Positron emission tomography imaging in gliomas: applications in clinical diagnosis, for assessment of prognosis and of treatment effects, and for detection of recurrences

Association of periprocedural perfusion non-improvement with recurrent stroke after endovascular treatment for Intracranial Atherosclerotic Stenosis

Background

Predictors of recurrent stroke after endovascular treatment for symptomatic intracranial atherosclerotic stenosis (ICAS) remain uncertain.

Objectives

Among baseline characteristics, lesion features, and cerebral perfusion changes, we try to explore which factors are associated with the risk of recurrent stroke in symptomatic ICAS after endovascular treatment.

Design

Consecutive patients with symptomatic ICAS of 70-99% receiving endovascular treatment were enrolled. All patients underwent whole-brain computer tomography perfusion (CTP) within 3 days before and 3 days after the endovascular treatment. Baseline characteristics, lesion features, and cerebral perfusion changes were collected.

Methods

Cerebral perfusion changes were evaluated with RAPID software and calculated as preprocedural cerebral blood flow (CBF) < 30%, time to maximum of the residue function (Tmax) > 6 s, and Tmax > 4 s volumes minus postprocedural. Cerebral perfusion changes were divided into periprocedural perfusion improvement (>0 ml) and non-improvement (⩽ 0 ml). Recurrent stroke within 180 days was collected. The Cox proportional hazards analysis analyses were performed to evaluate factors associated with recurrent stroke.

Results

From March 2021 to December 2021, 107 patients with symptomatic ICAS were enrolled. Of the 107 enrolled patients, 30 (28.0%) patients underwent balloon angioplasty alone and 77 patients (72.0%) underwent stenting. The perioperative complications occurred in three patients. Among CBF < 30%, Tmax > 6 s, and Tmax > 4 s volumes, Tmax > 4 s volume was available to evaluate cerebral perfusion changes. Periprocedural perfusion improvement was found in 77 patients (72.0%) and non-improvement in 30 patients (28.0%). Nine patients (8.4%) suffered from recurrent stroke in 180-day follow-up. In Cox proportional hazards analysis adjusted for age and sex, perfusion non-improvement was associated with recurrent stroke [hazards ratio (HR): 4.472; 95% CI: 1.069-18.718; p = 0.040].

Conclusion

In patients with symptomatic ICAS treated with endovascular treatment, recurrent stroke may be related to periprocedural cerebral perfusion non-improvement.

Registration

http://www.chictr.org.cn. Unique identifier: ChiCTR2100052925.

Static FET PET radiomics for the differentiation of treatment-related changes from glioma progression

PURPOSE

To investigate the potential of radiomics applied to static clinical PET data using the tracer O-(2-[¹⁸F]fluoroethyl)-L-tyrosine (FET) to differentiate treatment-related changes (TRC) from tumor progression (TP) in patients with gliomas.

PATIENTS AND METHODS

One hundred fifty-one (151) patients with histologically confirmed gliomas and post-therapeutic progressive MRI findings according to the response assessment in neuro-oncology criteria underwent a dynamic amino acid PET scan using the tracer O-(2-[¹⁸F]fluoroethyl)-L-tyrosine (FET). Thereof, 124 patients were investigated on a stand-alone PET scanner (data used for model development and validation), and 27 patients on a hybrid PET/MRI scanner (data used for model testing). Mean and maximum tumor to brain ratios (TBR_mean, TBR_max) were calculated using the PET data from 20 to 40 min after tracer injection. Logistic regression models were evaluated for the FET PET parameters TBR_mean, TBR_max, and for radiomics features of the tumor areas as well as combinations thereof to differentiate between TP and TRC. The best performing models in the validation dataset were finally applied to the test dataset. The diagnostic performance was assessed by receiver operating characteristic analysis.

RESULTS

Thirty-seven patients (25%) were diagnosed with TRC, and 114 (75%) with TP. The logistic regression model comprising the conventional FET PET parameters TBR_mean and TBR_max resulted in an AUC of 0.78 in both the validation (sensitivity, 64%; specificity, 80%) and the test dataset (sensitivity, 64%; specificity, 80%). The model combining the conventional FET PET parameters and two radiomics features yielded the best diagnostic performance in the validation dataset (AUC, 0.92; sensitivity, 91%; specificity, 80%) and demonstrated its generalizability in the independent test dataset (AUC, 0.85; sensitivity, 81%; specificity, 70%).

CONCLUSION

The developed radiomics classifier allows the differentiation between TRC and TP in pretreated gliomas based on routinely acquired static FET PET scans with a high diagnostic accuracy.

Differentiation of Treatment-Related Effects from Glioma Recurrence Using Machine Learning Classifiers Based Upon Pre-and Post-Contrast T1WI and T2 FLAIR Subtraction Features: A Two-Center Study

Purpose

We propose three support vector machine (SVM) classifiers, using pre-and post-contrast T2 fluid-attenuated inversion recovery (FLAIR) subtraction and/or pre-and post-contrast T1WI subtraction, to differentiate treatment-related effects (TRE) from glioma recurrence.

Materials and Methods

Fifty-six postoperative high-grade glioma patients with suspicious progression after radiotherapy and chemotherapy from two centers were studied. Pre-and post-contrast T1WI and T2 FLAIR were collected. Each pre-contrast image was voxel-wise subtracted from the co-registered post-contrast image. Dataset was randomly split into training, and testing on a 7:3 ratio, accordingly subjected to a five fold cross validation. Best feature subsets were selected by Pearson correlation coefficient and recursive feature elimination, whereupon a radiomics classifier was built with SVM. The discriminating performance was assessed with the area under receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).

