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Laprie A, Noel G, Chaltiel L, Truc G, Sunyach MP, Charissoux M, Magne N, Auberdiac P, Biau J, Ken S, Tensaouti F, Khalifa J, Sidibe I, Roux FE, Vieillevigne L, Catalaa I, Boetto S, Uro-Coste E, Supiot S, Bernier V, Filleron T, Mounier M, Poublanc M, Olivier P, Delord JP, Cohen-Jonathan-Moyal E. Randomized phase III trial of metabolic imaging-guided dose escalation of radio-chemotherapy in patients with newly diagnosed glioblastoma (SPECTRO GLIO trial). Neuro Oncol 2024; 26:153-163. [PMID: 37417948 PMCID: PMC10768994 DOI: 10.1093/neuonc/noad119] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Indexed: 07/08/2023] Open
Abstract
BACKGROUND Glioblastoma (GBM) systematically recurs after a standard 60 Gy radio-chemotherapy regimen. Since magnetic resonance spectroscopic imaging (MRSI) has been shown to predict the site of relapse, we analyzed the effect of MRSI-guided dose escalation on overall survival (OS) of patients with newly diagnosed GBM. METHODS In this multicentric prospective phase III trial, patients who had undergone biopsy or surgery for a GBM were randomly assigned to a standard dose (SD) of 60 Gy or a high dose (HD) of 60 Gy with an additional simultaneous integrated boost totaling 72 Gy to MRSI metabolic abnormalities, the tumor bed and residual contrast enhancements. Temozolomide was administered concomitantly and maintained for 6 months thereafter. RESULTS One hundred and eighty patients were included in the study between March 2011 and March 2018. After a median follow-up of 43.9 months (95% CI [42.5; 45.5]), median OS was 22.6 months (95% CI [18.9; 25.4]) versus 22.2 months (95% CI [18.3; 27.8]) for HD, and median progression-free survival was 8.6 (95% CI [6.8; 10.8]) versus 7.8 months (95% CI [6.3; 8.6]), in SD versus HD, respectively. No increase in toxicity rate was observed in the study arm. The pseudoprogression rate was similar across the SD (14.4%) and HD (16.7%) groups. For O(6)-methylguanine-DNA methyltransferase (MGMT) methylated patients, the median OS was 38 months (95% CI [23.2; NR]) for HD patients versus 28.5 months (95% CI [21.1; 35.7]) for SD patients. CONCLUSION The additional MRSI-guided irradiation dose totaling 72 Gy was well tolerated but did not improve OS in newly diagnosed GBM. TRIAL REGISTRATION NCT01507506; registration date: December 20, 2011. https://clinicaltrials.gov/ct2/show/NCT01507506?cond=NCT01507506&rank=1.
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Affiliation(s)
- Anne Laprie
- Institut Claudius Regaud, Institut Universitaire du Cancer de Toulouse-Oncopole, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, Toulouse, France
| | | | - Leonor Chaltiel
- Institut Claudius Regaud, Institut Universitaire du Cancer de Toulouse-Oncopole, Toulouse, France
| | - Gilles Truc
- Centre Georges-François Leclerc, Dijon, France
| | | | | | - Nicolas Magne
- Institut de Cancérologie de la Loire, Saint-Priest en Jarez, France
| | | | - Julian Biau
- Centre Jean-Perrin, Clermont-Ferrand, France
| | - Soléakhéna Ken
- Institut Claudius Regaud, Institut Universitaire du Cancer de Toulouse-Oncopole, RadOpt-CRCT-INSERM, Toulouse, France
| | - Fatima Tensaouti
- Institut Claudius Regaud, Institut Universitaire du Cancer de Toulouse-Oncopole & ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, Toulouse, France
| | - Jonathan Khalifa
- Institut Claudius Regaud, Institut Universitaire du Cancer de Toulouse-Oncopole, Toulouse, France
| | | | - Franck-Emmanuel Roux
- Centre Hospitalier Universitaire de Toulouse, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, Toulouse, France
| | - Laure Vieillevigne
- Institut Claudius Regaud, Institut Universitaire du Cancer de Toulouse-Oncopole, Toulouse, France
| | | | - Sergio Boetto
- Centre Hospitalier Universitaire de Toulouse, Toulouse, France
| | - Emmanuelle Uro-Coste
- Centre Hospitalier Universitaire de Toulouse, Institut Universitaire du Cancer de Toulouse-Oncopole, RadOpt-CRCT-INSERM, Toulouse, France
| | - Stéphane Supiot
- Institut de Cancerologie de l’Ouest, Nantes st Herblain, France
| | - Valérie Bernier
- Institut de Cancérologie de Lorraine Centre Alexis Vautrin, Nancy, France
| | - Thomas Filleron
- Institut Claudius Regaud, Institut Universitaire du Cancer de Toulouse-Oncopole, Toulouse, France
| | - Muriel Mounier
- Institut Claudius Regaud, Institut Universitaire du Cancer de Toulouse-Oncopole, Toulouse, France
| | - Muriel Poublanc
- Institut Claudius Regaud, Institut Universitaire du Cancer de Toulouse-Oncopole, Toulouse, France
| | - Pascale Olivier
- Service de Pharmacologie Médicale et Clinique, Centre Régional de Pharmacovigilance, de Pharmacoépidémiologie et d’Information sur le Médicament CHU de Toulouse, Toulouse, France
| | - Jean-Pierre Delord
- Institut Claudius Regaud, Institut Universitaire du Cancer de Toulouse-Oncopole, Toulouse, France
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Liu J, Cong C, Zhang J, Qiao J, Guo H, Wu H, Sang Z, Kang H, Fang J, Zhang W. Multimodel habitats constructed by perfusion and/or diffusion MRI predict isocitrate dehydrogenase mutation status and prognosis in high-grade gliomas. Clin Radiol 2024; 79:e127-e136. [PMID: 37923627 DOI: 10.1016/j.crad.2023.09.025] [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: 03/21/2023] [Revised: 08/15/2023] [Accepted: 09/22/2023] [Indexed: 11/07/2023]
Abstract
AIM To determine whether tumour vascular and cellular heterogeneity of high-grade glioma (HGG) is predictive of isocitrate dehydrogenase (IDH) mutation status and overall survival (OS) by using tumour habitat-based analysis constructed by perfusion and/or diffusion magnetic resonance imaging (MRI). MATERIALS AND METHODS Seventy-eight HGG patients that met the 2021 World Health Organization WHO Classification of Tumors of the Central Nervous System, 5th edition (WHO CNS5), were enrolled to predict IDH mutation status, of which 32 grade 4 patients with unmethylated O6-methylguanine-DNA methyltransferase (MGMT) promoter were enrolled for prognostic analysis. The deep-learning-based model nnU-Net and K-means clustering algorithm were applied to construct the Traditional Habitat, Vascular Habitat (VH), Cellular Density Habitat (DH), and their Combined Habitat (CH). Quantitative parameters were extracted and compared between IDH-mutant and IDH-wild-type patients, respectively, and the prediction potential was evaluated by receiver operating characteristic (ROC) curve analysis. OS was analysed using Kaplan-Meier survival analysis and the log-rank test. RESULTS Compared with IDH-mutants, median relative cerebral blood volume (rCBVmedian) values in the whole enhancing tumour (WET), VH1, VH3, CH1-4 habitats were significantly increased in IDH-wild-type HGGs (all p<0.05). Additionally, the accuracy of rCBVmedian values in CH1 outperformed other habitats in identifying IDH mutation status (p<0.001) at a cut-off value of 4.83 with AUC of 0.815. Kaplan-Meier survival analysis highlighted significant differences in OS between the populations dichotomised by the median of rCBVmedian in WET, VH1, CH1-3 habitats (all p<0.05). CONCLUSIONS The habitat imaging technique may improve the accuracy of predicting IDH mutation status and prognosis, and even provide a new direction for subsequent personalised precision treatment.
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Affiliation(s)
- J Liu
- Department of Radiology, Daping Hospital, Army Medical University, Chongqing, 400042, China; Chongqing Clinical Research Center for Imaging and Nuclear Medicine, Chongqing, 400042, China
| | - C Cong
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, 400042, China; School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing, 400054, China
| | - J Zhang
- Department of Radiology, General Hospital of Western Theater Command of PLA, Chengdu, 600083, China
| | - J Qiao
- Department of Radiology, Daping Hospital, Army Medical University, Chongqing, 400042, China; Chongqing Clinical Research Center for Imaging and Nuclear Medicine, Chongqing, 400042, China
| | - H Guo
- Department of Radiology, Daping Hospital, Army Medical University, Chongqing, 400042, China; Chongqing Clinical Research Center for Imaging and Nuclear Medicine, Chongqing, 400042, China
| | - H Wu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400042, China
| | - Z Sang
- Department of Radiology, Daping Hospital, Army Medical University, Chongqing, 400042, China; Chongqing Clinical Research Center for Imaging and Nuclear Medicine, Chongqing, 400042, China
| | - H Kang
- Department of Radiology, Daping Hospital, Army Medical University, Chongqing, 400042, China; Chongqing Clinical Research Center for Imaging and Nuclear Medicine, Chongqing, 400042, China
| | - J Fang
- Chongqing Clinical Research Center for Imaging and Nuclear Medicine, Chongqing, 400042, China; Department of Ultrasound, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - W Zhang
- Department of Radiology, Daping Hospital, Army Medical University, Chongqing, 400042, China; Chongqing Clinical Research Center for Imaging and Nuclear Medicine, Chongqing, 400042, China.
