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Conley A, Larson C, Oronsky B, Stirn M, Caroen S, Reid TR. Hypothesis: AdAPT-001 and pseudoprogression - when seeing is not necessarily believing. J Immunother Cancer 2024; 12:e008809. [PMID: 38886116 DOI: 10.1136/jitc-2024-008809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/02/2024] [Indexed: 06/20/2024] Open
Abstract
The purpose of this commentary is to highlight the high occurrence of clinical pseudoprogression and delayed responses that have been observed to date with the locally injected oncolytic adenovirus, AdAPT-001, currently in a Phase 1/2 clinical trial (NCT04673942) for the treatment of treatment-refractory tumors. Not surprisingly, these have led to confusion about response assessment and whether to continue patients on treatment. AdAPT-001 carries a transforming growth factor (TGF)-beta trap (TGF-β), which sequesters TGF-β, a cytokine that potently regulates inflammation, fibrosis, and immunosuppression in cancer. Pseudoprogression (PsP) or progression prior to response or stabilization, has been widely recognized with radiotherapy for primary brain tumors and immune checkpoint inhibitors (ICIs). PsP has also been described and documented in the context of oncolytic virotherapy but perhaps to a lesser extent. However, repeated intratumoral injections with these immunostimulatory agents may induce a more intense immune response and release more antigenic epitopes than with ICIs, for example, which are strictly T-cell directed rather than also tumor-directed like AdAPT-001.
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Affiliation(s)
- Anthony Conley
- The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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2
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Debreczeni-Máté Z, Törő I, Simon M, Gál K, Barabás M, Sipos D, Kovács A. Recurrence Patterns after Radiotherapy for Glioblastoma with [(11)C]methionine Positron Emission Tomography-Guided Irradiation for Target Volume Optimization. Diagnostics (Basel) 2024; 14:964. [PMID: 38732378 PMCID: PMC11083337 DOI: 10.3390/diagnostics14090964] [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: 04/16/2024] [Revised: 04/29/2024] [Accepted: 04/30/2024] [Indexed: 05/13/2024] Open
Abstract
11C methionine (11C-MET) is increasingly being used in addition to contrast-enhanced MRI to plan for radiotherapy of patients with glioblastomas. This study aimed to assess the recurrence pattern quantitatively. Glioblastoma patients undergoing 11C-MET PET examination before primary radiotherapy from 2018 to 2023 were included in the analysis. A clinical target volume was manually created and fused with MRI-based gross tumor volumes and MET PET-based biological target volume. The recurrence was noted as an area of contrast enhancement on the first MRI scan, which showed progression. The recurrent tumor was identified on the radiological MR images in terms of recurrent tumor volume, and recurrences were classified as central, in-field, marginal, or ex-field tumors. We then compared the MET-PET-defined biological target volume with the MRI-defined recurrent tumor volume regarding spatial overlap (the Dice coefficient) and the Hausdorff distance. Most recurrences occurred locally within the primary tumor area (64.8%). The mean Hausdorff distance was 39.4 mm (SD 32.25), and the mean Dice coefficient was 0.30 (SD 0.22). In patients with glioblastoma, the analysis of the recurrence pattern has been mainly based on FET-PET. Our study confirms that the recurrence pattern after gross tumor volume-based treatment contoured by MET-PET is consistent with the FET-PET-based treatment described in the literature.
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Affiliation(s)
- Zsanett Debreczeni-Máté
- Doctoral School of Health Sciences, Faculty of Health Sciences, University of Pécs, 7621 Pécs, Hungary; (Z.D.-M.)
| | - Imre Törő
- Department of Oncoradiology, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary
| | - Mihaly Simon
- Department of Oncoradiology, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary
| | - Kristof Gál
- Department of Oncoradiology, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary
| | - Marton Barabás
- Department of Oncoradiology, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary
| | - David Sipos
- Doctoral School of Health Sciences, Faculty of Health Sciences, University of Pécs, 7621 Pécs, Hungary; (Z.D.-M.)
