GNA14 and GNAQ somatic mutations cause spinal and intracranial extra-axial cavernous hemangiomas.

Extra-axial cavernous hemangiomas (ECHs) are complex vascular lesions mainly found in the spine and cavernous sinus. Their removal poses significant risk due to their vascularity and diffuse nature, and their genetic underpinnings remain incompletely understood. Our approach involved genetic analyses on 31 tissue samples of ECHs employing whole-exome sequencing and targeted deep sequencing. We explored downstream signaling pathways, gene expression changes, and resultant phenotypic shifts induced by these mutations, both in vitro and in vivo. In our cohort, 77.4% of samples had somatic missense variants in GNA14, GNAQ, or GJA4. Transcriptomic analysis highlighted significant pathway upregulation, with the GNAQ c.626A>G (p.Gln209Arg) mutation elevating PI3K-AKT-mTOR and angiogenesis-related pathways, while GNA14 c.614A>T (p.Gln205Leu) mutation led to MAPK and angiogenesis-related pathway upregulation. Using a mouse xenograft model, we observed enlarged vessels from these mutations. Additionally, we initiated rapamycin treatment in a 14-year-old individual harboring the GNAQ c.626A>G (p.Gln209Arg) variant, resulting in gradual regression of cutaneous cavernous hemangiomas and improved motor strength, with minimal side effects. Understanding these mutations and their pathways provides a foundation for developing therapies for ECHs resistant to current therapies. Indeed, the administration of rapamycin in an individual within this study highlights the promise of targeted treatments in treating these complex lesions.

Somatic BrafV600E mutation in the cerebral endothelium induces brain arteriovenous malformations.

Current treatments of brain arteriovenous malformation (BAVM) are associated with considerable risks and at times incomplete efficacy. Therefore, a clinically consistent animal model of BAVM is urgently needed to investigate its underlying biological mechanisms and develop innovative treatment strategies. Notably, existing mouse models have limited utility due to heterogenous and untypical phenotypes of AVM lesions. Here we developed a novel mouse model of sporadic BAVM that is consistent with clinical manifestations in humans. Mice with BrafV600E mutations in brain ECs developed BAVM closely resembled that of human lesions. This strategy successfully induced BAVMs in mice across different age groups and within various brain regions. Pathological features of BAVM were primarily dilated blood vessels with reduced vascular wall stability, accompanied by spontaneous hemorrhage and neuroinflammation. Single-cell sequencing revealed differentially expressed genes that were related to the cytoskeleton, cell motility, and intercellular junctions. Early administration of Dabrafenib was found to be effective in slowing the progression of BAVMs; however, its efficacy in treating established BAVM lesions remained uncertain. Taken together, our proposed approach successfully induced BAVM that closely resembled human BAVM lesions in mice, rendering the model suitable for investigating the pathogenesis of BAVM and assessing potential therapeutic strategies.

Machine Learning Approaches to Differentiate Sellar-Suprasellar Cystic Lesions on Magnetic Resonance Imaging.

Cystic lesions are common lesions of the sellar region with various pathological types, including pituitary apoplexy, Rathke's cleft cyst, cystic craniopharyngioma, etc. Suggested surgical approaches are not unique when dealing with different cystic lesions. However, cystic lesions with different pathological types were hard to differentiate on MRI with the naked eye by doctors. This study aimed to distinguish different pathological types of cystic lesions in the sellar region using preoperative magnetic resonance imaging (MRI). Radiomics and deep learning approaches were used to extract features from gadolinium-enhanced MRIs of 399 patients enrolled at Peking Union Medical College Hospital over the past 15 years. Paired imaging differentiations were performed on four subtypes, including pituitary apoplexy, cystic pituitary adenoma (cysticA), Rathke's cleft cyst, and cystic craniopharyngioma. Results showed that the model achieved an average AUC value of 0.7685. The model based on a support vector machine could distinguish cystic craniopharyngioma from Rathke's cleft cyst with the highest AUC value of 0.8584. However, distinguishing cystic apoplexy from pituitary apoplexy was difficult and almost unclassifiable with any algorithms on any feature set, with the AUC value being only 0.6641. Finally, the proposed methods achieved an average Accuracy of 0.7532, which outperformed the traditional clinical knowledge-based method by about 8%. Therefore, in this study, we first fill the gap in the existing literature and provide a non-invasive method for accurately differentiating between these lesions, which could improve preoperative diagnosis accuracy and help to make surgery plans in clinical work.

