Enhancing image quality in computed tomography angiography follow-ups after endovascular aneurysm repair: a comparative study of reconstruction techniques.

BACKGROUND: The image quality of computed tomography angiography (CTA) images following endovascular aneurysm repair (EVAR) is not satisfactory, since artifacts resulting from metallic implants obstruct the clear depiction of stent and isolation lumens, and also adjacent soft tissues. However, current techniques to reduce these artifacts still need further advancements due to higher radiation doses, longer processing times and so on. Thus, the aim of this study is to assess the impact of utilizing Single-Energy Metal Artifact Reduction (SEMAR) alongside a novel deep learning image reconstruction technique, known as the Advanced Intelligent Clear-IQ Engine (AiCE), on image quality of CTA follow-ups conducted after EVAR. MATERIALS: This retrospective study included 47 patients (mean age ± standard deviation: 68.6 ± 7.8 years; 37 males) who underwent CTA examinations following EVAR. Images were reconstructed using four different methods: hybrid iterative reconstruction (HIR), AiCE, the combination of HIR and SEMAR (HIR + SEMAR), and the combination of AiCE and SEMAR (AiCE + SEMAR). Two radiologists, blinded to the reconstruction techniques, independently evaluated the images. Quantitative assessments included measurements of image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), the longest length of artifacts (AL), and artifact index (AI). These parameters were subsequently compared across different reconstruction methods. RESULTS: The subjective results indicated that AiCE + SEMAR performed the best in terms of image quality. The mean image noise intensity was significantly lower in the AiCE + SEMAR group (25.35 ± 6.51 HU) than in the HIR (47.77 ± 8.76 HU), AiCE (42.93 ± 10.61 HU), and HIR + SEMAR (30.34 ± 4.87 HU) groups (p < 0.001). Additionally, AiCE + SEMAR exhibited the highest SNRs and CNRs, as well as the lowest AIs and ALs. Importantly, endoleaks and thrombi were most clearly visualized using AiCE + SEMAR. CONCLUSIONS: In comparison to other reconstruction methods, the combination of AiCE + SEMAR demonstrates superior image quality, thereby enhancing the detection capabilities and diagnostic confidence of potential complications such as early minor endleaks and thrombi following EVAR. This improvement in image quality could lead to more accurate diagnoses and better patient outcomes.

Quantitative evaluation of CTP derived time-density alterations versus CTP for collateral status prediction with stroke.

BACKGROUND AND OBJECTIVES: Collateral status is a pivotal determinant of clinical outcomes in acute ischemic stroke (AIS); however, its evaluation can be challenging. We investigated the predictive value of CT perfusion (CTP) derived time and density alterations versus CTP for collateral status prediction in AIS. METHODS: Consecutive patients with anterior circulation occlusion within 24 h were retrospectively included. Time-density curves of the CTP specified ischemic core, penumbra, and the corresponding contralateral unaffected brain were obtained. The collateral status was dichotomised into robust (4-5 scores) and poor (0-3 scores) using multiphase collateral scoring, as described by Menon et al.. Receiver operating characteristic curves and multivariable regression analysis were performed to assess the predictive ability of CTP-designated tissue time and density alterations, CTP for robust collaterals, and favourable outcomes (mRS score of 0-2 at 90 days). RESULTS: One-hundred patients (median age, 68 years; interquartile range, 57-80 years; 61 men) were included. A smaller ischemic core, shorter peak time delay, lower peak density decrease, lower cerebral blood volume ratio, and cerebral blood flow ratio in the CTP specified ischemic core were significantly associated with robust collaterals (PFDR ≤ 0.004). The peak time delay demonstrated the highest diagnostic value (AUC, 0.74; P < 0.001) with 66.7 % sensitivity and 73.7 % specificity. Furthermore, the peak time delay of less than 8.5 s was an independent predictor of robust collaterals and favourable clinical outcomes. CONCLUSIONS: Robust collateral status was significantly associated with the peak time delay in the ischemic core. It is a promising image marker for predicting collateral status and functional outcomes in AIS.

Looking through the imaging perspective: the importance of imaging necrosis in glioma diagnosis and prognostic prediction - single centre experience.

