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Foltyn-Dumitru M, Schell M, Rastogi A, Sahm F, Kessler T, Wick W, Bendszus M, Brugnara G, Vollmuth P. Impact of signal intensity normalization of MRI on the generalizability of radiomic-based prediction of molecular glioma subtypes. Eur Radiol 2024; 34:2782-2790. [PMID: 37672053 PMCID: PMC10957611 DOI: 10.1007/s00330-023-10034-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 05/09/2023] [Accepted: 06/16/2023] [Indexed: 09/07/2023]
Abstract
OBJECTIVES Radiomic features have demonstrated encouraging results for non-invasive detection of molecular biomarkers, but the lack of guidelines for pre-processing MRI-data has led to poor generalizability. Here, we assessed the influence of different MRI-intensity normalization techniques on the performance of radiomics-based models for predicting molecular glioma subtypes. METHODS Preoperative MRI-data from n = 615 patients with newly diagnosed glioma and known isocitrate dehydrogenase (IDH) and 1p/19q status were pre-processed using four different methods: no normalization (naive), N4 bias field correction (N4), N4 followed by either WhiteStripe (N4/WS), or z-score normalization (N4/z-score). A total of 377 Image-Biomarker-Standardisation-Initiative-compliant radiomic features were extracted from each normalized data, and 9 different machine-learning algorithms were trained for multiclass prediction of molecular glioma subtypes (IDH-mutant 1p/19q codeleted vs. IDH-mutant 1p/19q non-codeleted vs. IDH wild type). External testing was performed in public glioma datasets from UCSF (n = 410) and TCGA (n = 160). RESULTS Support vector machine yielded the best performance with macro-average AUCs of 0.84 (naive), 0.84 (N4), 0.87 (N4/WS), and 0.87 (N4/z-score) in the internal test set. Both N4/WS and z-score outperformed the other approaches in the external UCSF and TCGA test sets with macro-average AUCs ranging from 0.85 to 0.87, replicating the performance of the internal test set, in contrast to macro-average AUCs ranging from 0.19 to 0.45 for naive and 0.26 to 0.52 for N4 alone. CONCLUSION Intensity normalization of MRI data is essential for the generalizability of radiomic-based machine-learning models. Specifically, both N4/WS and N4/z-score approaches allow to preserve the high model performance, yielding generalizable performance when applying the developed radiomic-based machine-learning model in an external heterogeneous, multi-institutional setting. CLINICAL RELEVANCE STATEMENT Intensity normalization such as N4/WS or N4/z-score can be used to develop reliable radiomics-based machine learning models from heterogeneous multicentre MRI datasets and provide non-invasive prediction of glioma subtypes. KEY POINTS • MRI-intensity normalization increases the stability of radiomics-based models and leads to better generalizability. • Intensity normalization did not appear relevant when the developed model was applied to homogeneous data from the same institution. • Radiomic-based machine learning algorithms are a promising approach for simultaneous classification of IDH and 1p/19q status of glioma.
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Affiliation(s)
- Martha Foltyn-Dumitru
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
| | - Marianne Schell
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
| | - Aditya Rastogi
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
| | - Felix Sahm
- Department of Neuropathology, Heidelberg University Hospital, Heidelberg, DE, Germany
| | - Tobias Kessler
- Department of Neurology, Heidelberg University Hospital, Heidelberg, DE, Germany
| | - Wolfgang Wick
- Department of Neurology, Heidelberg University Hospital, Heidelberg, DE, Germany
- Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, DE, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany.
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany.
- Division of Medical Image Computing (MIC), German Cancer Research Center (DFKZ), Heidelberg, Germany.
