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Harris L, Shankar LK, Hildebrandt C, Rubinstein WS, Langlais K, Rodriguez H, Berger A, Freymann J, Huang EP, Williams PM, Zenklusen JC, Ochs R, Tezak Z, Sahiner B. Resource requirements to accelerate clinical applications of next-generation sequencing and radiomics: workshop commentary and review. J Natl Cancer Inst 2024; 116:1562-1570. [PMID: 38867688 DOI: 10.1093/jnci/djae136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 03/11/2024] [Accepted: 06/07/2024] [Indexed: 06/14/2024] Open
Abstract
The National Institutes of Health-US Food and Drug Administration Joint Leadership Council Next-Generation Sequencing and Radiomics Working Group was formed by the National Institutes of Health-Food and Drug Administration Joint Leadership Council to promote the development and validation of innovative next-generation sequencing tests, radiomic tools, and associated data analysis and interpretation enhanced by artificial intelligence and machine learning technologies. A 2-day workshop was held on September 29-30, 2021, to convene members of the scientific community to discuss how to overcome the "ground truth" gap that has frequently been acknowledged as 1 of the limiting factors impeding high-quality research, development, validation, and regulatory science in these fields. This report provides a summary of the resource gaps identified by the working group and attendees, highlights existing resources and the ways they can potentially be employed to accelerate growth in these fields, and presents opportunities to support next-generation sequencing and radiomic tool development and validation using technologies such as artificial intelligence and machine learning.
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Affiliation(s)
- Lyndsay Harris
- Cancer Diagnosis Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Lalitha K Shankar
- Cancer Imaging Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Claire Hildebrandt
- Cancer Diagnosis Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Wendy S Rubinstein
- Breast and Gynecologic Cancer Research Group, Division of Cancer Prevention, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Kristofor Langlais
- Office of In Vitro Diagnostics (OHT7), Office of Product Evaluation and Quality, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA
| | - Henry Rodriguez
- Office of Cancer Clinical Proteomics Research, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Adam Berger
- Division of Clinical and Healthcare Research Policy, Office of Science Policy, National Institutes of Health, Bethesda, MD, USA
| | - John Freymann
- Leidos Biomedical Research, Inc, Frederick National Laboratory for Cancer Research, Cancer Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Erich P Huang
- Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - P Mickey Williams
- Leidos Biomedical Research, Inc, Frederick National Laboratory for Cancer Research, Cancer Diagnosis Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jean Claude Zenklusen
- The Cancer Genome Atlas, Center for Cancer Genomics, Office of the Director, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Robert Ochs
- Office of Health Technology 8, Office of Product Evaluation and Quality, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA
| | - Zivana Tezak
- Office of In Vitro Diagnostics (OHT7), Office of Product Evaluation and Quality, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA
| | - Berkman Sahiner
- Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA
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Cuccarini V, Savoldi F, Mardor Y, Last D, Pellegatta S, Mazzi F, Bruzzone MG, Anghileri E, Pollo B, Maddaloni L, Russo C, Bocchi E, Pinzi V, Eoli M, Aquino D. Response assessment of GBM during immunotherapy by delayed contrast treatment response assessment maps. Front Neurol 2024; 15:1374737. [PMID: 38651109 PMCID: PMC11033465 DOI: 10.3389/fneur.2024.1374737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 03/18/2024] [Indexed: 04/25/2024] Open
Abstract
Introduction Assessing the treatment response of glioblastoma multiforme during immunotherapy (IT) is an open issue. Treatment response assessment maps (TRAMs) might help distinguish true tumor progression (TTP) and pseudoprogression (PsP) in this setting. Methods We recruited 16 naïve glioblastoma patients enrolled in a phase II trial consisting of the Stupp protocol (a standardized treatment for glioblastoma involving combined radiotherapy and chemotherapy with temozolomide, followed by adjuvant temozolomide) plus IT with dendritic cells. Patients were followed up till progression or death; seven underwent a second surgery for suspected progression. Clinical, immunological, and MRI data were collected from all patients and histology in case of second surgery. Patients were classified as responders (progression-free survival, PFS > 12 months), and non-responders (PFS ≤ 12), HIGH-NK (natural killer cells, i.e., immunological responders), and LOW-NK (immunological non-responders) based on immune cell counts in peripheral blood. TRAMs differentiate contrast-enhancing lesions with different washout dynamics into hypothesized tumoral (conventionally blue-colored) vs. treatment-related (red-colored). Results Using receiver operating characteristic (ROC) curves, a threshold of -0.066 in VBlue/VCE (volume of the blue portion of tumoral area/volume of contrast enhancement) variation between values obtained in the MRI performed before PsP/TTP and at TTP/PSP allowed to discriminate TTP from PsP with a sensitivity of 71.4% and a specificity of 100%. Among HIGH-NK patients, at month 6 there was a significant reduction compared to baseline and month 2 in median "blue" volumes. Discussion In conclusion, in our pilot study TRAMs support the discrimination between tumoral and treatment-related enhancing features in immunological responders vs. non-responders, the distinction between PsP and TTP, and might provide surrogate markers of immunological response.
