Pseudoprogression in Neuro-Oncology

Bush, Nancy Ann Oberheim; Chang, Susan M.; Clarke, Jennifer

doi:10.1016/b978-0-12-800945-1.00055-0

Cited by 2 publications

(4 citation statements)

References 81 publications

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Section: Histological and Pathological Findingsmentioning

confidence: 99%

“…19 Some authors supported the idea that RN was the continuum of PsP, but this was debated [14,47,141]. Both pathophysiological mechanisms, vascular injury and cellular effects, are still incompletely understood and characterized [141]. Inflammation and vascular endothelial damage lead to blood-brain barrier disruption, increased permeability, and vasogenic edema, resulting in MRI contrast enhancement [28,141].…”

Section: Histological and Pathological Findingsmentioning

confidence: 99%

“…Both pathophysiological mechanisms, vascular injury and cellular effects, are still incompletely understood and characterized [141]. Inflammation and vascular endothelial damage lead to blood-brain barrier disruption, increased permeability, and vasogenic edema, resulting in MRI contrast enhancement [28,141]. Cell necrosis and local tissue reaction increased cytokines and proinflammatory mediators that also increased vascular permeability.…”

Section: Histological and Pathological Findingsmentioning

confidence: 99%

“…PsP showed typical pathological findings after radiation, such as perivascular edema, bland necrosis, fibrosis, gliosis, endothelial thickening, hyalinization, platelet fibrin thrombi and occlusion [13,39,52]. Cell analysis showed paucicellularity, scattered little to no atypical cells with a lack of mitotic figures except in inflammatory cells, a reduced number of endothelial cell nuclei, low density of pleiomorphic tumor cells, low mitotic index, hyalinization of vessel walls, fibrinoid necrosis, fibrillary and gemistocytic astrocytes, pleiomorphic astrocytes, and mild perivascular lymphocyte and monocyte infiltrations [141]. There was a preponderance of reactive cells, including astrocytes, microglia and macrophages [144].…”

Section: Histological and Pathological Findingsmentioning

confidence: 99%

See 3 more Smart Citations

Pseudoprogression versus true progression in glioblastoma patients: A multiapproach literature review

Fèvre

Lhermitte

Ahle

et al. 2021

Critical Reviews in Oncology/Hematology

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Section: Histological and Pathological Findingsmentioning

confidence: 99%

Section: Histological and Pathological Findingsmentioning

confidence: 99%

Section: Histological and Pathological Findingsmentioning

confidence: 99%

Section: Histological and Pathological Findingsmentioning

confidence: 99%

See 2 more Smart Citations

Pseudoprogression versus true progression in glioblastoma patients: A multiapproach literature review

Fèvre

Lhermitte

Ahle

et al. 2021

Critical Reviews in Oncology/Hematology

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Feasibility of deep learning-based tumor segmentation for target delineation and response assessment in grade-4 glioma using multi-parametric MRI

Hannisdal

Goplen

Alam

et al. 2023

Neuro-Oncology Advances

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Background Tumor burden assessment is essential for radiation therapy (RT), treatment response evaluation, and clinical decision-making. However, manual tumor delineation remains laborious and challenging due to radiological complexity. The objective of this study was to investigate the feasibility of the HD-GLIO tool, an ensemble of pre-trained deep learning models based on the nnUNet-algorithm, for tumor segmentation, response prediction, and its potential for clinical deployment. Methods We analyzed the predicted contrast-enhanced (CE) and non-enhancing (NE) HD-GLIO output in 49 multi-parametric MRI examinations from 23 grade-4 glioma patients. The volumes were retrospectively compared to corresponding manual delineations by two independent operators, before prospectively testing the feasibility of clinical deployment of HD-GLIO-output to a RT setting. Results For CE, median Dice scores were 0.81 (95% CI 0.71-0.83) and 0.82 (95% CI 0.74-0.84) for Operator-1 and Operator-2, respectively. For NE, median Dice scores were 0.65 (95% CI 0.56-0,69) and 0.63 (95% CI 0.57-0.67), respectively. Comparing volume-sizes, we found excellent intra-class correlation coefficients (ICC) of 0.90 (p<0.001) and 0.95 (p<0.001), for CE, respectively, and 0.97 (p<0.001) and 0.90 (p<0.001), for NE, respectively. Moreover, there was a strong correlation between RANO-volumes and HD-GLIO-volumes (p<0.001, Spearman’s R2 =0.83). Longitudinal growth relations between CE- and NE-volumes distinguished patients by clinical response: Pearson correlations of CE- and NE-volumes were 0.55 (p=0.04) for responders, 0.91 (p>0.01) for non-responders, and 0.80 (p=0.05) for intermediate/mixed responders. Conclusions HD-GLIO was feasible for RT target delineation and MRI tumor volume assessment. CE/NE tumor compartment growth correlation showed potential to predict clinical response to treatment.

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Pseudoprogression in Neuro-Oncology

Cited by 2 publications

References 81 publications

Pseudoprogression versus true progression in glioblastoma patients: A multiapproach literature review

Pseudoprogression versus true progression in glioblastoma patients: A multiapproach literature review

Feasibility of deep learning-based tumor segmentation for target delineation and response assessment in grade-4 glioma using multi-parametric MRI

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