Feasibility of evaluating the histologic and genetic subtypes of WHO grade II-IV gliomas by diffusion-weighted imaging.

BACKGROUND: To explore the feasibility of diffusion-weighted imaging (DWI) metrics to predict the histologic subtypes and genetic status of gliomas (e.g., IDH, MGMT, and TERT) noninvasively. METHODS: One hundred and eleven patients with pathologically confirmed WHO grade II-IV gliomas were recruited retrospectively. Apparent diffusion coefficient (ADC) values were measured in solid parts of gliomas on co-registered T2-weighted images and were compared with each other in terms of WHO grading and genotypes using t-tests. Receiver operating characteristic analysis was performed to assess the diagnostic performances of ADC. Subsequently, multiple linear regression was used to find independent variables, which can directly affect ADC values. RESULTS: The values of overall mean ADC (omADC) and normalized ADC (nADC) of high grade gliomas and IDH wildtype gliomas were lower than low grade gliomas and IDH mutated gliomas (P < 0.05). nADC values showed better diagnostic performance than omADC in identifying tumor grade (AUC: 0.787 vs. 0.750) and IDH status (AUC: 0.836 vs. 0.777). ADC values had limited abilities in distinguishing TERT status (AUC = 0.607 for nADC and 0.617 for omADC) and MGMT status (AUC = 0.651 for nADC). Only tumor grade and IDH status were tightly associated with ADC values. CONCLUSION: DWI metrics can predict glioma grading and IDH mutation noninvasively, but have limited use in detecting TERT mutation and MGMT methylation.

Machine Learning in Rheumatic Diseases.

With advances in information technology, the demand for using data science to enhance healthcare and disease management is rapidly increasing. Among these technologies, machine learning (ML) has become ubiquitous and indispensable for solving complex problems in many scientific fields, including medical science. ML allows the development of guidelines and framing of the evaluation system for complex diseases based on massive data. In the analysis of rheumatic diseases, which are chronic and remarkably heterogeneous, ML can be anticipated to be extremely helpful in deciphering and revealing the inherent interrelationships in disease development and progression, which can further enhance the overall understanding of the disease, optimize patients' stratification, calibrate therapeutic strategies, and predict prognosis and outcomes. In this review, the basics of ML, its potential clinical applications in rheumatology, together with its strengths and limitations are summarized.

Quantitative Features From CHO PET Distinguish the WHO Grades of Primary Diffuse Glioma.

PURPOSE: The aim of this study was to investigate the quantitative 18F-fluoroethylcholine (CHO) PET characteristics for differentiating lower-grade glioma (LGG) from glioblastoma (GBM). PATIENTS AND METHODS: Thirty-nine patients who underwent CHO PET with histopathologically confirmed primary diffuse glioma were prospectively enrolled. The 3-dimensional region of interest was semiautomatically defined based on the SUV threshold, and a total of 74 quantitative features, including 13 shape features, 31 SUV-based features, and 30 normalized SUV-based features, were calculated. Wilcoxon rank sum test, receiver operating characteristic curve, and correlation coefficient analyses were applied to select independent representative features, and patient prognosis was stratified by the World Health Organization (WHO) grade and CHO features. RESULTS: A total of 89.2% of the quantitative features were significantly different between LGG and GBM, and the SUV-based features displayed higher area under the receiver operating characteristic curve (AUC) values than the other feature groups. Among the 5 traditional features, the SUVmax and the total lesion CHO uptake were the most distinguishing, with AUCs of 0.880 and 0.860 (0.938 and 0.927 after reclassification of 2 outliers), respectively, both of which could also stratify patient prognosis better than WHO grade. Five alternative features, including 2 shape features and 3 SUV-based features, were considered representative, with AUCs ranging from 0.754 to 0.854. CONCLUSIONS: Quantitative features from CHO PET are reliable in determining the WHO grade of primary diffuse gliomas. Our findings suggest that GBM has a larger volume, a more spherical shape, higher choline activity in most interval segments, and a more symmetrical distribution than LGG.

