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Tariq R. Predicting response to chemotherapy in brain tumor patients based on MRI features. Clin Neurol Neurosurg 2024; 244:108409. [PMID: 38959786 DOI: 10.1016/j.clineuro.2024.108409] [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: 06/20/2024] [Accepted: 06/27/2024] [Indexed: 07/05/2024]
Abstract
Chemotherapy in brain tumors is tailored based on tumor type, grade, and molecular markers, which are crucial for predicting responses and survival outcomes. This review summarizes the role of chemotherapy in gliomas, glioneuronal and neuronal tumors, ependymomas, choroid plexus tumors, medulloblastomas, and meningiomas, discussing standard treatment protocols and recent developments in targeted therapies.Furthermore, the studies reporting the integration of MRI-based radiomics and deep learning models for predicting treatment outcomes are reviewed. Advances in MRI-based radiomics and deep learning models have significantly enhanced the prediction of chemotherapeutic benefits, survival prediction following chemotherapy, and differentiating tumor progression with psuedoprogression. These non-invasive techniques offer valuable insights into tumor characteristics and treatment responses, facilitating personalized therapeutic strategies. Further research is warranted to refine these models and expand their applicability across different brain tumor types.
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Affiliation(s)
- Rabeet Tariq
- Department of Neurosurgery, Section of Surgery, Aga Khan University Hospital, Karachi, Pakistan.
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2
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Huang H, Xie Y, Chen X, Zhang D, Zhang X, Deng Y, Huang Z, Bi H, Hu X, Yan X, Liang H, Lv Z, Sun X, Zhang M, Hu D, Hu F. Identification and validation of DNA methylation-driven gene PCDHB4 as a novel tumor suppressor for glioblastoma diagnosis and prognosis. Mol Carcinog 2023; 62:1832-1845. [PMID: 37560880 DOI: 10.1002/mc.23618] [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: 03/24/2023] [Revised: 06/15/2023] [Accepted: 07/28/2023] [Indexed: 08/11/2023]
Abstract
Aberrant DNA methylation is a critical regulator of gene expression in the development and progression of glioblastoma (GBM). However, the impact of methylation-driven gene PCDHB4 changes on GBM occurrence and progression remains unclear. Therefore, this study aimed to identify the PCDHB4 gene for early diagnosis and prognostic evaluation and clarify its functional role in GBM. Methylation-driven gene PCDHB4 was selected for GBM using the multi-omics integration method based on publicly available data sets. The diagnostic capabilities of PCDHB4 methylation and 5-hydroxymethylcytosines were validated in tissue and blood cell-free DNA (cfDNA) samples, respectively. Combined survival analysis of PCDHB4 methylation and immune infiltration cells evaluated the prognostic predictive performance of GBM patients. We identified that the PCDHB4 gene achieved high discriminative capabilities for GBM and normal tissues with an area under the curve value of 0.941. PCDHB4 hypermethylation was observed in cfDNA blood samples from GBM patients. Compared with GBM patients with PCDHB4 hypermethylation level, patients with PCDHB4 hypomethylation level had significantly poorer overall survival (p = 0.035). In addition, GBM patients with PCDHB4 hypermethylation and high infiltration of CD4+ T cell activation level had a favorable survival (p = 0.026). Moreover, we demonstrated that mRNA expression of PCDHB4 was downregulated in GBM tissues and upregulated in GBM cell lines with PCDHB4 demethylation, and PCDHB4 overexpression inhibited GBM cell proliferation and migration. In summary, we discovered a novel methylation-driven gene PCDHB4 for the diagnosis and prognosis of GBM and demonstrated that PCDHB4 is a tumor suppressor in vitro experiments.
