Erratum to: Amino acid PET and MR perfusion imaging in brain tumours

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NIMG-17. DISCRIMINATION BETWEEN RADIATION INJURY AND BRAIN METASTASIS RECURRENCE BASED ON TEXTURAL FEATURE ANALYSIS OF FET PET – SUPERIOR TO STANDARD METHODS?

BACKGROUND:Since the differentiation of radiation injury and tumor recurrence using standard MRI alone is difficult, we investigated the potential of textural parameters of O-(2-[18F]fluoroethyl)-L-tyrosine (FET) PET for this issue.METHODS:Forty-seven patients (mean age, 55 ± 11 y) with single or multiple contrast-enhancing brain lesions (n=54) on MRI after radiotherapy of brain metastases (predominantly stereotactic radiosurgery) underwent FET PET in an ECAT EXACT HR+ scanner. VOIs were defined on summed images 20-40 min post-injection by a 3-dimensional auto-contouring process using a tumor-to-brain ratio (TBR) of 1.6. For each lesion, TBRs and time-activity curves of FET uptake and 59 textural parameters were determined using the CGITA toolbox (Fang et al., 2014 Biomed Res Int). Diagnostic accuracy of TBRs, kinetic patterns, time-to-peak, textural parameters and combinations thereof to discriminate between brain metastasis recurrence and radiation injury was evaluated by ROC analyses using histological results (21 lesions, 20 patients) or clinical course (33 lesions, 27 patients) as reference.RESULTS:The diagnostic accuracy increased from 81% for TBRmean alone to 85% when combined with the textural parameter Coarseness (sensitivity, 73%; specificity, 100%) or Short-zone emphasis (sensitivity, 77%; specificity, 96%). The accuracy of TBRmax alone was 83% and increased to 85% after combination with the textural parameters Coarseness (sensitivity, 73%; specificity, 100%), Short-zone emphasis (sensitivity, 77%; specificity, 96%) or Correlation (sensitivity, 77%; specificity, 96%). The analysis of the time-activity curves resulted in a diagnostic accuracy of 70% for the kinetic pattern alone and increased to 83% when combined with TBRmax. The accuracy of the time-to-peak was 67% and could not be further improved by combination with other parameters.CONCLUSIONS:Textural analysis in combination with TBRs may have the potential to increase the diagnostic accuracy for discrimination between brain metastasis recurrence and radiation injury, without the need for acquiring and analyzing more time-consuming dynamic FET PET scans

DISCRIMINATION BETWEEN RADIATION INJURY AND BRAIN METASTASIS RECURRENCE BASED ON TEXTURAL FEATURE ANALYSIS OF FET PET - SUPERIOR TO STANDARD METHODS?

BACKGROUND:Since the differentiation of radiation injury and tumor recurrence using standard MRI alone is difficult, we investigated the potential of textural parameters of O-(2-[18F]fluoroethyl)-L-tyrosine (FET) PET for this issue.METHODS:Forty-seven patients (mean age, 55 ± 11 y) with single or multiple contrast-enhancing brain lesions (n=54) on MRI after radiotherapy of brain metastases (predominantly stereotactic radiosurgery) underwent FET PET in an ECAT EXACT HR+ scanner. VOIs were defined on summed images 20-40 min post-injection by a 3-dimensional auto-contouring process using a tumor-to-brain ratio (TBR) of 1.6. For each lesion, TBRs and time-activity curves of FET uptake and 59 textural parameters were determined using the CGITA toolbox (Fang et al., 2014 Biomed Res Int). Diagnostic accuracy of TBRs, kinetic patterns, time-to-peak, textural parameters and combinations thereof to discriminate between brain metastasis recurrence and radiation injury was evaluated by ROC analyses using histological results (21 lesions, 20 patients) or clinical course (33 lesions, 27 patients) as reference.RESULTS:The diagnostic accuracy increased from 81% for TBRmean alone to 85% when combined with the textural parameter Coarseness (sensitivity, 73%; specificity, 100%) or Short-zone emphasis (sensitivity, 77%; specificity, 96%). The accuracy of TBRmax alone was 83% and increased to 85% after combination with the textural parameters Coarseness (sensitivity, 73%; specificity, 100%), Short-zone emphasis (sensitivity, 77%; specificity, 96%) or Correlation (sensitivity, 77%; specificity, 96%). The analysis of the time-activity curves resulted in a diagnostic accuracy of 70% for the kinetic pattern alone and increased to 83% when combined with TBRmax. The accuracy of the time-to-peak was 67% and could not be further improved by combination with other parameters.CONCLUSIONS:Textural analysis in combination with TBRs may have the potential to increase the diagnostic accuracy for discrimination between brain metastasis recurrence and radiation injury, without the need for acquiring and analyzing more time-consuming dynamic FET PET scans

F-18-FET PET Imaging in Differentiating Glioma Progression from Treatment-Related Changes: A Single-Center Experience

