Aims and objectives
In a proportion of patients,
the combination of chemotherapy and radiation provokes increased contrast agent uptake and apparent enlargement of the treated tumor; or the appearance of new lesions mimicking tumor progression.
This phenomenon,
referred to as pseudoprogression,
has become a major challenge in the follow-up of high-grade glioma undergoing treatment,
as only repeat biopsy is conclusive in differentiating pseudoprogression or true recurrence.
Serial imaging studies have been the mainstay in resolving the issue in day-to-day practice and may result in treatment delay for patients...
Methods and materials
In this IRB approved,
HIPAA compliant study,
50 high-grade glioma patients with 150 MRI studies who underwent total tumor resection and adjuvant chemoradiation,
and had multiple follow up MRI studies between 2010-2018,
were evaluated.
The 94 image acquisitions (62%) were done on a 1.5 Tesla magnet,
there were 53 studies (35%) performed on a 3Tesla magnet and 3 studies (-2%) were done on a 1 Tesla magnet.
Philips and GE MRI machines were used.
12 case of pseudoprogression were included in this study together with...
Results
20 female and 30 male patients were included.
The mean age for female patients was 56 years ( SD: 13) and the mean age for male patients was 48 years (SD: 14).
For the first visit,
none of the radiologists could identify true progression.
(p=0.87) For the first and second visits,
nodular enhancement (compared to a band-like) pattern was more likely suggested pseudoprogression (for the first visit OR:0.14,
p<0.00,
for the second visit OR:0.19,
p<0.04).
CBV and CBF values which were lower around the margins...
Conclusion
None of the MRI features,
including perfusions parameters,
could reliably differentiate pseudoprogression from tumor progression,
thereby suggesting that qualitative evaluation of MRI features are probably not sufficient for this distinction.
Machine learning paradigms would be the next step to achieve this objective.
Personal information
[email protected]
Ege University Hospital Radiology Department
35100 Bornova,
Izmir,
Turkey
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5-Radiomics in Brain Tumor: Image Assessment,
Quantitative Feature Descriptors,...