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Rastogi A, Brugnara G, Foltyn-Dumitru M, Mahmutoglu MA, Preetha CJ, Kobler E, Pflüger I, Schell M, Deike-Hofmann K, Kessler T, van den Bent MJ, Idbaih A, Platten M, Brandes AA, Nabors B, Stupp R, Bernhardt D, Debus J, Abdollahi A, Gorlia T, Tonn JC, Weller M, Maier-Hein KH, Radbruch A, Wick W, Bendszus M, Meredig H, Kurz FT, Vollmuth P. Deep-learning-based reconstruction of undersampled MRI to reduce scan times: a multicentre, retrospective, cohort study. Lancet Oncol 2024; 25:400-410. [PMID: 38423052 DOI: 10.1016/s1470-2045(23)00641-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 11/20/2023] [Accepted: 12/07/2023] [Indexed: 03/02/2024]
Abstract
BACKGROUND The extended acquisition times required for MRI limit its availability in resource-constrained settings. Consequently, accelerating MRI by undersampling k-space data, which is necessary to reconstruct an image, has been a long-standing but important challenge. We aimed to develop a deep convolutional neural network (dCNN) optimisation method for MRI reconstruction and to reduce scan times and evaluate its effect on image quality and accuracy of oncological imaging biomarkers. METHODS In this multicentre, retrospective, cohort study, MRI data from patients with glioblastoma treated at Heidelberg University Hospital (775 patients and 775 examinations) and from the phase 2 CORE trial (260 patients, 1083 examinations, and 58 institutions) and the phase 3 CENTRIC trial (505 patients, 3147 examinations, and 139 institutions) were used to develop, train, and test dCNN for reconstructing MRI from highly undersampled single-coil k-space data with various acceleration rates (R=2, 4, 6, 8, 10, and 15). Independent testing was performed with MRIs from the phase 2/3 EORTC-26101 trial (528 patients with glioblastoma, 1974 examinations, and 32 institutions). The similarity between undersampled dCNN-reconstructed and original MRIs was quantified with various image quality metrics, including structural similarity index measure (SSIM) and the accuracy of undersampled dCNN-reconstructed MRI on downstream radiological assessment of imaging biomarkers in oncology (automated artificial intelligence-based quantification of tumour burden and treatment response) was performed in the EORTC-26101 test dataset. The public NYU Langone Health fastMRI brain test dataset (558 patients and 558 examinations) was used to validate the generalisability and robustness of the dCNN for reconstructing MRIs from available multi-coil (parallel imaging) k-space data. FINDINGS In the EORTC-26101 test dataset, the median SSIM of undersampled dCNN-reconstructed MRI ranged from 0·88 to 0·99 across different acceleration rates, with 0·92 (95% CI 0·92-0·93) for 10-times acceleration (R=10). The 10-times undersampled dCNN-reconstructed MRI yielded excellent agreement with original MRI when assessing volumes of contrast-enhancing tumour (median DICE for spatial agreement of 0·89 [95% CI 0·88 to 0·89]; median volume difference of 0·01 cm3 [95% CI 0·00 to 0·03] equalling 0·21%; p=0·0036 for equivalence) or non-enhancing tumour or oedema (median DICE of 0·94 [95% CI 0·94 to 0·95]; median volume difference of -0·79 cm3 [95% CI -0·87 to -0·72] equalling -1·77%; p=0·023 for equivalence) in the EORTC-26101 test dataset. Automated volumetric tumour response assessment in the EORTC-26101 test dataset yielded an identical median time to progression of 4·27 months (95% CI 4·14 to 4·57) when using 10-times-undersampled dCNN-reconstructed or original MRI (log-rank p=0·80) and agreement in the time to progression in 374 (95·2%) of 393 patients with data. The dCNN generalised well to the fastMRI brain dataset, with significant improvements in the median SSIM when using multi-coil compared with single-coil k-space data (p<0·0001). INTERPRETATION Deep-learning-based reconstruction of undersampled MRI allows for a substantial reduction of scan times, with a 10-times acceleration demonstrating excellent image quality while preserving the accuracy of derived imaging biomarkers for the assessment of oncological treatment response. Our developments are available as open source software and hold considerable promise for increasing the accessibility to MRI, pending further prospective validation. FUNDING Deutsche Forschungsgemeinschaft (German Research Foundation) and an Else Kröner Clinician Scientist Endowed Professorship by the Else Kröner Fresenius Foundation.