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Heo D, Lee J, Yoo RE, Choi SH, Kim TM, Park CK, Park SH, Won JK, Lee JH, Lee ST, Choi KS, Lee JY, Hwang I, Kang KM, Yun TJ. Deep learning based on dynamic susceptibility contrast MR imaging for prediction of local progression in adult-type diffuse glioma (grade 4). Sci Rep 2023; 13:13864. [PMID: 37620555 PMCID: PMC10449894 DOI: 10.1038/s41598-023-41171-9] [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: 02/23/2023] [Accepted: 08/23/2023] [Indexed: 08/26/2023] Open
Abstract
Adult-type diffuse glioma (grade 4) has infiltrating nature, and therefore local progression is likely to occur within surrounding non-enhancing T2 hyperintense areas even after gross total resection of contrast-enhancing lesions. Cerebral blood volume (CBV) obtained from dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) is a parameter that is well-known to be a surrogate marker of both histologic and angiographic vascularity in tumors. We built two nnU-Net deep learning models for prediction of early local progression in adult-type diffuse glioma (grade 4), one using conventional MRI alone and one using multiparametric MRI, including conventional MRI and DSC-PWI. Local progression areas were annotated in a non-enhancing T2 hyperintense lesion on preoperative T2 FLAIR images, using the follow-up contrast-enhanced (CE) T1-weighted (T1W) images as the reference standard. The sensitivity was doubled with the addition of nCBV (80% vs. 40%, P = 0.02) while the specificity was decreased nonsignificantly (29% vs. 48%, P = 0.39), suggesting that fewer cases of early local progression would be missed with the addition of nCBV. While the diagnostic performance of CBV model is still poor and needs improving, the multiparametric deep learning model, which presumably learned from the subtle difference in vascularity between early local progression and non-progression voxels within perilesional T2 hyperintensity, may facilitate risk-adapted radiotherapy planning in adult-type diffuse glioma (grade 4) patients.
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Affiliation(s)
- Donggeon Heo
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jisoo Lee
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Roh-Eul Yoo
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea.
- Department of Radiology, Seoul National University Hospital, 101, Daehangno, Jongno-Gu, Seoul, 03080, Republic of Korea.
| | - Seung Hong Choi
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea.
- Department of Radiology, Seoul National University Hospital, 101, Daehangno, Jongno-Gu, Seoul, 03080, Republic of Korea.
- Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul, Republic of Korea.
- School of Chemical and Biological Engineering, Seoul National University, 1, Gwanak-Ro, Gwanak-Gu, Seoul, 302-909, Republic of Korea.
| | - Tae Min Kim
- Department of Internal Medicine, Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Chul-Kee Park
- Department of Neurosurgery, Biomedical Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Sung-Hye Park
- Department of Pathology, Seoul National University College of Medicine, Seoul, Korea
| | - Jae-Kyung Won
- Department of Pathology, Seoul National University College of Medicine, Seoul, Korea
| | - Joo Ho Lee
- Department of Radiation Oncology, Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Soon Tae Lee
- Department of Neurology, Seoul National University College of Medicine, Seoul, Korea
| | - Kyu Sung Choi
- Department of Radiology, Seoul National University Hospital, 101, Daehangno, Jongno-Gu, Seoul, 03080, Republic of Korea
| | - Ji Ye Lee
- Department of Radiology, Seoul National University Hospital, 101, Daehangno, Jongno-Gu, Seoul, 03080, Republic of Korea
| | - Inpyeong Hwang
- Department of Radiology, Seoul National University Hospital, 101, Daehangno, Jongno-Gu, Seoul, 03080, Republic of Korea
| | - Koung Mi Kang
- Department of Radiology, Seoul National University Hospital, 101, Daehangno, Jongno-Gu, Seoul, 03080, Republic of Korea
| | - Tae Jin Yun
- Department of Radiology, Seoul National University Hospital, 101, Daehangno, Jongno-Gu, Seoul, 03080, Republic of Korea
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Tensaouti F, Desmoulin F, Gilhodes J, Roques M, Ken S, Lotterie JA, Noël G, Truc G, Sunyach MP, Charissoux M, Magné N, Lubrano V, Péran P, Cohen-Jonathan Moyal E, Laprie A. Is pre-radiotherapy metabolic heterogeneity of glioblastoma predictive of progression-free survival? Radiother Oncol 2023; 183:109665. [PMID: 37024057 DOI: 10.1016/j.radonc.2023.109665] [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: 08/12/2022] [Revised: 03/25/2023] [Accepted: 03/28/2023] [Indexed: 04/08/2023]
Abstract
BACKGROUND AND PURPOSE All glioblastoma subtypes share the hallmark of aggressive invasion, meaning that it is crucial to identify their different components if we are to ensure effective treatment and improve survival. Proton MR spectroscopic imaging (MRSI) is a noninvasive technique that yields metabolic information and is able to identify pathological tissue with high accuracy. The aim of the present study was to identify clusters of metabolic heterogeneity, using a large MRSI dataset, and determine which of these clusters are predictive of progression-free survival (PFS). MATERIALS AND METHODS MRSI data of 180 patients acquired in a pre-radiotherapy examination were included in the prospective SPECTRO-GLIO trial. Eight features were extracted for each spectrum: Cho/NAA, NAA/Cr, Cho/Cr, Lac/NAA, and the ratio of each metabolite to the sum of all the metabolites. Clustering of data was performed using a mini-batch k-means algorithm. The Cox model and logrank test were used for PFS analysis. RESULTS Five clusters were identified as sharing similar metabolic information and being predictive of PFS. Two clusters revealed metabolic abnormalities. PFS was lower when Cluster 2 was the dominant cluster in patients' MRSI data. Among the metabolites, lactate (present in this cluster and in Cluster 5) was the most statistically significant predictor of poor outcome. CONCLUSION Results showed that pre-radiotherapy MRSI can be used to reveal tumor heterogeneity. Groups of spectra, which have the same metabolic information, reflect the different tissue components representative of tumor burden proliferation and hypoxia. Clusters with metabolic abnormalities and high lactate are predictive of PFS.
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Affiliation(s)
- Fatima Tensaouti
- Institut Claudius Regaud/Institut Universitaire du Cancer de Toulouse - Oncopôle, Radiation oncology, Toulouse, France; ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France.
| | - Franck Desmoulin
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France
| | - Julia Gilhodes
- Institut Claudius Regaud/Institut Universitaire du Cancer de Toulouse - Oncopôle, Biostatistics, Toulouse, France
| | - Margaux Roques
- CHU Toulouse, Neuroradiology, Toulouse, France; ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France
| | - Soleakhena Ken
- Institut Claudius Regaud/Institut Universitaire du Cancer de Toulouse - Oncopôle, Engineering and Medical Physics, Toulouse, France; Inserm U1037- Centre de Recherches contre le Cancer de Toulouse, Radiation oncology, Toulouse, France
| | - Jean-Albert Lotterie
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France; CHU Toulouse, Nuclear Medicine, Toulouse, France
| | | | - Gilles Truc
- Centre Georges-François Leclerc, Radiation Oncology, Dijon, France
| | | | - Marie Charissoux
- Institut du Cancer de Montpellier, Radiation Oncology, Montpellier, France
| | - Nicolas Magné
- Institut de Cancérologie de la Loire Lucien Neuwirth, Radiation Oncology, Saint-Priest-en-Jarez, France
| | - Vincent Lubrano
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France
| | - Patrice Péran
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France
| | - Elizabeth Cohen-Jonathan Moyal
- Institut Claudius Regaud/Institut Universitaire du Cancer de Toulouse - Oncopôle, Radiation oncology, Toulouse, France; Inserm U1037- Centre de Recherches contre le Cancer de Toulouse, Radiation oncology, Toulouse, France
| | - Anne Laprie
- Institut Claudius Regaud/Institut Universitaire du Cancer de Toulouse - Oncopôle, Radiation oncology, Toulouse, France; ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France
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5
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Sidibe I, Tensaouti F, Gilhodes J, Cabarrou B, Filleron T, Desmoulin F, Ken S, Noël G, Truc G, Sunyach MP, Charissoux M, Magné N, Lotterie JA, Roques M, Péran P, Cohen-Jonathan Moyal E, Laprie A. Pseudoprogression in GBM versus true progression in patients with glioblastoma: A multiapproach analysis. Radiother Oncol 2023; 181:109486. [PMID: 36706959 DOI: 10.1016/j.radonc.2023.109486] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 01/16/2023] [Accepted: 01/18/2023] [Indexed: 01/26/2023]
Abstract
BACKGROUND AND PURPOSE To investigate the feasibility of using a multiapproach analysis combining clinical data, diffusion- and perfusion-weighted imaging, and 3D magnetic resonance spectroscopic imaging to distinguish true tumor progression (TP) from pseudoprogression (PSP) in patients with glioblastoma. MATERIALS AND METHODS Progression was suspected within 6 months of radiotherapy in 46 of the 180 patients included in the Phase-III SpectroGlio trial (NCT01507506). Choline/creatine (Cho/Cr), choline/N-acetyl aspartate (Cho/NAA) and lactate/N-acetyl aspartate (Lac/NAA) ratios were extracted. Apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) maps were calculated. ADC, relative CBV values and tumor volume (TV) were collected at relapse. Differences between TP and PSP were evaluated using Mann-Whitney tests, and p values were adjusted with Bonferroni correction. RESULTS Patients with suspected progression underwent a new MRI scan 1 month after the first one. Of these, 28 were classified as PSP, and 18 as TP. After a median follow-up of 41 months, median overall survival was higher in PSP than in TP (25.2 vs 20.3 months; p = 0.0092). Lac/NAA and Cho/Cr ratios were higher in TP than in PSP (1.2 vs 0.5; p = 0.006; and 3 vs 2.2; p = 0.021). After multivariate regression analysis, TV was the most significant predictor of TP vs PSP, and the only one retained in the model (p = 0.028). CONCLUSION Three spectroscopic ratios could be used to differentiate PSP from TP. TV at relapse was the most predictive factor in the multivariate analysis, and overall survival was higher in PSP than in TP.
