Amide proton transfer-weighted imaging of pediatric brainstem glioma and its predicted value for H3 K27 alteration

BACKGROUND: Non-invasive determination of H3 K27 alteration of pediatric brainstem glioma (pedBSG) remains a clinical challenge. PURPOSE: To predict H3 K27-altered pedBSG using amide proton transfer-weighted (APTw) imaging. MATERIAL AND METHODS: This retrospective study included patients with pedBSG who underwent APTw imaging and had the H3 K27 alteration status determined by immunohistochemical staining. The presence or absence of foci of markedly increased APTw signal in the lesion was visually assessed. Quantitative APTw histogram parameters within the entire solid portion of tumors were extracted and compared between H3 K27-altered and wild-type groups using Student's t-test. The ability of APTw for differential diagnosis was evaluated using logistic regression. RESULTS: Sixty pedBSG patients included 48 patients with H3 K27-altered tumor (aged 2-48 years) and 12 patients with wild-type tumor (aged 3-53 years). Visual assessment showed that the foci of markedly increased APTw signal intensity were more common in the H3 K27-altered group than in wild-type group (60% vs. 16%, P = 0.007). Histogram parameters of APTw signal intensity in the H3 K27-altered group were significantly higher than those in the wild-type group (median, 2.74% vs. 2.22%, P = 0.02). The maximum (area under the receiver operating characteristic curve [AUC] = 0.72, P = 0.01) showed the highest diagnostic performance among histogram analysis. A combination of age, median and maximum APTw signal intensity could predict H3 K27 alteration with a sensitivity of 81%, specificity of 75% and AUC of 0.80. CONCLUSION: APTw imaging may serve as an imaging biomarker for H3 K27 alteration of pedBSGs

UV and NIR size of the low-mass field galaxies: the UV compact galaxies

Context. Most of the massive star-forming galaxies are found to have “inside-out” stellar mass growth modes, which means the inner parts of the galaxies mainly consist of the older stellar population, while the star forming in the outskirt of the galaxy is still ongoing. Aims. The high-resolution HST images from Hubble Deep UV Legacy Survey and Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey projects with the unprecedented depth in both F275W and F160W bands are the perfect data sets to study the forming and formed stellar distribution directly. Methods. We selected the low redshift (0.05 <  zspec <  0.3) galaxy sample from the GOODS-North field where the HST F275W and F160W images are available. Then we measured the half light radius in F275W and F160W bands, which are the indicators of the star formation and stellar mass. Results. By comparing the F275W and F160W half light radius, we find the massive galaxies are mainly follow the “inside-out” growth mode, which is consistent with the previous results. Moreover, the HST F275W and F160W images reveal that some of the low-mass galaxies (< 108 M⊙) have the “outside-in” growth mode: their images show a compact UV morphology, implying an ongoing star formation in the galaxy centre, while the stars in the outskirts of the galaxies are already formed. The two modes transit smoothly at stellar mass range about 108 − 9 M⊙ with a large scatter. We also try to identify the possible neighbour massive galaxies from the SDSS data, which represent the massive galaxy sample. We find that all of the spec-z selected galaxies have no massive galaxy nearby. Thus the “outside-in” mode we find in the low-mass galaxies are not likely originated from the environment

“Soap bubble” sign as an imaging marker for posterior fossa ependymoma Group B

Purpose: To investigate the predictive value of the “soap bubble” sign on molecular subtypes (Group A [PFA] and Group B [PFB]) of posterior fossa ependymomas (PF-EPNs). // Methods: MRI scans of 227 PF-EPNs (internal retrospective discovery set) were evaluated by two independent neuroradiologists to assess the “soap bubble” sign, which was defined as clusters of cysts of various sizes that look like “soap bubbles” on T2-weighted images. Two independent cohorts (external validation set [n = 31] and prospective validation set [n = 27]) were collected to validate the “soap bubble” sign. // Results: Across three datasets, the “soap bubble” sign was observed in 21 PFB cases (7.4% [21/285] of PF-EPNs and 12.9% [21/163] of PFB); none in PFA. Analysis of the internal retrospective discovery set demonstrated substantial interrater agreement (1st Rating: κ = 0.71 [0.53–0.90], 2nd Rating: κ = 0.83 [0.68–0.98]) and intrarater agreement (Rater 1: κ = 0.73 [0.55–0.91], Rater 2: κ = 0.74 [0.55–0.92]) for the “soap bubble” sign; all 13 cases positive for the “soap bubble” sign were PFB (p = 0.002; positive predictive value [PPV] = 100%, negative predictive value [NPV] = 44%, sensitivity = 10%, specificity = 100%). The findings from the external validation set and the prospective validation set were similar, all cases positive for the “soap bubble” sign were PFB (p < 0.001; PPV = 100%). // Conclusion: The “soap bubble” sign represents a highly specific imaging marker for the PFB molecular subtype of PF-EPNs

