Purpose
Superagers are older adults presenting excellent memory performance that may reflect resilience to the conventional pathways of aging. Our contribution aims to shape the evidence body of the ...known distinctive biomarkers of superagers and their connections with the Brain and Cognitive Reserve and Brain Maintenance concepts.
Methods
We performed a systematic literature search in PubMed and ScienceDirect with no limit on publication date for studies that evaluated potential biomarkers in superagers classified by validated neuropsychological tests. Methodological quality was assessed using the QUADAS-2 tool.
Results
Twenty-one studies were included, the majority in neuroimaging, followed by histological, genetic, cognition, and a single one on blood plasma analysis. Superagers exhibited specific regions of cortical preservation, rather than global cortical maintenance, standing out the anterior cingulate and hippocampus regions. Both superagers and controls showed similar levels of amyloid deposition. Moreover, the functional oscillation patterns in superagers resembled those described in young adults. Most of the quality assessment for the included studies showed medium risks of bias.
Conclusion
This systematic review supports selective cortical preservation in superagers, comprehending regions of the default mode, and salience networks, overlapped by stronger functional connectivity. In this context, the anterior cingulate cortex is highlighted as an imaging and histologic signature of these subjects. Besides, the biomarkers included pointed out that the Brain and Cognitive Reserve and Brain Maintenance concepts are independent and complementary in the superagers’ setting.
To investigate the prognostic utility of DTI and DSC-PWI perfusion-derived parameters in brain metastases patients.
Retrospective analyses of DTI-derived parameters (MD, FA, CL, CP, and CS) and ...DSC-perfusion PWI-derived rCBV
from 101 patients diagnosed with brain metastases prior to treatment were performed. Using semi-automated segmentation, DTI metrics and rCBV
were quantified from enhancing areas of the dominant metastatic lesion. For each metric, patients were classified as short- and long-term survivors based on analysis of the best coefficient for each parameter and percentile to separate the groups. Kaplan-Meier analysis was used to compare mOS between these groups. Multivariate survival analysis was subsequently conducted. A correlative histopathologic analysis was performed in a subcohort (
= 10) with DTI metrics and rCBV
on opposite ends of the spectrum.
Significant differences in mOS were observed for MD
(
< 0.05), FA (
< 0.01), CL (
< 0.05), and CP (
< 0.01) and trend toward significance for rCBV
(
= 0.07) between the two risk groups, in the univariate analysis. On multivariate analysis, the best predictive survival model was comprised of MD
(
= 0.05), rCBV
(
< 0.05), RPA (
< 0.0001), and number of lesions (
= 0.07). On histopathology, metastatic tumors showed significant differences in the amount of stroma depending on the combination of DTI metrics and rCBVmax values. Patients with high stromal content demonstrated poorer mOS.
Pretreatment DTI-derived parameters, notably MD
and rCBVmax, are promising imaging markers for prognostication of OS in patients with brain metastases. Stromal cellularity may be a contributing factor to these differences.
The correlation of DTI-derived metrics and perfusion MRI with patient outcomes has not been investigated in patients with treatment naïve brain metastasis. DTI and DSC-PWI can aid in therapeutic decision-making by providing additional clinical guidance.
Technological advances in the delivery of radiation and other novel cancer therapies have significantly improved the 5‐year survival rates over the last few decades. Although recent developments have ...helped to better manage the acute effects of radiation, the late effects such as impairment in cognition continue to remain of concern. Accruing data in the literature have implicated derangements in hemodynamic parameters and metabolic activity of the irradiated normal brain as predictive of cognitive impairment. Multiparametric imaging modalities have allowed us to precisely quantify functional and metabolic information, enhancing the anatomic and morphologic data provided by conventional MRI sequences, thereby contributing as noninvasive imaging‐based biomarkers of radiation‐induced brain injury. In this review, we have elaborated on the mechanisms of radiation‐induced brain injury and discussed several novel imaging modalities, including MR spectroscopy, MR perfusion imaging, functional MR, SPECT, and PET that provide pathophysiological and functional insights into the postradiation brain, and its correlation with radiation dose as well as clinical neurocognitive outcomes. Additionally, we explored some innovative imaging modalities, such as quantitative blood oxygenation level‐dependent imaging, susceptibility‐based oxygenation measurement, and T2‐based oxygenation measurement, that hold promise in delineating the potential mechanisms underlying deleterious neurocognitive changes seen in the postradiation setting. We aim that this comprehensive review of a range of imaging modalities will help elucidate the hemodynamic and metabolic injury mechanisms underlying cognitive impairment in the irradiated normal brain in order to optimize treatment regimens and improve the quality of life for these patients.
