PURPOSETo provide evidence-based recommendations for practicing physicians and other healthcare providers on the management of salivary gland malignancy.METHODSASCO convened an Expert Panel of medi...

https://ascopubs.org/doi/pdfdirect/10.1200/JCO.21.00449

Management of Salivary Gland Malignancy: ASCO Guideline.

The most frequent adult-type primary CNS tumours are diffuse gliomas, but a large variety of rarer CNS tumour types exists. The classification of these tumours is increasingly based on molecular diagnostics, which is reflected in the extensive molecular foundation of the recent WHO 2021 classification of CNS tumours. Resection as extensive as is safely possible is the cornerstone of treatment in most gliomas, and is now also recommended early in the treatment of patients with radiological evidence of histologically low-grade tumours. For the adult-type diffuse glioma, standard of care is a combination of radiotherapy and chemotherapy. Although treatment with curative intent is not available, combined modality treatment has resulted in long-term survival (>10-20 years) for some patients with isocitrate dehydrogenase (IDH) mutant tumours. Other rarer tumours require tailored approaches, best delivered in specialised centres. Targeted treatments based on molecular alterations still only play a minor role in the treatment landscape of adult-type diffuse glioma, and today are mainly limited to patients with tumours with BRAFV600E (ie, Val600Glu) mutations. Immunotherapy for CNS tumours is still in its infancy, and so far, trials with checkpoint inhibitors and vaccination studies have not shown improvement in patient outcomes in glioblastoma. Current research is focused on improving our understanding of the immunosuppressive tumour environment, the molecular heterogeneity of tumours, and the role of tumour microtube network connections between cells in the tumour microenvironment. These factors all appear to play a role in treatment resistance, and indicate that novel approaches are needed to further improve outcomes of patients with CNS tumours. 

Primary brain tumours in adults

Background The aim of this study was to predict isocitrate dehydrogenase (IDH) genotypes of gliomas using an interpretable deep learning application for dynamic susceptibility contrast (DSC) perfusion MRI. Methods Four hundred sixty-three patients with gliomas who underwent preoperative MRI were enrolled in the study. All the patients had immunohistopathologic diagnoses of either IDH-wildtype or IDH-mutant gliomas. Tumor subregions were segmented using a convolutional neural network followed by manual correction. DSC perfusion MRI was performed to obtain T2* susceptibility signal intensity-time curves from each subregion of the tumors: enhancing tumor, non-enhancing tumor, peritumoral edema, and whole tumor. These, with arterial input functions, were fed into a neural network as multidimensional inputs. A convolutional long short-term memory model with an attention mechanism was developed to predict IDH genotypes. Receiver operating characteristics analysis was performed to evaluate the model. Results The IDH genotype predictions had an accuracy, sensitivity, and specificity of 92.8%, 92.6%, and 93.1%, respectively, in the validation set (area under the curve [AUC], 0.98; 95% confidence interval [CI], 0.969-0.991) and 91.7%, 92.1%, and 91.5%, respectively, in the test set (AUC, 0.95; 95% CI, 0.898-0.982). In temporal feature analysis, T2* susceptibility signal intensity-time curves obtained from DSC perfusion MRI with attention weights demonstrated high attention on the combination of the end of the pre-contrast baseline, up/downslopes of signal drops, and/or post-bolus plateaus for the curves used to predict IDH genotype. Conclusions We developed an explainable recurrent neural network model based on DSC perfusion MRI to predict IDH genotypes in gliomas.

Prediction of IDH genotype in gliomas with dynamic susceptibility contrast perfusion MR imaging using an explainable recurrent neural network.