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Affiliation(s)
- Valeria Cuccarini
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Filippo Savoldi
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Yael Mardor
- Advanced Technology Center, Sheba Medical Center, Ramat Gan, Israel
- Tel Aviv University, Tel Aviv, Israel
| | - David Last
- Advanced Technology Center, Sheba Medical Center, Ramat Gan, Israel
| | - Serena Pellegatta
- Molecular Neuro-Oncology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Federica Mazzi
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Maria Grazia Bruzzone
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Elena Anghileri
- Molecular Neuro-Oncology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Bianca Pollo
- Neuro-Pathology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Luisa Maddaloni
- Molecular Neuro-Oncology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Camilla Russo
- Dipartimento di Ingegneria Elettrica e delle Tecnologie dell’Informazione (DIETI), Università Degli Studi di Napoli “Federico II”, Naples, Italy
| | - Elisa Bocchi
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Valentina Pinzi
- Radiotherapy Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Marica Eoli
- Molecular Neuro-Oncology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Domenico Aquino
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
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3
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Lin CH, Yan JL, Yap WK, Kang CJ, Chang YC, Tsai TY, Chang KP, Liao CT, Hsu CL, Chou WC, Wang HM, Huang PW, Fan KH, Huang BS, Tung-Chieh Chang J, Tu SJ, Lin CY. Prognostic value of interim CT-based peritumoral and intratumoral radiomics in laryngeal and hypopharyngeal cancer patients undergoing definitive radiotherapy. Radiother Oncol 2023; 189:109938. [PMID: 37806562 DOI: 10.1016/j.radonc.2023.109938] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 09/20/2023] [Accepted: 10/02/2023] [Indexed: 10/10/2023]
Abstract
BACKGROUND AND PURPOSE We aimed to investigate the prognostic value of peritumoral and intratumoral computed tomography (CT)-based radiomics during the course of radiotherapy (RT) in patients with laryngeal and hypopharyngeal cancer (LHC). MATERIALS AND METHODS A total of 92 eligible patients were 1:1 randomly assigned into training and validation cohorts. Pre-RT and mid-RT radiomic features were extracted from pre-treatment and interim CT. LASSO-Cox regression was used for feature selection and model construction. Time-dependent area under the receiver operating curve (AUC) analysis was applied to evaluate the models' prognostic performances. Risk stratification ability on overall survival (OS) and progression-free survival (PFS) were assessed using the Kaplan-Meier method and Cox regression. The associations between radiomics and clinical parameters as well as circulating lymphocyte counts were also evaluated. RESULTS The mid-RT peritumoral (AUC: 0.77) and intratumoral (AUC: 0.79) radiomic models yielded better performance for predicting OS than the pre-RT intratumoral model (AUC: 0.62) in validation cohort. This was confirmed by Kaplan-Meier analysis, in which risk stratification depended on the mid-RT peritumoral (p = 0.009) and intratumoral (p = 0.003) radiomics could be improved for OS, in comparison to the pre-RT intratumoral radiomics (p = 0.199). Multivariate analysis identified mid-RT peritumoral and intratumoral radiomic models as independent prognostic factors for both OS and PFS. Mid-RT peritumoral and intratumoral radiomics were correlated with treatment-related lymphopenia. CONCLUSION Mid-RT peritumoral and intratumoral radiomic models are promising image biomarkers that could have clinical utility for predicting OS and PFS in patients with LHC treated with RT.
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Affiliation(s)
- Chia-Hsin Lin
- Proton and Radiation Therapy Center, Chang Gung Memorial Hospital-Linkou Medical Center, Department of Radiation Oncology, Chang Gung University, Taoyuan, Taiwan.
| | - Jiun-Lin Yan
- Department of Neurosurgery, Keelung Chang Gung Memorial Hospital, Keelung, Taiwan; School of Traditional Chinese Medicine, College of Medicine, Chang Gung University, Taoyuan, Taiwan.
| | - Wing-Keen Yap
- Proton and Radiation Therapy Center, Chang Gung Memorial Hospital-Linkou Medical Center, Department of Radiation Oncology, Chang Gung University, Taoyuan, Taiwan.
| | - Chung-Jan Kang
- Department of Otorhinolaryngology, Head and Neck Surgery, Chang Gung Memorial Hospital, Medical College of Chang Gung University, Taoyuan, Taiwan.
| | - Yun-Chen Chang
- Department of Otorhinolaryngology, Head and Neck Surgery, Chang Gung Memorial Hospital, Medical College of Chang Gung University, Taoyuan, Taiwan.
| | - Tsung-You Tsai
- Department of Otorhinolaryngology, Head and Neck Surgery, Chang Gung Memorial Hospital, Medical College of Chang Gung University, Taoyuan, Taiwan.
| | - Kai-Ping Chang
- Department of Otorhinolaryngology, Head and Neck Surgery, Chang Gung Memorial Hospital, Medical College of Chang Gung University, Taoyuan, Taiwan.
| | - Chun-Ta Liao
- Department of Otorhinolaryngology, Head and Neck Surgery, Chang Gung Memorial Hospital, Medical College of Chang Gung University, Taoyuan, Taiwan.
| | - Cheng-Lung Hsu
- Department of Hematology-Oncology, Chang Gung Memorial Hospital, Medical College of Chang Gung University, Taoyuan, Taiwan.