- Department of Medical Imaging, Faculty of Health Sciences, University of Pécs, 7621 Pécs, Hungary
| | - Arpad Kovács
- Doctoral School of Health Sciences, Faculty of Health Sciences, University of Pécs, 7621 Pécs, Hungary; (Z.D.-M.)
- Department of Oncoradiology, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary
- Department of Medical Imaging, Faculty of Health Sciences, University of Pécs, 7621 Pécs, Hungary
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Frosina G. Advancements in Image-Based Models for High-Grade Gliomas Might Be Accelerated. Cancers (Basel) 2024; 16:1566. [PMID: 38672647 PMCID: PMC11048778 DOI: 10.3390/cancers16081566] [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: 03/05/2024] [Revised: 04/08/2024] [Accepted: 04/18/2024] [Indexed: 04/28/2024] Open
Abstract
The first half of 2022 saw the publication of several major research advances in image-based models and artificial intelligence applications to optimize treatment strategies for high-grade gliomas, the deadliest brain tumors. We review them and discuss the barriers that delay their entry into clinical practice; particularly, the small sample size and the heterogeneity of the study designs and methodologies used. We will also write about the poor and late palliation that patients suffering from high-grade glioma can count on at the end of life, as well as the current legislative instruments, with particular reference to Italy. We suggest measures to accelerate the gradual progress in image-based models and end of life care for patients with high-grade glioma.
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Affiliation(s)
- Guido Frosina
- Mutagenesis & Cancer Prevention Unit, IRCCS Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, 16132 Genova, Italy
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Liu Y, Zhao R, Xie Z, Pang Z, Chen S, Xu Q, Zhang Z. Significance of circulating tumor cells detection in tumor diagnosis and monitoring. BMC Cancer 2023; 23:1195. [PMID: 38057833 DOI: 10.1186/s12885-023-11696-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 11/29/2023] [Indexed: 12/08/2023] Open
Abstract
To detect circulating tumor cells (CTCs) in the peripheral blood of patients with tumor, and to analyze the significance of CTC detection in tumor diagnosis and monitoring. In the present study, peripheral blood was collected from 125 patients with tumor, and CTCs were isolated and identified. Differences in CTC number and subtype detection were analyzed for different tumor diseases and stages. CTCs were detected in 122 of the 125 patients with tumor, with a positive rate of 97.6%. The number of CTCs increases in patients with vascular metastasis. The number of mesenchymal CTCs increases in patients with lymph node or vascular metastasis. The average ratio of epithelial CTCs in each positive sample decreases in the later stages of cancer compared with the earlier stages, while the average ratio of mesenchymal CTCs increases in the later stages of cancer compared with the earlier stages. The results showed that CTCs with mesenchymal phenotypes are closely related to lymph node or vascular metastasis. CTC detection can help with early diagnosis of tumor diseases. Continuous monitoring of changes in CTCs number and subtypes can assist clinical judgment of tumor disease development status and prognosis.
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Affiliation(s)
- Yuanrui Liu
- Department of Laboratory Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Rong Zhao
- Clinical Laboratory, Guangzhou 8th People's Hospital, Guangzhou Medical University, Guangzhou, Guangdong, 510060, China
| | - Zaichun Xie
- Clinical Laboratory, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, 510405, P.R. China
| | - Zhiyu Pang
- Clinical Laboratory, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, 510405, P.R. China
| | - Shengjie Chen
- Clinical Laboratory, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, 510405, P.R. China
| | - Qian Xu
- Clinical Laboratory, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, 510405, P.R. China
| | - Zhanfeng Zhang
- Clinical Laboratory, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, 510405, P.R. China.