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Affiliation(s)
- Hao Huang
- Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Medical School, Shenzhen, Guangdong, People's Republic of China
- Department of General Practice, The Affiliated Luohu Hospital of Shenzhen University Medical School, Shenzhen, Guangdong, People's Republic of China
| | - Yilin Xie
- Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Medical School, Shenzhen, Guangdong, People's Republic of China
| | - Xi Chen
- Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Medical School, Shenzhen, Guangdong, People's Republic of China
| | - Dongdong Zhang
- Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Medical School, Shenzhen, Guangdong, People's Republic of China
- Department of General Practice, The Affiliated Luohu Hospital of Shenzhen University Medical School, Shenzhen, Guangdong, People's Republic of China
| | - Xueying Zhang
- Department of General Practice, The Affiliated Luohu Hospital of Shenzhen University Medical School, Shenzhen, Guangdong, People's Republic of China
| | - Ying Deng
- Department of Epidemiology and Health Statistics, Fujian Provincial Key Laboratory of Environment Factors and Cancer, School of Public Health, Fujian Medical University, Fuzhou, Fujian, People's Republic of China
| | - Zhicong Huang
- Department of Epidemiology and Health Statistics, Fujian Provincial Key Laboratory of Environment Factors and Cancer, School of Public Health, Fujian Medical University, Fuzhou, Fujian, People's Republic of China
| | - Haoran Bi
- Department of Biostatistics, Xuzhou Medical University, Xuzhou, Jiangsu, People's Republic of China
| | - Xing Hu
- Department of General Practice, The Affiliated Luohu Hospital of Shenzhen University Medical School, Shenzhen, Guangdong, People's Republic of China
| | - Xiangwei Yan
- Department of Oncology Radiotherapy, Hainan Cancer Hospital, Haikou, Hainan, People's Republic of China
| | - Hongsheng Liang
- Department of Neurosurgery, First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, People's Republic of China
| | - Zhonghua Lv
- Department of Neurosurgery, Third Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, People's Republic of China
| | - Xizhuo Sun
- Department of General Practice, The Affiliated Luohu Hospital of Shenzhen University Medical School, Shenzhen, Guangdong, People's Republic of China
| | - Ming Zhang
- Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Medical School, Shenzhen, Guangdong, People's Republic of China
| | - Dongsheng Hu
- Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Medical School, Shenzhen, Guangdong, People's Republic of China
- Department of General Practice, The Affiliated Luohu Hospital of Shenzhen University Medical School, Shenzhen, Guangdong, People's Republic of China
| | - Fulan Hu
- Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Medical School, Shenzhen, Guangdong, People's Republic of China
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3
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Grand S, Nedunchelian M, Charara S, Demaison R, Jean C, Galloux A, Kastler A, Attye A, Berthet C, Krainik A. Tumor or not a tumor: Pitfalls and differential diagnosis in neuro-oncology. Rev Neurol (Paris) 2023; 179:378-393. [PMID: 37030987 DOI: 10.1016/j.neurol.2023.03.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 03/16/2023] [Accepted: 03/17/2023] [Indexed: 04/08/2023]
Abstract
The majority of intracranial expansive lesions are tumors. However, a wide range of lesions can mimic neoplastic pathology. Differentiating pseudotumoral lesions from brain tumors is crucial to patient management. This article describes the most common intracranial pseudotumors, with a focus on the imaging features that serve as clues to detect pseudotumors.
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Seyve A, Dehais C, Chinot O, Djelad A, Cohen-Moyal E, Bronnimann C, Gourmelon C, Emery E, Colin P, Boone M, Vauléon E, Langlois O, di Stefano AL, Seizeur R, Ghiringhelli F, D’Hombres A, Feuvret L, Guyotat J, Capelle L, Carpentier C, Garnier L, Honnorat J, Meyronet D, Mokhtari K, Figarella-Branger D, Ducray F. Incidence and characteristics of pseudoprogression in IDH-mutant high-grade gliomas: A POLA network study. Neuro Oncol 2023; 25:495-507. [PMID: 35953421 PMCID: PMC10013645 DOI: 10.1093/neuonc/noac194] [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: 06/07/2022] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Incidence and characteristics of pseudoprogression in isocitrate dehydrogenase-mutant high-grade gliomas (IDHmt HGG) remain to be specifically described. METHODS We analyzed pseudoprogression characteristics and explored the possibility of pseudoprogression misdiagnosis in IDHmt HGG patients, treated with radiotherapy (RT) (with or without chemotherapy [CT]), included in the French POLA network. Pseudoprogression was analyzed in patients with MRI available for review (reference cohort, n = 200). Pseudoprogression misdiagnosis was estimated in this cohort and in an independent cohort (control cohort, n = 543) based on progression-free survival before and after first progression. RESULTS In the reference cohort, 38 patients (19%) presented a pseudoprogression after a median time of 10.5 months after RT. Pseudoprogression characteristics were similar across IDHmt HGG subtypes. In most patients, it consisted of the appearance of one or several infracentimetric, asymptomatic, contrast-enhanced lesions occurring within 2 years after RT. The only factor associated with pseudoprogression occurrence was adjuvant PCV CT. Among patients considered as having a first true progression, 7 out of 41 (17%) in the reference cohort and 35 out of 203 (17%) in the control cohort were retrospectively suspected to have a misdiagnosed pseudoprogression. Patients with a misdiagnosed pseudoprogression were characterized by a time to event and an outcome similar to that of patients with a pseudoprogression but presented with larger and more symptomatic lesions. CONCLUSION In patients with an IDHmt HGG, pseudoprogression occurs later than in IDH-wildtype glioblastomas and seems not only frequent but also frequently misdiagnosed. Within the first 2 years after RT, the possibility of a pseudoprogression should be carefully considered.