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Affiliation(s)
- Aditya Rastogi
- Division for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany; Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Gianluca Brugnara
- Division for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany; Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Martha Foltyn-Dumitru
- Division for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany; Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Mustafa Ahmed Mahmutoglu
- Division for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany; Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Chandrakanth J Preetha
- Division for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany; Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Erich Kobler
- Department of Neuroradiology, University Medical Center Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Irada Pflüger
- Division for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany; Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Marianne Schell
- Division for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany; Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Katerina Deike-Hofmann
- Department of Neuroradiology, University Medical Center Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany; German Center for Neurodegenerative Diseases, Bonn, Germany
| | - Tobias Kessler
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany; Clinical Cooperation Unit Neurooncology, German Cancer Consortium within German Cancer Research Center, Heidelberg, Germany
| | | | - Ahmed Idbaih
- Assistance Publique-Hôpitaux de Paris, Service de Neurologie 1, Hôpital Pitié-Salpêtrière, Sorbonne Université, Paris, France
| | - Michael Platten
- Department of Neurology, Medical Faculty Mannheim, Mannheim Center for Translational Neuroscience, University of Heidelberg, Mannheim, Germany; Clinical Cooperation Unit Neuroimmunology and Brain Tumor Immunology, German Cancer Consortium within German Cancer Research Center, Heidelberg, Germany
| | - Alba A Brandes
- Department of Medical Oncology, Azienda UnitàSanitaria Locale of Bologna, Bologna, Italy
| | - Burt Nabors
- Department of Neurology, Division of Neuro-Oncology, University of Alabama at Birmingham, Birmingham, AL, USA; O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Roger Stupp
- Lou and Jean Malnati Brain Tumor Institute, Robert H Lurie Comprehensive Cancer Center, Northwestern Medicine and Northwestern University, Chicago, USA; Department of Neurological Surgery, Northwestern Medicine and Northwestern University, Chicago, USA; Department of Neurology, Northwestern Medicine and Northwestern University, Chicago, USA
| | - Denise Bernhardt
- Department of Radiation Oncology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Jürgen Debus
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany; Heidelberg Institute of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany; Heidelberg Ion-Beam Therapy Center, Heidelberg University Hospital, Heidelberg, Germany
| | - Amir Abdollahi
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany; Heidelberg Institute of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany; Heidelberg Ion-Beam Therapy Center, Heidelberg University Hospital, Heidelberg, Germany
| | - Thierry Gorlia
- European Organization for Research and Treatment of Cancer, Brussels, Belgium
| | - Jörg-Christian Tonn
- Department of Neurosurgery, Ludwig-Maximilians-University, Munich, Germany; German Cancer Consortium within German Center Research Center, partner site Munich, Germany
| | - Michael Weller
- Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland
| | - Klaus H Maier-Hein
- Medical Image Computing, German Cancer Research Center, Heidelberg, Germany; Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Alexander Radbruch
- Department of Neuroradiology, University Medical Center Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Wolfgang Wick
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany; Clinical Cooperation Unit Neurooncology, German Cancer Consortium within German Cancer Research Center, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Hagen Meredig
- Division for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany; Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Felix T Kurz
- Division of Diagnostic and Interventional Neuroradiology, Geneva University Hospitals, Geneva, Switzerland; Department of Radiology, German Cancer Research Center, Heidelberg, Germany
| | - Philipp Vollmuth
- Division for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany; Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany; Department of Neuroradiology, University Medical Center Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany; Medical Image Computing, German Cancer Research Center, Heidelberg, Germany.
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Reinecke R, Jahnke K, Foltyn-Dumitru M, Lachner K, Armbrust M, Weber KJ, Zeiner PS, Czabanka M, Brunnberg U, Hartmann S, Steinbach JP, Ronellenfitsch MW. Intrathecal IgM synthesis as a diagnostic marker in patients with suspected CNS lymphoma. J Neurochem 2024. [PMID: 38332527 DOI: 10.1111/jnc.16069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 01/19/2024] [Accepted: 01/23/2024] [Indexed: 02/10/2024]
Abstract
For CNS lymphomas (CNSL), there is a high need for minimally invasive and easily obtainable diagnostic markers. Intrathecal IgM synthesis can easily be determined in routine CSF diagnostics. The aim of this study was to systematically investigate the diagnostic potential of intrathecal IgM synthesis in primary and secondary CNSL (PCNSL and SCNSL). In this retrospective study, patients with a biopsy-proven diagnosis of PCNSL or SCNSL were compared with patients with other neurological diseases in whom CNSL was initially the primary radiological differential diagnosis based on MRI. Sensitivity and specificity of intrathecal IgM synthesis were calculated using receiver operating characteristic curves. Seventy patients with CNSL were included (49 PCNSL and 21 SCNSL) and compared to 70 control patients. The sensitivity and specificity for the diagnosis of CNSL were 49% and 87%, respectively, for the entire patient population and 66% and 91% after selection for cases with tumor access to the CSF system and isolated intrathecal IgM synthesis. In cases with MRI-based radiological suspicion of CNSL, intrathecal IgM synthesis has good specificity but limited sensitivity. Because of its low-threshold availability, analysis of intrathecal IgM synthesis has the potential to lead to higher diagnostic accuracy, especially in resource-limited settings, and deserves further study.