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Pan I, Huang RY. Artificial intelligence in neuroimaging of brain tumors: reality or still promise? Curr Opin Neurol 2023; 36:549-556. [PMID: 37973024 DOI: 10.1097/wco.0000000000001213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2023]
Abstract
PURPOSE OF REVIEW To provide an updated overview of artificial intelligence (AI) applications in neuro-oncologic imaging and discuss current barriers to wider clinical adoption. RECENT FINDINGS A wide variety of AI applications in neuro-oncologic imaging have been developed and researched, spanning tasks from pretreatment brain tumor classification and segmentation, preoperative planning, radiogenomics, prognostication and survival prediction, posttreatment surveillance, and differentiating between pseudoprogression and true disease progression. While earlier studies were largely based on data from a single institution, more recent studies have demonstrated that the performance of these algorithms are also effective on external data from other institutions. Nevertheless, most of these algorithms have yet to see widespread clinical adoption, given the lack of prospective studies demonstrating their efficacy and the logistical difficulties involved in clinical implementation. SUMMARY While there has been significant progress in AI and neuro-oncologic imaging, clinical utility remains to be demonstrated. The next wave of progress in this area will be driven by prospective studies measuring outcomes relevant to clinical practice and go beyond retrospective studies which primarily aim to demonstrate high performance.
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Affiliation(s)
- Ian Pan
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School
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Young JS, Al-Adli N, Scotford K, Cha S, Berger MS. Pseudoprogression versus true progression in glioblastoma: what neurosurgeons need to know. J Neurosurg 2023; 139:748-759. [PMID: 36790010 PMCID: PMC10412732 DOI: 10.3171/2022.12.jns222173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 12/12/2022] [Indexed: 02/16/2023]
Abstract
Management of patients with glioblastoma (GBM) is complex and involves implementing standard therapies including resection, radiation therapy, and chemotherapy, as well as novel immunotherapies and targeted small-molecule inhibitors through clinical trials and precision medicine approaches. As treatments have advanced, the radiological and clinical assessment of patients with GBM has become even more challenging and nuanced. Advances in spatial resolution and both anatomical and physiological information that can be derived from MRI have greatly improved the noninvasive assessment of GBM before, during, and after therapy. Identification of pseudoprogression (PsP), defined as changes concerning for tumor progression that are, in fact, transient and related to treatment response, is critical for successful patient management. These temporary changes can produce new clinical symptoms due to mass effect and edema. Differentiating this entity from true tumor progression is a major decision point in the patient's management and prognosis. Providers may choose to start an alternative therapy, transition to a clinical trial, consider repeat resection, or continue with the current therapy in hopes of resolution. In this review, the authors describe the invasive and noninvasive techniques neurosurgeons need to be aware of to identify PsP and facilitate surgical decision-making.
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Affiliation(s)
- Jacob S. Young
- Department of Neurological Surgery, University of California, San Francisco, California
| | - Nadeem Al-Adli
- Department of Neurological Surgery, University of California, San Francisco, California
- School of Medicine, Texas Christian University, Fort Worth, Texas
| | - Katie Scotford
- Department of Neurological Surgery, University of California, San Francisco, California
| | - Soonmee Cha
- Department of Neurological Surgery, University of California, San Francisco, California
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California
| | - Mitchel S. Berger
- Department of Neurological Surgery, University of California, San Francisco, California
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Warner E, Lee J, Krishnan S, Wang N, Mohammed S, Srinivasan A, Bapuraj J, Rao A. Low-parameter supervised learning models can discriminate pseudoprogression and true progression in non-perfusion-based MRI. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083692 DOI: 10.1109/embc40787.2023.10340435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Discrimination of pseudoprogression and true progression is one challenge to the treatment of malignant gliomas. Although some techniques such as circulating tumor DNA (ctDNA) and perfusion-weighted imaging (PWI) demonstrate promise in distinguishing PsP from TP, we investigate robust and replicable alternatives to distinguish the two entities based on more widely-available media. In this study, we use low-parametric supervised learning techniques based on geographically-weighted regression (GWR) to investigate the utility of both conventional MRI sequences as well as a diffusion-weighted sequence (apparent diffusion coefficient or ADC) in the discrimination of PsP v TP. GWR applied to MRI modality pairs is a unique approach for small sample sizes and is a novel approach in this arena. From our analysis, all modality pairs involving ADC maps, and those involving post-contrast T1 regressed onto T2 showed potential promise. This work on ADC data adds to a growing body of research suggesting the predictive benefits of ADC, and suggests further research on the relationships between post-contrast T1 and T2.Clinical relevance- Few studies have investigated predictive potential of conventional MRI and ADC to detect PsP. Our study adds to the growing research on the topic and presents a new perspective to research by exploiting the utility of ADC in PsP v TP distinction. In addition, our GWR methodology for low-parametric supervised computer vision models demonstrates a unique approach for image processing of small sample sizes.