Background A readily implemented MRI biomarker for glioma genotyping is currently lacking. Purpose To evaluate clinically available MRI parameters for predicting isocitrate dehydrogenase (IDH) status in patients with glioma. Materials and Methods In this retrospective study of patients studied from July 2008 to February 2019, untreated World Health Organization (WHO) grade II/III gliomas were analyzed by three neuroradiologists blinded to tissue results. Apparent diffusion coefficient (ADC) minimum (ADCmin) and mean (ADCmean) regions of interest were defined in tumor and normal appearing white matter (ADCNAWM). A visual rating of anatomic features (T1 weighted, T1 weighted with contrast enhancement, T2 weighted, and fluid-attenuated inversion recovery) was performed. Interobserver comparison (intraclass correlation coefficient and Cohen κ) was followed by nonparametric (Kruskal-Wallis analysis of variance) testing of associations between ADC metrics and glioma genotypes, including Bonferroni correction for multiple testing. Descriptors with sufficient concordance (intraclass correlation coefficient, >0.8; κ > 0.6) underwent univariable analysis. Predictive variables (P < .05) were entered into a multivariable logistic regression and tested in an additional test sample of patients with glioma. Results The study included 290 patients (median age, 40 years; interquartile range, 33-52 years; 169 male patients) with 82 IDH wild-type, 107 IDH mutant/1p19q intact, and 101 IDH mutant/1p19q codeleted gliomas. Two predictive models incorporating ADCmean-to-ADCNAWM ratio, age, and morphologic characteristics, with model A mandating calcification result and model B recording cyst formation, classified tumor type with areas under the receiver operating characteristic curve of 0.94 (95% confidence interval [CI]: 0.91, 0.97) and 0.96 (95% CI: 0.93, 0.98), respectively. In the test sample of 49 gliomas (nine IDH wild type, 21 IDH mutant/1p19q intact, and 19 IDH mutant/1p19q codeleted), the classification accuracy was 40 of 49 gliomas (82%; 95% CI: 71%, 92%) for model A and 42 of 49 gliomas (86%; 95% CI: 76%, 96%) for model B. Conclusion Two algorithms that incorporated apparent diffusion coefficient values, age, and tumor morphologic characteristics predicted isocitrate dehydrogenase status in World Health Organization grade II/III gliomas on the basis of standard clinical MRI sequences alone. © RSNA, 2020 Online supplemental material is available for this article.

/pdf/world-health-organization-grade-ii-iii-glioma-molecular-t7do4gd4i7.pdf

World Health Organization Grade II/III Glioma Molecular Status: Prediction by MRI Morphologic Features and Apparent Diffusion Coefficient.

An estimated 8 to 15% of women, and 3 to 10% of men currently suffer from Temporomandibular disorders, and it has been reported that sounds are a common finding and have been observed in 28% to 50% of the adult population; sounds are almost always connected to "internal derangements" a condition in which the articular disk displaced from its position on the mandibular condyle. Due to the multifactorial etiology of temporomandibular joint dysfunction, any accurate diagnosis based on clinical examination alone often proves to be difficult, so a clinical examination should be utilized together with other imaging methods to determine the relationship between the disk and condyle before and after treatment. In general, management of temporomandibular joint-related conditions is necessary when pain or dysfunction is present, so many surgical or nonsurgical methods of treatment have been reported. The Occlusal splint is 1 of the most widespread treatment it induces a slight vertical condylar distraction and eliminates the occlusal factor, which can be responsible for Temporomandibular joint disturbances, removing the influence of the teeth on the joint position by the slight distraction of the joint which, in turn, enables damaged connective tissue to heal. 

Temporomandibular Joint Disk Displacement: Etiology, Diagnosis, Imaging, and Therapeutic Approaches

Intracranial solitary fibrous tumor/hemangiopericytoma (SFT/HPC) and meningioma are difficult to distinguish owing to their overlapping imaging manifestation on routine magnetic resonance imaging. The purpose of this study was to assess whether SFT/HPC can be differentiated from meningioma with diffusion-weighted imaging (DWI) and susceptibility-weighted imaging (SWI). We retrospectively reviewed DWI, SWI, conventreional MR, and CT imaging features of 16 patients with SFT/HPC and 96 patients with meningioma. The apparent diffusion coefficient (ADC) value, normalized ADC (nADC) value, and degree of intratumoral susceptibility signal intensity (ITSS) were compared between SFT/HPCs and meningiomas using two-sample t tests, and among SFT/HPCs, low-grade and high-grade meningioma were tested using one-way analysis of variance (ANOVA). Receiver operating characteristic (ROC) curve and logistic regression analyses were performed to determine the differentiation capacity. The ADC value, nADC value, and the degree of ITSS in SFT/HPC were significantly higher than those in low-grade and high-grade meningiomas (all p 1.15 for nADC provided 75.00% sensitivity and 60.42% specificity for differentiating SFT/HPC from meningioma. Compared with nADC, the degree of ITSS had a moderate sensitivity (62.50%) and a higher specificity (85.42%) using the threshold value of > 1.00. Furthermore, combining DWI and SWI can achieve a relatively high differentiation capacity with a sensitivity of 81.25% and specificity of 78.12%. The nADC ratios and ITSS are useful for differentiating SFT/HPC from meningioma. Combining ITSS and nADC value appears to be a promising option for differential diagnosis.

Differentiating intracranial solitary fibrous tumor/hemangiopericytoma from meningioma using diffusion-weighted imaging and susceptibility-weighted imaging.