Thin-Slice Magnetic Resonance Imaging-Based Radiomics Signature Predicts Chromosomal 1p/19q Co-deletion Status in Grade II and III Gliomas.

Objective: Chromosomal 1p/19q co-deletion is recognized as a diagnostic, prognostic, and predictive biomarker in lower grade glioma (LGG). This study aims to construct a radiomics signature to non-invasively predict the 1p/19q co-deletion status in LGG. Methods: Ninety-six patients with pathology-confirmed LGG were retrospectively included and randomly assigned into training (n = 78) and validation (n = 18) dataset. Three-dimensional contrast-enhanced T1 (3D-CE-T1)-weighted magnetic resonance (MR) images and T2-weighted MR images were acquired, and simulated-conventional contrast-enhanced T1 (SC-CE-T1)-weighted images were generated. One hundred and seven shape, first-order, and texture radiomics features were extracted from each imaging modality and selected using the least absolute shrinkage and selection operator on the training dataset. A 3D-radiomics signature based on 3D-CE-T1 and T2-weighted features and a simulated-conventional (SC) radiomics signature based on SC-CE-T1 and T2-weighted features were established using random forest. The radiomics signatures were validated independently and evaluated using receiver operating characteristic (ROC) curves. Tumors with IDH mutations were also separately assessed. Results: Four radiomics features were selected to construct the 3D-radiomics signature and displayed accuracies of 0.897 and 0.833, areas under the ROC curves (AUCs) of 0.940 and 0.889 in the training and validation datasets, respectively. The SC-radiomics signature was constructed with 4 features, but the AUC values were lower than that of the 3D signature. In the IDH-mutated subgroup, the 3D-radiomics signature presented AUCs of 0.950-1.000. Conclusions: The MRI-based radiomics signature can differentiate 1p/19q co-deletion status in LGG with or without predetermined IDH status. 3D-CE-T1-weighted radiomics features are more favorable than SC-CE-T1-weighted features in the establishment of radiomics signatures.

Conventional magnetic resonance imaging-based radiomic signature predicts telomerase reverse transcriptase promoter mutation status in grade II and III gliomas.

PURPOSE: Telomerase reverse transcriptase (TERT) promoter mutation status is an important biomarker for the precision diagnosis and prognosis prediction of lower grade glioma (LGG). This study aimed to construct a radiomic signature to noninvasively predict the TERT promoter status in LGGs. METHODS: Eighty-three local patients with pathology-confirmed LGG were retrospectively included as a training cohort, and 33 patients from The Cancer Imaging Archive (TCIA) were used as for independent validation. Three types of regions of interest (ROIs), which covered the tumor, peri-tumoral area, and tumor plus peri-tumoral area, were delineated on three-dimensional contrast-enhanced T1 (3D-CE-T1)-weighted and T2-weighted images. One hundred seven shape, first-order, and texture radiomic features from each modality under each ROI were extracted and selected through least absolute shrinkage and selection operator. Radiomic signatures were constructed with multiple classifiers and evaluated using receiver operating characteristic (ROC) analysis. The tumors were also stratified according to IDH status. RESULTS: Three radiomic signatures, namely, tumoral radiomic signature, tumoral plus peri-tumoral radiomic signature, and fusion radiomic signature, were built, all of which exhibited good accuracy and balanced sensitivity and specificity. The tumoral signature displayed the best performance, with area under the ROC curves (AUC) of 0.948 (0.903-0.993) in the training cohort and 0.827 (0.667-0.988) in the validation cohort. In the IDH subgroups, the AUCs of the tumoral signature ranged from 0.750 to 0.940. CONCLUSION: The MRI-based radiomic signature is reliable for noninvasive evaluation of TERT promoter mutations in LGG regardless of the IDH status. The inclusion of peri-tumoral area did not significantly improve the performance.