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de Godoy LL, Mohan S, Wang S, Nasrallah MP, Sakai Y, O'Rourke DM, Bagley S, Desai A, Loevner LA, Poptani H, Chawla S. Validation of multiparametric MRI based prediction model in identification of pseudoprogression in glioblastomas. J Transl Med 2023; 21:287. [PMID: 37118754 PMCID: PMC10142504 DOI: 10.1186/s12967-023-03941-x] [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: 11/19/2022] [Accepted: 01/30/2023] [Indexed: 04/30/2023] Open
Abstract
BACKGROUND Accurate differentiation of pseudoprogression (PsP) from tumor progression (TP) in glioblastomas (GBMs) is essential for appropriate clinical management and prognostication of these patients. In the present study, we sought to validate the findings of our previously developed multiparametric MRI model in a new cohort of GBM patients treated with standard therapy in identifying PsP cases. METHODS Fifty-six GBM patients demonstrating enhancing lesions within 6 months after completion of concurrent chemo-radiotherapy (CCRT) underwent anatomical imaging, diffusion and perfusion MRI on a 3 T magnet. Subsequently, patients were classified as TP + mixed tumor (n = 37) and PsP (n = 19). When tumor specimens were available from repeat surgery, histopathologic findings were used to identify TP + mixed tumor (> 25% malignant features; n = 34) or PsP (< 25% malignant features; n = 16). In case of non-availability of tumor specimens, ≥ 2 consecutive conventional MRIs using mRANO criteria were used to determine TP + mixed tumor (n = 3) or PsP (n = 3). The multiparametric MRI-based prediction model consisted of predictive probabilities (PP) of tumor progression computed from diffusion and perfusion MRI derived parameters from contrast enhancing regions. In the next step, PP values were used to characterize each lesion as PsP or TP+ mixed tumor. The lesions were considered as PsP if the PP value was < 50% and TP+ mixed tumor if the PP value was ≥ 50%. Pearson test was used to determine the concordance correlation coefficient between PP values and histopathology/mRANO criteria. The area under ROC curve (AUC) was used as a quantitative measure for assessing the discriminatory accuracy of the prediction model in identifying PsP and TP+ mixed tumor. RESULTS Multiparametric MRI model correctly predicted PsP in 95% (18/19) and TP+ mixed tumor in 57% of cases (21/37) with an overall concordance rate of 70% (39/56) with final diagnosis as determined by histopathology/mRANO criteria. There was a significant concordant correlation coefficient between PP values and histopathology/mRANO criteria (r = 0.56; p < 0.001). The ROC analyses revealed an accuracy of 75.7% in distinguishing PsP from TP+ mixed tumor. Leave-one-out cross-validation test revealed that 73.2% of cases were correctly classified as PsP and TP + mixed tumor. CONCLUSIONS Our multiparametric MRI based prediction model may be helpful in identifying PsP in GBM patients.
