Differentiation of pseudoprogression and real progression in glioblastoma using ADC parametric response maps

Reimer, Caroline; Deike-Hofmann, Katerina; Graf, Markus; Reimer, Peter; Wiestler, Benedikt; Floca, Ralf; Kickingereder, Philipp; Schlemmer, Heinz Peter; Wick, Wolfgang; Bendszus, Martin; Radbruch, Alexander

doi:10.1371/journal.pone.0174620

Cited by 44 publications

(23 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The spatial pattern of ADC values is of diagnostic interest, both for a single timepoint assessment and follow‐up. Serial voxel‐wise mapping of diffusion could be superior to identify subtle focal changes, but entirely depends on accurate coregistration and requires additional processing time . Parametric mapping is considered unsuitable for patients with significant changes in mass effect and brain shift between scans.…”

Section: Current Mri Approaches To Pseudoprogressionmentioning

confidence: 99%

Pseudoprogression of brain tumors

Thust

Bent

Smits

2018

Magnetic Resonance Imaging

227

194

View full text Add to dashboard Cite

This review describes the definition, incidence, clinical implications, and magnetic resonance imaging (MRI) findings of pseudoprogression of brain tumors, in particular, but not limited to, high‐grade glioma. Pseudoprogression is an important clinical problem after brain tumor treatment, interfering not only with day‐to‐day patient care but also the execution and interpretation of clinical trials. Radiologically, pseudoprogression is defined as a new or enlarging area(s) of contrast agent enhancement, in the absence of true tumor growth, which subsides or stabilizes without a change in therapy. The clinical definitions of pseudoprogression have been quite variable, which may explain some of the differences in reported incidences, which range from 9–30%. Conventional structural MRI is insufficient for distinguishing pseudoprogression from true progressive disease, and advanced imaging is needed to obtain higher levels of diagnostic certainty. Perfusion MRI is the most widely used imaging technique to diagnose pseudoprogression and has high reported diagnostic accuracy. Diagnostic performance of MR spectroscopy (MRS) appears to be somewhat higher, but MRS is less suitable for the routine and universal application in brain tumor follow‐up. The combination of MRS and diffusion‐weighted imaging and/or perfusion MRI seems to be particularly powerful, with diagnostic accuracy reaching up to or even greater than 90%. While diagnostic performance can be high with appropriate implementation and interpretation, even a combination of techniques, however, does not provide 100% accuracy. It should also be noted that most studies to date are small, heterogeneous, and retrospective in nature. Future improvements in diagnostic accuracy can be expected with harmonization of acquisition and postprocessing, quantitative MRI and computer‐aided diagnostic technology, and meticulous evaluation with clinical and pathological data. Level of Evidence: 3 Technical Efficacy: Stage 2J. Magn. Reson. Imaging 2018;48:571–589.

show abstract

Section: Current Mri Approaches To Pseudoprogressionmentioning

confidence: 99%

Pseudoprogression of brain tumors

Thust

Bent

Smits

2018

Magnetic Resonance Imaging

227

194

View full text Add to dashboard Cite

show abstract

“…The discrimination is challenging because both lesions demonstrate similar contrast enhancement (CE) on gadolinium-enhanced T1-weighted magnetic resonance imaging (MRI) 2 , 3 . Although pathologic confirmation is the most reliable method to diagnose PD or PsPD, numerous non-invasive attempts have been made for discrimination using diffusion-weighted imaging 4 – 6 , perfusion imaging 7 , 8 , or positron emission tomography (PET) 9 , 10 . While these advanced imaging techniques have shown certain values, most experts do not agree with that traditional MRI such as T1-weighted or T2-weighted MRI can distinguish PsPD from PD 11 .…”

Section: Introductionmentioning

confidence: 99%

Prediction of Pseudoprogression versus Progression using Machine Learning Algorithm in Glioblastoma