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Affiliation(s)
- Ingrid Sidibe
- Radiation Oncology Department, Claudius Regaud Institute/Toulouse University Cancer Institute - Oncopôle, Toulouse, France; Toulouse NeuroImaging Center (ToNIC), University of Toulouse Paul Sabatier & INSERM, Toulouse, France
| | - Fatima Tensaouti
- Radiation Oncology Department, Claudius Regaud Institute/Toulouse University Cancer Institute - Oncopôle, Toulouse, France; Toulouse NeuroImaging Center (ToNIC), University of Toulouse Paul Sabatier & INSERM, Toulouse, France
| | - Julia Gilhodes
- Biostatistics Department, Claudius Regaud Institute/Toulouse University Cancer Institute - Oncopôle, Toulouse, France
| | - Bastien Cabarrou
- Biostatistics Department, Claudius Regaud Institute/Toulouse University Cancer Institute - Oncopôle, Toulouse, France
| | - Thomas Filleron
- Biostatistics Department, Claudius Regaud Institute/Toulouse University Cancer Institute - Oncopôle, Toulouse, France
| | - Franck Desmoulin
- Toulouse NeuroImaging Center (ToNIC), University of Toulouse Paul Sabatier & INSERM, Toulouse, France
| | - Soleakhena Ken
- Radiation Oncology Department, Claudius Regaud Institute/Toulouse University Cancer Institute - Oncopôle, Toulouse, France; Radiation Oncology Department, Toulouse Center for Cancer Research & INSERM, Toulouse, France
| | - Georges Noël
- Radiation Oncology Department, ICANS, Strasbourg, France
| | - Gilles Truc
- Radiation Oncology Department, Georges-François Leclerc Center, Dijon, France
| | | | | | - Nicolas Magné
- Radiation Oncology Department, Lucien Neuwirth Loire Cancer Institute, Saint-Priest-en-Jarez, France
| | - Jean-Albert Lotterie
- Toulouse NeuroImaging Center (ToNIC), University of Toulouse Paul Sabatier & INSERM, Toulouse, France
| | - Margaux Roques
- Toulouse NeuroImaging Center (ToNIC), University of Toulouse Paul Sabatier & INSERM, Toulouse, France
| | - Patrice Péran
- Toulouse NeuroImaging Center (ToNIC), University of Toulouse Paul Sabatier & INSERM, Toulouse, France
| | - Elizabeth Cohen-Jonathan Moyal
- Radiation Oncology Department, Claudius Regaud Institute/Toulouse University Cancer Institute - Oncopôle, Toulouse, France; Radiation Oncology Department, Toulouse Center for Cancer Research & INSERM, Toulouse, France
| | - Anne Laprie
- Radiation Oncology Department, Claudius Regaud Institute/Toulouse University Cancer Institute - Oncopôle, Toulouse, France; Toulouse NeuroImaging Center (ToNIC), University of Toulouse Paul Sabatier & INSERM, Toulouse, France.
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Waqar M, Van Houdt PJ, Hessen E, Li KL, Zhu X, Jackson A, Iqbal M, O’Connor J, Djoukhadar I, van der Heide UA, Coope DJ, Borst GR. Visualising spatial heterogeneity in glioblastoma using imaging habitats. Front Oncol 2022; 12:1037896. [PMID: 36505856 PMCID: PMC9731157 DOI: 10.3389/fonc.2022.1037896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 10/31/2022] [Indexed: 11/26/2022] Open
Abstract
Glioblastoma is a high-grade aggressive neoplasm characterised by significant intra-tumoral spatial heterogeneity. Personalising therapy for this tumour requires non-invasive tools to visualise its heterogeneity to monitor treatment response on a regional level. To date, efforts to characterise glioblastoma's imaging features and heterogeneity have focussed on individual imaging biomarkers, or high-throughput radiomic approaches that consider a vast number of imaging variables across the tumour as a whole. Habitat imaging is a novel approach to cancer imaging that identifies tumour regions or 'habitats' based on shared imaging characteristics, usually defined using multiple imaging biomarkers. Habitat imaging reflects the evolution of imaging biomarkers and offers spatially preserved assessment of tumour physiological processes such perfusion and cellularity. This allows for regional assessment of treatment response to facilitate personalised therapy. In this review, we explore different methodologies to derive imaging habitats in glioblastoma, strategies to overcome its technical challenges, contrast experiences to other cancers, and describe potential clinical applications.
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Affiliation(s)
- Mueez Waqar
- Department of Neurosurgery, Geoffrey Jefferson Brain Research Centre, Manchester Centre for Clinical Neurosciences, Northern Care Alliance NHS Foundation Trust, Manchester Academic Health Sciences Centre, Manchester, United Kingdom
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health and Manchester Cancer Research Centre, University of Manchester, Manchester, United Kingdom
| | - Petra J. Van Houdt
- Department of Radiation Oncology, the Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Eline Hessen
- Department of Radiation Oncology, the Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Ka-Loh Li
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health and Manchester Cancer Research Centre, University of Manchester, Manchester, United Kingdom
| | - Xiaoping Zhu
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health and Manchester Cancer Research Centre, University of Manchester, Manchester, United Kingdom
| | - Alan Jackson
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health and Manchester Cancer Research Centre, University of Manchester, Manchester, United Kingdom
- Department of Neuroradiology, Geoffrey Jefferson Brain Research Centre, Manchester Centre for Clinical Neurosciences, Northern Care Alliance NHS Foundation Trust, Manchester Academic Health Sciences Centre, Manchester, United Kingdom
| | - Mudassar Iqbal
- Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health and Manchester Cancer Research Centre, University of Manchester, Manchester, United Kingdom
| | - James O’Connor
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health and Manchester Cancer Research Centre, University of Manchester, Manchester, United Kingdom
- Department of Radiology, The Christie NHS Foundation Trust, Manchester, United Kingdom
| | - Ibrahim Djoukhadar
- Department of Neuroradiology, Geoffrey Jefferson Brain Research Centre, Manchester Centre for Clinical Neurosciences, Northern Care Alliance NHS Foundation Trust, Manchester Academic Health Sciences Centre, Manchester, United Kingdom
| | - Uulke A. van der Heide
- Department of Radiation Oncology, the Netherlands Cancer Institute, Amsterdam, Netherlands
| | - David J. Coope
- Department of Neurosurgery, Geoffrey Jefferson Brain Research Centre, Manchester Centre for Clinical Neurosciences, Northern Care Alliance NHS Foundation Trust, Manchester Academic Health Sciences Centre, Manchester, United Kingdom
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health and Manchester Cancer Research Centre, University of Manchester, Manchester, United Kingdom
| | - Gerben R. Borst
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health and Manchester Cancer Research Centre, University of Manchester, Manchester, United Kingdom
- Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester, United Kingdom
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Henriksen OM, del Mar Álvarez-Torres M, Figueiredo P, Hangel G, Keil VC, Nechifor RE, Riemer F, Schmainda KM, Warnert EAH, Wiegers EC, Booth TC. High-Grade Glioma Treatment Response Monitoring Biomarkers: A Position Statement on the Evidence Supporting the Use of Advanced MRI Techniques in the Clinic, and the Latest Bench-to-Bedside Developments. Part 1: Perfusion and Diffusion Techniques. Front Oncol 2022; 12:810263. [PMID: 35359414 PMCID: PMC8961422 DOI: 10.3389/fonc.2022.810263] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 01/05/2022] [Indexed: 01/16/2023] Open
Abstract
Objective Summarize evidence for use of advanced MRI techniques as monitoring biomarkers in the clinic, and highlight the latest bench-to-bedside developments. Methods Experts in advanced MRI techniques applied to high-grade glioma treatment response assessment convened through a European framework. Current evidence regarding the potential for monitoring biomarkers in adult high-grade glioma is reviewed, and individual modalities of perfusion, permeability, and microstructure imaging are discussed (in Part 1 of two). In Part 2, we discuss modalities related to metabolism and/or chemical composition, appraise the clinic readiness of the individual modalities, and consider post-processing methodologies involving the combination of MRI approaches (multiparametric imaging) or machine learning (radiomics). Results High-grade glioma vasculature exhibits increased perfusion, blood volume, and permeability compared with normal brain tissue. Measures of cerebral blood volume derived from dynamic susceptibility contrast-enhanced MRI have consistently provided information about brain tumor growth and response to treatment; it is the most clinically validated advanced technique. Clinical studies have proven the potential of dynamic contrast-enhanced MRI for distinguishing post-treatment related effects from recurrence, but the optimal acquisition protocol, mode of analysis, parameter of highest diagnostic value, and optimal cut-off points remain to be established. Arterial spin labeling techniques do not require the injection of a contrast agent, and repeated measurements of cerebral blood flow can be performed. The absence of potential gadolinium deposition effects allows widespread use in pediatric patients and those with impaired renal function. More data are necessary to establish clinical validity as monitoring biomarkers. Diffusion-weighted imaging, apparent diffusion coefficient analysis, diffusion tensor or kurtosis imaging, intravoxel incoherent motion, and other microstructural modeling approaches also allow treatment response assessment; more robust data are required to validate these alone or when applied to post-processing methodologies. Conclusion Considerable progress has been made in the development of these monitoring biomarkers. Many techniques are in their infancy, whereas others have generated a larger body of evidence for clinical application.
