scholarly journals Characterization of the serum metabolome following radiation treatment in patients with high-grade gliomas

2016 ◽  
Vol 11 (1) ◽  
Author(s):  
Lina Mörén ◽  
Carl Wibom ◽  
Per Bergström ◽  
Mikael Johansson ◽  
Henrik Antti ◽  
...  
2012 ◽  
Vol 2 (1) ◽  
Author(s):  
James C Marsh ◽  
Julius V. Turian ◽  
Arnold M. Herskovic ◽  
Julie A. Wendt ◽  
Rohit Godbole ◽  
...  

2006 ◽  
Vol 33 (6Part23) ◽  
pp. 2290-2290
Author(s):  
V Nagesh ◽  
T Chenevert ◽  
L Junck ◽  
C Tsien ◽  
T Lawrence ◽  
...  

2019 ◽  
Vol 9 (1) ◽  
pp. 16-23 ◽  
Author(s):  
Kripa Guram ◽  
Mark Smith ◽  
Timothy Ginader ◽  
Kellie Bodeker ◽  
Darrin Pelland ◽  
...  

Cancers ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 5921
Author(s):  
Anahita Fathi Kazerooni ◽  
Stephen J. Bagley ◽  
Hamed Akbari ◽  
Sanjay Saxena ◽  
Sina Bagheri ◽  
...  

Machine learning (ML) integrated with medical imaging has introduced new perspectives in precision diagnostics of high-grade gliomas, through radiomics and radiogenomics. This has raised hopes for characterizing noninvasive and in vivo biomarkers for prediction of patient survival, tumor recurrence, and genomics and therefore encouraging treatments tailored to individualized needs. Characterization of tumor infiltration based on pre-operative multi-parametric magnetic resonance imaging (MP-MRI) scans may allow prediction of the loci of future tumor recurrence and thereby aid in planning the course of treatment for the patients, such as optimizing the extent of resection and the dose and target area of radiation. Imaging signatures of tumor genomics can help in identifying the patients who benefit from certain targeted therapies. Specifying molecular properties of gliomas and prediction of their changes over time and with treatment would allow optimization of treatment. In this article, we provide neuro-oncology, neuropathology, and computational perspectives on the promise of radiomics and radiogenomics for allowing personalized treatments of patients with gliomas and discuss the challenges and limitations of these methods in multi-institutional clinical trials and suggestions to mitigate the issues and the future directions.


Oncotarget ◽  
2017 ◽  
Vol 8 (42) ◽  
pp. 71597-71617 ◽  
Author(s):  
Aurélia Nguyen ◽  
François Marie Moussallieh ◽  
Alan Mackay ◽  
A. Ercument Cicek ◽  
Andres Coca ◽  
...  

2020 ◽  
Vol 149 (2) ◽  
pp. 305-314 ◽  
Author(s):  
Chia-Lin Tseng ◽  
James Stewart ◽  
Gillian Whitfield ◽  
Joost J. C. Verhoeff ◽  
Joseph Bovi ◽  
...  

Abstract Introduction This study proposes contouring recommendations for radiation treatment planning target volumes and organs-at-risk (OARs) for both low grade and high grade gliomas. Methods Ten cases consisting of 5 glioblastomas and 5 grade II or III gliomas, including their respective gross tumor volume (GTV), clinical target volume (CTV), and OARs were each contoured by 6 experienced neuro-radiation oncologists from 5 international institutions. Each case was first contoured using only MRI sequences (MRI-only), and then re-contoured with the addition of a fused planning CT (CT-MRI). The level of agreement among all contours was assessed using simultaneous truth and performance level estimation (STAPLE) with the kappa statistic and Dice similarity coefficient. Results A high level of agreement was observed between the GTV and CTV contours in the MRI-only workflow with a mean kappa of 0.88 and 0.89, respectively, with no statistically significant differences compared to the CT-MRI workflow (p = 0.88 and p = 0.82 for GTV and CTV, respectively). Agreement in cochlea contours improved from a mean kappa of 0.39 to 0.41, to 0.69 to 0.71 with the addition of CT information (p < 0.0001 for both cochleae). Substantial to near perfect level of agreement was observed in all other contoured OARs with a mean kappa range of 0.60 to 0.90 in both MRI-only and CT-MRI workflows. Conclusions Consensus contouring recommendations for low grade and high grade gliomas were established using the results from the consensus STAPLE contours, which will serve as a basis for further study and clinical trials by the MR-Linac Consortium.


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