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Affiliation(s)
- Raphael Reinecke
- Dr. Senckenberg Institute of Neurooncology, Goethe University Frankfurt, University Hospital, Frankfurt am Main, Germany
- Department of Neurology, Goethe University Frankfurt, University Hospital, Frankfurt am Main, Germany
- University Cancer Center (UCT), Goethe University Frankfurt, University Hospital, Frankfurt am Main, Germany
| | - Kolja Jahnke
- Department of Neurology, Goethe University Frankfurt, University Hospital, Frankfurt am Main, Germany
| | - Martha Foltyn-Dumitru
- Dr. Senckenberg Institute of Neurooncology, Goethe University Frankfurt, University Hospital, Frankfurt am Main, Germany
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Karsten Lachner
- Institute of Neuroradiology, Goethe University Frankfurt, University Hospital, Frankfurt am Main, Germany
| | - Moritz Armbrust
- Neurological Institute (Edinger Institute), Goethe University Frankfurt, University Hospital, Frankfurt am Main, Germany
| | - Katharina J Weber
- University Cancer Center (UCT), Goethe University Frankfurt, University Hospital, Frankfurt am Main, Germany
- Neurological Institute (Edinger Institute), Goethe University Frankfurt, University Hospital, Frankfurt am Main, Germany
- German Cancer Consortium (DKTK), partner site Frankfurt/Mainz, a partnership between DKFZ and University Hospital Frankfurt, Frankfurt am Main, Germany
- Frankfurt Cancer Institute (FCI), Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Pia S Zeiner
- Dr. Senckenberg Institute of Neurooncology, Goethe University Frankfurt, University Hospital, Frankfurt am Main, Germany
- Department of Neurology, Goethe University Frankfurt, University Hospital, Frankfurt am Main, Germany
- University Cancer Center (UCT), Goethe University Frankfurt, University Hospital, Frankfurt am Main, Germany
- German Cancer Consortium (DKTK), partner site Frankfurt/Mainz, a partnership between DKFZ and University Hospital Frankfurt, Frankfurt am Main, Germany
- Frankfurt Cancer Institute (FCI), Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Marcus Czabanka
- Department of Neurosurgery, Goethe University Frankfurt, University Hospital, Frankfurt am Main, Germany
| | - Uta Brunnberg
- University Cancer Center (UCT), Goethe University Frankfurt, University Hospital, Frankfurt am Main, Germany
- Department of Medicine, Hematology and Oncology, Goethe University Frankfurt, University Hospital, Frankfurt am Main, Germany
| | - Sylvia Hartmann
- Dr. Senckenberg Institute of Pathology, Goethe University Frankfurt, University Hospital, Frankfurt am Main, Germany
| | - Joachim P Steinbach
- Department of Neurology, Goethe University Frankfurt, University Hospital, Frankfurt am Main, Germany
- University Cancer Center (UCT), Goethe University Frankfurt, University Hospital, Frankfurt am Main, Germany
- German Cancer Consortium (DKTK), partner site Frankfurt/Mainz, a partnership between DKFZ and University Hospital Frankfurt, Frankfurt am Main, Germany
- Frankfurt Cancer Institute (FCI), Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Michael W Ronellenfitsch
- Dr. Senckenberg Institute of Neurooncology, Goethe University Frankfurt, University Hospital, Frankfurt am Main, Germany
- Department of Neurology, Goethe University Frankfurt, University Hospital, Frankfurt am Main, Germany
- University Cancer Center (UCT), Goethe University Frankfurt, University Hospital, Frankfurt am Main, Germany
- German Cancer Consortium (DKTK), partner site Frankfurt/Mainz, a partnership between DKFZ and University Hospital Frankfurt, Frankfurt am Main, Germany
- Frankfurt Cancer Institute (FCI), Goethe University Frankfurt, Frankfurt am Main, Germany
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Foltyn-Dumitru M, Schell M, Sahm F, Kessler T, Wick W, Bendszus M, Rastogi A, Brugnara G, Vollmuth P. Advancing noninvasive glioma classification with diffusion radiomics: Exploring the impact of signal intensity normalization. Neurooncol Adv 2024; 6:vdae043. [PMID: 38596719 PMCID: PMC11003539 DOI: 10.1093/noajnl/vdae043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2024] Open
Abstract