| | - Wen-Chi Chou
- Department of Hematology-Oncology, Chang Gung Memorial Hospital, Medical College of Chang Gung University, Taoyuan, Taiwan.
| | - Hung-Ming Wang
- Department of Hematology-Oncology, Chang Gung Memorial Hospital, Medical College of Chang Gung University, Taoyuan, Taiwan.
| | - Pei-Wei Huang
- Department of Hematology-Oncology, Chang Gung Memorial Hospital, Medical College of Chang Gung University, Taoyuan, Taiwan.
| | - Kang-Hsing Fan
- Department of Radiation Oncology, New Taipei Municipal Tucheng Hospital, New Taipei City, Taiwan.
| | - Bing-Shen Huang
- Proton and Radiation Therapy Center, Chang Gung Memorial Hospital-Linkou Medical Center, Department of Radiation Oncology, Chang Gung University, Taoyuan, Taiwan; Graduate Institute of Clinical Medical Science, Chang Gung University, Taoyuan, Taiwan.
| | - Joseph Tung-Chieh Chang
- Proton and Radiation Therapy Center, Chang Gung Memorial Hospital-Linkou Medical Center, Department of Radiation Oncology, Chang Gung University, Taoyuan, Taiwan; Department of Radiation Oncology, Xiamen Chang Gung Memorial Hospital, Xiamen, Fujian, China.
| | - Shu-Ju Tu
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan; Department of Medical Imaging and Intervention, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan.
| | - Chien-Yu Lin
- Proton and Radiation Therapy Center, Chang Gung Memorial Hospital-Linkou Medical Center, Department of Radiation Oncology, Chang Gung University, Taoyuan, Taiwan.
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Khalili N, Kazerooni AF, Familiar A, Haldar D, Kraya A, Foster J, Koptyra M, Storm PB, Resnick AC, Nabavizadeh A. Radiomics for characterization of the glioma immune microenvironment. NPJ Precis Oncol 2023; 7:59. [PMID: 37337080 DOI: 10.1038/s41698-023-00413-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Accepted: 06/02/2023] [Indexed: 06/21/2023] Open
Abstract
Increasing evidence suggests that besides mutational and molecular alterations, the immune component of the tumor microenvironment also substantially impacts tumor behavior and complicates treatment response, particularly to immunotherapies. Although the standard method for characterizing tumor immune profile is through performing integrated genomic analysis on tissue biopsies, the dynamic change in the immune composition of the tumor microenvironment makes this approach not feasible, especially for brain tumors. Radiomics is a rapidly growing field that uses advanced imaging techniques and computational algorithms to extract numerous quantitative features from medical images. Recent advances in machine learning methods are facilitating biological validation of radiomic signatures and allowing them to "mine" for a variety of significant correlates, including genetic, immunologic, and histologic data. Radiomics has the potential to be used as a non-invasive approach to predict the presence and density of immune cells within the microenvironment, as well as to assess the expression of immune-related genes and pathways. This information can be essential for patient stratification, informing treatment decisions and predicting patients' response to immunotherapies. This is particularly important for tumors with difficult surgical access such as gliomas. In this review, we provide an overview of the glioma microenvironment, describe novel approaches for clustering patients based on their tumor immune profile, and discuss the latest progress on utilization of radiomics for immune profiling of glioma based on current literature.
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Affiliation(s)
- Nastaran Khalili
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Anahita Fathi Kazerooni
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA
- AI2D Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Ariana Familiar
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Debanjan Haldar
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Institute of Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Adam Kraya
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Jessica Foster
- Division of Oncology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Mateusz Koptyra
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Phillip B Storm
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Adam C Resnick
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Ali Nabavizadeh
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA.
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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Vladimirov N, Perlman O. Molecular MRI-Based Monitoring of Cancer Immunotherapy Treatment Response. Int J Mol Sci 2023; 24:3151. [PMID: 36834563 PMCID: PMC9959624 DOI: 10.3390/ijms24043151] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 01/29/2023] [Accepted: 02/02/2023] [Indexed: 02/09/2023] Open
Abstract
Immunotherapy constitutes a paradigm shift in cancer treatment. Its FDA approval for several indications has yielded improved prognosis for cases where traditional therapy has shown limited efficiency. However, many patients still fail to benefit from this treatment modality, and the exact mechanisms responsible for tumor response are unknown. Noninvasive treatment monitoring is crucial for longitudinal tumor characterization and the early detection of non-responders. While various medical imaging techniques can provide a morphological picture of the lesion and its surrounding tissue, a molecular-oriented imaging approach holds the key to unraveling biological effects that occur much earlier in the immunotherapy timeline. Magnetic resonance imaging (MRI) is a highly versatile imaging modality, where the image contrast can be tailored to emphasize a particular biophysical property of interest using advanced engineering of the imaging pipeline. In this review, recent advances in molecular-MRI based cancer immunotherapy monitoring are described. Next, the presentation of the underlying physics, computational, and biological features are complemented by a critical analysis of the results obtained in preclinical and clinical studies. Finally, emerging artificial intelligence (AI)-based strategies to further distill, quantify, and interpret the image-based molecular MRI information are discussed in terms of perspectives for the future.