In glioma patients, differentiation between tumor progression (TP) and treatment-related changes (TRCs) remains challenging. Difficulties in classifying imaging alterations may result in a delay or an unnecessary discontinuation of treatment. PET using O-(2-F-18-fluoroethyl)-L-tyrosine (F-18-FET) has been shown to be a useful tool for detecting TP and TRCs. Methods: We retrospectively evaluated 127 consecutive patients with World Health Organization grade II-IV glioma who underwent F-18-FET PET imaging to distinguish between TP and TRCs. F-18-FET PET findings were verified by neuropathology (40 patients) or clinicoradiologic follow-up (87 patients). Maximum tumor-to-brain ratios (TBRmax) of F-18-FET uptake and the slope of the time-activity curves (20-50 min after injection) were determined. The diagnostic accuracy of F-18-FET PET parameters was evaluated by receiver-operating-characteristic analysis and chi(2) testing. The prognostic value of F-18-FET PET was estimated using the Kaplan-Meier method. Results: TP was diagnosed in 94 patients (74%) and TRCs in 33 (26%). For differentiating TP from TRCs, receiver-operating-characteristic analysis yielded an optimal F-18-FET TBRmax cutoff of 1.95 (sensitivity, 70%; specificity, 71%; accuracy, 70%; area under the curve, 0.75 +/- 0.05). The highest accuracy was achieved by a combination of TBRmax and slope (sensitivity, 86%; specificity, 67%; accuracy, 81%). However, accuracy was poorer when tumors harbored isocitrate dehydrogenase (IDH) mutations (91% in IDH-wild-type tumors, 67% in IDH-mutant tumors, P, 0.001). F-18-FET PET results correlated with overall survival (P, 0.001). Conclusion: In our neurooncology department, the diagnostic performance of F-18-FET PET was convincing but slightly inferior to that of previous reports

Effect of Zolpidem in the Aftermath of Traumatic Brain Injury: An MEG Study

In the past two decades, many studies have shown the paradoxical efficacy of zolpidem, a hypnotic used to induce sleep, in transiently alleviating various disorders of consciousness such as traumatic brain injury (TBI), dystonia, and Parkinson’s disease. The mechanism of action of this effect of zolpidem is of great research interest. In this case study, we use magnetoencephalography (MEG) to investigate a fully conscious, ex-coma patient who suffered from neurological difficulties for a few years due to traumatic brain injury. For a few years after injury, the patient was under medication with zolpidem that drastically improved his symptoms. MEG recordings taken before and after zolpidem showed a reduction in power in the theta-alpha (4–12 Hz) and lower beta (15–20 Hz) frequency bands. An increase in power after zolpidem intake was found in the higher beta/lower gamma (20–43 Hz) frequency band. Source level functional connectivity measured using weighted-phase lag index showed changes after zolpidem intake. Stronger connectivity between left frontal and temporal brain regions was observed. We report that zolpidem induces a change in MEG resting power and functional connectivity in the patient. MEG is an informative and sensitive tool to detect changes in brain activity for TBI

Clinical applications and prospects of PET imaging in patients with IDH-mutant gliomas

PET imaging using radiolabeled amino acids in addition to MRI has become a valuable diagnostic tool in the clinical management of patients with brain tumors. This review provides a comprehensive overview of PET studies in glioma patients with a mutation in the isocitrate dehydrogenase gene (IDH). A considerable fraction of these tumors typically show no contrast enhancement on MRI, especially when classified as grade 2 according to the World Health Organization classification of Central Nervous System tumors. Major diagnostic challenges in this situation are differential diagnosis, target definition for diagnostic biopsies, delineation of glioma extent for treatment planning, differentiation of treatment-related changes from tumor progression, and the evaluation of response to alkylating agents. The main focus of this review is the role of amino acid PET in this setting. Furthermore, in light of clinical trials using IDH inhibitors targeting the mutated IDH enzyme for treating patients with IDH-mutant gliomas, we also aim to give an outlook on PET probes specifically targeting the IDH mutation, which appear potentially helpful for response assessment

Clinical applications and prospects of PET imaging in patients with IDH-mutant gliomas

PET imaging using radiolabeled amino acids in addition to MRI has become a valuable diagnostic tool in the clinical management of patients with brain tumors. This review provides a comprehensive overview of PET studies in glioma patients with a mutation in the isocitrate dehydrogenase gene (IDH). A considerable fraction of these tumors typically show no contrast enhancement on MRI, especially when classified as grade 2 according to the World Health Organization classification of Central Nervous System tumors. Major diagnostic challenges in this situation are differential diagnosis, target definition for diagnostic biopsies, delineation of glioma extent for treatment planning, differentiation of treatment-related changes from tumor progression, and the evaluation of response to alkylating agents. The main focus of this review is the role of amino acid PET in this setting. Furthermore, in light of clinical trials using IDH inhibitors targeting the mutated IDH enzyme for treating patients with IDH-mutant gliomas, we also aim to give an outlook on PET probes specifically targeting the IDH mutation, which appear potentially helpful for response assessment

Radiomics in neuro-oncology: Basics, workflow, and applications

Over the last years, the amount, variety, and complexity of neuroimaging data acquired in patients with brain tumors for routine clinical purposes and the resulting number of imaging parameters have substantially increased. Consequently, a timely and cost-effective evaluation of imaging data is hardly feasible without the support of methods from the field of artificial intelligence (AI). AI can facilitate and shorten various time-consuming steps in the image processing workflow, e.g., tumor segmentation, thereby optimizing productivity. Besides, the automated and computer-based analysis of imaging data may help to increase data comparability as it is independent of the experience level of the evaluating clinician. Importantly, AI offers the potential to extract new features from the routinely acquired neuroimages of brain tumor patients. In combination with patient data such as survival, molecular markers, or genomics, mathematical models can be generated that allow, for example, the prediction of treatment response or prognosis, as well as the noninvasive assessment of molecular markers. The subdiscipline of AI dealing with the computation, identification, and extraction of image features, as well as the generation of prognostic or predictive mathematical models, is termed radiomics. This review article summarizes the basics, the current workflow, and methods used in radiomics with a focus on feature-based radiomics in neuro-oncology and provides selected examples of its clinical application