Results

In all, 186 features were extracted on each subtraction map. Top nine T1WI subtraction features, top thirteen T2 FLAIR subtraction features and top thirteen combination features were selected to build optimal SVM classifiers accordingly. The accuracies/AUCs/sensitivity/specificity/PPV/NPV of SVM based on sole T1WI subtraction were 80.00%/80.00% (CI: 0.5370–1.0000)/100%/70.00%/62.50%/100%. Those results of SVM based on sole T2 FLAIR subtraction were 86.67%/84.00% (CI: 0.5962–1.0000)/100%/80%/71.43%/100%. Those results of SVM based on both T1WI subtraction and T2 FLAIR subtraction were 93.33%/94.00% (CI: 0.7778–1.0000)/100%/90%/83.33%/100%, respectively.

Conclusion

Pre- and post-contrast T2 FLAIR subtraction provided added value for diagnosis between recurrence and TRE. SVM based on a combination of T1WI and T2 FLAIR subtraction maps was superior to the sole use of T1WI or T2 FLAIR for differentiating TRE from recurrence. The SVM classifier based on combination of pre-and post-contrast subtraction T2 FLAIR and T1WI imaging allowed for the accurate differential diagnosis of TRE from recurrence, which is of paramount importance for treatment management of postoperative glioma patients after radiation therapy.

The Molecular Effects of Ionizing Radiations on Brain Cells: Radiation Necrosis vs. Tumor Recurrence

The central nervous system (CNS) is generally resistant to the effects of radiation, but higher doses, such as those related to radiation therapy, can cause both acute and long-term brain damage. The most important results is a decline in cognitive function that follows, in most cases, cerebral radionecrosis. The essence of radio-induced brain damage is multifactorial, being linked to total administered dose, dose per fraction, tumor volume, duration of irradiation and dependent on complex interactions between multiple brain cell types. Cognitive impairment has been described following brain radiotherapy, but the mechanisms leading to this adverse event remain mostly unknown. In the event of a brain tumor, on follow-up radiological imaging often cannot clearly distinguish between recurrence and necrosis, while, especially in patients that underwent radiation therapy (RT) post-surgery, positron emission tomography (PET) functional imaging, is able to differentiate tumors from reactive phenomena. More recently, efforts have been done to combine both morphological and functional data in a single exam and acquisition thanks to the co-registration of PET/MRI. The future of PET imaging to differentiate between radionecrosis and tumor recurrence could be represented by a third-generation PET tracer already used to reveal the spatial extent of brain inflammation. The aim of the following review is to analyze the effect of ionizing radiations on CNS with specific regard to effect of radiotherapy, focusing the attention on the mechanism underling the radionecrosis and the brain damage, and show the role of nuclear medicine techniques to distinguish necrosis from recurrence and to early detect of cognitive decline after treatment.

Radiopharmaceuticals and Fluorescein Sodium Mediated Triple-Modality Molecular Imaging Allows Precise Image-Guided Tumor Surgery

Radical resection is the most effective method for malignant tumor treatments. However, conventional imaging cannot fully satisfy the clinical needs of surgical navigation. This study presents a novel triple-modality positron emission tomography (PET)-Cerenkov radiation energy transfer (CRET)-confocal laser endomicroscopy (CLE) imaging strategy for intraoperative tumor imaging and surgical navigation. Using clinical radiopharmaceuticals and fluorescein sodium (FS), this strategy can accurately detect the tumor and guide the tumor surgery. The FS emission property under Cerenkov radiation excitation is investigated using 2-deoxy-2-18F-fluoroglucose and 11C-choline. Performances of the PET-CRET-CLE imaging and the CRET-CLE image-guided surgery are evaluated on mouse models. The CRET signal at 8 mm depth is stronger than the Cerenkov luminescence at 1 mm depth in phantoms. In vivo experiments indicate that 0.5 mL kg-1 of 10% FS generates the strongest CRET signal, which can be observed immediately after FS injection. A surgical navigation study shows that the tumors are precisely detected and resected using intraoperative CRET-CLE. In summary, a PET-CRET-CLE triple-modality imaging strategy is developed. This strategy can detect the tumors and precisely guide the tumor resection using clinical pharmaceuticals. This triple-modality imaging shows high potential in surgical navigation research and clinical translation.

Laboratory and Neuroimaging Biomarkers in Neuropsychiatric Systemic Lupus Erythematosus: Where Do We Stand, Where To Go?

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by multi-systemic involvement. Nervous system involvement in SLE leads to a series of uncommon and heterogeneous neuropsychiatric (NP) manifestations. Current knowledge on the underlying pathogenic processes and their subsequent pathophysiological changes leading to NP-SLE manifestations is incomplete. Several putative laboratory biomarkers have been proposed as contributors to the genesis of SLE-related nervous system damage. Alongside the laboratory biomarkers, several neuroimaging tools have shown to reflect the nature of tissue microstructural damage associated with SLE, and thus were suggested to contribute to the understanding of the pathophysiological changes and subsequently help in clinical decision making. However, the number of useful biomarkers in NP-SLE in clinical practice is disconcertingly modest. In some cases it is not clear whether the biomarker is truly involved in pathogenesis, or the result of non-specific pathophysiological changes in the nervous system (e.g., neuroinflammation) or whether it is the consequence of a concomitant underlying abnormality related to SLE activity. In order to improve the diagnosis of NP-SLE and provide a better targeted care to these patients, there is still a need to develop and validate a range of biomarkers that reliably capture the different aspects of disease heterogeneity. This article critically reviews the current state of knowledge on laboratory and neuroimaging biomarkers in NP-SLE, discusses the factors that need to be addressed to make these biomarkers suitable for clinical application, and suggests potential future research paths to address important unmet needs in the NP-SLE field.