BACKGROUND AND PURPOSE: On DWI and DSC-PWI, hemangioblastomas and brain metastases may exhibit different signal intensities depending on their cellularity and angiogenesis. The purpose of this study was to evaluate whether a hemangioblastoma can be differentiated from a single brain metastasis with DWI and DSC-PWI. MATERIALS AND METHODS: We retrospectively reviewed DWI, DSC-PWI, and conventional MR imaging of 21 patients with hemangioblastomas and 30 patients with a single brain metastasis. Variables of minimum ADC and relative ADC were acquired by DWI and the parameter of relative maximum CBV, by DSC-PWI. Minimum ADC, relative ADC, and relative maximum CBV values were compared between hemangioblastomas and brain metastases by using the nonparametric Mann-Whitney test. The sensitivity, specificity, positive and negative predictive values, accuracy, and the area under the receiver operating characteristic curve were determined. RESULTS: Both the minimum ADC values and relative ADC ratios were significantly higher in hemangioblastomas compared with brain metastases (P CONCLUSIONS: DWI and DSC-PWI are helpful in the characterization and differentiation of hemangioblastomas from brain metastases. DWI appears to be the most efficient MR imaging technique for providing a distinct differentiation of the 2 tumor types.

http://www.ajnr.org/content/ajnr/37/10/1844.full.pdf

Differentiating Hemangioblastomas from Brain Metastases Using Diffusion-Weighted Imaging and Dynamic Susceptibility Contrast-Enhanced Perfusion-Weighted MR Imaging.

Objectives To evaluate the discriminative value of conventional magnetic resonance imaging between benign and malignant palatal tumors. Study Design Conventional magnetic resonance imaging features of 130 patients with palatal tumors confirmed by histopathologic examination were retrospectively reviewed. Clinical data and imaging findings were assessed between benign and malignant tumors and between benign and low-grade malignant salivary gland tumors. The variables that were significant in differentiating benign from malignant lesions were further identified using logistic regression analysis. Moreover, imaging features of each common palatal histologic entity were statistically analyzed with the rest of the tumors to define their typical imaging features. Results Older age, partially defined and ill-defined margins, and absence of a capsule were highly suggestive of malignant palatal tumors, especially ill-defined margins (β = 6.400). The precision in determining malignant palatal tumors achieved a sensitivity of 92.8% and a specificity of 85.6%. In addition, irregular shape, ill-defined margins, lack of a capsule, perineural spread, and invasion of surrounding structures were more often associated with low-grade malignant salivary gland tumors. Conclusion Conventional magnetic resonance imaging is useful for differentiating benign from malignant palatal tumors as well as benign salivary gland tumors from low-grade salivary gland malignancies.

Differentiation between benign and malignant palatal tumors using conventional MRI: a retrospective analysis of 130 cases.

Objectives The performance of multiparametric MRI-based radiomics models for predicting H3 K27M mutant status in diffuse midline glioma (DMG) has not been thoroughly evaluated. The optimal combination of multiparametric MRI and machine learning techniques remains undetermined. We compared the performance of various radiomics models across different MRI sequences and different machine learning techniques. Methods A total of 102 patients with pathologically confirmed DMG were retrospectively enrolled (27 with H3 K27M-mutant and 75 with H3 K27M wild-type). Radiomics features were extracted from eight sequences, and 18 feature sets were conducted by independent combination. There were three feature matrix normalization algorithms, two dimensionality-reduction methods, four feature selectors, and seven classifiers, consisting of 168 machine learning pipelines. Radiomics models were established across different feature sets and machine learning pipelines. The performance of models was evaluated using receiver operating characteristic curves with area under the curve (AUC) and compared with DeLong’s test. Results The multiparametric MRI-based radiomics models could accurately predict the H3 K27M mutant status in DMG (highest AUC: 0.807–0.969, for different sequences or sequence combinations). However, the results varied significantly between different machine learning techniques. When suitable machine learning techniques were used, the conventional MRI-based radiomics models shared similar performance to advanced MRI-based models (highest AUC: 0.875–0.915 vs. 0.807–0.926; DeLong’s test, p > 0.05). Most models had a better performance when generated with a combination of MRI sequences. The optimal model in the present study used a combination of all sequences (AUC = 0.969). Conclusions The multiparametric MRI-based radiomics models could be useful for predicting H3 K27M mutant status in DMG, but the performance varied across different sequences and machine learning techniques.

Multiparametric MRI-Based Radiomics Model for Predicting H3 K27M Mutant Status in Diffuse Midline Glioma: A Comparative Study Across Different Sequences and Machine Learning Techniques

Hemangioblastoma (HBL) within the central nervous system is a benign vascular neoplasm that usually occurs in the cerebellum. Supratentorial occurrence of HBL is an extremely rare event. Till date, approximately 129 cases of supratentorial HBL have been reported in the literature. Here, we present three new cases of supratentorial hemangioblatomas, one of which was found to have the lesions in a unique location of the choroidal fissure. The clinical, histopathological, and neuroradiological characteristics, as well as management of this rare disease are discussed with a review of the pertinent literature.

Supratentorial hemangioblastomas: three case reports and review of the literature.

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