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Affiliation(s)
- Antoine Seyve
- Department of Neuro-Oncology, East Group Hospital, Hospices Civils de Lyon, Lyon, France
| | - Caroline Dehais
- Department of Neurology 2-Mazarin, APHP, University Hospital Pitié Salpêtrière-Charles Foix, Paris, France
| | - Olivier Chinot
- Department of Neuro-Oncology, AP-HM, University Hospital Timone, Marseille, France
| | - Apolline Djelad
- Department of Neurosurgery, University Hospital of Lille, Lille, France
| | - Elisabeth Cohen-Moyal
- Department of Radiotherapy, Claudius Regaud Institut, Cancer University Institut of Toulouse, Oncopole 1, Paul Sabatier University, Toulouse III, Toulouse, France
| | - Charlotte Bronnimann
- Department of Medical Oncology, University Hospital of Bordeaux, Bordeaux, France
| | - Carole Gourmelon
- Department of Medical Oncology, West Cancerology Institut René Gauducheau, Saint-Herblain, France
| | - Evelyne Emery
- Department of Neurosurgery, Caen University Hospital, Caen, France
| | - Philippe Colin
- Department of Radiotherapy, Courlancy Institut of Cancer, Rouen, France
| | - Mathieu Boone
- Medical Oncology Department, Amiens University Hospital, Amiens, France
| | | | - Olivier Langlois
- Department of Neurosurgery, University Hospital of Rouen, Rouen, France
| | | | - Romuald Seizeur
- Neurosurgery Department, Hôpital de la cavale blanche, CHU Brest, Brest, France
| | | | - Anne D’Hombres
- Department of Radiotherapy, South Group Hospital, Hospices Civils de Lyon, Lyon, France
| | - Loic Feuvret
- Department of Radiotherapy, APHP, University Hospital Pitié Salpêtrière-Charles Foix, Paris, France
| | - Jacques Guyotat
- Department of Neurosurgery, East Group Hospital, Hospices Civils de Lyon, Lyon, France
| | - Laurent Capelle
- Department of Neurosurgery, APHP, University Hospital Pitié Salpêtrière-Charles Foix, Paris, France
| | - Catherine Carpentier
- Department of Neurology 2-Mazarin, National Institute of Health and Medical Research (Inserm), CNRS, Brain and Spinal Cord Institute, University Hospital Pitié Salpêtrière-Charles Foix, Sorbonne University, Paris, France
| | - Louis Garnier
- Department of Neuro-Oncology, East Group Hospital, Hospices Civils de Lyon, Lyon, France
| | - Jérôme Honnorat
- Department of Neuro-Oncology, East Group Hospital, Hospices Civils de Lyon, Lyon, France
- SynatAc Team, Institute NeuroMyoGène, MeLis INSERM U1314/CNRS UMR 5284, Université de Lyon, Université Claude Bernard Lyon 1, Lyon, France
| | - David Meyronet
- Pathology Department, East Group Hospital, Hospices Civils de Lyon, Lyon, France
- Centre de recherche en Cancérologie de Lyon, INSERM U1052, CNRS UMR 5286, Cancer Cell Plasticity Department, Transcriptome Diversity in Stem Cells Laboratory, Lyon, France
| | - Karima Mokhtari
- Pathology Department, APHP, University Hospital Pitié Salpêtrière-Charles Foix, Paris, France
| | - Dominique Figarella-Branger
- APHM, CNRS, INP, Inst Neurophysiopathol, CHU Timone, Service d’Anatomie Pathologique et de Neuropathologie, Aix-Marseille University, Marseille, France
| | - François Ducray
- Department of Neuro-Oncology, East Group Hospital, Hospices Civils de Lyon, Lyon, France
- Centre de recherche en Cancérologie de Lyon, INSERM U1052, CNRS UMR 5286, Cancer Cell Plasticity Department, Transcriptome Diversity in Stem Cells Laboratory, Lyon, France
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5
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McKenney AS, Weg E, Bale TA, Wild AT, Um H, Fox MJ, Lin A, Yang JT, Yao P, Birger ML, Tixier F, Sellitti M, Moss NS, Young RJ, Veeraraghavan H. Radiomic Analysis to Predict Histopathologically Confirmed Pseudoprogression in Glioblastoma Patients. Adv Radiat Oncol 2023; 8:100916. [PMID: 36711062 PMCID: PMC9873493 DOI: 10.1016/j.adro.2022.100916] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 01/18/2022] [Indexed: 02/01/2023] Open
Abstract
Purpose Pseudoprogression mimicking recurrent glioblastoma remains a diagnostic challenge that may adversely confound or delay appropriate treatment or clinical trial enrollment. We sought to build a radiomic classifier to predict pseudoprogression in patients with primary isocitrate dehydrogenase wild type glioblastoma. Methods and Materials We retrospectively examined a training cohort of 74 patients with isocitrate dehydrogenase wild type glioblastomas with brain magnetic resonance imaging including dynamic contrast enhanced T1 perfusion before resection of an enhancing lesion indeterminate for recurrent tumor or pseudoprogression. A recursive feature elimination random forest classifier was built using nested cross-validation without