On the relation between MR spectroscopy features and the distance to MRI-visible solid tumor in GBM patients
Corresponding Author
Nuno Pedrosa de Barros
University Institute for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
Correspondence Nuno Pedrosa de Barros, Neuroradiology, Inselspital, Freiburgstr. 4, CH-3010, Bern, Switzerland. Email: [email protected] Twitter: @nunopbarrosSearch for more papers by this authorRaphael Meier
University Institute for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
Search for more papers by this authorMartin Pletscher
University Institute for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
Search for more papers by this authorSamuel Stettler
University Institute for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
Search for more papers by this authorUrspeter Knecht
University Institute for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
Search for more papers by this authorEvelyn Herrmann
Department of Radiation Oncology, University of Bern, Bern, Switzerland
Search for more papers by this authorPhilippe Schucht
Department of Neurosurgery, University of Bern, Bern, Switzerland
Search for more papers by this authorMauricio Reyes
Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland
Search for more papers by this authorJan Gralla
University Institute for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
Search for more papers by this authorRoland Wiest
University Institute for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
Search for more papers by this authorJohannes Slotboom
University Institute for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
Search for more papers by this authorCorresponding Author
Nuno Pedrosa de Barros
University Institute for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
Correspondence Nuno Pedrosa de Barros, Neuroradiology, Inselspital, Freiburgstr. 4, CH-3010, Bern, Switzerland. Email: [email protected] Twitter: @nunopbarrosSearch for more papers by this authorRaphael Meier
University Institute for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
Search for more papers by this authorMartin Pletscher
University Institute for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
Search for more papers by this authorSamuel Stettler
University Institute for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
Search for more papers by this authorUrspeter Knecht
University Institute for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
Search for more papers by this authorEvelyn Herrmann
Department of Radiation Oncology, University of Bern, Bern, Switzerland
Search for more papers by this authorPhilippe Schucht
Department of Neurosurgery, University of Bern, Bern, Switzerland
Search for more papers by this authorMauricio Reyes
Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland
Search for more papers by this authorJan Gralla
University Institute for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
Search for more papers by this authorRoland Wiest
University Institute for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
Search for more papers by this authorJohannes Slotboom
University Institute for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
Search for more papers by this authorFunding information: EU Marie Curie FP7-PEOPLE-2012-ITN project TRANSACT, Grant/Award Number: PITN-GA-2012-316679; Swiss National Science Foundation, Grant/Award Number: 140958
Correction added after online publication 2 July 2018. The authors’ corrections were not fully addressed prior to publication. The supporting figure captions were updated for conciseness.
Abstract
Purpose
To improve the detection of peritumoral changes in GBM patients by exploring the relation between MRSI information and the distance to the solid tumor volume (STV) defined using structural MRI (sMRI).
Methods
Twenty-three MRSI studies (PRESS, TE 135 ms) acquired from different patients with untreated GBM were used in this study. For each MRSI examination, the STV was identified by segmenting the corresponding sMRI images using BraTumIA, an automatic segmentation method. The relation between different metabolite ratios and the distance to STV was analyzed. A regression forest was trained to predict the distance from each voxel to STV based on 14 metabolite ratios. Then, the trained model was used to determine the expected distance to tumor (EDT) for each voxel of the MRSI test data. EDT maps were compared against sMRI segmentation.
Results
The features showing abnormal values at the longest distances to the tumor were: %NAA, Glx/NAA, Cho/NAA, and Cho/Cr. These four features were also the most important for the prediction of the distances to STV. Each EDT value was associated with a specific metabolic pattern, ranging from normal brain tissue to actively proliferating tumor and necrosis. Low EDT values were highly associated with malignant features such as elevated Cho/NAA and Cho/Cr.
Conclusion
The proposed method enables the automatic detection of metabolic patterns associated with different distances to the STV border and may assist tumor delineation of infiltrative brain tumors such as GBM.
Supporting Information
Additional Supporting Information may be found online in the supporting information tab for this article.
| Filename | Description |
|---|---|
| mrm27359-sup-0001-suppinfo1.pdf2.7 MB |
FIGURE S1.1 and S1.2 Value of several features as a function of the sMRI segmentation class (green), and the distance to STV (blue). For each value of the horizontal axis, the 5th, 25th, 50th, 75th and 95th percentiles are shown as depicted in the legend. For each feature, the horizontal level lines in red mark the 5th, 25th, 50th, 75th and 95th percentiles in healthy volunteers (includes GM, WM and CSF). FIGURE S2.1 to S2.14 MRS-FSD fitting results for %Cho (FIGURE S2.1), %Cr (FIGURE S2.2), %Glx (FIGURE S2.3), %NAA (FIGURE S2.4), %Lac (FIGURE S2.5), %Lip (FIGURE S2.6), Cho/Cr (FIGURE S2.7), Cho/NAA (FIGURE S2.8), Glx/Cr (FIGURE S2.9), Glx/NAA (FIGURE S2.10), Lac/Cr (FIGURE S2.11), Lac/NAA (FIGURE S2.12), Lip/Cr (FIGURE S2.13) and Lip/NAA (FIGURE S2.14). In each figure, each plot corresponds to one fold of determination (R2). |
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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