This study aimed to investigate the potential of quantitative radiomic data extracted from conventional MR images in discriminating IDH-mutant grade 4 astrocytomas from IDH-wild-type glioblastomas ...(GBMs). A cohort of 57 treatment-naïve patients with IDH-mutant grade 4 astrocytomas (
= 23) and IDH-wild-type GBMs (
= 34) underwent anatomical imaging on a 3T MR system with standard parameters. Post-contrast T1-weighted and T2-FLAIR images were co-registered. A semi-automatic segmentation approach was used to generate regions of interest (ROIs) from different tissue components of neoplasms. A total of 1050 radiomic features were extracted from each image. The data were split randomly into training and testing sets. A deep learning-based data augmentation method (CTGAN) was implemented to synthesize 200 datasets from the training sets. A total of 18 classifiers were used to distinguish two genotypes of grade 4 astrocytomas. From generated data using 80% training set, the best discriminatory power was obtained from core tumor regions overlaid on post-contrast T1 using the K-best feature selection algorithm and a Gaussian naïve Bayes classifier (AUC = 0.93, accuracy = 0.92, sensitivity = 1, specificity = 0.86, PR_AUC = 0.92). Similarly, high diagnostic performances were obtained from original and generated data using 50% and 30% training sets. Our findings suggest that conventional MR imaging-based radiomic features combined with machine/deep learning methods may be valuable in discriminating IDH-mutant grade 4 astrocytomas from IDH-wild-type GBMs.
To emphasize the most appropriate magnetic resonance imaging (MRI) diffusion protocol for the detection of lesions that cause transient global amnesia, in order to perform an accurate examination, as ...well as to determine the ideal time point after the onset of symptoms to perform the examination.
We evaluated five patients with a diagnosis of transient global amnesia treated between 2012 and 2015. We analyzed demographic characteristics, clinical data, symptom onset, diffusion techniques, and radiological findings. Examination techniques included a standard diffusion sequence (b value = 1000 s/mm
; slice thickness = 5 mm) and a optimized diffusion sequence (b value = 2000 s/mm
; slice thickness = 3 mm).
Brain MRI was performed at 24 h or 36 h after symptom onset, except in one patient, in whom it was performed at 12 h after (at which point no changes were seen) and repeated at 36 h after symptom onset (at which point it showed alterations in the right hippocampus). The standard and optimized diffusion sequences were both able to demonstrate focal changes in the hippocampi in all of the patients but one, in whom the changes were demonstrated only in the optimized sequence.
MRI can confirm a clinical hypothesis of transient global amnesia. Knowledge of the optimal diffusion parameters and the ideal timing of diffusion-weighted imaging (> 24 h after symptom onset) are essential to improving diagnostic efficiency.
Accurate differentiation of pseudoprogression (PsP) from tumor progression (TP) in glioblastomas (GBMs) is essential for appropriate clinical management and prognostication of these patients. In the ...present study, we sought to validate the findings of our previously developed multiparametric MRI model in a new cohort of GBM patients treated with standard therapy in identifying PsP cases.
Fifty-six GBM patients demonstrating enhancing lesions within 6 months after completion of concurrent chemo-radiotherapy (CCRT) underwent anatomical imaging, diffusion and perfusion MRI on a 3 T magnet. Subsequently, patients were classified as TP + mixed tumor (n = 37) and PsP (n = 19). When tumor specimens were available from repeat surgery, histopathologic findings were used to identify TP + mixed tumor (> 25% malignant features; n = 34) or PsP (< 25% malignant features; n = 16). In case of non-availability of tumor specimens, ≥ 2 consecutive conventional MRIs using mRANO criteria were used to determine TP + mixed tumor (n = 3) or PsP (n = 3). The multiparametric MRI-based prediction model consisted of predictive probabilities (PP) of tumor progression computed from diffusion and perfusion MRI derived parameters from contrast enhancing regions. In the next step, PP values were used to characterize each lesion as PsP or TP+ mixed tumor. The lesions were considered as PsP if the PP value was < 50% and TP+ mixed tumor if the PP value was ≥ 50%. Pearson test was used to determine the concordance correlation coefficient between PP values and histopathology/mRANO criteria. The area under ROC curve (AUC) was used as a quantitative measure for assessing the discriminatory accuracy of the prediction model in identifying PsP and TP+ mixed tumor.
