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Jayachandran Preetha C, Meredig H, Brugnara G, Mahmutoglu MA, Foltyn M, Isensee F, Kessler T, Pflüger I, Schell M, Neuberger U, Petersen J, Wick A, Heiland S, Debus J, Platten M, Idbaih A, Brandes AA, Winkler F, van den Bent MJ, Nabors B, Stupp R, Maier-Hein KH, Gorlia T, Tonn JC, Weller M, Wick W, Bendszus M, Vollmuth P. Deep-learning-based synthesis of post-contrast T1-weighted MRI for tumour response assessment in neuro-oncology: a multicentre, retrospective cohort study. Lancet Digit Health 2021; 3:e784-e794. [PMID: 34688602 DOI: 10.1016/s2589-7500(21)00205-3] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 07/14/2021] [Accepted: 08/10/2021] [Indexed: 12/30/2022]
Abstract
BACKGROUND Gadolinium-based contrast agents (GBCAs) are widely used to enhance tissue contrast during MRI scans and play a crucial role in the management of patients with cancer. However, studies have shown gadolinium deposition in the brain after repeated GBCA administration with yet unknown clinical significance. We aimed to assess the feasibility and diagnostic value of synthetic post-contrast T1-weighted MRI generated from pre-contrast MRI sequences through deep convolutional neural networks (dCNN) for tumour response assessment in neuro-oncology. METHODS In this multicentre, retrospective cohort study, we used MRI examinations to train and validate a dCNN for synthesising post-contrast T1-weighted sequences from pre-contrast T1-weighted, T2-weighted, and fluid-attenuated inversion recovery sequences. We used MRI scans with availability of these sequences from 775 patients with glioblastoma treated at Heidelberg University Hospital, Heidelberg, Germany (775 MRI examinations); 260 patients who participated in the phase 2 CORE trial (1083 MRI examinations, 59 institutions); and 505 patients who participated in the phase 3 CENTRIC trial (3147 MRI examinations, 149 institutions). Separate training runs to rank the importance of individual sequences and (for a subset) diffusion-weighted imaging were conducted. Independent testing was performed on MRI data from the phase 2 and phase 3 EORTC-26101 trial (521 patients, 1924 MRI examinations, 32 institutions). The similarity between synthetic and true contrast enhancement on post-contrast T1-weighted MRI was quantified using the structural similarity index measure (SSIM). Automated tumour segmentation and volumetric tumour response assessment based on synthetic versus true post-contrast T1-weighted sequences was performed in the EORTC-26101 trial and agreement was assessed with Kaplan-Meier plots. FINDINGS The median SSIM score for predicting contrast enhancement on synthetic post-contrast T1-weighted sequences in the EORTC-26101 test set was 0·818 (95% CI 0·817-0·820). Segmentation of the contrast-enhancing tumour from synthetic post-contrast T1-weighted sequences yielded a median tumour volume of 6·31 cm3 (5·60 to 7·14), thereby underestimating the true tumour volume by a median of -0·48 cm3 (-0·37 to -0·76) with the concordance correlation coefficient suggesting a strong linear association between tumour volumes derived from synthetic versus true post-contrast T1-weighted sequences (0·782, 0·751-0·807, p<0·0001). Volumetric tumour response assessment in the EORTC-26101 trial showed a median time to progression of 4·2 months (95% CI 4·1-5·2) with synthetic post-contrast T1-weighted and 4·3 months (4·1-5·5) with true post-contrast T1-weighted sequences (p=0·33). The strength of the association between the time to progression as a surrogate endpoint for predicting the patients' overall survival in the EORTC-26101 cohort was similar when derived from synthetic post-contrast T1-weighted sequences (hazard ratio of 1·749, 95% CI 1·282-2·387, p=0·0004) and model C-index (0·667, 0·622-0·708) versus true post-contrast T1-weighted MRI (1·799, 95% CI 1·314-2·464, p=0·0003) and model C-index (0·673, 95% CI 0·626-0·711). INTERPRETATION Generating synthetic post-contrast T1-weighted MRI from pre-contrast MRI using dCNN is feasible and quantification of the contrast-enhancing tumour burden from synthetic post-contrast T1-weighted MRI allows assessment of the patient's response to treatment with no significant difference by comparison with true post-contrast T1-weighted sequences with administration of GBCAs. This finding could guide the application of dCNN in radiology to potentially reduce the necessity of GBCA administration. FUNDING Deutsche Forschungsgemeinschaft.