and with O6-methylguanine-DNA methyltransferase status to predict pseudoprogression. Results A classifier constructed with cross-validation on the training cohort achieved an area under the receiver operating curve of 81% for predicting pseudoprogression. This was further improved to 89% with the addition of O6-methylguanine-DNA methyltransferase status into the classifier. Conclusions Our results suggest that radiomic analysis of contrast T1-weighted images and magnetic resonance imaging perfusion images can assist the prompt diagnosis of pseudoprogression. Validation on external and independent data sets is necessary to verify these advanced analyses, which can be performed on routinely acquired clinical images and may help inform clinical treatment decisions.
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Affiliation(s)
- Anna Sophia McKenney
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Radiology, New York-Presbyterian Hospital/Weill Cornell Medical Center, New York, New York
| | - Emily Weg
- Department of Radiation Oncology, University of Washington, Seattle, Washington
| | - Tejus A. Bale
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
- Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Aaron T. Wild
- Department Southeast Radiation Oncology, Levine Cancer Institute, Charlotte, North Carolina
| | - Hyemin Um
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Michael J. Fox
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Andrew Lin
- Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Jonathan T. Yang
- Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Peter Yao
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Maxwell L. Birger
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Florent Tixier
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Matthew Sellitti
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Nelson S. Moss
- Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Neurosurgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Robert J. Young
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Radiology, New York-Presbyterian Hospital/Weill Cornell Medical Center, New York, New York
- Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, New York
- Corresponding author: Robert J. Young, MD
| | - Harini Veeraraghavan
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
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6
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Flies CM, van Leuken KH, Voorde MT, Verhoeff JJC, De Vos FYF, Seute T, Robe PA, Witkamp TD, Hendrikse J, Dankbaar JW, Snijders TJ. Conventional MRI Criteria to Differentiate Progressive Disease from Treatment-Induced Effects in High-Grade (WHO Grade 3-4) Gliomas. Neurology 2022; 99:e77-e88. [PMID: 35437259 PMCID: PMC9259090 DOI: 10.1212/wnl.0000000000200359] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 02/22/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND Post-treatment radiological deterioration of patients with an irradiated high-grade (WHO grade 3-4) glioma (HGG) may be the result of true progressive disease (PD) or treatment-induced effects (TIE). Differentiation between these entities is of great importance, but remains a diagnostic challenge. This study assesses the diagnostic value of conventional MRI characteristics to differentiate PD from TIE in HGGs. MATERIAL AND METHODS In this single-centre, retrospective, consecutive cohort study, we included adults with a HGG, who were treated with (chemo-)radiotherapy and subsequently developed a new or increasing contrast-enhancing lesion on conventional follow-up MRI. TIE and PD were defined radiologically as stable/decreased for ≥6 weeks or RANO-progression, and histologically as TIE without viable tumour or PD. Two neuroradiologists assessed twenty-one preselected MRI characteristics of the progressive lesions. The statistical analysis included logistic regression to develop a) a full multivariable model b) a diagnostic model with model reduction, and a Cohen's Kappa interrater reliability (IRR) coefficient. RESULTS 210 patients (median age=61, IQR=54-68, 189 males) with 284 lesions were included, of which 141 (50%) had PD. Median time to PD was 2 (0.7-6.1) and to TIE 0.9 (0.7-3.5) months after radiotherapy. After multivariable modelling and model reduction, the following determinants prevailed: Radiation dose (Odds ratio (OR)=0.68, 95%-CI=0.49-0.93), longer time to progression (TTP, OR=3.56, 95%-CI=1.84-6.88), marginal enhancement (OR=2.04, 95%-CI=1.09-3.83), soap bubble enhancement (OR=2.63, 95%-CI=1.39-4.98) and isointense apparent diffusion coefficient (ADC)-signal (OR=2.11, 95%-CI=1.05-4.24). ORs>1 