Deep-Learning Approach to Automatic Identification of Facial Anomalies in Endocrine Disorders.

BACKGROUND: Deep learning has the potential to assist the medical diagnostic process. We aimed to identify facial anomalies associated with endocrinal disorders using a deep-learning approach to facilitate the process of diagnosis and follow-up. METHODS: We collected facial images of patients with hypercortisolism and acromegaly, and we augmented these images with additional negative samples from public databases. A model with a pretrained deep-learning network was constructed to automatically identify these hypersecretion statuses based on characteristic facial changes. We compared its performance to that of endocrine experts and further investigated key factors upon which the best performing model focused. FINDINGS: The model achieved areas under the receiver operating characteristic curve of 0.9647 (Cushing's syndrome) and 0.9556 (acromegaly), accuracies of 0.9593 (Cushing's syndrome) and 0.9479 (acromegaly), and recalls of 0.7593 (Cushing's syndrome) and 0.8089 (acromegaly). It performed better than any level of our endocrine experts. Furthermore, the regions of interest on the part of the machine were primarily the same as those upon which the humans focused. INTERPRETATION: Our findings suggest that the deep-learning model learned the facial characters based merely on labeled data without learning prerequisite medical knowledge, and its performance was comparable with professional medical practitioners. The model has the potential to assist in the diagnosis and follow-up of these hypersecretion statuses.

Fusion Radiomics Features from Conventional MRI Predict MGMT Promoter Methylation Status in Lower Grade Gliomas.

PURPOSE: The methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter has been proven to be a prognostic and predictive biomarker for lower grade glioma (LGG). This study aims to build a radiomics model to preoperatively predict the MGMT promoter methylation status in LGG. METHOD: 122 pathology-confirmed LGG patients were retrospectively reviewed, with 87 local patients as the training dataset, and 35 from The Cancer Imaging Archive as independent validation. A total of 1702 radiomics features were extracted from three-dimensional contrast-enhanced T1 (3D-CE-T1)-weighted and T2-weighted MRI images, including 14 shape, 18 first order, 75 texture, and 744 wavelet features respectively. The radiomics features were selected with the least absolute shrinkage and selection operator algorithm, and prediction models were constructed with multiple classifiers. Models were evaluated using receiver operating characteristic (ROC). RESULTS: Five radiomics prediction models, namely, 3D-CE-T1-weighted single radiomics model, T2-weighted single radiomics model, fusion radiomics model, linear combination radiomics model, and clinical integrated model, were built. The fusion radiomics model, which constructed from the concatenation of both series, displayed the best performance, with an accuracy of 0.849 and an area under the curve (AUC) of 0.970 (0.939-1.000) in the training dataset, and an accuracy of 0.886 and an AUC of 0.898 (0.786-1.000) in the validation dataset. Linear combination of single radiomics models and integration of clinical factors did not improve. CONCLUSIONS: Conventional MRI radiomics models are reliable for predicting the MGMT promoter methylation status in LGG patients. The fusion of radiomics features from different series may increase the prediction performance.

18F-FDG-PET-based radiomics features to distinguish primary central nervous system lymphoma from glioblastoma.