Background This study investigates the influence of diffusion-weighted Magnetic Resonance Imaging (DWI-MRI) on radiomic-based prediction of glioma types according to molecular status and assesses the impact of DWI intensity normalization on model generalizability. Methods Radiomic features, compliant with image biomarker standardization initiative standards, were extracted from preoperative MRI of 549 patients with diffuse glioma, known IDH, and 1p19q-status. Anatomical sequences (T1, T1c, T2, FLAIR) underwent N4-Bias Field Correction (N4) and WhiteStripe normalization (N4/WS). Apparent diffusion coefficient (ADC) maps were normalized using N4 or N4/z-score. Nine machine-learning algorithms were trained for multiclass prediction of glioma types (IDH-mutant 1p/19q codeleted, IDH-mutant 1p/19q non-codeleted, IDH-wild type). Four approaches were compared: Anatomical, anatomical + ADC naive, anatomical + ADC N4, and anatomical + ADC N4/z-score. The University of California San Francisco (UCSF)-glioma dataset (n = 409) was used for external validation. Results Naïve-Bayes algorithms yielded overall the best performance on the internal test set. Adding ADC radiomics significantly improved AUC from 0.79 to 0.86 (P = .011) for the IDH-wild-type subgroup, but not for the other 2 glioma subgroups (P > .05). In the external UCSF dataset, the addition of ADC radiomics yielded a significantly higher AUC for the IDH-wild-type subgroup (P ≤ .001): 0.80 (N4/WS anatomical alone), 0.81 (anatomical + ADC naive), 0.81 (anatomical + ADC N4), and 0.88 (anatomical + ADC N4/z-score) as well as for the IDH-mutant 1p/19q non-codeleted subgroup (P < .012 each). Conclusions ADC radiomics can enhance the performance of conventional MRI-based radiomic models, particularly for IDH-wild-type glioma. The benefit of intensity normalization of ADC maps depends on the type and context of the used data.
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Affiliation(s)
- Martha Foltyn-Dumitru
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Division for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Marianne Schell
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Division for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Felix Sahm
- Department of Neuropathology, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Tobias Kessler
- Department of Neurology and Neurooncology Program, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
- Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Wolfgang Wick
- Department of Neurology and Neurooncology Program, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
- Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Aditya Rastogi
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Division for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Division for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Division for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Division for Computational Radiology & Clinical AI, Department of Neuroradiology, Bonn University Hospital, Bonn, Germany
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Foltyn-Dumitru M, Kessler T, Sahm F, Wick W, Heiland S, Bendszus M, Vollmuth P, Schell M. Cluster-based prognostication in glioblastoma: Unveiling heterogeneity based on diffusion and perfusion similarities. Neuro Oncol 2023:noad259. [PMID: 38153923 DOI: 10.1093/neuonc/noad259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Indexed: 12/30/2023] Open
Abstract
BACKGROUND While the association between diffusion and perfusion MRI and survival in glioblastoma is established, prognostic models for patients are lacking. This study employed clustering of functional imaging to identify distinct functional phenotypes in untreated glioblastomas, assessing their prognostic significance for overall survival. METHODS A total of 289 patients with glioblastoma who underwent preoperative multimodal MR imaging were included. Mean values of apparent diffusion coefficient (ADC) normalized relative cerebral blood volume (nrCBV), and relative cerebral blood flow (rCBF) were calculated for different tumor compartments and the entire tumor. Distinct imaging patterns were identified using Partition Around Medoids (PAM) clustering on the training dataset, and their ability to predict overall survival was assessed. Additionally, tree-based machine-learning models were trained to ascertain the significance of features pertaining to cluster membership. RESULTS Using the training dataset (231/289) we identified two stable imaging phenotypes through PAM clustering with significantly different overall survival (OS). Validation in an independent test set revealed a high-risk group with a median OS of 10.2 months and a low-risk group with a median OS of 26.6 months (p=0.012). Patients in the low-risk cluster had high diffusion and low perfusion values throughout, while the high-risk cluster displayed the reverse pattern. Including cluster membership in all multivariate Cox regression analyses improved performance (p≤ 0.004 each). CONCLUSIONS Our research demonstrates that data-driven clustering can identify clinically relevant, distinct imaging phenotypes, highlighting the potential role of diffusion and perfusion MRI in predicting survival rates of glioblastoma patients.
