference was 8.1 mL (2.0 vs 10.1 ml, p < 0.001) when using the bidimensional products. The bidimensional product reached a moderate inter-observer agreement with a Kappa value of 0.689. For 32% of the tumors, the two neuroradiologists calculated a difference of more than 25% using bidimensional products. CONCLUSIONS Clinical radiological reporting and the bidimensional product exhibit similar accuracy. The bidimensional product has moderate inter-observer agreement and is prone to underestimating tumor progression, as an average glioblastoma had to grow 10 mL in order to be classified as progressed. These findings underline the assumption that one should try to move towards volumetric assessment of glioblastoma growth in the future.
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Affiliation(s)
- Erik Magnus Berntsen
- Department of Radiology and Nuclear Medicine, St. Olavs University Hospital, Olav Kyrres Gate, 7006, Trondheim, Norway.
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, 7491, Trondheim, Norway.
| | - Anne Line Stensjøen
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, 7491, Trondheim, Norway
| | - Maren Staurset Langlo
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, 7491, Trondheim, Norway
| | - Solveig Quam Simonsen
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, 7491, Trondheim, Norway
| | - Pål Christensen
- Department of Radiology and Nuclear Medicine, St. Olavs University Hospital, Olav Kyrres Gate, 7006, Trondheim, Norway
| | - Viggo Andreas Moholdt
- Department of Radiology and Nuclear Medicine, St. Olavs University Hospital, Olav Kyrres Gate, 7006, Trondheim, Norway
| | - Ole Solheim
- Department of Neurosurgery, St. Olavs University Hospital, Olav Kyrres Gate, 7006, Trondheim, Norway
- National Competence Centre for Ultrasound and Image Guided Therapy, St. Olavs University Hospital, 7006, Trondheim, Norway
- Department of Neuroscience, Faculty of Medicine, Norwegian University of Science and Technology, 7491, Trondheim, Norway
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D'Arco F, Culleton S, De Cocker LJL, Mankad K, Davila J, Tamrazi B. Current concepts in radiologic assessment of pediatric brain tumors during treatment, part 1. Pediatr Radiol 2018; 48:1833-1843. [PMID: 29980859 DOI: 10.1007/s00247-018-4194-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2018] [Revised: 05/26/2018] [Accepted: 06/21/2018] [Indexed: 12/26/2022]
Abstract
Pediatric brain tumors differ from those in adults by location, phenotype and genotype. In addition, they show dissimilar imaging characteristics before and after treatment. While adult brain tumor treatment effects are primarily assessed on MRI by measuring the contrast-enhancing components in addition to abnormalities on T2-weighted and fluid-attenuated inversion recovery images, these methods cannot be simply extrapolated to pediatric central nervous system tumors. A number of researchers have attempted to solve the problem of tumor assessment during treatment in pediatric neuro-oncology; specifically, the Response Assessment in Pediatric Neuro-Oncology (RAPNO) working group was recently established to deal with the distinct challenges in evaluating treatment-related changes on imaging, but no established criteria are available. In this article we review the current methods to evaluate brain tumor therapy and the numerous challenges that remain. In part 1, we examine the role of T2-weighted imaging and fluid-attenuated inversion recovery sequences, contrast enhancement, volumetrics and diffusion imaging techniques. We pay particular attention to several specific pediatric brain tumors, such as optic pathway glioma, diffuse midline glioma and medulloblastoma. Finally, we review the best means to assess leptomeningeal seeding.
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Affiliation(s)
- Felice D'Arco
- Department of Neuroradiology, Great Ormond Street Hospital for Children NHS Trust, Great Ormond Street, London, WC1N 3JH, UK. felice.d'
| | - Sinead Culleton
- Department of Neuroradiology, Great Ormond Street Hospital for Children NHS Trust, Great Ormond Street, London, WC1N 3JH, UK
| | | | - Kshitij Mankad
- Department of Neuroradiology, Great Ormond Street Hospital for Children NHS Trust, Great Ormond Street, London, WC1N 3JH, UK
| | - Jorge Davila
- Department of Medical Imaging, Children's Hospital of Eastern Ontario, Ottawa, Ontario, Canada
| | - Benita Tamrazi
- Department of Radiology, Children's Hospital Los Angeles, Los Angeles, CA, USA
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