BACKGROUND: The aim of the study was to investigate the diagnostic value of imaging necrosis (Imnecrosis) in grading, predict the genotype and prognosis of gliomas, and further assess tumor necrosis by dynamic contrast-enhanced MR perfusion imaging (DCE-MRI). PATIENTS AND METHODS: We retrospectively included 150 patients (104 males, mean age: 46 years old) pathologically proved as adult diffuse gliomas and all diagnosis was based on the 2021 WHO central nervous system (CNS) classification. The pathological necrosis (Panecrosis) and gene mutation information were collected. All patients underwent conventional and DCE-MRI examinations and had been followed until May 31, 2021. The Imnecrosis was determined by two experienced neuroradiologists. DCE-MRI derived metric maps have been post-processed, and the mean value of each metric in the tumor parenchyma, peritumoral and contralateral area were recorded. RESULTS: There was a strong degree of inter-observer agreement in defining Imnecrosis (Kappa = 0.668, p < 0.001) and a strong degree of agreement between Imnecrosis and Panecrosis (Kappa = 0.767, p < 0.001). Compared to low-grade gliomas, high-grade gliomas had more Imnecrosis (85.37%, p < 0.001), and Imnecrosis significantly increased with the grade of gliomas increasing. And Imnecrosis was significantly more identified in IDH-wildtype, 1p19q-non-codeletion, and CDKN2A/B-homozygous-deletion gliomas. Using multivariate Cox regression analysis, Imnecrosis was an independent and unfavorable prognosis factor (Hazard Ratio = 2.113, p = 0.046) in gliomas. Additionally, extravascular extracellular volume fraction (ve) in tumor parenchyma derived from DCE-MRI demonstrated the highest diagnostic efficiency in identifying Panecrosis and Imnecrosis with high specificity (83.3% and 91.9%, respectively). CONCLUSIONS: Imnecrosis can provide supplementary evidence beyond Panecrosis in grading, predicting the genotype and prognosis of gliomas, and ve in tumor parenchyma can help to predict tumor necrosis with high specificity.

Tumor Multiregional Mean Apparent Propagator (MAP) Features in Evaluating Gliomas-A Comparative Study With Diffusion Kurtosis Imaging (DKI).

BACKGROUND: Glioma classification affects treatment and prognosis. Reliable imaging methods for preoperatively evaluating gliomas are essential. PURPOSE: To evaluate tumor multiregional mean apparent propagator (MAP) features in glioma diagnosis and to compare those with diffusion-kurtosis imaging (DKI). STUDY TYPE: Retrospective study. SUBJECTS: 70 untreated glioma patients (31 LGGs (low-grade gliomas), 34 women; mean age, 47 ± 12 years, training (60%, n = 42) and testing cohorts (40%, n = 28)). FIELD STRENGTH/SEQUENCE: 3-T, diffusion-MRI using q-space Cartesian grid sampling with 11 different b-values. ASSESSMENT: Tumor multiregional MAP (mean squared displacement (MSD); q-space inverse variance (QIV); non-Gaussianity (NG); axial/radial non-Gaussianity (NGAx, NGRad); return-to-origin/axis/plane probability (RTOP, RTAP, and RTPP)); and DKI metrics (axial/mean/radial kurtosis (AK, MK, and RK)) on tumor parenchyma (TP) and peritumoral areas (PT) in histopathologically gliomas grading and genotyping were assessed. STATISTICAL TESTS: Mann-Whitney U; Kruskal-Wallis; Benjamini-Hochberg; Bonferroni-correction; receiver operating curve (ROC) and area under curve (AUC); DeLong's test; Random Forest (RF). P value<0.05 was considered statistically significant after multiple comparisons correction. RESULTS: Compared with LGGs, MSD, and QIV were significantly lower in TP, whereas NG, NGAx, NGRad, RTOP, RTAP, RTPP, and DKI metrics were significantly higher in HGGs (high-grade gliomas) (P ≤ 0.007), as well as in isocitrate-dehydrogenase (IDH)-mutated than IDH-wildtype gliomas (P ≤ 0.039). These trends were reversed for PT (tumor grades, P ≤ 0.011; IDH-mutation status, P ≤ 0.012). ROC analysis showed that, in TP, DKI metrics performed best in TP (AUC 0.83), whereas in PT, RTPP performed best (AUC 0.77) in glioma grading. AK performed best in TP (AUC 0.77), whereas MSD and RTPP performed best in PT (AUC 0.73) in IDH genotyping. Further RF analysis with DKI and MAP demonstrated good performance in grading (AUC 0.91, Accuracy 82%) and IDH genotyping (AUC 0.87, Accuracy 79%). DATA CONCLUSION: Tumor multiregional MAP features could effectively evaluate gliomas. The performance of MAP may be similar to DKI in TP, while in PT, MAP may outperform DKI. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 2.

SAD score of intracranial aneurysms for rupture risk assessment based on high-resolution vessel wall imaging.