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Affiliation(s)
- Otto M. Henriksen
- Department of Clinical Physiology, Nuclear Medicine and PET, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | | | - Patricia Figueiredo
- Department of Bioengineering and Institute for Systems and Robotics-Lisboa, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Gilbert Hangel
- Department of Neurosurgery, Medical University, Vienna, Austria
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University, Vienna, Austria
| | - Vera C. Keil
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam, Netherlands
| | - Ruben E. Nechifor
- International Institute for the Advanced Studies of Psychotherapy and Applied Mental Health, Department of Clinical Psychology and Psychotherapy, Babes-Bolyai University, Cluj-Napoca, Romania
| | - Frank Riemer
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Kathleen M. Schmainda
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, United States
| | | | - Evita C. Wiegers
- Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Thomas C. Booth
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School of Biomedical Engineering and Imaging Sciences, St. Thomas’ Hospital, King’s College London, London, United Kingdom
- Department of Neuroradiology, King’s College Hospital NHS Foundation Trust, London, United Kingdom
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8
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Castellano A, Bailo M, Cicone F, Carideo L, Quartuccio N, Mortini P, Falini A, Cascini GL, Minniti G. Advanced Imaging Techniques for Radiotherapy Planning of Gliomas. Cancers (Basel) 2021; 13:cancers13051063. [PMID: 33802292 PMCID: PMC7959155 DOI: 10.3390/cancers13051063] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Revised: 02/24/2021] [Accepted: 02/26/2021] [Indexed: 02/07/2023] Open
Abstract
The accuracy of target delineation in radiation treatment (RT) planning of cerebral gliomas is crucial to achieve high tumor control, while minimizing treatment-related toxicity. Conventional magnetic resonance imaging (MRI), including contrast-enhanced T1-weighted and fluid-attenuated inversion recovery (FLAIR) sequences, represents the current standard imaging modality for target volume delineation of gliomas. However, conventional sequences have limited capability to discriminate treatment-related changes from viable tumors, owing to the low specificity of increased blood-brain barrier permeability and peritumoral edema. Advanced physiology-based MRI techniques, such as MR spectroscopy, diffusion MRI and perfusion MRI, have been developed for the biological characterization of gliomas and may circumvent these limitations, providing additional metabolic, structural, and hemodynamic information for treatment planning and monitoring. Radionuclide imaging techniques, such as positron emission tomography (PET) with amino acid radiopharmaceuticals, are also increasingly used in the workup of primary brain tumors, and their integration in RT planning is being evaluated in specialized centers. This review focuses on the basic principles and clinical results of advanced MRI and PET imaging techniques that have promise as a complement to RT planning of gliomas.
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Affiliation(s)
- Antonella Castellano
- Neuroradiology Unit, IRCCS Ospedale San Raffaele and Vita-Salute San Raffaele University, 20132 Milan, Italy; (A.C.); (A.F.)
| | - Michele Bailo
- Department of Neurosurgery and Gamma Knife Radiosurgery, IRCCS Ospedale San Raffaele and Vita-Salute San Raffaele University, 20132 Milan, Italy; (M.B.); (P.M.)
| | - Francesco Cicone
- Department of Experimental and Clinical Medicine, “Magna Graecia” University of Catanzaro, and Nuclear Medicine Unit, University Hospital “Mater Domini”, 88100 Catanzaro, Italy;
- Correspondence: ; Tel.: +39-0-961-369-4155
| | - Luciano Carideo
- National Cancer Institute, G. Pascale Foundation, 80131 Naples, Italy;
| | - Natale Quartuccio
- A.R.N.A.S. Ospedale Civico Di Cristina Benfratelli, 90144 Palermo, Italy;
| | - Pietro Mortini
- Department of Neurosurgery and Gamma Knife Radiosurgery, IRCCS Ospedale San Raffaele and Vita-Salute San Raffaele University, 20132 Milan, Italy; (M.B.); (P.M.)
| | - Andrea Falini
- Neuroradiology Unit, IRCCS Ospedale San Raffaele and Vita-Salute San Raffaele University, 20132 Milan, Italy; (A.C.); (A.F.)
| | - Giuseppe Lucio Cascini
- Department of Experimental and Clinical Medicine, “Magna Graecia” University of Catanzaro, and Nuclear Medicine Unit, University Hospital “Mater Domini”, 88100 Catanzaro, Italy;
| | - Giuseppe Minniti
- Radiation Oncology Unit, Department of Medicine, Surgery and Neurosciences, University of Siena, Policlinico Le Scotte, 53100 Siena, Italy;
- IRCCS Neuromed, 86077 Pozzilli (IS), Italy
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9
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Stadlbauer A, Kinfe TM, Eyüpoglu I, Zimmermann M, Kitzwögerer M, Podar K, Buchfelder M, Heinz G, Oberndorfer S, Marhold F. Tissue Hypoxia and Alterations in Microvascular Architecture Predict Glioblastoma Recurrence in Humans. Clin Cancer Res 2020; 27:1641-1649. [PMID: 33293375 DOI: 10.1158/1078-0432.ccr-20-3580] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 11/03/2020] [Accepted: 12/04/2020] [Indexed: 11/16/2022]
Abstract
PURPOSE Insufficient control of infiltrative glioblastoma (GBM) cells is a major cause of treatment failure and tumor recurrence. Hence, detailed insights into pathophysiologic changes that precede GBM recurrence are needed to develop more precise neuroimaging modalities for tailored diagnostic monitoring and therapeutic approaches. EXPERIMENTAL DESIGN Overall, 168 physiologic MRI follow-up examinations of 56 patients with GBM who developed recurrence after standard therapy were retrospectively evaluated, that is, two post-standard-therapeutic follow-ups before and one at radiological recurrence. MRI biomarkers for microvascular architecture and perfusion, neovascularization activity, oxygen metabolism, and hypoxia were determined for brain areas that developed in the further course into recurrence and for the recurrent GBM itself. The temporal pattern of biomarker changes was fitted with locally estimated scatterplot smoothing functions and analyzed for pathophysiologic changes preceding radiological GBM recurrence. RESULTS Our MRI approach demonstrated early pathophysiologic changes prior to radiological GBM recurrence in all patients. Analysis of the time courses revealed a model for the pathophysiology of GBM recurrence: 190 days prior to radiological recurrence, vascular cooption by GBM cells induced vessel regression, detected as decreasing vessel density/perfusion and increasing hypoxia. Seventy days later, neovascularization activity was upregulated, which reincreased vessel density and perfusion. Hypoxia, however, continued to intensify for 30 days and peaked 90 days before radiological recurrence. CONCLUSIONS Hypoxia may represent an early sign for GBM recurrence. This might become useful in the development of new combined diagnostic-therapeutic approaches for tailored clinical management of recurrent GBM. Further preclinical and in-human studies are required for validation and evaluation.