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Affiliation(s)
- Martha Foltyn-Dumitru
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE
| | - Tobias Kessler
- Department of Neurology and Neurooncology Program, Heidelberg University Hospital, Heidelberg University, Heidelberg, DE
- Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, DE
| | - Felix Sahm
- Department of Neuropathology, Heidelberg University Hospital, Heidelberg, DE
| | - Wolfgang Wick
- Department of Neurology and Neurooncology Program, Heidelberg University Hospital, Heidelberg University, Heidelberg, DE
- Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, DE
| | - Sabine Heiland
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE
| | - Marianne Schell
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE
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Park YW, Vollmuth P, Foltyn-Dumitru M, Sahm F, Choi KS, Park JE, Ahn SS, Chang JH, Kim SH. The 2021 WHO Classification for Gliomas and Implications on Imaging Diagnosis: Part 3-Summary of Imaging Findings on Glioneuronal and Neuronal Tumors. J Magn Reson Imaging 2023; 58:1680-1702. [PMID: 37715567 DOI: 10.1002/jmri.29016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 07/22/2023] [Accepted: 07/24/2023] [Indexed: 09/17/2023] Open
Abstract
The fifth edition of the World Health Organization classification of central nervous system tumors published in 2021 reflects the current transitional state between traditional classification system based on histopathology and the state-of-the-art molecular diagnostics. This Part 3 Review focuses on the molecular diagnostics and imaging findings of glioneuronal and neuronal tumors. Histological and molecular features in glioneuronal and neuronal tumors often overlap with pediatric-type diffuse low-grade gliomas and circumscribed astrocytic gliomas (discussed in the Part 2 Review). Due to this overlap, in several tumor types of glioneuronal and neuronal tumors the diagnosis may be inconclusive with histopathology and genetic alterations, and imaging features may be helpful to distinguish difficult cases. Thus, it is crucial for radiologists to understand the underlying molecular diagnostics as well as imaging findings for application on clinical practice. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Yae Won Park
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University College of Medicine, Heidelberg, Germany
| | - Martha Foltyn-Dumitru
- Department of Neuroradiology, Heidelberg University College of Medicine, Heidelberg, Germany
| | - Felix Sahm
- Department of Neuropathology, Heidelberg University College of Medicine, Heidelberg, Germany
| | - Kyu Sung Choi
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Ji Eun Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Seoul, Korea
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Jong Hee Chang
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea
| | - Se Hoon Kim
- Department of Pathology, Yonsei University College of Medicine, Seoul, Korea
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7
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Park YW, Vollmuth P, Foltyn-Dumitru M, Sahm F, Ahn SS, Chang JH, Kim SH. The 2021 WHO Classification for Gliomas and Implications on Imaging Diagnosis: Part 2-Summary of Imaging Findings on Pediatric-Type Diffuse High-Grade Gliomas, Pediatric-Type Diffuse Low-Grade Gliomas, and Circumscribed Astrocytic Gliomas. J Magn Reson Imaging 2023; 58:690-708. [PMID: 37069764 DOI: 10.1002/jmri.28740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 03/26/2023] [Accepted: 03/27/2023] [Indexed: 04/19/2023] Open
Abstract
The fifth edition of the World Health Organization (WHO) classification of central nervous system tumors published in 2021 advances the role of molecular diagnostics in the classification of gliomas by emphasizing integrated diagnoses based on histopathology and molecular information and grouping tumors based on genetic alterations. This Part 2 review focuses on the molecular diagnostics and imaging findings of pediatric-type diffuse high-grade gliomas, pediatric-type diffuse low-grade gliomas, and circumscribed astrocytic gliomas. Each tumor type in pediatric-type diffuse high-grade glioma mostly harbors a distinct molecular marker. On the other hand, in pediatric-type diffuse low-grade gliomas and circumscribed astrocytic gliomas, molecular diagnostics may be extremely complicated at a glance in the 2021 WHO classification. It is crucial for radiologists to understand the molecular diagnostics and imaging findings and leverage the knowledge in clinical practice. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Yae Won Park
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Philipp Vollmuth
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University College of Medicine, Heidelberg, Germany
