1
|
Rejimon AC, Ramesh KK, Trivedi AG, Huang V, Schreibmann E, Weinberg BD, Kleinberg LR, Shu HKG, Shim H, Olson JJ. The Utility of Spectroscopic MRI in Stereotactic Biopsy and Radiotherapy Guidance in Newly Diagnosed Glioblastoma. Tomography 2024; 10:428-443. [PMID: 38535775 PMCID: PMC10975697 DOI: 10.3390/tomography10030033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 03/18/2024] [Accepted: 03/19/2024] [Indexed: 04/01/2024] Open
Abstract
Current diagnostic and therapeutic approaches for gliomas have limitations hindering survival outcomes. We propose spectroscopic magnetic resonance imaging as an adjunct to standard MRI to bridge these gaps. Spectroscopic MRI is a volumetric MRI technique capable of identifying tumor infiltration based on its elevated choline (Cho) and decreased N-acetylaspartate (NAA). We present the clinical translatability of spectroscopic imaging with a Cho/NAA ≥ 5x threshold for delineating a biopsy target in a patient diagnosed with non-enhancing glioma. Then, we describe the relationship between the undertreated tumor detected with metabolite imaging and overall survival (OS) from a pilot study of newly diagnosed GBM patients treated with belinostat and chemoradiation. Each cohort (control and belinostat) were split into subgroups using the median difference between pre-radiotherapy Cho/NAA ≥ 2x and the treated T1-weighted contrast-enhanced (T1w-CE) volume. We used the Kaplan-Meier estimator to calculate median OS for each subgroup. The median OS was 14.4 months when the difference between Cho/NAA ≥ 2x and T1w-CE volumes was higher than the median compared with 34.3 months when this difference was lower than the median. The T1w-CE volumes were similar in both subgroups. We find that patients who had lower volumes of undertreated tumors detected via spectroscopy had better survival outcomes.
Collapse
Affiliation(s)
- Abinand C. Rejimon
- Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA; (A.C.R.); (K.K.R.); (E.S.); (H.-K.G.S.); (H.S.)
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Karthik K. Ramesh
- Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA; (A.C.R.); (K.K.R.); (E.S.); (H.-K.G.S.); (H.S.)
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Anuradha G. Trivedi
- Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA; (A.C.R.); (K.K.R.); (E.S.); (H.-K.G.S.); (H.S.)
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Vicki Huang
- Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA; (A.C.R.); (K.K.R.); (E.S.); (H.-K.G.S.); (H.S.)
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Eduard Schreibmann
- Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA; (A.C.R.); (K.K.R.); (E.S.); (H.-K.G.S.); (H.S.)
| | - Brent D. Weinberg
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA 30322, USA;
- Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Lawrence R. Kleinberg
- Department of Radiation Oncology, Johns Hopkins University, Baltimore, MD 21218, USA;
| | - Hui-Kuo G. Shu
- Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA; (A.C.R.); (K.K.R.); (E.S.); (H.-K.G.S.); (H.S.)
- Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Hyunsuk Shim
- Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA; (A.C.R.); (K.K.R.); (E.S.); (H.-K.G.S.); (H.S.)
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA 30322, USA;
- Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Jeffrey J. Olson
- Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
- Department of Neurosurgery, Emory University, Atlanta, GA 30322, USA
| |
Collapse
|
2
|
Arnold TC, Muthukrishnan R, Pattnaik AR, Sinha N, Gibson A, Gonzalez H, Das SR, Litt B, Englot DJ, Morgan VL, Davis KA, Stein JM. Deep learning-based automated segmentation of resection cavities on postsurgical epilepsy MRI. Neuroimage Clin 2022; 36:103154. [PMID: 35988342 PMCID: PMC9402390 DOI: 10.1016/j.nicl.2022.103154] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 07/26/2022] [Accepted: 08/12/2022] [Indexed: 12/14/2022]
Abstract
Accurate segmentation of surgical resection sites is critical for clinical assessments and neuroimaging research applications, including resection extent determination, predictive modeling of surgery outcome, and masking image processing near resection sites. In this study, an automated resection cavity segmentation algorithm is developed for analyzing postoperative MRI of epilepsy patients and deployed in an easy-to-use graphical user interface (GUI) that estimates remnant brain volumes, including postsurgical hippocampal remnant tissue. This retrospective study included postoperative T1-weighted MRI from 62 temporal lobe epilepsy (TLE) patients who underwent resective surgery. The resection site was manually segmented and reviewed by a neuroradiologist (JMS). A majority vote ensemble algorithm was used to segment surgical resections, using 3 U-Net convolutional neural networks trained on axial, coronal, and sagittal slices, respectively. The algorithm was trained using 5-fold cross validation, with data partitioned into training (N = 27) testing (N = 9), and validation (N = 9) sets, and evaluated on a separate held-out test set (N = 17). Algorithm performance was assessed using Dice-Sørensen coefficient (DSC), Hausdorff distance, and volume estimates. Additionally, we deploy a fully-automated, GUI-based pipeline that compares resection segmentations with preoperative imaging and reports estimates of resected brain structures. The cross-validation and held-out test median DSCs were 0.84 ± 0.08 and 0.74 ± 0.22 (median ± interquartile range) respectively, which approach inter-rater reliability between radiologists (0.84-0.86) as reported in the literature. Median 95 % Hausdorff distances were 3.6 mm and 4.0 mm respectively, indicating high segmentation boundary confidence. Automated and manual resection volume estimates were highly correlated for both cross-validation (r = 0.94, p < 0.0001) and held-out test subjects (r = 0.87, p < 0.0001). Automated and manual segmentations overlapped in all 62 subjects, indicating a low false negative rate. In control subjects (N = 40), the classifier segmented no voxels (N = 33), <50 voxels (N = 5), or a small volumes<0.5 cm3 (N = 2), indicating a low false positive rate that can be controlled via thresholding. There was strong agreement between postoperative hippocampal remnant volumes determined using automated and manual resection segmentations (r = 0.90, p < 0.0001, mean absolute error = 6.3 %), indicating that automated resection segmentations can permit quantification of postoperative brain volumes after epilepsy surgery. Applications include quantification of postoperative remnant brain volumes, correction of deformable registration, and localization of removed brain regions for network modeling.