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Affiliation(s)
- Nikita Vladimirov
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Or Perlman
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv 6997801, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
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Hormuth DA, Farhat M, Christenson C, Curl B, Chad Quarles C, Chung C, Yankeelov TE. Opportunities for improving brain cancer treatment outcomes through imaging-based mathematical modeling of the delivery of radiotherapy and immunotherapy. Adv Drug Deliv Rev 2022; 187:114367. [PMID: 35654212 PMCID: PMC11165420 DOI: 10.1016/j.addr.2022.114367] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/25/2022] [Accepted: 05/25/2022] [Indexed: 11/01/2022]
Abstract
Immunotherapy has become a fourth pillar in the treatment of brain tumors and, when combined with radiation therapy, may improve patient outcomes and reduce the neurotoxicity. As with other combination therapies, the identification of a treatment schedule that maximizes the synergistic effect of radiation- and immune-therapy is a fundamental challenge. Mechanism-based mathematical modeling is one promising approach to systematically investigate therapeutic combinations to maximize positive outcomes within a rigorous framework. However, successful clinical translation of model-generated combinations of treatment requires patient-specific data to allow the models to be meaningfully initialized and parameterized. Quantitative imaging techniques have emerged as a promising source of high quality, spatially and temporally resolved data for the development and validation of mathematical models. In this review, we will present approaches to personalize mechanism-based modeling frameworks with patient data, and then discuss how these techniques could be leveraged to improve brain cancer outcomes through patient-specific modeling and optimization of treatment strategies.
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Affiliation(s)
- David A Hormuth
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Maguy Farhat
- Departments of Radiation Oncology, MD Anderson Cancer Center, Houston, TX 77230, USA
| | - Chase Christenson
- Departments of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
| | - Brandon Curl
- Departments of Radiation Oncology, MD Anderson Cancer Center, Houston, TX 77230, USA
| | - C Chad Quarles
- Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, AZ 85013, USA
| | - Caroline Chung
- Departments of Radiation Oncology, MD Anderson Cancer Center, Houston, TX 77230, USA
| | - Thomas E Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Oncology, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Imaging Physics, MD Anderson Cancer Center, Houston, TX 77230, USA
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Hu W, Liu H, Li Z, Liu J, Chen L. Impact of molecular and clinical variables on survival outcome with immunotherapy for glioblastoma patients: A systematic review and meta-analysis. CNS Neurosci Ther 2022; 28:1476-1491. [PMID: 35822692 PMCID: PMC9437230 DOI: 10.1111/cns.13915] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 06/22/2022] [Accepted: 06/24/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Given that only a subset of patients with glioblastoma multiforme (GBM) responds to immuno-oncology, this study aimed to assess the impact of multiple factors on GBM immunotherapy prognosis and investigate the potential predictors. METHODS A quantitative meta-analysis was conducted using the random-effects model. Several potential factors were also reviewed qualitatively. RESULTS A total of 39 clinical trials were included after screening 1317 papers. Patients with O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation [hazard ratio (HR) for overall survival (OS) = 2.30, p < 0.0001; HR for progression-free survival (PFS) = 2.10, p < 0.0001], gross total resection (HR for OS = 0.70, p = 0.02; HR for PFS = 0.56, p = 0.004), and no baseline steroid use (HR for OS = 0.52, p = 0.0002; HR for PFS = 0.61, p = 0.02) had a relatively significant favorable OS and PFS following immunotherapy. Patients with a Karnofsky Performance Status score < 80 (HR = 1.73, p = 0.0007) and undergoing two prior relapses (HR = 2.08, p = 0.003) were associated with worse OS. Age, gender, tumor programmed death-ligand 1 expression, and history of chemotherapy were not associated with survival outcomes. Notably, immunotherapy significantly improved the OS among patients undergoing two prior recurrences (HR = 0.40, p = 0.008) but not among patients in any other subgroups, as opposed to non-immunotherapy. CONCLUSION Several factors were associated with prognostic outcomes of GBM patients receiving immunotherapy; multiple recurrences might be a candidate predictor. More marker-driven prospective studies are warranted.
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Affiliation(s)
- Wentao Hu
- School of Medicine, Nankai University, Tianjin, China.,Department of Neurosurgery, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Hongyu Liu
- Department of Neurosurgery, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Ze Li
- Department of Neurosurgery, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Jialin Liu
- Department of Neurosurgery, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Ling Chen
- Department of Neurosurgery, First Medical Center of Chinese PLA General Hospital, Beijing, China
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Li AY, Iv M. Conventional and Advanced Imaging Techniques in Post-treatment Glioma Imaging. FRONTIERS IN RADIOLOGY 2022; 2:883293. [PMID: 37492665 PMCID: PMC10365131 DOI: 10.3389/fradi.2022.883293] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 06/06/2022] [Indexed: 07/27/2023]
Abstract
Despite decades of advancement in the diagnosis and therapy of gliomas, the most malignant primary brain tumors, the overall survival rate is still dismal, and their post-treatment imaging appearance remains very challenging to interpret. Since the limitations of conventional magnetic resonance imaging (MRI) in the distinction between recurrence and treatment effect have been recognized, a variety of advanced MR and functional imaging techniques including diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), perfusion-weighted imaging (PWI), MR spectroscopy (MRS), as well as a variety of radiotracers for single photon emission computed tomography (SPECT) and positron emission tomography (PET) have been investigated for this indication along with voxel-based and more quantitative analytical methods in recent years. Machine learning and radiomics approaches in recent years have shown promise in distinguishing between recurrence and treatment effect as well as improving prognostication in a malignancy with a very short life expectancy. This review provides a comprehensive overview of the conventional and advanced imaging techniques with the potential to differentiate recurrence from treatment effect and includes updates in the state-of-the-art in advanced imaging with a brief overview of emerging experimental techniques. A series of representative cases are provided to illustrate the synthesis of conventional and advanced imaging with the clinical context which informs the radiologic evaluation of gliomas in the post-treatment setting.