OBJECTIVE: We aimed to develop a comprehensive model that integrates the radiological, morphological, and clinical factors to assess rupture risk for intracranial aneurysms. METHODS: We prospectively enrolled patients with intracranial saccular aneurysms who underwent high-resolution vessel wall imaging (HR-VWI) preoperatively. Clinical characteristics, aneurysm features and aneurysm wall enhancement scale (AWES) were recorded. AWES was categorized into three grades (no/faint/strong enhancement) by comparing AWE to enhancement of the pituitary infundibulum or choroid plexus on HR-VWI. Univariate and multivariate logistic regression analyses were performed to determine risk factors associated with aneurysmal rupture. RESULTS: A total of 25 ruptured and 116 unruptured aneurysms were included. Multivariate logistic regression analysis revealed that non-ICA site (OR 6.25, 95% CI 1.35-28.30, P = 0.019), AWES (OR 5.99, 95% CI 2.51-14.29, P < 0.001) and daughter sac or lobulated shape (OR 6.22, 95% CI 1.68-23.16, P = 0.006) were independent factors associated with ruptured aneurysms. The "SAD" model was generated and named after the first letters of each of these factors. SAD scores of 0-4 predicted 0, 2%, 12%, 42% and 100% ruptured aneurysms, respectively. The area under the receiver operating characteristic curve for the SAD model was 0.8822. CONCLUSION: The SAD model aids in distinguishing aneurysm rupture status and in managing unruptured aneurysms. Larger cohort studies are needed to confirm its applicability in predicting the rupture risk of unruptured aneurysms.

Quantitative susceptibility mapping evaluation of glioma.

OBJECTIVES: To comprehensively evaluate the glioma using quantitative susceptibility mapping (QSM). MATERIALS AND METHODS: Forty-two patients (18 women; mean age, 45 years) with pathologically confirmed gliomas were retrospectively included. All the patients underwent conventional and advanced MRI examinations (QSM, DWI, MRS, etc.). Five patients underwent paired QSM (pre- and post-enhancement). Four Visually Accessible Rembrandt Image (VASARI) features and intratumoural susceptibility signal (ITSS) were observed. Three ROIs each were manually drawn separately in the tumour parenchyma with relatively high and low magnetic susceptibility. The association between the tumour's magnetic susceptibility and other MRI parameters was also analysed. RESULTS: Morphologically, gliomas with heterogeneous ITSS were more similar to high-grade gliomas (p = 0.006, AUC: 0.72, sensitivity: 70%, and specificity: 73%). Heterogeneous ITSS was significantly associated with tumour haemorrhage, necrosis, diffusion restriction, and avid enhancement but did not change between pre- and post-enhanced QSM. Quantitatively, tumour parenchyma magnetic susceptibility had limited value in grading gliomas and identifying IDH mutation status, whereas the relatively low magnetic susceptibility of the tumour parenchyma helped identify oligodendrogliomas in IDH mutated gliomas (AUC = 0.78) with high specificity (100%). The relatively high tumour magnetic susceptibility significantly increased after enhancement (p = 0.039). Additionally, we found that the magnetic susceptibility of the tumour parenchyma was significantly correlated with ADC (r = 0.61) and Cho/NAA (r = 0.40). CONCLUSIONS: QSM is a promising candidate for the comprehensive evaluation of gliomas, except for IDH mutation status. The magnetic susceptibility of tumour parenchyma may be affected by tumour cell proliferation. KEY POINTS: • Morphologically, gliomas with a heterogeneous intratumoural susceptibility signal (ITSS) are more similar to high-grade gliomas (p = 0.006; AUC, 0.72; sensitivity, 70%; and specificity, 73%). Heterogeneous ITSS was significantly associated with tumour haemorrhage, necrosis, diffusion restriction, and avid enhancement but did not change between pre- and post-enhanced QSM. • Tumour parenchyma's relatively low magnetic susceptibility helped identify oligodendroglioma with high specificity. • Tumour parenchyma magnetic susceptibility was significantly correlated with ADC (r = 0.61) and Cho/NAA (r = 0.40).

Hemorrhage in pheochromocytoma surgery: evaluation of preoperative risk factors.

