1
|
Bisgaard ALH, Brink C, Schytte T, Bahij R, Weisz Ejlsmark M, Bernchou U, Bertelsen AS, Pfeiffer P, Mahmood F. Prediction of overall survival in patients with locally advanced pancreatic cancer using longitudinal diffusion-weighted MRI. Front Oncol 2024; 14:1401464. [PMID: 39091912 PMCID: PMC11291378 DOI: 10.3389/fonc.2024.1401464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 07/08/2024] [Indexed: 08/04/2024] Open
Abstract
Background and purpose Biomarkers for prediction of outcome in patients with pancreatic cancer are wanted in order to personalize the treatment. This study investigated the value of longitudinal diffusion-weighted magnetic resonance imaging (DWI) for prediction of overall survival (OS) in patients with locally advanced pancreatic cancer (LAPC) treated with stereotactic body radiotherapy (SBRT). Materials and methods The study included 45 patients with LAPC who received 5 fractions of 10 Gy on a 1.5T MRI-Linac. DWI was acquired prior to irradiation at each fraction. The analysis included baseline values and time-trends of the apparent diffusion coefficient (ADC) and DWI parameters obtained using a decomposition method. A multivariable Cox proportional hazards model for OS was made using best-subset selection, using cross-validation based on Bootstrap. Results The median OS from the first day of SBRT was 15.5 months (95% CI: 13.2-20.6), and the median potential follow-up time was 19.8 months. The best-performing multivariable model for OS included two decomposition-based DWI parameters: one baseline and one time-trend parameter. The C-Harrell index describing the model's discriminating power was 0.754. High baseline ADC values were associated with reduced OS, whereas no association between the ADC time-trend and OS was observed. Conclusion Decomposition-based DWI parameters indicated value in the prediction of OS in LAPC. A DWI time-trend parameter was included in the best-performing model, indicating a potential benefit of acquiring longitudinal DWI during the SBRT course. These findings support both baseline and longitudinal DWI as candidate prognostic biomarkers, which may become tools for personalization of the treatment of patients with LAPC.
Collapse
Affiliation(s)
- Anne L. H. Bisgaard
- Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, Odense, Denmark
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Carsten Brink
- Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, Odense, Denmark
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Tine Schytte
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
- Department of Oncology, Odense University Hospital, Odense, Denmark
| | - Rana Bahij
- Department of Oncology, Odense University Hospital, Odense, Denmark
| | - Mathilde Weisz Ejlsmark
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
- Department of Oncology, Odense University Hospital, Odense, Denmark
| | - Uffe Bernchou
- Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, Odense, Denmark
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Anders S. Bertelsen
- Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, Odense, Denmark
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Per Pfeiffer
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
- Department of Oncology, Odense University Hospital, Odense, Denmark
| | - Faisal Mahmood
- Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, Odense, Denmark
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| |
Collapse
|
2
|
Rahbek S, Mahmood F, Tomaszewski MR, Hanson LG, Madsen KH. Decomposition-based framework for tumor classification and prediction of treatment response from longitudinal MRI. Phys Med Biol 2023; 68. [PMID: 36595245 DOI: 10.1088/1361-6560/acaa85] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 12/09/2022] [Indexed: 12/14/2022]
Abstract
Objective.In the field of radiation oncology, the benefit of MRI goes beyond that of providing high soft-tissue contrast images for staging and treatment planning. With the recent clinical introduction of hybrid MRI linear accelerators it has become feasible to map physiological parameters describing diffusion, perfusion, and relaxation during the entire course of radiotherapy, for example. However, advanced data analysis tools are required for extracting qualified prognostic and predictive imaging biomarkers from longitudinal MRI data. In this study, we propose a new prediction framework tailored to exploit temporal dynamics of tissue features from repeated measurements. We demonstrate the framework using a newly developed decomposition method for tumor characterization.Approach.Two previously published MRI datasets with multiple measurements during and after radiotherapy, were used for development and testing:T2-weighted multi-echo images obtained for two mouse models of pancreatic cancer, and diffusion-weighted images for patients with brain metastases. Initially, the data was decomposed using the novel monotonous slope non-negative matrix factorization (msNMF) tailored for MR data. The following processing consisted of a tumor heterogeneity assessment using descriptive statistical measures, robust linear modelling to capture temporal changes of these, and finally logistic regression analysis for stratification of tumors and volumetric outcome.Main Results.The framework was able to classify the two pancreatic tumor types with an area under curve (AUC) of 0.999,P< 0.001 and predict the tumor volume change with a correlation coefficient of 0.513,P= 0.034. A classification of the human brain metastases into responders and non-responders resulted in an AUC of 0.74,P= 0.065.Significance.A general data processing framework for analyses of longitudinal MRI data has been developed and applications were demonstrated by classification of tumor type and prediction of radiotherapy response. Further, as part of the assessment, the merits of msNMF for tumor tissue decomposition were demonstrated.
