Glioblastoma (GBM) poses a significant challenge in treatment due to the difficulty in distinguishing true progression (TP) from pseudoprogression (PP) during chemoradiotherapy. TP signifies tumour growth with poor treatment response, while PP is characterized by tumour necrosis with a favourable response to treatment. This ambiguity makes it crucial to identify predictive markers early in the treatment process. A recent study delves into the world of delta radiomics, exploring their potential as prognostic indicators during MR-Linac radiotherapy for GBM.
What were the methods?
The study focused on a cohort of GBM patients undergoing 30 fractions of chemoRT on an MR-Linac.
MR-Linac integrates an MRI scanner with a linear accelerator. The linear accelerator is the machine that delivers high-energy radiation to the tumour. By combining it with an MRI, doctors can see detailed images of the tumour and surrounding anatomy in real-time, both before and during the radiation treatment. This enables them to make adjustments to the treatment plan based on the current position and shape of the tumour.
Two regions of interest were identified on daily treatment scans: the tumour and the post-surgical resection cavity. Patient responses were retrospectively classified as no progression (NP), TP, or PP.
Pseudoprogression has been a consistent clinical problem when monitoring brain tumours after people have received chemotherapy and radiotherapy. It occurs when imaging tests suggest the size of the tumour has increased, but the cancer hasn’t actually spread or grown, sometimes leading to unfortunate interference with patient care and the interpretation of brain scans.
The main cause of this phenomenon is the anti-cancer chemotherapy drug, Temozolomide, which is a part of the standard care for treatment of Glioma. The drug may cause an initial increase in the size of the tumour, followed by a decrease in tumour burden. Ultimately, this sometimes leads to a premature decision to discontinue the drugs which may actually be benefitting the individual.
What were the results?
Out of 36 screened patients, 27 were included in the study. Ten had NP, 11 had TP, and 6 had PP. The study employed a machine learning model, revealing that six of the ten indicated early changes in the lesion/tumour microenvironment.
Conclusion from the study
The study demonstrates that delta radiomic features extracted from MR-Linac imaging hold promise in predicting the differentiation between PP and TP in GBM patients during treatment.
This early identification, especially within the first 10 fractions, could empower physicians to adapt or intensify treatment in real-time for patients with poor responses. The findings pave the way for future research with larger patient cohorts and additional MRI contrasts, such as MR-Linac multiparametric MRI, to further enhance predictive capabilities.
To summarise, this research explores a new way of looking at brain tumour images during treatment. The study suggests that certain features, when analysed using advanced imaging techniques, can help doctors identify whether the tumour is responding well to treatment or not. This early detection could enable doctors to adjust the treatment plan in real-time for better outcomes. While more research is needed, this approach shows promise in improving the way we manage and treat glioblastoma.
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