| | - Martha Foltyn-Dumitru
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University College of Medicine, Heidelberg, Germany
| | - Felix Sahm
- Department of Neuropathology, Heidelberg University College of Medicine, Heidelberg, Germany
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Jong Hee Chang
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, South Korea
| | - Se Hoon Kim
- Department of Pathology, Yonsei University College of Medicine, Seoul, South Korea
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8
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Park YW, Vollmuth P, Foltyn-Dumitru M, Sahm F, Ahn SS, Chang JH, Kim SH. The 2021 WHO Classification for Gliomas and Implications on Imaging Diagnosis: Part 1-Key Points of the Fifth Edition and Summary of Imaging Findings on Adult-Type Diffuse Gliomas. J Magn Reson Imaging 2023; 58:677-689. [PMID: 37069792 DOI: 10.1002/jmri.28743] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 03/31/2023] [Accepted: 04/03/2023] [Indexed: 04/19/2023] Open
Abstract
The fifth edition of the World Health Organization (WHO) classification of central nervous system tumors published in 2021 advances the role of molecular diagnostics in the classification of gliomas by emphasizing integrated diagnoses based on histopathology and molecular information and grouping tumors based on genetic alterations. Importantly, molecular biomarkers that provide important prognostic information are now a parameter for establishing tumor grades in gliomas. Understanding the 2021 WHO classification is crucial for radiologists for daily imaging interpretation as well as communication with clinicians. Although imaging features are not included in the 2021 WHO classification, imaging can serve as a powerful tool to impact the clinical practice not only prior to tissue confirmation but beyond. This review represents the first of a three-installment review series on the 2021 WHO classification for gliomas, glioneuronal tumors, and neuronal tumors and implications on imaging diagnosis. This Part 1 Review focuses on the major changes to the classification of gliomas and imaging findings on adult-type diffuse gliomas. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Yae Won Park
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Philipp Vollmuth
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University College of Medicine, Heidelberg, Germany
| | - Martha Foltyn-Dumitru
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University College of Medicine, Heidelberg, Germany
| | - Felix Sahm
- Department of Neuropathology, Heidelberg University College of Medicine, Heidelberg, Germany
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Jong Hee Chang
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea
| | - Se Hoon Kim
- Department of Pathology, Yonsei University College of Medicine, Seoul, Korea
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Schell M, Foltyn-Dumitru M, Bendszus M, Vollmuth P. Automated hippocampal segmentation algorithms evaluated in stroke patients. Sci Rep 2023; 13:11712. [PMID: 37474622 PMCID: PMC10359355 DOI: 10.1038/s41598-023-38833-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 07/16/2023] [Indexed: 07/22/2023] Open
Abstract
Deep learning segmentation algorithms can produce reproducible results in a matter of seconds. However, their application to more complex datasets is uncertain and may fail in the presence of severe structural abnormalities-such as those commonly seen in stroke patients. In this investigation, six recent, deep learning-based hippocampal segmentation algorithms were tested on 641 stroke patients of a multicentric, open-source dataset ATLAS 2.0. The comparisons of the volumes showed that the methods are not interchangeable with concordance correlation coefficients from 0.266 to 0.816. While the segmentation algorithms demonstrated an overall good performance (volumetric similarity [VS] 0.816 to 0.972, DICE score 0.786 to 0.921, and Hausdorff distance [HD] 2.69 to 6.34), no single out-performing algorithm was identified: FastSurfer performed best in VS, QuickNat in DICE and average HD, and Hippodeep in HD. Segmentation performance was significantly lower for ipsilesional segmentation, with a decrease in performance as a function of lesion size due to the pathology-based domain shift. Only QuickNat showed a more robust performance in volumetric similarity. Even though there are many pre-trained segmentation methods, it is important to be aware of the possible decrease in performance for the segmentation results on the lesion side due to the pathology-based domain shift. The segmentation algorithm should be selected based on the research question and the evaluation parameter needed. More research is needed to improve current hippocampal segmentation methods.