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Affiliation(s)
- Anna Y. Li
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, United States
| | - Michael Iv
- Division of Neuroimaging and Neurointervention, Department of Radiology, Stanford University School of Medicine, Stanford, CA, United States
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Malik DG, Rath TJ, Urcuyo Acevedo JC, Canoll PD, Swanson KR, Boxerman JL, Quarles CC, Schmainda KM, Burns TC, Hu LS. Advanced MRI Protocols to Discriminate Glioma From Treatment Effects: State of the Art and Future Directions. FRONTIERS IN RADIOLOGY 2022; 2:809373. [PMID: 37492687 PMCID: PMC10365126 DOI: 10.3389/fradi.2022.809373] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 03/01/2022] [Indexed: 07/27/2023]
Abstract
In the follow-up treatment of high-grade gliomas (HGGs), differentiating true tumor progression from treatment-related effects, such as pseudoprogression and radiation necrosis, presents an ongoing clinical challenge. Conventional MRI with and without intravenous contrast serves as the clinical benchmark for the posttreatment surveillance imaging of HGG. However, many advanced imaging techniques have shown promise in helping better delineate the findings in indeterminate scenarios, as posttreatment effects can often mimic true tumor progression on conventional imaging. These challenges are further confounded by the histologic admixture that can commonly occur between tumor growth and treatment-related effects within the posttreatment bed. This review discusses the current practices in the surveillance imaging of HGG and the role of advanced imaging techniques, including perfusion MRI and metabolic MRI.
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Affiliation(s)
- Dania G. Malik
- Department of Radiology, Mayo Clinic, Phoenix, AZ, United States
| | - Tanya J. Rath
- Department of Radiology, Mayo Clinic, Phoenix, AZ, United States
| | - Javier C. Urcuyo Acevedo
- Mathematical Neurooncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ, United States
| | - Peter D. Canoll
- Departments of Pathology and Cell Biology, Columbia University, New York, NY, United States
| | - Kristin R. Swanson
- Mathematical Neurooncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ, United States
| | - Jerrold L. Boxerman
- Department of Diagnostic Imaging, Brown University, Providence, RI, United States
| | - C. Chad Quarles
- Department of Neuroimaging Research & Barrow Neuroimaging Innovation Center, Barrow Neurologic Institute, Phoenix, AZ, United States
| | - Kathleen M. Schmainda
- Department of Biophysics & Radiology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Terry C. Burns
- Departments of Neurologic Surgery and Neuroscience, Mayo Clinic, Rochester, MN, United States
| | - Leland S. Hu
- Department of Radiology, Mayo Clinic, Phoenix, AZ, United States
- Mathematical Neurooncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ, United States
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10
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Li Y, Ma Y, Wu Z, Xie R, Zeng F, Cai H, Lui S, Song B, Chen L, Wu M. Advanced Imaging Techniques for Differentiating Pseudoprogression and Tumor Recurrence After Immunotherapy for Glioblastoma. Front Immunol 2021; 12:790674. [PMID: 34899760 PMCID: PMC8656432 DOI: 10.3389/fimmu.2021.790674] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 11/08/2021] [Indexed: 02/05/2023] Open
Abstract
Glioblastoma (GBM) is the most common malignant tumor of the central nervous system with poor prognosis. Although the field of immunotherapy in glioma is developing rapidly, glioblastoma is still prone to recurrence under strong immune intervention. The major challenges in the process of immunotherapy are evaluating the curative effect, accurately distinguishing between treatment-related reactions and tumor recurrence, and providing guidance for clinical decision-making. Since the conventional magnetic resonance imaging (MRI) is usually difficult to distinguish between pseudoprogression and the true tumor progression, many studies have used various advanced imaging techniques to evaluate treatment-related responses. Meanwhile, criteria for efficacy evaluation of immunotherapy are constantly updated and improved. A standard imaging scheme to evaluate immunotherapeutic response will benefit patients finally. This review mainly summarizes the application status and future trend of several advanced imaging techniques in evaluating the efficacy of GBM immunotherapy.