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Affiliation(s)
- Andreas Stadlbauer
- Department of Neurosurgery, Friedrich-Alexander University (FAU) Erlangen-Nürnberg, Erlangen, Germany.
- Institute of Medical Radiology, University Clinic St. Pölten, Karl Landsteiner University of Health Sciences, St. Pölten, Austria
| | - Thomas M Kinfe
- Department of Neurosurgery, Friedrich-Alexander University (FAU) Erlangen-Nürnberg, Erlangen, Germany
- Division of Functional Neurosurgery and Stereotaxy, Friedrich-Alexander University (FAU) Erlangen-Nürnberg, Erlangen, Germany
| | - Ilker Eyüpoglu
- Department of Neurosurgery, Friedrich-Alexander University (FAU) Erlangen-Nürnberg, Erlangen, Germany
| | - Max Zimmermann
- Department of Neurosurgery, Friedrich-Alexander University (FAU) Erlangen-Nürnberg, Erlangen, Germany
- Department of Preclinical Imaging and Radiopharmacy, University of Tübingen, Tübingen, Germany
| | - Melitta Kitzwögerer
- Department of Pathology, University Clinic of St. Pölten, St. Pölten, Austria
| | - Klaus Podar
- Department of Internal Medicine 2, University Hospital Krems, Karl Landsteiner University of Health Sciences, Krems, Austria
| | - Michael Buchfelder
- Department of Neurosurgery, Friedrich-Alexander University (FAU) Erlangen-Nürnberg, Erlangen, Germany
| | - Gertraud Heinz
- Institute of Medical Radiology, University Clinic St. Pölten, Karl Landsteiner University of Health Sciences, St. Pölten, Austria
| | - Stefan Oberndorfer
- Department of Neurology, University Clinic of St. Pölten, Karl Landsteiner University of Health Sciences, St. Pölten, Austria
| | - Franz Marhold
- Department of Neurosurgery, University Clinic of St. Pölten, Karl Landsteiner University of Health Sciences, St. Pölten, Austria
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10
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Dercle L, Henry T, Carré A, Paragios N, Deutsch E, Robert C. Reinventing radiation therapy with machine learning and imaging bio-markers (radiomics): State-of-the-art, challenges and perspectives. Methods 2020; 188:44-60. [PMID: 32697964 DOI: 10.1016/j.ymeth.2020.07.003] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Revised: 07/02/2020] [Accepted: 07/06/2020] [Indexed: 12/14/2022] Open
Abstract
Radiation therapy is a pivotal cancer treatment that has significantly progressed over the last decade due to numerous technological breakthroughs. Imaging is now playing a critical role on deployment of the clinical workflow, both for treatment planning and treatment delivery. Machine-learning analysis of predefined features extracted from medical images, i.e. radiomics, has emerged as a promising clinical tool for a wide range of clinical problems addressing drug development, clinical diagnosis, treatment selection and implementation as well as prognosis. Radiomics denotes a paradigm shift redefining medical images as a quantitative asset for data-driven precision medicine. The adoption of machine-learning in a clinical setting and in particular of radiomics features requires the selection of robust, representative and clinically interpretable biomarkers that are properly evaluated on a representative clinical data set. To be clinically relevant, radiomics must not only improve patients' management with great accuracy but also be reproducible and generalizable. Hence, this review explores the existing literature and exposes its potential technical caveats, such as the lack of quality control, standardization, sufficient sample size, type of data collection, and external validation. Based upon the analysis of 165 original research studies based on PET, CT-scan, and MRI, this review provides an overview of new concepts, and hypotheses generating findings that should be validated. In particular, it describes evolving research trends to enhance several clinical tasks such as prognostication, treatment planning, response assessment, prediction of recurrence/relapse, and prediction of toxicity. Perspectives regarding the implementation of an AI-based radiotherapy workflow are presented.
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Affiliation(s)
- Laurent Dercle
- Department of Radiology, New York Presbyterian Hospital, Columbia University Medical Center, New York, USA
| | - Theophraste Henry
- Molecular Radiotherapy and Innovative Therapeutics, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France; Department of Nuclear Medicine and Endocrine Oncology, Gustave Roussy Cancer Campus, Villejuif, France
| | - Alexandre Carré
- Molecular Radiotherapy and Innovative Therapeutics, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France; Department of Radiation Oncology, Gustave Roussy Cancer Campus, Villejuif, France
| | | | - Eric Deutsch
- Molecular Radiotherapy and Innovative Therapeutics, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France; Department of Radiation Oncology, Gustave Roussy Cancer Campus, Villejuif, France
| | - Charlotte Robert
- Molecular Radiotherapy and Innovative Therapeutics, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France; Department of Radiation Oncology, Gustave Roussy Cancer Campus, Villejuif, France.
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11
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Yu Y, Ma Y, Sun M, Jiang W, Yuan T, Tong D. Meta-analysis of the diagnostic performance of diffusion magnetic resonance imaging with apparent diffusion coefficient measurements for differentiating glioma recurrence from pseudoprogression. Medicine (Baltimore) 2020; 99:e20270. [PMID: 32501974 PMCID: PMC7306328 DOI: 10.1097/md.0000000000020270] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 02/13/2020] [Accepted: 04/15/2020] [Indexed: 11/25/2022] Open
Abstract
OBJECTIVE The accurate differentiation of glioma recurrence from pseudoprogression (PSP) after therapy remains a considerable clinical challenge. Several studies have shown that diffusion magnetic resonance imaging (MRI) has potential value in distinguishing these 2 outcomes. The current meta-analysis examined the diagnostic accuracy of diffusion MRI with the apparent diffusion coefficient (ADC) in the differentiation of glioma recurrence from PSP. METHOD PubMed, Embase, Cochrane Library, and Chinese Biomedical databases were reviewed to identify studies that fulfilled our inclusion/exclusion criteria and were published on or before May 5, 2019. Threshold effects; heterogeneity; pooled sensitivity (SENS), specificity, positive likelihood ratio, and negative likelihood ratio; and diagnostic odds ratio were calculated. The overall diagnostic usefulness of diffusion MRI-derived ADC values was assessed by calculating the area under the curve (AUC) following summary receiver operating characteristic (SROC) analysis. RESULTS Six eligible studies examined a total of 214 patients. Calculation of pooled values indicated the SENS was 0.95 (95% confidence interval [CI] = 0.89-0.98), specificity was 0.83 (95% CI = 0.72-0.91), positive likelihood ratio was 4.82 (95% CI = 2.93-7.93), negative likelihood ratio was 0.08 (95% CI = 0.04-0.17), and diagnostic odds ratio was 59.63 (95% CI = 22.63-157.37). The SROC AUC was 0.9322. Publication bias was not significant, and SENS analysis indicated the results were relatively stable. CONCLUSIONS Our meta-analysis indicated that diffusion MRI with quantitative ADC is an effective approach for differentiation of glioma recurrence from PSP, and can be used as an auxiliary tool to diagnose glioma progression.
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Affiliation(s)
| | | | - Mengyao Sun
- Department of Internal Oncology, The First Hospital of Jilin University, Changchun, Jilin, China
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12
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Benzakoun J, Robert C, Legrand L, Pallud J, Meder JF, Oppenheim C, Dhermain F, Edjlali M. Anatomical and functional MR imaging to define tumoral boundaries and characterize lesions in neuro-oncology. Cancer Radiother 2020; 24:453-462. [PMID: 32278653 DOI: 10.1016/j.canrad.2020.03.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Accepted: 03/04/2020] [Indexed: 12/19/2022]
Abstract
Neuroimaging and especially MRI has emerged as a necessary imaging modality to detect, measure, characterize and monitor brain tumours. Advanced MRI sequences such as perfusion MRI, diffusion MRI and spectroscopy as well as new post-processing techniques such as automatic segmentation of tumours and radiomics play a crucial role in characterization and follow up of brain tumours. The purpose of this review is to provide an overview on anatomical and functional MRI use for brain tumours boundaries determination and tumour characterization in the specific context of radiotherapy. The usefulness of anatomical and functional MRI on particular challenges posed by radiotherapy such as pseudo progression and pseudo esponse and new treatment strategies such as dose painting is also described.