Multiparametric MRI model correctly predicted PsP in 95% (18/19) and TP+ mixed tumor in 57% of cases (21/37) with an overall concordance rate of 70% (39/56) with final diagnosis as determined by histopathology/mRANO criteria. There was a significant concordant correlation coefficient between PP values and histopathology/mRANO criteria (r = 0.56; p < 0.001). The ROC analyses revealed an accuracy of 75.7% in distinguishing PsP from TP+ mixed tumor. Leave-one-out cross-validation test revealed that 73.2% of cases were correctly classified as PsP and TP + mixed tumor.
Our multiparametric MRI based prediction model may be helpful in identifying PsP in GBM patients.
Purpose: The isocitrate dehydrogenase (IDH) mutation has become one of the most important prognostic biomarkers in glioma management, indicating better treatment response and prognosis. IDH mutations ...confer neomorphic activity leading to the conversion of alpha-ketoglutarate (α-KG) to 2-hydroxyglutarate (2HG). The purpose of this study was to investigate the clinical potential of proton MR spectroscopy (1H-MRS) in identifying IDH-mutant gliomas by detecting characteristic resonances of 2HG and its complex interplay with other clinically relevant metabolites. Materials and Methods: Thirty-two patients with suspected infiltrative glioma underwent a single-voxel (SVS, n = 17) and/or single-slice-multivoxel (1H-MRSI, n = 15) proton MR spectroscopy (1H-MRS) sequence with an optimized echo-time (97 ms) on 3T-MRI. Spectroscopy data were analyzed using the linear combination (LC) model. Cramér–Rao lower bound (CRLB) values of <40% were considered acceptable for detecting 2HG and <20% for other metabolites. Immunohistochemical analyses for determining IDH mutational status were subsequently performed from resected tumor specimens and findings were compared with the results from spectral data. Mann–Whitney and chi-squared tests were performed to ascertain differences in metabolite levels between IDH-mutant and IDH-wild-type gliomas. Receiver operating characteristic (ROC) curve analyses were also performed. Results: Data from eight cases were excluded due to poor spectral quality or non-tumor-related etiology, and final data analyses were performed from 24 cases. Of these cases, 9/12 (75%) were correctly identified as IDH-mutant or IDH-wildtype gliomas through SVS and 10/12 (83%) through 1H-MRSI with an overall concordance rate of 79% (19/24). The sensitivity, specificity, positive predictive value, and negative predictive value were 80%, 77%, 86%, and 70%, respectively. The metabolite 2HG was found to be significant in predicting IDH-mutant gliomas through the chi-squared test (p < 0.01). The IDH-mutant gliomas also had a significantly higher NAA/Cr ratio (1.20 ± 0.09 vs. 0.75 ± 0.12 p = 0.016) and lower Glx/Cr ratio (0.86 ± 0.078 vs. 1.88 ± 0.66; p = 0.029) than those with IDH wild-type gliomas. The areas under the ROC curves for NAA/Cr and Glx/Cr were 0.808 and 0.786, respectively. Conclusions: Noninvasive optimized 1H-MRS may be useful in predicting IDH mutational status and 2HG may serve as a valuable diagnostic and prognostic biomarker in patients with gliomas
The highly aggressive nature of glioblastoma carries a dismal prognosis despite aggressive multimodal therapy. Alternative treatment regimens, such as immunotherapies, are known to intensify the ...inflammatory response in the treatment field. Follow-up imaging in these scenarios often mimics disease progression on conventional MRI, making accurate evaluation extremely challenging. To this end, revised criteria for assessment of treatment response in high-grade gliomas were successfully proposed by the RANO Working Group to distinguish pseudoprogression from true progression, with intrinsic constraints related to the postcontrast T1-weighted MRI sequence. To address these existing limitations, our group proposes a more objective and quantifiable "treatment agnostic" model, integrating into the RANO criteria advanced multimodal neuroimaging techniques, such as diffusion tensor imaging (DTI), dynamic susceptibility contrast-perfusion weighted imaging (DSC-PWI), dynamic contrast enhanced (DCE)-MRI, MR spectroscopy, and amino acid-based positron emission tomography (PET) imaging tracers, along with artificial intelligence (AI) tools (radiomics, radiogenomics, and radiopathomics) and molecular information to address this complex issue of treatment-related changes versus tumor progression in "real-time", particularly in the early posttreatment window. Our perspective delineates the potential of incorporating multimodal neuroimaging techniques to improve consistency and automation for the assessment of early treatment response in neuro-oncology.
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