Evaluation of pre-radiotherapy apparent diffusion coefficient (ADC): patterns of recurrence and survival outcomes analysis in patients treated for glioblastoma multiforme
Abstract:The presence of an ADC hypointensity on pre-radiotherapy diffusion-weighted imaging is associated with the location of tumor recurrence as demonstrated by frequent overlap in this series, and is associated with a trend toward inferior outcomes. This abnormality may reflect a high risk region of hypercellularity and warrants consideration with respect to radiotherapy planning.
“…However, this is insufficient as recurrence is still inevitable and occurs in about 90% of the patients within or directly adjacent to the resection area [3]. Low ADC indicative of a high amount of tumour cells in recurrent glioblastoma seen in 95% within the 60 Gy isodose line is associated with poorer outcome [20]. This might be due to the variable treatment response of glioblastoma cells [8], which is also suggested by our increased standard deviation postradiotherapy.…”
Increased ADC values, representing a decrease in infiltrative tumor load, were demonstrated in a limited direct periresectional area. This finding adds to previous studies evaluating ADC response in the larger high-T2 area in relation to survival.
“…However, this is insufficient as recurrence is still inevitable and occurs in about 90% of the patients within or directly adjacent to the resection area [3]. Low ADC indicative of a high amount of tumour cells in recurrent glioblastoma seen in 95% within the 60 Gy isodose line is associated with poorer outcome [20]. This might be due to the variable treatment response of glioblastoma cells [8], which is also suggested by our increased standard deviation postradiotherapy.…”
Increased ADC values, representing a decrease in infiltrative tumor load, were demonstrated in a limited direct periresectional area. This finding adds to previous studies evaluating ADC response in the larger high-T2 area in relation to survival.
“…The use of out-of-bag error as well as validation on the testing cohort demonstrated the predictive potential of the model. Previous studies have shown that imaging features such as ADC hypointensity, 18 contrast material enhanced T1-weighted subtraction volume, 37 change in T1 enhancing volume, residual T1 enhancing volume, 25 and ratio of peaks fit in ADC histograms 13,14,38 are associated with survival outcomes in recurrent glioblastoma. These studies have focused on only a few imaging features without integrating several features into a multivariate model.…”
With the use of machine learning techniques to analyze imaging features derived from pre- and posttherapy multimodal MRI, we were able to develop a predictive model for patient OS that could potentially assist clinical decision making.
“…A Polish study demonstrated the discordance between gross tumor volume (GTVs) delineated from MRI as compared to 18F-fluoroethylthyrosine-PET (FET-PET), a functional imaging modality; FET-PET was better associated with the site of eventual failure, suggesting that traditional target volumes may not be adequate. 26 ADC maps generated from diffusion imaging can identify areas of restricted diffusion that may predict for areas of eventual recurrence with high concordance; 27,28 for adequate post-operative recovery. Radiotherapy planning includes registration (aka "fusion") of the post-operative MRI (T1CE and FLAIR sequences) with the planning simulation CT, which allows for delineation of the FLAIR abnormality and residual enhancement in treatment planning.…”
Section: Mr Imaging To Define the At Risk Target Volumes And Organs Amentioning
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