OBJECTIVE: Pheochromocytoma surgery carries a higher risk of hemorrhage. Our objective was to identify preoperative risk factors for hemorrhage during pheochromocytoma surgery. METHODS: Patients who underwent surgery and with postoperative pathological confirmation were enrolled. A total of 251 patients from our center were included in the investigation, and 120 patients from the First Affiliated Hospital, Sun Yat-sen University were included as an external validation dataset. Family and medical history, demographics, hemodynamics, biochemical parameters, image data, anesthesia and operation records, postoperative outcomes were collected. Postoperative complications were graded by the Clavien-Dindo classification. Correlation between intraoperative hemorrhage volume and postoperative outcomes was assessed. The features associated with intraoperative hemorrhage were identified by linear regression. All features that were statistically significant in the multiple linear regression were then used to construct models and nomograms for predicting intraoperative hemorrhage. The constructed models were evaluated by Akaike Information Criterion. Finally, internal and external validations were carried out by tenfold cross-validation. RESULTS: Intraoperative hemorrhage volume was positively correlated with the postoperative hospitalization time (R = 0.454, P < 0.001) and the Clavien-Dindo grades (R = 0.664, P < 0.001). Features associated with intraoperative hemorrhage were male gender (ß = 0.533, OR = 1.722, P = 0.002), tumor diameter (ß = 0.027, OR = 1.027, P < 0.001), preoperative CCB use (ß = 0.318, OR = 1.308, P = 0.123) and open surgery (ß = 1.175, OR = 3.234, P < 0.001). Validations showed reliable results (internal (R = 0.612, RMSE = 1.355, MAE = 1.111); external (R = 0.585, RMSE = 1.398, MAE = 0.964)). CONCLUSION: More intraoperative hemorrhage is correlated with longer postoperative hospitalization time and more severe postoperative complications. Male gender, larger tumor, preoperative CCB use and open surgery are preoperative risk factors for hemorrhage in PCC surgery.

Patients with degenerative cervical myelopathy exhibit neurophysiological improvement upon extension and flexion: a retrospective cohort study with a minimum 1-year follow-up.

BACKGROUND: Cervical extension and flexion are presumably harmful to patients with degenerative cervical myelopathy (DCM) because they worsen medullary compression visible on dynamic magnetic resonance imaging (MRI). Dynamic somatosensory evoked potentials (SSEPs) are an objective tool to measure the electrophysiological function of the spinal cord at different neck positions. In contrast to previous hypotheses, a considerable proportion of patients with DCM present improved SSEPs upon extension and flexion compared to a neutral position. METHODS: Patients with DCM who underwent preoperative dynamic SSEP examinations and subsequent decompression surgeries between 2015 and 2019 were retrospectively evaluated. We compared extension and flexion SSEPs with neutral SSEPs in each patient and classified them into extension-improved (EI) or extension-nonimproved (EN) and flexion-improved (FI) or flexion-nonimproved (FN) groups. Preoperative clinical evaluations, decompression surgical methods and one-year follow-up clinical data were recorded. Cervical spondylolisthesis and cervical alignment types were evaluated on plain cervical lateral radiographs. The number of stenotic segments, Mühle stenosis grade and disc degeneration stage of the most severe segment, and presence of ligamentum flavum hypertrophy and intramedullary T2 weighted imaging (T2WI) hyperintensity were evaluated on lateral and axial MRI. Data were compared between the EN and EN groups or FI and FN groups with T-tests, chi-square tests or Kruskal-Wallis tests. Prediction criteria were determined with logistic regression analyses. RESULTS: Forty-nine patients were included, and 9 (18.4%) and 11 (22.4%) showed improved extension and flexion SSEPs compared to their own neutral SSEPs, respectively. Interestingly, EI or FI patients had significantly better one-year postoperative mJOA recoveries than EN or FN patients (T-test, P < 0.001). Moreover, the disease duration (T-test, P = 0.024), involved segment number (Kruskal-Wallis test, P < 0.001), and cervical alignment type (chi-square test, P = 0.005) varied significantly between the EI and EN groups. The FI group presented a significantly higher Mühle stenosis grade than the FN group (Kruskal-Wallis test, P = 0.038). Furthermore, ≤ 2 involved segments and straight or sigmoid cervical alignment were significant criteria predicting improved extension SSEPs (probability: 85.7%), while Mühle stenosis Grade 3 and disease duration ≤6 months were significant criteria predicting improved flexion SSEPs (probability: 85.7%). CONCLUSIONS: Our findings provide evidence for neurophysiological improvement in patients with DCM at extension and flexion and its significance in predicting prognoses. Moreover, certain clinical and radiographic criteria may help predict neurophysiological improvement upon extension or flexion. TRIAL REGISTRATION: " [2020]151 ". Retrospectively registered on April 30, 2020.