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Affiliation(s)
- J Benzakoun
- Radiology Department, GHU de Paris, centre hospitalier Sainte-Anne, 1, rue Cabanis, 75014 Paris, France; Université de Paris, 85, boulevard Saint-Germain, 75006 Paris, France; Imabrain, Institut de psychiatrie et neurosciences de Paris (IPNP), 102-108, rue de la Santé, 75014 Paris, France; Inserm, U1266, 102, rue de la Santé, 75013 Paris, France.
| | - C Robert
- Medical Physics Department, Gustave-Roussy, 114, rue Édouard-Vaillant, 94805 Villejuif, France; Molecular Radiotherapy, Gustave-Roussy, 114, rue Édouard-Vaillant, 94805 Villejuif, France; Inserm, 114, rue Édouard-Vaillant, 94805 Villejuif, France; Paris-Sud University, Paris-Saclay University, 114, rue Édouard-Vaillant, 94805 Villejuif, France
| | - L Legrand
- Radiology Department, GHU de Paris, centre hospitalier Sainte-Anne, 1, rue Cabanis, 75014 Paris, France; Université de Paris, 85, boulevard Saint-Germain, 75006 Paris, France; Imabrain, Institut de psychiatrie et neurosciences de Paris (IPNP), 102-108, rue de la Santé, 75014 Paris, France; Inserm, U1266, 102, rue de la Santé, 75013 Paris, France
| | - J Pallud
- Université de Paris, 85, boulevard Saint-Germain, 75006 Paris, France; Imabrain, Institut de psychiatrie et neurosciences de Paris (IPNP), 102-108, rue de la Santé, 75014 Paris, France; Inserm, U1266, 102, rue de la Santé, 75013 Paris, France; Neurosurgery Department, GHU de Paris, centre hospitalier Sainte-Anne, 1, rue Cabanis, 75014 Paris, France
| | - J-F Meder
- Radiology Department, GHU de Paris, centre hospitalier Sainte-Anne, 1, rue Cabanis, 75014 Paris, France; Université de Paris, 85, boulevard Saint-Germain, 75006 Paris, France; Imabrain, Institut de psychiatrie et neurosciences de Paris (IPNP), 102-108, rue de la Santé, 75014 Paris, France; Inserm, U1266, 102, rue de la Santé, 75013 Paris, France
| | - C Oppenheim
- Radiology Department, GHU de Paris, centre hospitalier Sainte-Anne, 1, rue Cabanis, 75014 Paris, France; Université de Paris, 85, boulevard Saint-Germain, 75006 Paris, France; Imabrain, Institut de psychiatrie et neurosciences de Paris (IPNP), 102-108, rue de la Santé, 75014 Paris, France; Inserm, U1266, 102, rue de la Santé, 75013 Paris, France
| | - F Dhermain
- Radiotherapy Department, Gustave-Roussy, 114, rue Édouard-Vaillant, 94805 Villejuif, France
| | - M Edjlali
- Radiology Department, GHU de Paris, centre hospitalier Sainte-Anne, 1, rue Cabanis, 75014 Paris, France; Université de Paris, 85, boulevard Saint-Germain, 75006 Paris, France; Imabrain, Institut de psychiatrie et neurosciences de Paris (IPNP), 102-108, rue de la Santé, 75014 Paris, France; Inserm, U1266, 102, rue de la Santé, 75013 Paris, France
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13
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Whole brain apparent diffusion coefficient measurements correlate with survival in glioblastoma patients. J Neurooncol 2019; 146:157-162. [PMID: 31797235 PMCID: PMC6938471 DOI: 10.1007/s11060-019-03357-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Accepted: 11/26/2019] [Indexed: 12/14/2022]
Abstract
INTRODUCTION Glioblastoma (GBM) is the most common malignant primary brain tumor, and methods to improve the early detection of disease progression and evaluate treatment response are highly desirable. We therefore explored changes in whole-brain apparent diffusion coefficient (ADC) values with respect to survival (progression-free [PFS], overall [OS]) in a cohort of GBM patients followed at regular intervals until disease progression. METHODS A total of 43 subjects met inclusion criteria and were analyzed retrospectively. Histogram data were extracted from standardized whole-brain ADC maps including skewness, kurtosis, entropy, median, mode, 15th percentile (p15) and 85th percentile (p85) values, and linear regression slopes (metrics versus time) were fitted. Regression slope directionality (positive/negative) was subjected to univariate Cox regression. The final model was determined by aLASSO on metrics above threshold. RESULTS Skewness, kurtosis, median, p15 and p85 were all below threshold for both PFS and OS and were analyzed further. Median regression slope directionality best modeled PFS (p = 0.001; HR 3.3; 95% CI 1.6-6.7), while p85 was selected for OS (p = 0.002; HR 0.29; 95% CI 0.13-0.64). CONCLUSIONS Our data show tantalizing potential in the use of whole-brain ADC measurements in the follow up of GBM patients, specifically serial median ADC values which correlated with PFS, and serial p85 values which correlated with OS. Whole-brain ADC measurements are fast and easy to perform, and free of ROI-placement bias.
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14
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Laprie A, Ken S, Filleron T, Lubrano V, Vieillevigne L, Tensaouti F, Catalaa I, Boetto S, Khalifa J, Attal J, Peyraga G, Gomez-Roca C, Uro-Coste E, Noel G, Truc G, Sunyach MP, Magné N, Charissoux M, Supiot S, Bernier V, Mounier M, Poublanc M, Fabre A, Delord JP, Cohen-Jonathan Moyal E. Dose-painting multicenter phase III trial in newly diagnosed glioblastoma: the SPECTRO-GLIO trial comparing arm A standard radiochemotherapy to arm B radiochemotherapy with simultaneous integrated boost guided by MR spectroscopic imaging. BMC Cancer 2019; 19:167. [PMID: 30791889 PMCID: PMC6385401 DOI: 10.1186/s12885-019-5317-x] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2018] [Accepted: 01/24/2019] [Indexed: 02/05/2023] Open
Abstract
Background Glioblastoma, a high-grade glial infiltrating tumor, is the most frequent malignant brain tumor in adults and carries a dismal prognosis. External beam radiotherapy (EBRT) increases overall survival but this is still low due to local relapses, mostly occurring in the irradiation field. As the ratio of spectra of choline/N acetyl aspartate> 2 (CNR2) on MR spectroscopic imaging has been described as predictive for the site of local relapse, we hypothesized that dose escalation on these regions would increase local control and hence global survival. Methods/design In this multicenter prospective phase III trial for newly diagnosed glioblastoma, 220 patients having undergone biopsy or surgery are planned for randomization to two arms. Arm A is the Stupp protocol (EBRT 60 Gy on contrast enhancement + 2 cm margin with concomitant temozolomide (TMZ) and 6 months of TMZ maintenance); Arm B is the same treatment with an additional simultaneous integrated boost of intensity-modulated radiotherapy (IMRT) of 72Gy/2.4Gy delivered on the MR spectroscopic imaging metabolic volumes of CHO/NAA > 2 and contrast-enhancing lesions or resection cavity. Stratification is performed on surgical and MGMT status. Discussion This is a dose-painting trial, i.e. delivery of heterogeneous dose guided by metabolic imaging. The principal endpoint is overall survival. An online prospective quality control of volumes and dose is performed in the experimental arm. The study will yield a large amount of longitudinal multimodal MR imaging data including planning CT, radiotherapy dosimetry, MR spectroscopic, diffusion and perfusion imaging. Trial registration NCT01507506, registration date December 20, 2011.
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Affiliation(s)
- Anne Laprie
- Radiation Oncology Department, Institut Claudius Regaud- Institut Universitaire du Cancer de Toulouse-Oncopole, Toulouse, France. .,ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, INSERM, UPS, Toulouse, France.