indicate higher odds of PD. The Hosmer&Lemeshow test showed good calibration (p=0.947) and the area under the ROC-curve was 0.722 (95%-CI=0.66-0.78). In the glioblastoma subgroup, TTP, marginal enhancement and ADC-signal were significant. IRR analysis between neuroradiologists revealed moderate to near-perfect agreement for the predictive items, but poor agreement for others. DISCUSSION Several characteristics from conventional MRI are significant predictors for the discrimination between PD and TIE. However, IRR was variable. Conventional MRI characteristics from this study should be incorporated into a multimodal diagnostic model with advanced imaging techniques. CLASSIFICATION OF EVIDENCE This study provides Class II evidence that in patients with irradiated HGGs, radiation dose, longer time to progression, marginal enhancement, soap bubble enhancement and isointense apparent ADC-signal distinguish PD from TIE.
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Affiliation(s)
- Christina M Flies
- Department of Neurology & Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
| | - Karlijn H van Leuken
- Department of Neurology & Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands.,Stichting Beroepsopleiding Huisarts, the Netherlands
| | - Marlies Ten Voorde
- Department of Neurology & Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands.,Mission of the Netherlands Reformed Congregations, in Guinea (Conakry)
| | - Joost J C Verhoeff
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Filip Y F De Vos
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Tatjana Seute
- Department of Neurology & Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
| | - Pierre A Robe
- Department of Neurology & Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
| | - Theodoor D Witkamp
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jeroen Hendrikse
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jan Willem Dankbaar
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Tom J Snijders
- Department of Neurology & Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
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Bodensohn R, Forbrig R, Quach S, Reis J, Boulesteix AL, Mansmann U, Hadi I, Fleischmann D, Mücke J, Holzgreve A, Albert N, Ruf V, Dorostkar M, Corradini S, Herms J, Belka C, Thon N, Niyazi M. MRI-based contrast clearance analysis shows high differentiation accuracy between radiation-induced reactions and progressive disease after cranial radiotherapy. ESMO Open 2022; 7:100424. [PMID: 35248822 PMCID: PMC9058918 DOI: 10.1016/j.esmoop.2022.100424] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 01/31/2022] [Accepted: 02/01/2022] [Indexed: 11/26/2022] Open
Abstract
Background Pseudoprogression (PsP) or radiation necrosis (RN) may frequently occur after cranial radiotherapy and show a similar imaging pattern compared with progressive disease (PD). We aimed to evaluate the diagnostic accuracy of magnetic resonance imaging-based contrast clearance analysis (CCA) in this clinical setting. Patients and methods Patients with equivocal imaging findings after cranial radiotherapy were consecutively included into this monocentric prospective study. CCA was carried out by software-based automated subtraction of imaging features in late versus early T1-weighted sequences after contrast agent application. Two experienced neuroradiologists evaluated CCA with respect to PsP/RN and PD being blinded for histological findings. The radiological assessment was compared with the histopathological results, and its accuracy was calculated statistically. Results A total of 33 patients were included; 16 (48.5%) were treated because of a primary brain tumor (BT), and 17 (51.1%) because of a secondary BT. In one patient, CCA was technically infeasible. The accuracy of CCA in predicting the histological result was 0.84 [95% confidence interval (CI) 0.67-0.95; one-sided P = 0.051; n = 32]. Sensitivity and specificity of CCA were 0.93 (95% CI 0.66-1.00) and 0.78 (95% CI 0.52-0.94), respectively. The accuracy in patients with secondary BTs was 0.94 (95% CI 0.71-1.00) and nonsignificantly higher compared with patients with primary BT with an accuracy of 0.73 (95% CI 0.45-0.92), P = 0.16. Conclusions In this study, CCA was a highly accurate, easy, and helpful method for distinguishing PsP or RN from PD after cranial radiotherapy, especially in patients with secondary tumors after radiosurgical treatment. CCA is accurate in distinguishing treatment reactions from true PD. CCA was more accurate for irradiated metastases than primary BTs. CCA is not feasible for lesions with no contrast media uptake.
