TY - JOUR
T1 - Deep-learning-based synthesis of post-contrast T1-weighted MRI for tumour response assessment in neuro-oncology
T2 - a multicentre, retrospective cohort study
AU - Jayachandran Preetha, Chandrakanth
AU - Meredig, Hagen
AU - Brugnara, Gianluca
AU - Mahmutoglu, Mustafa A.
AU - Foltyn, Martha
AU - Isensee, Fabian
AU - Kessler, Tobias
AU - Pflüger, Irada
AU - Schell, Marianne
AU - Neuberger, Ulf
AU - Petersen, Jens
AU - Wick, Antje
AU - Heiland, Sabine
AU - Debus, Jürgen
AU - Platten, Michael
AU - Idbaih, Ahmed
AU - Brandes, Alba A.
AU - Winkler, Frank
AU - van den Bent, Martin J.
AU - Nabors, Burt
AU - Stupp, Roger
AU - Maier-Hein, Klaus H.
AU - Gorlia, Thierry
AU - Tonn, Jörg Christian
AU - Weller, Michael
AU - Wick, Wolfgang
AU - Bendszus, Martin
AU - Vollmuth, Philipp
N1 - Funding:
Deutsche Forschungsgemeinschaft.
Role of the funding source:
The funders of this study or the three clinical trials from
which the data were obtained had no role in study design,
data collection, data analysis, data interpretation, or
writing of the report.
Publisher Copyright:
© 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license
PY - 2021/12
Y1 - 2021/12
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85119472751&partnerID=8YFLogxK
U2 - 10.1016/S2589-7500(21)00205-3
DO - 10.1016/S2589-7500(21)00205-3
M3 - Article
C2 - 34688602
AN - SCOPUS:85119472751
SN - 2589-7500
VL - 3
SP - e784-e794
JO - The Lancet Digital Health
JF - The Lancet Digital Health
IS - 12
ER -