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Affiliation(s)
| | - Hagen Meredig
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Mustafa A Mahmutoglu
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Martha Foltyn
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Fabian Isensee
- Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Tobias Kessler
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany; Clinical Cooperation Unit Neurooncology, German Cancer Research Center, Heidelberg, Germany
| | - Irada Pflüger
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Marianne Schell
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Ulf Neuberger
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Jens Petersen
- Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Antje Wick
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
| | - Sabine Heiland
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Jürgen Debus
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany; Heidelberg Institute of Radiation Oncology, Heidelberg, Germany; Heidelberg Ion-Beam Therapy Center, Heidelberg, Germany
| | - Michael Platten
- Clinical Cooperation Unit Neuroimmunology and Brain Tumor Immunology, German Cancer Research Center, Heidelberg, Germany; Department of Neurology, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Ahmed Idbaih
- Sorbonne Université, Inserm, Institut du Cerveau, Assistance Publique-Hôpitaux de Paris, Hôpitaux Universitaires La Pitié Salpêtrière-Charles Foix, Service de Neurologie 2-Mazarin, Paris, France
| | - Alba A Brandes
- Department of Medical Oncology, Azienda USL of Bologna, Bologna, Italy
| | - Frank Winkler
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany; Clinical Cooperation Unit Neurooncology, German Cancer Research Center, Heidelberg, Germany
| | | | - Burt Nabors
- Department of Neurology and O'Neal Comprehensive Cancer Center, Division of Neuro-Oncology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Roger Stupp
- Malnati Brain Tumor Institute of the Lurie Comprehensive Cancer Center, Department of Neurological Surgery and Department of Neurology, Northwestern Medicine and Northwestern University, Chicago, IL, USA
| | - Klaus H Maier-Hein
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany; Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Thierry Gorlia
- European Organisation for Research and Treatment of Cancer, Brussels, Belgium
| | | | - Michael Weller
- Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland
| | - Wolfgang Wick
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany; Clinical Cooperation Unit Neurooncology, German Cancer Research Center, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany.
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Brugnara G, Neuberger U, Mahmutoglu MA, Foltyn M, Herweh C, Nagel S, Schönenberger S, Heiland S, Ulfert C, Ringleb PA, Bendszus M, Möhlenbruch MA, Pfaff JA, Vollmuth P. Multimodal Predictive Modeling of Endovascular Treatment Outcome for Acute Ischemic Stroke Using Machine-Learning. Stroke 2020; 51:3541-3551. [DOI: 10.1161/strokeaha.120.030287] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Background and Purpose:
This study assessed the predictive performance and relative importance of clinical, multimodal imaging, and angiographic characteristics for predicting the clinical outcome of endovascular treatment for acute ischemic stroke.
Methods:
A consecutive series of 246 patients with acute ischemic stroke and large vessel occlusion in the anterior circulation who underwent endovascular treatment between April 2014 and January 2018 was analyzed. Clinical, conventional imaging (electronic Alberta Stroke Program Early CT Score, acute ischemic volume, site of vessel occlusion, and collateral score), and advanced imaging characteristics (CT-perfusion with quantification of ischemic penumbra and infarct core volumes) before treatment as well as angiographic (interval groin puncture-recanalization, modified Thrombolysis in Cerebral Infarction score) and postinterventional clinical (National Institutes of Health Stroke Scale score after 24 hours) and imaging characteristics (electronic Alberta Stroke Program Early CT Score, final infarction volume after 18–36 hours) were assessed. The modified Rankin Scale (mRS) score at 90 days (mRS-90) was used to measure patient outcome (favorable outcome: mRS-90 ≤2 versus unfavorable outcome: mRS-90 >2). Machine-learning with gradient boosting classifiers was used to assess the performance and relative importance of the extracted characteristics for predicting mRS-90.