Prediction of H3K27M-mutant brainstem glioma by amide proton transfer–weighted imaging and its derived radiomics

Purpose: H3K27M-mutant associated brainstem glioma (BSG) carries a very poor prognosis. We aimed to predict H3K27M mutation status by amide proton transfer–weighted (APTw) imaging and radiomic features. Methods: Eighty-one BSG patients with APTw imaging at 3T MR and known H3K27M status were retrospectively studied. APTw values (mean, median, and max) and radiomic features within manually delineated 3D tumor masks were extracted. Comparison of APTw measures between H3K27M-mutant and wildtype groups was conducted by two-sample Student’s T/Mann–Whitney U test and receiver operating characteristic curve (ROC) analysis. H3K27M-mutant prediction using APTw-derived radiomics was conducted using a machine learning algorithm (support vector machine) in randomly selected train (n = 64) and test (n = 17) sets. Sensitivity analysis with additional random splits of train and test sets, 2D tumor masks, and other classifiers were conducted. Finally, a prospective cohort including 29 BSG patients was acquired for validation of the radiomics algorithm. Results: BSG patients with H3K27M-mutant were younger and had higher max APTw values than those with wildtype. APTw-derived radiomic measures reflecting tumor heterogeneity could predict H3K27M mutation status with an accuracy of 0.88, sensitivity of 0.92, and specificity of 0.80 in the test set. Sensitivity analysis confirmed the predictive ability (accuracy range: 0.71–0.94). In the independent prospective validation cohort, the algorithm reached an accuracy of 0.86, sensitivity of 0.88, and specificity of 0.85 for predicting H3K27M-mutation status. Conclusion: BSG patients with H3K27M-mutant had higher max APTw values than those with wildtype. APTw-derived radiomics could accurately predict a H3K27M-mutant status in BSG patients

Prediction of H3K27M Alteration Status in Brainstem Glioma Using Multi-Shell Diffusion MRI Metrics

Background: Multi-shell diffusion characteristics may help characterize brainstem gliomas (BSGs) and predict H3K27M status. // Purpose: To identify the diffusion characteristics of BSG patients and investigate the predictive values of various diffusion metrics for H3K27M status in BSG. // Study Type: Prospective. // Population: Eighty-four BSG patients (median age 10.5 years [IQR 6.8–30.0 years]) were included, of whom 56 were pediatric and 28 were adult patients. // Field Strength/Sequence: 3 T, multi-shell diffusion imaging. // Assessment: Diffusion kurtosis imaging and neurite orientation dispersion and density imaging analyses were performed. Age, gender, and diffusion metrics, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity, radial diffusivity (RD), mean kurtosis (MK), axial kurtosis (AK), radial kurtosis, intracellular volume fraction (ICVF), orientation dispersion index, and isotropic volume fraction (ISOVF), were compared between H3K27M-altered and wildtype BSG patients. // Statistical Tests: Chi-square test, Mann–Whitney U test, multivariate analysis of variance (MANOVA), step-wise multivariable logistic regression. P-values <0.05 were considered significant. // Results: 82.4% pediatric and 57.1% adult patients carried H3K27M alteration. In the whole group, the H3K27M-altered BSGs demonstrated higher FA, AK and lower RD, ISOVF. The combination of age and median ISOVF showed fair performance for H3K27M prediction (AUC = 0.78). In the pediatric group, H3K27M-altered BSGs showed higher FA, AK, MK, ICVF and lower RD, MD, ISOVF. The combinations of median ISOVF, 5th percentile of FA, median MK and median MD showed excellent predictive power (AUC = 0.91). In the adult group, H3K27M-altered BSGs showed higher ICVF and lower RD, MD. The 75th percentile of RD demonstrated fair performance for H3K27M status prediction (AUC = 0.75). // Data Conclusion: Different alteration patterns of diffusion measures were identified between H3K27M-altered and wildtype BSGs, which collectively had fair to excellent predictive value for H3K27M alteration status, especially in pediatric patients. // Evidence Level: 2 // Technical Efficacy: Stage