18F-Trifluoromethylated D-Cysteine as a Promising New PET Tracer for Glioma Imaging: Comparative Analysis With MRI and Histopathology in Orthotopic C6 Models.

Comparing MRI and histopathology, this study aims to comprehensively explore the potential application of 18F-trifluoromethylated D-cysteine (S-[18F]CF3-D-CYS) in evaluating glioma by using orthotopic C6 glioma models. Sprague-Dawley (SD) rats (n = 9) were implanted with C6 glioma cells. Tumor growth was monitored every week by multiparameter MRI [including dynamic contrast-enhanced MRI (DCE-MRI)], [18F]FDG, S-[18F]CF3-D-CYS, and [18F]FDOPA PET imaging. Repeated scans of the same rat with the two or three [18F]-labeled radiotracers were investigated. Initial regions of interest were manually delineated on T2WI and set on the same level of PET images, and tumor-to-normal brain uptake ratios (TNRs) were calculated to semiquantitatively assess the tracer accumulation in the tumor. The tumor volume in PET and histopathology was calculated. HE and Ki67 immunohistochemical staining were further performed. The correlations between the uptake of S-[18F]CF3-D-CYS and Ki67 were analyzed. Dynamic S-[18F]CF3-D-CYS PET imaging showed tumor uptake rapidly reached a peak, maintained plateau during 10-30 min after injection, then decreased slowly. Compared with [18F]FDG and [18F]FDOPA PET imaging, S-[18F]CF3-D-CYS PET demonstrated the highest TNRs (P < 0.05). There were no significant differences in the tumor volume measured on S-[18F]CF3-D-CYS PET or HE specimen. Furthermore, our results showed that the uptake of S-[18F]CF3-D-CYS was significantly positively correlated with tumor Ki67, and the poor accumulated S-[18F]CF3-D-CYS was consistent with tumor hemorrhage. There was no significant correlation between the S-[18F]CF3-D-CYS uptakes and the Ktrans values derived from DCE-MRI. In comparison with MRI and histopathology, S-[18F]CF3-D-CYS PET performs well in the diagnosis and evaluation of glioma. S-[18F]CF3-D-CYS PET may serve as a valuable tool in the clinical management of gliomas.

Diagnostic accuracy and potential covariates for machine learning to identify IDH mutations in glioma patients: evidence from a meta-analysis.

OBJECTIVES: To assess the diagnostic accuracy of machine learning (ML) in predicting isocitrate dehydrogenase (IDH) mutations in patients with glioma and to identify potential covariates that could influence the diagnostic performance of ML. METHODS: A systematic search of PubMed, Web of Science, and the Cochrane library up to 1 August 2019 was conducted to collect all the articles investigating the diagnostic performance of ML for prediction of IDH mutation in glioma. The search strategy combined synonyms for 'machine learning', 'glioma', and 'IDH'. Pooled sensitivity, specificity, and their 95% confidence intervals (CIs) were calculated, and the area under the receiver operating characteristic curve (AUC) was obtained. RESULTS: Nine original articles assessing a total of 996 patients with glioma were included. Among these studies, five divided the participants into training and validation sets, while the remaining four studies only had a training set. The AUC of ML for predicting IDH mutation in the training and validation sets was 93% (95% CI 91-95%) and 89% (95% CI 86-92%), respectively. The pooled sensitivity and specificity were, respectively, 87% (95% CI 82-91%) and 88% (95% CI 83-92%) in the training set and 87% (95% CI 76-93%) and 90% (95% CI 72-97%) in the validation set. In subgroup analyses in the training set, the combined use of clinical and imaging features with ML yielded higher sensitivity (90% vs. 83%) and specificity (90% vs. 82%) than the use of imaging features alone. In addition, ML performed better for high-grade gliomas than for low-grade gliomas, and ML that used conventional MRI sequences demonstrated higher specificity for predicting IDH mutation than ML using conventional and advanced MRI sequences. CONCLUSIONS: ML demonstrated an excellent diagnostic performance in predicting IDH mutation of glioma. Clinical information, MRI sequences, and glioma grade were the main factors influencing diagnostic specificity. KEY POINTS: • Machine learning demonstrated an excellent diagnostic performance for prediction of IDH mutation in glioma (the pooled sensitivity and specificity were 88% and 87%, respectively). • Machine learning that used conventional MRI sequences demonstrated higher specificity in predicting IDH mutation than that based on conventional and advanced MRI sequences (89% vs. 85%). • Integration of clinical and imaging features in machine learning yielded a higher sensitivity (90% vs. 83%) and specificity (90% vs. 82%) than that achieved by using imaging features alone.