Results:
Baseline clinical and conventional imaging characteristics predicted mRS-90 with an area under the receiver operating characteristics curve of 0.740 (95% CI, 0.733–0.747) and an accuracy of 0.711 (95% CI, 0.705–0.717). Advanced imaging with CT-perfusion did not improved the predictive performance (area under the receiver operating characteristics curve, 0.747 [95% CI, 0.740–0.755]; accuracy, 0.720 [95% CI, 0.714–0.727];
P
=0.150). Further inclusion of angiographic and postinterventional characteristics significantly improved the predictive performance (area under the receiver operating characteristics curve, 0.856 [95% CI, 0.850–0.861]; accuracy, 0.804 [95% CI, 0.799–0.810];
P
<0.001). The most important parameters for predicting mRS 90 were National Institutes of Health Stroke Scale score after 24 hours (importance =100%), premorbid mRS score (importance =44%) and final infarction volume on postinterventional CT after 18 to 36 hours (importance =32%).
Conclusions:
Integrative assessment of clinical, multimodal imaging, and angiographic characteristics with machine-learning allowed to accurately predict the clinical outcome following endovascular treatment for acute ischemic stroke. Thereby, premorbid mRS was the most important clinical predictor for mRS-90, and the final infarction volume was the most important imaging predictor, while the extent of hemodynamic impairment on CT-perfusion before treatment had limited importance.
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Affiliation(s)
- Gianluca Brugnara
- Department of Neuroradiology (G.B., U.N., M.A.M., M.F., Ch.H., S.H., C.U., M.B., M.A.M., J.A.R.P., P.V.), Heidelberg University Hospital, Germany
| | - Ulf Neuberger
- Department of Neuroradiology (G.B., U.N., M.A.M., M.F., Ch.H., S.H., C.U., M.B., M.A.M., J.A.R.P., P.V.), Heidelberg University Hospital, Germany
| | - Mustafa A. Mahmutoglu
- Department of Neuroradiology (G.B., U.N., M.A.M., M.F., Ch.H., S.H., C.U., M.B., M.A.M., J.A.R.P., P.V.), Heidelberg University Hospital, Germany
| | - Martha Foltyn
- Department of Neuroradiology (G.B., U.N., M.A.M., M.F., Ch.H., S.H., C.U., M.B., M.A.M., J.A.R.P., P.V.), Heidelberg University Hospital, Germany
| | - Christian Herweh
- Department of Neuroradiology (G.B., U.N., M.A.M., M.F., Ch.H., S.H., C.U., M.B., M.A.M., J.A.R.P., P.V.), Heidelberg University Hospital, Germany
| | - Simon Nagel
- Neurology Clinic (S.N., S.S., P.A.R.), Heidelberg University Hospital, Germany
| | | | - Sabine Heiland
- Department of Neuroradiology (G.B., U.N., M.A.M., M.F., Ch.H., S.H., C.U., M.B., M.A.M., J.A.R.P., P.V.), Heidelberg University Hospital, Germany
| | - Christian Ulfert
- Department of Neuroradiology (G.B., U.N., M.A.M., M.F., Ch.H., S.H., C.U., M.B., M.A.M., J.A.R.P., P.V.), Heidelberg University Hospital, Germany
| | | | - Martin Bendszus
- Department of Neuroradiology (G.B., U.N., M.A.M., M.F., Ch.H., S.H., C.U., M.B., M.A.M., J.A.R.P., P.V.), Heidelberg University Hospital, Germany
| | - Markus A. Möhlenbruch
- Department of Neuroradiology (G.B., U.N., M.A.M., M.F., Ch.H., S.H., C.U., M.B., M.A.M., J.A.R.P., P.V.), Heidelberg University Hospital, Germany
| | - Johannes A.R. Pfaff
- Department of Neuroradiology (G.B., U.N., M.A.M., M.F., Ch.H., S.H., C.U., M.B., M.A.M., J.A.R.P., P.V.), Heidelberg University Hospital, Germany
| | - Philipp Vollmuth
- Department of Neuroradiology (G.B., U.N., M.A.M., M.F., Ch.H., S.H., C.U., M.B., M.A.M., J.A.R.P., P.V.), Heidelberg University Hospital, Germany
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