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8
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Le Fèvre C, Lhermitte B, Ahle G, Chambrelant I, Cebula H, Antoni D, Keller A, Schott R, Thiery A, Constans JM, Noël G. Pseudoprogression versus true progression in glioblastoma patients: A multiapproach literature review: Part 1 - Molecular, morphological and clinical features. Crit Rev Oncol Hematol 2020; 157:103188. [PMID: 33307200 DOI: 10.1016/j.critrevonc.2020.103188] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 11/12/2020] [Accepted: 11/23/2020] [Indexed: 01/04/2023] Open
Abstract
With new therapeutic protocols, more patients treated for glioblastoma have experienced a suspicious radiologic image of progression (pseudoprogression) during follow-up. Pseudoprogression should be differentiated from true progression because the disease management is completely different. In the case of pseudoprogression, the follow-up continues, and the patient is considered stable. In the case of true progression, a treatment adjustment is necessary. Presently, a pseudoprogression diagnosis certainly needs to be pathologically confirmed. Some important efforts in the radiological, histopathological, and genomic fields have been made to differentiate pseudoprogression from true progression, and the assessment of response criteria exists but remains limited. The aim of this paper is to highlight clinical and pathological markers to differentiate pseudoprogression from true progression through a literature review.
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Affiliation(s)
- Clara Le Fèvre
- Department of Radiotherapy, ICANS, Institut Cancérologie Strasbourg Europe, 17 Rue Albert Calmette, 67200, Strasbourg Cedex, France
| | - Benoît Lhermitte
- Département of Pathology, Hautepierre University Hospital, 1, Avenue Molière, 67200, Strasbourg, France
| | - Guido Ahle
- Departement of Neurology, Hôpitaux Civils de Colmar, 39 Avenue de la Liberté, 68024, Colmar, France
| | - Isabelle Chambrelant
- Department of Radiotherapy, ICANS, Institut Cancérologie Strasbourg Europe, 17 Rue Albert Calmette, 67200, Strasbourg Cedex, France
| | - Hélène Cebula
- Departement of Neurosurgery, Hautepierre University Hospital, 1, Avenue Molière, 67200, Strasbourg, France
| | - Delphine Antoni
- Department of Radiotherapy, ICANS, Institut Cancérologie Strasbourg Europe, 17 Rue Albert Calmette, 67200, Strasbourg Cedex, France
| | - Audrey Keller
- Department of Radiotherapy, ICANS, Institut Cancérologie Strasbourg Europe, 17 Rue Albert Calmette, 67200, Strasbourg Cedex, France
| | - Roland Schott
- Departement of Medical Oncology, ICANS, Institut Cancérologie Strasbourg Europe, 17 rue Albert Calmette, 67200, Strasbourg Cedex, France
| | - Alicia Thiery
- Department of Public Health, ICANS, Institut Cancérologie Strasbourg Europe, 17 rue Albert Calmette, 67200, Strasbourg Cedex, France
| | - Jean-Marc Constans
- Department of Radiology, Amiens-Pïcardie University Hospital, 1 rond point du Professeur Christian Cabrol, 80054 Amiens Cedex 1, France
| | - Georges Noël
- Department of Radiotherapy, ICANS, Institut Cancérologie Strasbourg Europe, 17 Rue Albert Calmette, 67200, Strasbourg Cedex, France.
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9
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Sharma HS, Muresanu DF, Castellani RJ, Nozari A, Lafuente JV, Tian ZR, Sahib S, Bryukhovetskiy I, Bryukhovetskiy A, Buzoianu AD, Patnaik R, Wiklund L, Sharma A. Pathophysiology of blood-brain barrier in brain tumor. Novel therapeutic advances using nanomedicine. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2020; 151:1-66. [PMID: 32448602 DOI: 10.1016/bs.irn.2020.03.001] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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