A Fully Automated Deep-Learning Model for Predicting the Molecular Subtypes of Posterior Fossa Ependymomas Using T2-Weighted Images

PURPOSE: We aimed to develop and validate a deep learning (DL) model to automatically segment posterior fossa ependymoma (PF-EPN) and predict its molecular subtypes (Group A [PFA] and Group B [PFB]) from preoperative MR images. EXPERIMENTAL DESIGN: We retrospectively identified 227 PF-EPNs (development and internal test sets) with available preoperative T2-weighted (T2w) MR images and molecular status to develop and test a 3D nnU-Net (referred to as T2-nnU-Net) for tumor segmentation and molecular subtype prediction. The network was externally tested using an external independent set (n=40; subset-1 [n=31] and subset-2 [n=9]) and prospectively enrolled cases (prospective validation set [n=27]). The Dice similarity coefficient was used to evaluate the segmentation performance. Receiver operating characteristic analysis for molecular subtype prediction was performed. RESULTS: For tumor segmentation, the T2-nnU-Net achieved a dice score of 0.94±0.02 in the internal test set. For molecular subtype prediction, the T2-nnU-Net achieved an AUC of 0.93 and accuracy of 0.89 in the internal test set, an AUC of 0.99 and accuracy of 0.93 in the external test set. In the prospective validation set, the model achieved an AUC of 0.93 and an accuracy of 0.89. The predictive performance of T2-nnU-Net was superior or comparable to that of demographic and multiple radiological features (AUCs ranging from 0.87 to 0.95). CONCLUSIONS: A fully automated DL model was developed and validated to accurately segment PF-EPNs and predict molecular subtypes using only T2w MR images, which could help in clinical decision-making

Automated Classification of Intramedullary Spinal Cord Tumors and Inflammatory Demyelinating Lesions Using Deep Learning

Accurate differentiation of intramedullary spinal cord tumors and inflammatory demyelinating lesions and their subtypes are warranted because of their overlapping characteristics at MRI but with different treatments and prognosis. The authors aimed to develop a pipeline for spinal cord lesion segmentation and classification using two-dimensional MultiResUNet and DenseNet121 networks based on T2-weighted images. A ret-rospective cohort of 490 patients (118 patients with astrocytoma, 130 with ependymoma, 101 with multiple sclerosis [MS], and 141 with neu-romyelitis optica spectrum disorders [NMOSD]) was used for model development, and a prospective cohort of 157 patients (34 patients with astrocytoma, 45 with ependymoma, 33 with MS, and 45 with NMOSD) was used for model testing. In the test cohort, the model achieved Dice scores of 0.77, 0.80, 0.50, and 0.58 for segmentation of astrocytoma, ependymoma, MS, and NMOSD, respectively, against manual la-beling. Accuracies of 96% (area under the receiver operating characteristic curve [AUC], 0.99), 82% (AUC, 0.90), and 79% (AUC, 0.85) were achieved for the classifications of tumor versus demyelinating lesion, astrocytoma versus ependymoma, and MS versus NMOSD, respectively. In a subset of radiologically difficult cases, the classifier showed an accuracy of 79%–95% (AUC, 0.78–0.97). The established deep learning pipeline for segmentation and classification of spinal cord lesions can support an accurate radiologic diagnosis

Small cytosolic double-stranded DNA represses cyclic GMP-AMP synthase activation and induces autophagy

Summary: The cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) pathway is a major mediator of inflammation following stimulation with >45 bp double-stranded DNA (dsDNA). Herein, we identify a class of ∼20–40 bp small cytosolic dsDNA (scDNA) molecules that compete with long dsDNA (200–1,500 bp herring testis [HT]-DNA) for binding to cGAS, thus repressing HT-DNA-induced cGAS activation. The scDNA promotes cGAS and Beclin-1 interaction, releasing Rubicon, a negative regulator of phosphatidylinositol 3-kinase class III (PI3KC3), from the Beclin-1-PI3KC3 complex. This leads to PI3KC3 activation and induces autophagy, causing degradation of STING and long cytosolic dsDNA. Moreover, DNA damage decreases, and autophagy inducers increase scDNA levels. scDNA transfection and treatment with autophagy inducers attenuate DNA damage-induced cGAS activation. Thus, scDNA molecules serve as effective brakes for cGAS activation, preventing excessive inflammatory cytokine production following DNA damage. Our findings may have therapeutic implications for cytosolic DNA-associated inflammatory diseases