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Affiliation(s)
- Yan Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Yiqi Ma
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Zijun Wu
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Ruoxi Xie
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Fanxin Zeng
- Department of Clinic Medical Center, Dazhou Central Hospital, Dazhou, China
| | - Huawei Cai
- Laboratory of Clinical Nuclear Medicine, Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Su Lui
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Bin Song
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Lei Chen
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, China.,Department of Clinical Research Management, West China Hospital, Sichuan University, Chengdu, China
| | - Min Wu
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China.,Department of Clinic Medical Center, Dazhou Central Hospital, Dazhou, China
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11
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Quintarelli C, Camera A, Ciccone R, Alessi I, Del Bufalo F, Carai A, Del Baldo G, Mastronuzzi A, De Angelis B. Innovative and Promising Strategies to Enhance Effectiveness of Immunotherapy for CNS Tumors: Where Are We? Front Immunol 2021; 12:634031. [PMID: 34163465 PMCID: PMC8216238 DOI: 10.3389/fimmu.2021.634031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 04/30/2021] [Indexed: 12/12/2022] Open
Abstract
Although there are several immunotherapy approaches for the treatment of Central Nervous System (CNS) tumors under evaluation, currently none of these approaches have received approval from the regulatory agencies. CNS tumors, especially glioblastomas, are tumors characterized by highly immunosuppressive tumor microenvironment, limiting the possibility of effectively eliciting an immune response. Moreover, the peculiar anatomic location of these tumors poses relevant challenges in terms of safety, since uncontrolled hyper inflammation could lead to cerebral edema and cranial hypertension. The most promising strategies of immunotherapy in neuro-oncology consist of the use of autologous T cells redirected against tumor cells through chimeric antigen receptor (CAR) constructs or genetically modified T-cell receptors. Trials based on native or genetically engineered oncolytic viruses and on vaccination with tumor-associated antigen peptides are also under evaluation. Despite some sporadic complete remissions achieved in clinical trials, the outcome of patients with CNS tumors treated with different immunotherapeutic approaches remains poor. Based on the lessons learned from these unsatisfactory experiences, novel immune-therapy approaches aimed at overcoming the profound immunosuppressive microenvironment of these diseases are bringing new hope to reach the cure for CNS tumors.
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Affiliation(s)
- Concetta Quintarelli
- Department Onco-Hematology, Cell and Gene Therapy, IRCCS, Bambino Gesù Children's Hospital, Rome, Italy.,Department of Clinical Medicine and Surgery, University of Naples Federico II, Naples, Italy
| | - Antonio Camera
- Department Onco-Hematology, Cell and Gene Therapy, IRCCS, Bambino Gesù Children's Hospital, Rome, Italy
| | - Roselia Ciccone
- Department Onco-Hematology, Cell and Gene Therapy, IRCCS, Bambino Gesù Children's Hospital, Rome, Italy
| | - Iside Alessi
- Department Onco-Hematology, Cell and Gene Therapy, IRCCS, Bambino Gesù Children's Hospital, Rome, Italy
| | - Francesca Del Bufalo
- Department Onco-Hematology, Cell and Gene Therapy, IRCCS, Bambino Gesù Children's Hospital, Rome, Italy
| | - Andrea Carai
- Neurosurgery Unit, Department of Neurological and Psychiatric Sciences, IRCCS Bambino Gesù Children's Hospital, Rome, Italy
| | - Giada Del Baldo
- Department Onco-Hematology, Cell and Gene Therapy, IRCCS, Bambino Gesù Children's Hospital, Rome, Italy
| | - Angela Mastronuzzi
- Department Onco-Hematology, Cell and Gene Therapy, IRCCS, Bambino Gesù Children's Hospital, Rome, Italy
| | - Biagio De Angelis
- Department Onco-Hematology, Cell and Gene Therapy, IRCCS, Bambino Gesù Children's Hospital, Rome, Italy
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12
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Wang JH, Wahid KA, van Dijk LV, Farahani K, Thompson RF, Fuller CD. Radiomic biomarkers of tumor immune biology and immunotherapy response. Clin Transl Radiat Oncol 2021; 28:97-115. [PMID: 33937530 PMCID: PMC8076712 DOI: 10.1016/j.ctro.2021.03.006] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 03/20/2021] [Accepted: 03/24/2021] [Indexed: 02/08/2023] Open
Abstract
Immunotherapies are leading to improved outcomes for many cancers, including those with devastating prognoses. As therapies like immune checkpoint inhibitors (ICI) become a mainstay in treatment regimens, many concurrent challenges have arisen - for instance, delineating clinical responders from non-responders. Predicting response has proven to be difficult given a lack of consistent and accurate biomarkers, heterogeneity of the tumor microenvironment (TME), and a poor understanding of resistance mechanisms. For the most part, imaging data have remained an untapped, yet abundant, resource to address these challenges. In recent years, quantitative image analyses have highlighted the utility of medical imaging in predicting tumor phenotypes, prognosis, and therapeutic response. These studies have been fueled by an explosion of resources in high-throughput mining of image features (i.e. radiomics) and artificial intelligence. In this review, we highlight current progress in radiomics to understand tumor immune biology and predict clinical responses to immunotherapies. We also discuss limitations in these studies and future directions for the field, particularly if high-dimensional imaging data are to play a larger role in precision medicine.