Collapse
Affiliation(s)
- Sofie Rahbek
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, DK-2800, Denmark
| | - Faisal Mahmood
- Department of Clinical Research, University of Southern Denmark, Odense, DK-5000, Denmark.,Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, Odense C, DK-5000, Denmark
| | - Michal R Tomaszewski
- Translation Imaging Department, Merck & Co, West Point, PA, United States of America.,Cancer Physiology Department, H. Lee Moffitt Cancer Center and Research Institute, 12902 USF Magnolia Dr, Tampa, FL 33612, United States of America
| | - Lars G Hanson
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, DK-2800, Denmark.,Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, DK-2650, Denmark
| | - Kristoffer H Madsen
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, DK-2650, Denmark.,Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, DK-2800, Denmark
| |
Collapse
|
3
|
Liu H, Grouza V, Tuznik M, Siminovitch KA, Bagheri H, Peterson A, Rudko DA. Self-labelled encoder-decoder (SLED) for multi-echo gradient echo-based myelin water imaging. Neuroimage 2022; 264:119717. [PMID: 36367497 DOI: 10.1016/j.neuroimage.2022.119717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 10/07/2022] [Accepted: 10/27/2022] [Indexed: 11/09/2022] Open
Abstract
PURPOSE Reconstruction of high quality myelin water imaging (MWI) maps is challenging, particularly for data acquired using multi-echo gradient echo (mGRE) sequences. A non-linear least squares fitting (NLLS) approach has often been applied for MWI. However, this approach may produce maps with limited detail and, in some cases, sub-optimal signal to noise ratio (SNR), due to the nature of the voxel-wise fitting. In this study, we developed a novel, unsupervised learning method called self-labelled encoder-decoder (SLED) to improve gradient echo-based MWI data fitting. METHODS Ultra-high resolution, MWI data was collected from five mouse brains with variable levels of myelination, using a mGRE sequence. Imaging data was acquired using a 7T preclinical MRI system. A self-labelled, encoder-decoder network was implemented in TensorFlow for calculation of myelin water fraction (MWF) based on the mGRE signal decay. A simulated MWI phantom was also created to evaluate the performance of MWF estimation. RESULTS Compared to NLLS, SLED demonstrated improved MWF estimation, in terms of both stability and accuracy in phantom tests. In addition, SLED produced less noisy MWF maps from high resolution MR microscopy images of mouse brain tissue. It specifically resulted in lower noise amplification for all mouse genotypes that were imaged and yielded mean MWF values in white matter ROIs that were highly correlated with those derived from standard NLLS fitting. Lastly, SLED also exhibited higher tolerance to low SNR data. CONCLUSION Due to its unsupervised and self-labeling nature, SLED offers a unique alternative to analyze gradient echo-based MWI data, providing accurate and stable MWF estimations.
Collapse
Affiliation(s)
- Hanwen Liu
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada; Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Vladimir Grouza
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada; Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Marius Tuznik
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada; Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Katherine A Siminovitch
- Departments of Medicine and Immunology, University of Toronto, Toronto, ON, Canada; Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada
| | - Hooman Bagheri
- Department of Human Genetics, McGill University, Montreal, QC, Canada
| | - Alan Peterson
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada; Department of Human Genetics, McGill University, Montreal, QC, Canada; Gerald Bronfman Department of Oncology, McGill University, Montreal, QC, Canada
| | - David A Rudko
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada; Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada; Department of Biomedical Engineering, McGill University, Montreal, QC, Canada.
| |
Collapse
|