Collapse
Affiliation(s)
- T Campbell Arnold
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ramya Muthukrishnan
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Computer Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Akash R Pattnaik
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Nishant Sinha
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Adam Gibson
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Hannah Gonzalez
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sandhitsu R Das
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Brian Litt
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Dario J Englot
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Biomedical Engineering, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Victoria L Morgan
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Biomedical Engineering, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Kathryn A Davis
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Joel M Stein
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| |
Collapse
|
3
|
Luo M, Narasimhan S, Larson PS, Martin AJ, Konrad PE, Miga MI. Impact of brain shift on neural pathways in deep brain stimulation: a preliminary analysis via multi-physics finite element models. J Neural Eng 2021; 18. [PMID: 33740780 DOI: 10.1088/1741-2552/abf066] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 03/19/2021] [Indexed: 11/12/2022]
Abstract
OBJECTIVE The effectiveness of deep brain stimulation (DBS) depends on electrode placement accuracy, which can be compromised by brain shift during surgery. While there have been efforts in assessing the impact of electrode misplacement due to brain shift using preop- and postop- imaging data, such analysis using preop- and intraop- imaging data via biophysical modeling has not been conducted. This work presents a preliminary study that applies a multi-physics analysis framework using finite element biomechanical and bioelectric models to examine the impact of realistic intraoperative shift on neural pathways determined by tractography. APPROACH The study examined six patients who had undergone interventional magnetic resonance (iMR)-guided DBS surgery. The modeling framework utilized a biomechanical approach to update preoperative MR to reflect shift-induced anatomical changes. Using this anatomically deformed image and its undeformed counterpart, bioelectric effects from shifting electrode leads could be simulated and neural activation differences were approximated. Specifically, for each configuration, volume of tissue activation (VTA) was computed and subsequently used for tractography estimation. Total tract volume and overlapping volume with motor regions as well as connectivity profile were compared. In addition, volumetric overlap between different fiber bundles among configurations was computed and correlated to estimated shift. MAIN RESULT The study found deformation-induced differences in tract volume, motor region overlap, and connectivity behavior, suggesting the impact of shift. There is a strong correlation (R=-0.83) between shift from intended target and intended neural pathway recruitment, where at threshold of ~2.94 mm, intended recruitment completely degrades. The determined threshold is consistent with and provides quantitative support to prior observations and literature that deviations of 2-3 mm are detrimental. SIGNIFICANCE The findings support and advance prior studies and understanding to illustrate the need to account for shift in DBS and the potentiality of computational modeling for estimating influence of shift on neural activation.
Collapse
Affiliation(s)
- Ma Luo
- Biomedical Engineering, Vanderbilt University, 5824 Stevenson Center, Nashville, Tennessee, 37232, UNITED STATES
| | - Saramati Narasimhan
- Department of Neurological Surgery, Vanderbilt University Medical Center, Village at Vanderbilt, 1500 21st Ave. South, Nashville, Tennessee, 37212, UNITED STATES
| | - Paul S Larson
- Department of Neurological Surgery, University of California San Francisco, Box 0112, 505 Parnassus Ave, Room M779, San Francisco, California, 94143, UNITED STATES
| | - Alastiar J Martin
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 505 Parnassus Avenue, San Francisco, California, 94143, UNITED STATES
| | - Peter E Konrad
- Department of Neurosurgery, West Virginia University, PO Box 9183, Morgantown, West Virginia, 26506, UNITED STATES
| | - Michael I Miga
- Department of Biomedical Engineering, Vanderbilt University, 5901 Stevenson Center, Nashville, Tennessee, 37235, UNITED STATES
| |
Collapse
|