Jang

Jeon

Kim

et al. 2018

Sci Rep

103

View full text Add to dashboard Cite

We aimed to investigate the feasibility of machine learning (ML) algorithm to distinguish pseudoprogression (PsPD) from progression (PD) in patients with glioblastoma (GBM). We recruited the patients diagnosed as primary GBM who received gross total resection (GTR) and concurrent chemoradiotherapy in two institutions from April 2010 to April 2017 and presented suspicious contrast-enhanced lesion on brain magnetic resonance imaging (MRI) during follow-up. Patients from two institutions were allocated to training (N = 59) and testing (N = 19) datasets, respectively. We developed a convolutional neural network combined with a long short-term memory ML structure. MRI data, which was 9 axial post-contrast T1-weighted images in our study, and clinical features were incorporated (Model 1). In the testing set, the trained Model 1 resulted in AUC of 0.83, AUPRC of 0.87, and F1-score of 0.74 using optimal threshold. The performance was superior to that of Model 2 (CNN-LSTM model with MRI data alone) and Model 3 (random forest model with clinical feature alone). The developed algorithm involving MRI data and clinical features could help making decision during follow-up of patients with GBM treated with GTR and concurrent CCRT.

show abstract

“…Although the method to calculate ADC is controversial, it can be performed manually or through specific automated software with additional histogram analysis 37 ; the proposed ADC cutoff of most studies to differentiate progressive disease from pseudoprogression is about 1.3 Â 10 À3 mm 2 /s. 38,39 Some patients undergoing bevacizumab therapy can develop lesions with markedly high restricted diffusion (ADC levels under 0.7 Â 10 À3 mm 2 /s) within a few weeks to several months after drug initiation.…”

Section: Fig 12 Dwi Changes After Anti-vegf Agent Administration In mentioning

confidence: 99%

Response Assessment in Neuro-Oncology Criteria for Gliomas: Practical Approach Using Conventional and Advanced Techniques

Leao

Craig

Godoy

et al. 2019

AJNR Am J Neuroradiol

112

View full text Add to dashboard Cite

The Response Assessment in Neuro-Oncology criteria were developed as an objective tool for radiologic assessment of treatment response in high-grade gliomas. Imaging plays a critical role in the management of the patient with glioma, from initial diagnosis to posttreatment follow-up, which can be particularly challenging for radiologists. Interpreting findings after surgery, radiation, and chemotherapy requires profound knowledge about the tumor biology, as well as the peculiar changes expected to ensue as a consequence of each treatment technique. In this article, we discuss the imaging findings associated with tumor progression, tumor response, pseudoprogression, and pseudoresponse according to the Response Assessment in Neuro-Oncology criteria for high-grade and lower-grade gliomas. We describe relevant practical issues when evaluating patients with glioma, such as the need for imaging in the first 48 hours, the radiation therapy planning and isodose curves, the significance of T2/FLAIR hyperintense lesions, the impact of the timing for the evaluation after radiation therapy, and the definition of progressive disease on the histologic specimen. We also illustrate the correlation among the findings on conventional MR imaging with advanced techniques, such as perfusion, diffusion-weighted imaging, spectroscopy, and amino acid PET. Because many of the new lesions represent a mixture of tumor cells and tissue with radiation injury, the radiologist aims to identify the predominant component of the lesion and categorize the findings according to Response Assessment in Neuro-Oncology criteria so that the patient can receive the best treatment. ABBREVIATIONS: DCE 4 dynamic contrast-enhanced; GBM 4 glioblastoma; RANO 4 Response Assessment in Neuro-Oncology; rCBV 4 relative CBV; VEGF 4 vascular endothelial growth factor G lial tumors are the most common intra-axial primary tumors of the CNS, with an age-adjusted estimated incidence varying from 0.8 to 5.5 per 100,000, according to the reporting country/organization. 1,2 The most common and aggressive histologic type is glioblastoma (GBM), which accounts for about 60% of the cases. 1 Despite decades of substantial advances in diagnostic radiology, surgery, radiation therapy, chemotherapy, and clinical management of oncology patients, the fatality rate for gliomas remains relentlessly high, especially for GBM. A recent study reported a 1-year survival rate after diagnosis of 41.4% and a 5-year survival rate of only

show abstract

Differentiation of pseudoprogression and real progression in glioblastoma using ADC parametric response maps

Cited by 44 publications

References 40 publications

Pseudoprogression of brain tumors

Pseudoprogression of brain tumors

Prediction of Pseudoprogression versus Progression using Machine Learning Algorithm in Glioblastoma

Response Assessment in Neuro-Oncology Criteria for Gliomas: Practical Approach Using Conventional and Advanced Techniques

Contact Info

Product

Resources

About