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Affiliation(s)
- Marianne Schell
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
| | - Martha Foltyn-Dumitru
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany.
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10
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Leone R, Meredig H, Foltyn-Dumitru M, Sahm F, Hamelmann S, Kurz F, Kessler T, Bonekamp D, Schlemmer HP, Hansen MB, Wick W, Bendszus M, Vollmuth P, Brugnara G. Assessing the added value of ADC, CBV and radiomic MR features for differentiation of pseudoprogression vs. true tumor progression in patients with glioblastoma. Neurooncol Adv 2023; 5:vdad016. [PMID: 36968291 PMCID: PMC10034916 DOI: 10.1093/noajnl/vdad016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2023] Open
Abstract
Abstract
Background
Pseudoprogression (PsPD) is a major diagnostic challenge in the follow-up of patients with glioblastoma (GB) after chemo-radiotherapy (CRT). Conventional imaging signs and parameters derived from diffusion and perfusion-MRI have yet to prove their reliability in clinical practice for an accurate differential diagnosis. Here, we tested these parameters and combined them with radiomic features (RFs), clinical data, and MGMT-promoter methylation-status using machine- and deep-learning models to distinguish PsPD from PD.
Methods
In a single-center analysis, 105 patients with GB who developed a suspected imaging PsPD in the first 7 months after standard-CRT were identified retrospectively. Imaging data included standard MRI anatomical sequences, apparent diffusion coefficient (ADC) and normalized relative cerebral blood volume (nrCBV) maps. Median values (ADC, nrCBV) and RFs (all sequences) were calculated from deep-learning-based tumor segmentations. Generalized linear models with LASSO feature-selection and deep-learning models were built integrating clinical data, MGMT-methylation-status, median ADC and nrCBV values and RFs.
Results
A model based on clinical data and MGMT-methylation-status yielded an AUC=0.69 (95%CI 0.55-0.83) for detecting PsPD, and the addition of median ADC and nrCBV values resulted in a non-significant increase in performance (AUC=0.71,95%CI 0.57-0.85, p=0.416). Combining clinical/MGMT information with RFs derived from ADC, nrCBV and from all available sequences both resulted in significantly (both p<0.005) lower model performances, with AUC=0.52 (0.38-0.66) and AUC=0.54 (0.40-0.68), respectively. Deep-learning imaging models resulted in AUCs≤0.56.
Conclusion
Currently available imaging biomarkers could not reliably differentiate PsPD from true tumor progression in patients with glioblastoma; larger collaborative efforts are needed to build more reliable models.
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Affiliation(s)
- Riccardo Leone
- Department of Neuroradiology, Heidelberg University Hospital , Heidelberg, Germany
- Division for Computational Neuroimaging, Heidelberg University Hospital , Heidelberg, Germany
| | - Hagen Meredig
- Department of Neuroradiology, Heidelberg University Hospital , Heidelberg, Germany
- Division for Computational Neuroimaging, Heidelberg University Hospital , Heidelberg, Germany
| | - Martha Foltyn-Dumitru
- Department of Neuroradiology, Heidelberg University Hospital , Heidelberg, Germany
- Division for Computational Neuroimaging, Heidelberg University Hospital , Heidelberg, Germany
| | - Felix Sahm
- Department of Pathology, Heidelberg University Hospital , Heidelberg, Germany
| | - Stefan Hamelmann
- Department of Pathology, Heidelberg University Hospital , Heidelberg, Germany
| | - Felix Kurz
- Department of Radiology, German Cancer Research Center (DKFZ) , Heidelberg, Germany
| | - Tobias Kessler
- Department of Neurology, Heidelberg University Hospital , Heidelberg, Germany
| | - David Bonekamp
- Department of Radiology, German Cancer Research Center (DKFZ) , Heidelberg, Germany
| | | | - Mikkel Bo Hansen
- Center for Functionally Integrative Neuroscience (CFIN), University of Aarhus , Aarhus, Denmark
| | - Wolfgang Wick
- Department of Neurology, Heidelberg University Hospital , Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital , Heidelberg, Germany
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital , Heidelberg, Germany
- Division for Computational Neuroimaging, Heidelberg University Hospital , Heidelberg, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital , Heidelberg, Germany
- Division for Computational Neuroimaging, Heidelberg University Hospital , Heidelberg, Germany
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