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Affiliation(s)
- Jarey H. Wang
- Medical Scientist Training Program, Baylor College of Medicine, Houston, TX, United States
| | - Kareem A. Wahid
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Lisanne V. van Dijk
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Keyvan Farahani
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Bethesda, MD, United States
| | - Reid F. Thompson
- Department of Radiation Medicine, Oregon Health & Science University, Portland, OR, United States
| | - Clifton David Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
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13
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García-Figueiras R, Baleato-González S, Luna A, Muñoz-Iglesias J, Oleaga L, Vallejo Casas JA, Martín-Noguerol T, Broncano J, Areses MC, Vilanova JC. Assessing Immunotherapy with Functional and Molecular Imaging and Radiomics. Radiographics 2020; 40:1987-2010. [PMID: 33035135 DOI: 10.1148/rg.2020200070] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Immunotherapy is changing the treatment paradigm for cancer and has introduced new challenges in medical imaging. Because not all patients benefit from immunotherapy, pretreatment imaging should be performed to identify not only prognostic factors but also factors that allow prediction of response to immunotherapy. Follow-up studies must allow detection of nonresponders, without confusion of pseudoprogression with real progression to prevent premature discontinuation of treatment that can benefit the patient. Conventional imaging techniques and classic tumor response criteria are limited for the evaluation of the unusual patterns of response that arise from the specific mechanisms of action of immunotherapy, so advanced imaging methods must be developed to overcome these shortcomings. The authors present the fundamentals of the tumor immune microenvironment and immunotherapy and how they influence imaging findings. They also discuss advances in functional and molecular imaging techniques for the assessment of immunotherapy in clinical practice, including their use to characterize immune phenotypes, assess patient prognosis and response to therapy, and evaluate immune-related adverse events. Finally, the development of radiomics and radiogenomics in these therapies and the future role of imaging biomarkers for immunotherapy are discussed. Online supplemental material is available for this article. ©RSNA, 2020.
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Affiliation(s)
- Roberto García-Figueiras
- From the Department of Radiology, Oncologic Imaging, Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706, Santiago de Compostela, Spain (R.G.F., S.B.G.); Department of Radiology, HT Medica, Jaén, Spain (A.L, J.B.); Department of Nuclear Medicine, Complexo Hospitalario Universitario de Vigo, Vigo, Spain (J.M.I.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.); Unidad de Gestión Clínica de Medicina Nuclear, Hospital Universitario Reina Sofía de Córdoba, Córdoba, Spain (J.A.V.C.); MRI Unit, HT Medica, Jaén, Spain (T.M.N.); Department of Medical Oncology, Complexo Hospitalario Universitario de Ourense, Ourense, Spain (M.C.A.); and Department of Radiology, Clínica Girona, Institute of Diagnostic Imaging, Girona, Spain (J.C.V.)
| | - Sandra Baleato-González
- From the Department of Radiology, Oncologic Imaging, Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706, Santiago de Compostela, Spain (R.G.F., S.B.G.); Department of Radiology, HT Medica, Jaén, Spain (A.L, J.B.); Department of Nuclear Medicine, Complexo Hospitalario Universitario de Vigo, Vigo, Spain (J.M.I.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.); Unidad de Gestión Clínica de Medicina Nuclear, Hospital Universitario Reina Sofía de Córdoba, Córdoba, Spain (J.A.V.C.); MRI Unit, HT Medica, Jaén, Spain (T.M.N.); Department of Medical Oncology, Complexo Hospitalario Universitario de Ourense, Ourense, Spain (M.C.A.); and Department of Radiology, Clínica Girona, Institute of Diagnostic Imaging, Girona, Spain (J.C.V.)
| | - Antonio Luna
- From the Department of Radiology, Oncologic Imaging, Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706, Santiago de Compostela, Spain (R.G.F., S.B.G.); Department of Radiology, HT Medica, Jaén, Spain (A.L, J.B.); Department of Nuclear Medicine, Complexo Hospitalario Universitario de Vigo, Vigo, Spain (J.M.I.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.); Unidad de Gestión Clínica de Medicina Nuclear, Hospital Universitario Reina Sofía de Córdoba, Córdoba, Spain (J.A.V.C.); MRI Unit, HT Medica, Jaén, Spain (T.M.N.); Department of Medical Oncology, Complexo Hospitalario Universitario de Ourense, Ourense, Spain (M.C.A.); and Department of Radiology, Clínica Girona, Institute of Diagnostic Imaging, Girona, Spain (J.C.V.)
| | - José Muñoz-Iglesias
- From the Department of Radiology, Oncologic Imaging, Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706, Santiago de Compostela, Spain (R.G.F., S.B.G.); Department of Radiology, HT Medica, Jaén, Spain (A.L, J.B.); Department of Nuclear Medicine, Complexo Hospitalario Universitario de Vigo, Vigo, Spain (J.M.I.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.); Unidad de Gestión Clínica de Medicina Nuclear, Hospital Universitario Reina Sofía de Córdoba, Córdoba, Spain (J.A.V.C.); MRI Unit, HT Medica, Jaén, Spain (T.M.N.); Department of Medical Oncology, Complexo Hospitalario Universitario de Ourense, Ourense, Spain (M.C.A.); and Department of Radiology, Clínica Girona, Institute of Diagnostic Imaging, Girona, Spain (J.C.V.)