| | - Soléakhéna Ken
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, INSERM, UPS, Toulouse, France.,Department of Engineering and Medical Physics, Institut Claudius Regaud- Institut Universitaire du Cancer de Toulouse-OncopoleCancer de Toulouse-Oncopole, Toulouse, France
| | - Thomas Filleron
- Biostatistics Unit, Institut Claudius Regaud- Institut Universitaire du Cancer de Toulouse-Oncopole, Toulouse, France
| | - Vincent Lubrano
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, INSERM, UPS, Toulouse, France.,Neurosurgery Department, Centre Hospitalier Universitaire de Toulouse, Toulouse, France
| | - Laure Vieillevigne
- Department of Engineering and Medical Physics, Institut Claudius Regaud- Institut Universitaire du Cancer de Toulouse-OncopoleCancer de Toulouse-Oncopole, Toulouse, France
| | - Fatima Tensaouti
- Radiation Oncology Department, Institut Claudius Regaud- Institut Universitaire du Cancer de Toulouse-Oncopole, Toulouse, France.,ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, INSERM, UPS, Toulouse, France
| | - Isabelle Catalaa
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, INSERM, UPS, Toulouse, France.,Neuroimaging Department, Centre Hospitalier Universitaire de Toulouse, Toulouse, France
| | - Sergio Boetto
- Neurosurgery Department, Centre Hospitalier Universitaire de Toulouse, Toulouse, France
| | - Jonathan Khalifa
- Radiation Oncology Department, Institut Claudius Regaud- Institut Universitaire du Cancer de Toulouse-Oncopole, Toulouse, France
| | - Justine Attal
- Radiation Oncology Department, Institut Claudius Regaud- Institut Universitaire du Cancer de Toulouse-Oncopole, Toulouse, France
| | - Guillaume Peyraga
- Radiation Oncology Department, Institut Claudius Regaud- Institut Universitaire du Cancer de Toulouse-Oncopole, Toulouse, France
| | - Carlos Gomez-Roca
- Medical Oncology Department, Institut Claudius Regaud- Institut Universitaire du Cancer de Toulouse-Oncopole, Toulouse, France
| | - Emmanuelle Uro-Coste
- Pathology department, Centre Hospitalier Universitaire de Toulouse, Toulouse, France
| | - Georges Noel
- Radiation Oncology Department, Centre Paul Strauss, Strasbourg, France
| | - Gilles Truc
- Radiation Oncology Department Centre Georges-François Leclerc, Dijon, France
| | | | - Nicolas Magné
- Radiation Oncology Department, Institut de Cancérologie de la Loire, Saint-Priest en Jarez, France
| | - Marie Charissoux
- Radiation Oncology Department - Centre Val d'aurelle, Montpellier, France
| | - Stéphane Supiot
- Radiation Oncology Department, Institut de Cancerologie de l'Ouest, Nantes st Herblain, France
| | - Valérie Bernier
- Radiation Oncology Department, Institut de cancérologie de Lorraine centre Alexis Vautrin, Nancy, France
| | - Muriel Mounier
- Clinical Research Department, Institut Claudius Regaud, Institut Universitaire du Cancer de Toulouse-Oncopole, Toulouse, France
| | - Muriel Poublanc
- Clinical Research Department, Institut Claudius Regaud, Institut Universitaire du Cancer de Toulouse-Oncopole, Toulouse, France
| | - Amandine Fabre
- Clinical Research Department, Institut Claudius Regaud, Institut Universitaire du Cancer de Toulouse-Oncopole, Toulouse, France
| | - Jean-Pierre Delord
- Medical Oncology Department, Institut Claudius Regaud- Institut Universitaire du Cancer de Toulouse-Oncopole, Toulouse, France
| | - Elizabeth Cohen-Jonathan Moyal
- Radiation Oncology Department, Institut Claudius Regaud- Institut Universitaire du Cancer de Toulouse-Oncopole, Toulouse, France.,INSERM UMR1037, Cancer Research Center of Toulouse, Oncopole, Toulouse, France
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15
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Impact on survival of early tumor growth between surgery and radiotherapy in patients with de novo glioblastoma. J Neurooncol 2019; 142:489-497. [PMID: 30783874 DOI: 10.1007/s11060-019-03120-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2018] [Accepted: 02/02/2019] [Indexed: 12/18/2022]
Abstract
PURPOSE Systematic pre-radiotherapy MRI in patients with newly resected glioblastoma (OMS 2016) sometimes reveals tumor growth in the period between surgery and radiotherapy. We evaluated the relation between early tumor growth and overall survival (OS) with the aim of finding predictors of regrowth. METHODS Seventy-five patients from 25 to 84 years old (Median age 62 years) with preoperative, immediate postoperative, and preradiotherapy MRI were included. Volumetric measurements were made on each of the three MRI scans and clinical and molecular parameters were collected for each case. RESULTS Fifty-four patients (72%) had an early regrowth with a median contrast enhancement volume of 3.61 cm3-range 0.12-71.93 cm3. The median OS was 24 months in patients with no early tumor growth and 17.1 months in those with early tumor regrowth (p = 0.0024). In the population with initial complete resection (27 patients), the median OS was 25.3 months (19 patients) in those with no early tumor growth between surgery and radiotherapy compared to 16.3 months (8 patients) in those with tumor regrowth. In multivariate analysis, the initial extent of resection (p < 0.001) and the delay between postoperative MRI and preradiotherapy MRI (p < 0.001) were significant independent prognostic factors of regrowth and of poorer outcome. CONCLUSIONS We demonstrated that, in addition to the well known issue of incomplete resection, longer delays between surgery and adjuvant treatment is an independent factors of tumor regrowth and a risk factor of poorer outcomes for the patients. To overcome the delay factor, we suggest shortening the usual time between surgery and radiotherapy.
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16
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Press RH, Zhong J, Gurbani SS, Weinberg BD, Eaton BR, Shim H, Shu HKG. The Role of Standard and Advanced Imaging for the Management of Brain Malignancies From a Radiation Oncology Standpoint. Neurosurgery 2018; 85:165-179. [DOI: 10.1093/neuros/nyy461] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Accepted: 08/30/2018] [Indexed: 01/20/2023] Open
Abstract
Abstract
Radiation therapy (RT) plays a critical role in the overall management of many central nervous system (CNS) tumors. Advances in RT treatment planning, with techniques such as intensity modulated radiation therapy, volumetric modulated arc therapy, and stereotactic radiosurgery, now allow the delivery of highly conformal dose with great precision. These techniques rely on high-resolution 3-dimensional anatomical imaging modalities such as computed tomography or magnetic resonance imaging (MRI) scans to accurately and reliably define CNS targets and normal tissue avoidance structures. The integration of cross-sectional imaging into radiation oncology has directly translated into improvements in the therapeutic window of RT, and the union between radiation oncology and imaging is only expected to grow stronger. In addition, advanced imaging modalities including diffusion, perfusion, and spectroscopic MRIs as well as positron emission tomography (PET) scans with novel tracers are being utilized to provide additional insight into tumor biology and behavior beyond anatomy. Together, these standard and advanced imaging modalities hold significant potential to improve future RT delivery and response assessment. In this review, we will discuss the current utilization of standard/advanced imaging for CNS tumors from a radiation oncology perspective as well as the implications of novel MRI and PET modalities currently under investigation.
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Affiliation(s)
- Robert H Press
- Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, Georgia
| | - Jim Zhong
- Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, Georgia
| | - Saumya S Gurbani
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, Georgia
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia
| | - Brent D Weinberg
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, Georgia
| | - Bree R Eaton
- Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, Georgia
| | - Hyunsuk Shim
- Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, Georgia
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, Georgia
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia
| | - Hui-Kuo G Shu
- Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, Georgia
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17
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Peyraga G, Robaine N, Khalifa J, Cohen-Jonathan-Moyal E, Payoux P, Laprie A. Molecular PET imaging in adaptive radiotherapy: brain. THE QUARTERLY JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING : OFFICIAL PUBLICATION OF THE ITALIAN ASSOCIATION OF NUCLEAR MEDICINE (AIMN) [AND] THE INTERNATIONAL ASSOCIATION OF RADIOPHARMACOLOGY (IAR), [AND] SECTION OF THE SOCIETY OF RADIOPHARMACEUTICAL CHEMISTRY AND BIOLOGY 2018; 62:337-348. [PMID: 30497232 DOI: 10.23736/s1824-4785.18.03116-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
INTRODUCTION Owing to their heterogeneity and radioresistance, the prognosis of primitive brain tumors, which are mainly glial tumors, remains poor. Dose escalation in radioresistant areas is a potential issue for improving local control and overall survival. This review focuses on advances in biological and metabolic imaging of brain tumors that are proving to be essential for defining tumor target volumes in radiation therapy (RT) and for increasing the use of DPRT (dose painting RT) and ART (adaptative RT), to optimize dose in radio-resistant areas. EVIDENCE ACQUISITION Various biological imaging modalities such as PET (hypoxia, glucidic metabolism, protidic metabolism, cellular proliferation, inflammation, cellular membrane synthesis) and MRI (spectroscopy) may be used to identify these areas of radioresistance. The integration of these biological imaging modalities improves the diagnosis, prognosis and treatment of brain tumors. EVIDENCE SYNTHESIS Technological improvements (PET and MRI), the development of research, and intensive cooperation between different departments are necessary before using daily metabolic imaging (PET and MRI) to treat patients with brain tumors. CONCLUSIONS The adaptation of treatment volumes during RT (ART) seems promising, but its development requires improvements in several areas and an interdisciplinary approach involving radiology, nuclear medicine and radiotherapy. We review the literature on biological imaging to outline the perspectives for using DPRT and ART in brain tumors.