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Affiliation(s)
- Laiz Laura de Godoy
- Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Suyash Mohan
- Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Sumei Wang
- Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - MacLean P Nasrallah
- Clinical Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Yu Sakai
- Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Donald M O'Rourke
- Neurosurgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Stephen Bagley
- Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Arati Desai
- Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Laurie A Loevner
- Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Harish Poptani
- Molecular and Clinical Cancer Medicine, University of Liverpool, Liverpool, UK
| | - Sanjeev Chawla
- Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
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Qi D, Li J, Quarles CC, Fonkem E, Wu E. Assessment and prediction of glioblastoma therapy response: challenges and opportunities. Brain 2023; 146:1281-1298. [PMID: 36445396 PMCID: PMC10319779 DOI: 10.1093/brain/awac450] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 11/03/2022] [Accepted: 11/10/2022] [Indexed: 11/30/2022] Open
Abstract
Glioblastoma is the most aggressive type of primary adult brain tumour. The median survival of patients with glioblastoma remains approximately 15 months, and the 5-year survival rate is <10%. Current treatment options are limited, and the standard of care has remained relatively constant since 2011. Over the last decade, a range of different treatment regimens have been investigated with very limited success. Tumour recurrence is almost inevitable with the current treatment strategies, as glioblastoma tumours are highly heterogeneous and invasive. Additionally, another challenging issue facing patients with glioblastoma is how to distinguish between tumour progression and treatment effects, especially when relying on routine diagnostic imaging techniques in the clinic. The specificity of routine imaging for identifying tumour progression early or in a timely manner is poor due to the appearance similarity of post-treatment effects. Here, we concisely describe the current status and challenges in the assessment and early prediction of therapy response and the early detection of tumour progression or recurrence. We also summarize and discuss studies of advanced approaches such as quantitative imaging, liquid biomarker discovery and machine intelligence that hold exceptional potential to aid in the therapy monitoring of this malignancy and early prediction of therapy response, which may decisively transform the conventional detection methods in the era of precision medicine.
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Affiliation(s)
- Dan Qi
- Department of Neurosurgery and Neuroscience Institute, Baylor Scott & White Health, Temple, TX 76502, USA
| | - Jing Li
- School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - C Chad Quarles
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Ekokobe Fonkem
- Department of Neurosurgery and Neuroscience Institute, Baylor Scott & White Health, Temple, TX 76502, USA
- Department of Medical Education, School of Medicine, Texas A&M University, Bryan, TX 77807, USA
| | - Erxi Wu
- Department of Neurosurgery and Neuroscience Institute, Baylor Scott & White Health, Temple, TX 76502, USA
- Department of Medical Education, School of Medicine, Texas A&M University, Bryan, TX 77807, USA
- Department of Pharmaceutical Sciences, Irma Lerma Rangel School of Pharmacy, Texas A&M University, College Station, TX 77843, USA
- Department of Oncology and LIVESTRONG Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA
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Noushmehr H, Herrgott G, Morosini NS, Castro AV. Noninvasive approaches to detect methylation-based markers to monitor gliomas. Neurooncol Adv 2022; 4:ii22-ii32. [PMID: 36380867 PMCID: PMC9650474 DOI: 10.1093/noajnl/vdac021] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/07/2023] Open
Abstract
In this review, we summarize the current approaches used to detect glioma tissue-derived DNA methylation markers in liquid biopsy specimens with the aim to diagnose, prognosticate and potentially track treatment response and evolution of patients with gliomas.
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Affiliation(s)
- Houtan Noushmehr
- Department of Neurosurgery, Omics Laboratory, Henry Ford Health System, Detroit, Michigan, USA
| | - Grayson Herrgott
- Department of Neurosurgery, Omics Laboratory, Henry Ford Health System, Detroit, Michigan, USA
| | - Natalia S Morosini
- Department of Neurosurgery, Omics Laboratory, Henry Ford Health System, Detroit, Michigan, USA
| | - Ana Valeria Castro
- Department of Neurosurgery, Omics Laboratory, Henry Ford Health System, Detroit, Michigan, USA
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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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