| | - Laura Oleaga
- From the Department of Radiology, Oncologic Imaging, Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706, Santiago de Compostela, Spain (R.G.F., S.B.G.); Department of Radiology, HT Medica, Jaén, Spain (A.L, J.B.); Department of Nuclear Medicine, Complexo Hospitalario Universitario de Vigo, Vigo, Spain (J.M.I.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.); Unidad de Gestión Clínica de Medicina Nuclear, Hospital Universitario Reina Sofía de Córdoba, Córdoba, Spain (J.A.V.C.); MRI Unit, HT Medica, Jaén, Spain (T.M.N.); Department of Medical Oncology, Complexo Hospitalario Universitario de Ourense, Ourense, Spain (M.C.A.); and Department of Radiology, Clínica Girona, Institute of Diagnostic Imaging, Girona, Spain (J.C.V.)
| | - Juan Antonio Vallejo Casas
- From the Department of Radiology, Oncologic Imaging, Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706, Santiago de Compostela, Spain (R.G.F., S.B.G.); Department of Radiology, HT Medica, Jaén, Spain (A.L, J.B.); Department of Nuclear Medicine, Complexo Hospitalario Universitario de Vigo, Vigo, Spain (J.M.I.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.); Unidad de Gestión Clínica de Medicina Nuclear, Hospital Universitario Reina Sofía de Córdoba, Córdoba, Spain (J.A.V.C.); MRI Unit, HT Medica, Jaén, Spain (T.M.N.); Department of Medical Oncology, Complexo Hospitalario Universitario de Ourense, Ourense, Spain (M.C.A.); and Department of Radiology, Clínica Girona, Institute of Diagnostic Imaging, Girona, Spain (J.C.V.)
| | - Teodoro Martín-Noguerol
- From the Department of Radiology, Oncologic Imaging, Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706, Santiago de Compostela, Spain (R.G.F., S.B.G.); Department of Radiology, HT Medica, Jaén, Spain (A.L, J.B.); Department of Nuclear Medicine, Complexo Hospitalario Universitario de Vigo, Vigo, Spain (J.M.I.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.); Unidad de Gestión Clínica de Medicina Nuclear, Hospital Universitario Reina Sofía de Córdoba, Córdoba, Spain (J.A.V.C.); MRI Unit, HT Medica, Jaén, Spain (T.M.N.); Department of Medical Oncology, Complexo Hospitalario Universitario de Ourense, Ourense, Spain (M.C.A.); and Department of Radiology, Clínica Girona, Institute of Diagnostic Imaging, Girona, Spain (J.C.V.)
| | - Jordi Broncano
- From the Department of Radiology, Oncologic Imaging, Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706, Santiago de Compostela, Spain (R.G.F., S.B.G.); Department of Radiology, HT Medica, Jaén, Spain (A.L, J.B.); Department of Nuclear Medicine, Complexo Hospitalario Universitario de Vigo, Vigo, Spain (J.M.I.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.); Unidad de Gestión Clínica de Medicina Nuclear, Hospital Universitario Reina Sofía de Córdoba, Córdoba, Spain (J.A.V.C.); MRI Unit, HT Medica, Jaén, Spain (T.M.N.); Department of Medical Oncology, Complexo Hospitalario Universitario de Ourense, Ourense, Spain (M.C.A.); and Department of Radiology, Clínica Girona, Institute of Diagnostic Imaging, Girona, Spain (J.C.V.)
| | - María Carmen Areses
- From the Department of Radiology, Oncologic Imaging, Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706, Santiago de Compostela, Spain (R.G.F., S.B.G.); Department of Radiology, HT Medica, Jaén, Spain (A.L, J.B.); Department of Nuclear Medicine, Complexo Hospitalario Universitario de Vigo, Vigo, Spain (J.M.I.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.); Unidad de Gestión Clínica de Medicina Nuclear, Hospital Universitario Reina Sofía de Córdoba, Córdoba, Spain (J.A.V.C.); MRI Unit, HT Medica, Jaén, Spain (T.M.N.); Department of Medical Oncology, Complexo Hospitalario Universitario de Ourense, Ourense, Spain (M.C.A.); and Department of Radiology, Clínica Girona, Institute of Diagnostic Imaging, Girona, Spain (J.C.V.)
| | - Joan C Vilanova
- From the Department of Radiology, Oncologic Imaging, Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706, Santiago de Compostela, Spain (R.G.F., S.B.G.); Department of Radiology, HT Medica, Jaén, Spain (A.L, J.B.); Department of Nuclear Medicine, Complexo Hospitalario Universitario de Vigo, Vigo, Spain (J.M.I.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.); Unidad de Gestión Clínica de Medicina Nuclear, Hospital Universitario Reina Sofía de Córdoba, Córdoba, Spain (J.A.V.C.); MRI Unit, HT Medica, Jaén, Spain (T.M.N.); Department of Medical Oncology, Complexo Hospitalario Universitario de Ourense, Ourense, Spain (M.C.A.); and Department of Radiology, Clínica Girona, Institute of Diagnostic Imaging, Girona, Spain (J.C.V.)
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