The differential diagnosis of primary central nervous system lymphoma from glioblastoma multiforme (GBM) is essential due to the difference in treatment strategies. This study retrospectively reviewed 77 patients (24 with lymphoma and 53 with GBM) to identify the stable and distinguishable characteristics of lymphoma and GBM in 18F-fluorodeocxyglucose (FDG) positron emission tomography (PET) images using a radiomics approach. Three groups of maps, namely, a standardized uptake value (SUV) map, an SUV map calibrated with the normal contralateral cortex (ncc) activity (SUV/ncc map), and an SUV map calibrated with the normal brain mean (nbm) activity (SUV/nbm map), were generated, and a total of 107 radiomics features were extracted from each SUV map. The margins of the ROI were adjusted to assess the stability of the features, and the area under the curve (AUC) of the receiver operating characteristic curve of each feature was compared with the SUVmax to evaluate the distinguishability of the features. Nighty-five radiomics features from the SUV map were significantly different between lymphoma and GBM, 46 features were numeric stable after marginal adjustment, and 31 features displayed better performance than SUVmax. Features extracted from the SUV map demonstrated higher AUCs than features from the further calibrated maps. Tumors with solid metabolic patterns were also separately evaluated and revealed similar results. Thirteen radiomics features that were stable and distinguishable than SUVmax in every circumstance were selected to distinguish lymphoma from glioblastoma, and they suggested that lymphoma has a higher SUV in most interval segments and is more mathematically heterogeneous than GBM. This study suggested that 18F-FDG-PET-based radiomics is a reliable noninvasive method to distinguish lymphoma and GBM.

18F-FDG-PET-based Radiomics signature predicts MGMT promoter methylation status in primary diffuse glioma.

BACKGROUND: The methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter has emerged as a favorable independent prognostic and predictive biomarker in glioma. This study aimed to build a radiomics signature based on 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) for noninvasive measurement of the MGMT promoter methylation status in glioma. METHODS: One hundred and seven pathology-confirmed primary diffuse glioma patients were retrospectively included and randomly assigned to the primary (n = 71) or validation cohort (n = 36). The MGMT promoter methylation status was measured by pyrosequencing. A total of 1561 radiomics features were extracted from the three-dimensional region of interest (ROI) on the standard uptake value (SUV) maps that were generated from the original 18F-FDG PET data. A radiomics signature, a clinical signature and a fusion signature that combined the clinical and radiomics features together were generated. The performance of the three signatures was evaluated by receiver operating characteristic (ROC) curve analysis, and the patient prognosis was stratified based on the MGMT promoter methylation status and the signature with the best performance. RESULTS: Five radiomics features were selected to construct the radiomics signature, and displayed the best performance with area under the receiver operating characteristic (ROC) curve (AUC) reaching 0.94 and 0.86 in the primary and validation cohorts, respectively, which outweigh the performances of clinical signature and fusion signature. With a median follow-up time of 32.4 months, the radiomics signature stratified the glioma patients into two risk groups with significantly different prognoses (p = 0.04). CONCLUSIONS: 18F-FDG-PET-based radiomics is a promising approach for preoperatively evaluating the MGMT promoter methylation status in glioma and predicting the prognosis of glioma patients noninvasively.

Genotypic and phenotypic characterization of Chinese patients with osteogenesis imperfecta.

Osteogenesis imperfecta (OI) is a rare hereditary skeletal dysplasia, characterized by recurrent fractures and bone deformity. This study presents a clinical characterization and mutation analysis of 668 patients, aiming to establish the mutation spectrum and to elucidate genotype-phenotype correlations in Chinese OI patients. We identified 274 sequence variants (230 in type I collagen encoding genes and 44 in noncollagen genes), including 102 novel variants, in 340 probands with a detection rate of 90%. Compared with 47 loss-of-function variants detected in COL1A1, neither nonsense nor frameshift variants were found in COL1A2 (p < 0.0001). The major cause of autosomal recessive OI was biallelic variants in WNT1 (56%, 20/36). It is noteworthy that three genomic rearrangements, including one gross deletion and one gross duplication in COL1A1 as well as one gross deletion in FKBP10, were detected in this study. Of ten individuals with glycine substitutions that lie towards the N-terminal end of the triple-helical region of the α1(I) chain, none exhibited hearing loss, suggesting a potential genotype-phenotype correlation. The findings in this study expanded the mutation spectrum and identified novel correlations between genotype and phenotype in Chinese OI patients.