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Affiliation(s)
- Guillaume Peyraga
- Department of Radiation Therapy, Claudius Regaud Institute, Institut Universitaire du Cancer de Toulouse Oncopole, Toulouse, France
| | - Nesrine Robaine
- Department of Nuclear Medicine, Claudius Regaud Institute, Institut Universitaire du Cancer de Toulouse Oncopole, Toulouse, France
| | - Jonathan Khalifa
- Department of Radiation Therapy, Claudius Regaud Institute, Institut Universitaire du Cancer de Toulouse Oncopole, Toulouse, France.,Paul Sabatier University, Toulouse III, Toulouse, France
| | - Elizabeth Cohen-Jonathan-Moyal
- Department of Radiation Therapy, Claudius Regaud Institute, Institut Universitaire du Cancer de Toulouse Oncopole, Toulouse, France.,Paul Sabatier University, Toulouse III, Toulouse, France
| | - Pierre Payoux
- Department of Nuclear Medicine, Purpan University Hospital Center, Toulouse, France
| | - Anne Laprie
- Department of Radiation Therapy, Claudius Regaud Institute, Institut Universitaire du Cancer de Toulouse Oncopole, Toulouse, France - .,Paul Sabatier University, Toulouse III, Toulouse, France
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18
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Lundemann M, Munck Af Rosenschöld P, Muhic A, Larsen VA, Poulsen HS, Engelholm SA, Andersen FL, Kjær A, Larsson HBW, Law I, Hansen AE. Feasibility of multi-parametric PET and MRI for prediction of tumour recurrence in patients with glioblastoma. Eur J Nucl Med Mol Imaging 2018; 46:603-613. [PMID: 30276440 DOI: 10.1007/s00259-018-4180-3] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2018] [Accepted: 09/21/2018] [Indexed: 12/21/2022]
Abstract
BACKGROUND Recurrence in glioblastoma patients often occur close to the original tumour and indicates that the current treatment is inadequate for local tumour control. In this study, we explored the feasibility of using multi-modality imaging at the time of radiotherapy planning. Specifically, we aimed to identify parameters from pre-treatment PET and MRI with potential to predict tumour recurrence. MATERIALS AND METHODS Sixteen patients were prospectively recruited and treated according to established guidelines. Multi-parametric imaging with 18F-FET PET/CT and 18F-FDG PET/MR including diffusion and dynamic contrast enhanced perfusion MRI were performed before radiotherapy. Correlations between imaging parameters were calculated. Imaging was related to the voxel-wise outcome at the time of tumour recurrence. Within the radiotherapy target, median differences of imaging parameters in recurring and non-recurring voxels were calculated for contrast-enhancing lesion (CEL), non-enhancing lesion (NEL), and normal appearing grey and white matter. Logistic regression models were created to predict the patient-specific probability of recurrence. The most important parameters were identified using standardized model coefficients. RESULTS Significant median differences between recurring and non-recurring voxels were observed for FDG, FET, fractional anisotropy, mean diffusivity, mean transit time, extra-vascular, extra-cellular blood volume and permeability derived from scans prior to chemo-radiotherapy. Tissue-specific patterns of voxel-wise correlations were observed. The most pronounced correlations were observed for 18F-FDG- and 18F-FET-uptake in CEL and NEL. Voxel-wise modelling of recurrence probability resulted in area under the receiver operating characteristic curve of 0.77 from scans prior to therapy. Overall, FET proved to be the most important parameter for recurrence prediction. CONCLUSION Multi-parametric imaging before radiotherapy is feasible and significant differences in imaging parameters between recurring and non-recurring voxels were observed. Combining parameters in a logistic regression model enabled patient-specific maps of recurrence probability, where 18F-FET proved to be most important. This strategy could enable risk-adapted radiotherapy planning.
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Affiliation(s)
- Michael Lundemann
- Department of Clinical Physiology, Nuclear Medicine & PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark. .,Department of Oncology, Section for Radiotherapy, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark. .,Niels Bohr Institute, Department of Science, University of Copenhagen, Copenhagen, Denmark.
| | - Per Munck Af Rosenschöld
- Niels Bohr Institute, Department of Science, University of Copenhagen, Copenhagen, Denmark.,Radiation Physics, Department of Hematology, Oncology and Radiation Physics, Skåne University Hospital, Scania, Sweden
| | - Aida Muhic
- Department of Oncology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Vibeke A Larsen
- Department of Radiology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Hans S Poulsen
- Department of Oncology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Svend-Aage Engelholm
- Department of Oncology, Section for Radiotherapy, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark.,Department of Oncology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Flemming L Andersen
- Department of Clinical Physiology, Nuclear Medicine & PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Andreas Kjær
- Department of Clinical Physiology, Nuclear Medicine & PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark.,Cluster for Molecular Imaging, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Henrik B W Larsson
- Department of Clinical Physiology, Nuclear Medicine & PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Ian Law
- Department of Clinical Physiology, Nuclear Medicine & PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Adam E Hansen
- Department of Clinical Physiology, Nuclear Medicine & PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
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Diffusion-weighted MRI and ADC versus FET-PET and GdT1w-MRI for gross tumor volume (GTV) delineation in re-irradiation of recurrent glioblastoma. Radiother Oncol 2018; 130:121-131. [PMID: 30219612 DOI: 10.1016/j.radonc.2018.08.019] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Revised: 07/18/2018] [Accepted: 08/27/2018] [Indexed: 02/07/2023]
Abstract
BACKGROUND AND PURPOSE GTV definition for re-irradiation treatment planning in recurrent glioblastoma (rGBM) is usually based on contrast-enhanced MRI (GdT1w-MRI) and, for an increased specificity, on amino acid PET. Diffusion-weighted (DWI) MRI and ADC maps can reveal regions of high cellularity as surrogate for active tumor. The objective of this study was to compare the localization and quality of diffusion restriction foci (GTV-ADClow) with FET-PET (GTV-PET) and GdT1w-MRI (GTV-GdT1w-MRI). MATERIAL AND METHODS We prospectively evaluated 41 patients, who received a fractionated stereotactic re-irradiation for rGBM. GTV-PET was generated automatically (tumor-to-background ratio 1.7-1.8) and manually customized. GTV-ADClow was manually defined based on DWI data (3D diffusion gradients, b = 0, 1000 s/mm2) and parametric ADC maps. The localization of recurrence was correlated with initial GdT1w-MRI and PET data. RESULTS In 30/41 patients, DWI-MRI showed areas with restricted diffusion (mean ADC-value 0.74 ± 0.22 mm2/s). 66% of GTVs-ADClow were located outside the GdT1w-MRI volume and 76% outside increased FET uptake regions. Furthermore, GTVs-ADClow were only partially included in the high dose volume and received in mean 82% of the reference dose. An adjusted volume including GdT1w-MRI, PET-positive and restricted diffusion areas would imply a GTV increase of 48%. GTV-PET and GdT1w-MRI correlated better with the localization of re-recurrence in comparison to GTV-ADClow. CONCLUSION Unexpectedly, GTV-ADClow overlapped only partially with FET-PET and GdT1w-MRI in rGBM. Moreover, GTV-ADClow correlated poorly with later rGBM-recurrences. Seeing as a restricted diffusion is known to correlate with hypercellularity, this imaging discrepancy could only be further explained in histopathological studies.
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Thust SC, van den Bent MJ, Smits M. Pseudoprogression of brain tumors. J Magn Reson Imaging 2018; 48:571-589. [PMID: 29734497 PMCID: PMC6175399 DOI: 10.1002/jmri.26171] [Citation(s) in RCA: 177] [Impact Index Per Article: 29.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2018] [Accepted: 04/07/2018] [Indexed: 12/11/2022] Open
Abstract
This review describes the definition, incidence, clinical implications, and magnetic resonance imaging (MRI) findings of pseudoprogression of brain tumors, in particular, but not limited to, high-grade glioma. Pseudoprogression is an important clinical problem after brain tumor treatment, interfering not only with day-to-day patient care but also the execution and interpretation of clinical trials. Radiologically, pseudoprogression is defined as a new or enlarging area(s) of contrast agent enhancement, in the absence of true tumor growth, which subsides or stabilizes without a change in therapy. The clinical definitions of pseudoprogression have been quite variable, which may explain some of the differences in reported incidences, which range from 9-30%. Conventional structural MRI is insufficient for distinguishing pseudoprogression from true progressive disease, and advanced imaging is needed to obtain higher levels of diagnostic certainty. Perfusion MRI is the most widely used imaging technique to diagnose pseudoprogression and has high reported diagnostic accuracy. Diagnostic performance of MR spectroscopy (MRS) appears to be somewhat higher, but MRS is less suitable for the routine and universal application in brain tumor follow-up. The combination of MRS and diffusion-weighted imaging and/or perfusion MRI seems to be particularly powerful, with diagnostic accuracy reaching up to or even greater than 90%. While diagnostic performance can be high with appropriate implementation and interpretation, even a combination of techniques, however, does not provide 100% accuracy. It should also be noted that most studies to date are small, heterogeneous, and retrospective in nature. Future improvements in diagnostic accuracy can be expected with harmonization of acquisition and postprocessing, quantitative MRI and computer-aided diagnostic technology, and meticulous evaluation with clinical and pathological data. LEVEL OF EVIDENCE 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.
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Affiliation(s)
- Stefanie C. Thust
- Lysholm Neuroradiology DepartmentNational Hospital for Neurology and NeurosurgeryLondonUK
- Department of Brain Rehabilitation and RepairUCL Institute of NeurologyLondonUK
- Imaging DepartmentUniversity College London HospitalLondonUK
| | - Martin J. van den Bent
- Department of NeurologyThe Brain Tumor Centre at Erasmus MC Cancer InstituteRotterdamThe Netherlands
| | - Marion Smits
- Department of Radiology and Nuclear Medicine, Erasmus MCUniversity Medical Centre RotterdamRotterdamThe Netherlands
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