Pediatric-type diffuse low-grade glioma with MYB/MYBL1 alteration: report of 2 cases.

2016 World Health Organization (WHO) Classification of Tumors of the Central Nervous System (CNS) has shown how molecular features can impact the classification of brain tumors. The continued combination of molecular features with histopathology has led to distinguish tumors with similar histopathologic features but distinct clinical prognosis. The 2021 revised 5. edition of the WHO classification further includes molecular features for CNS tumor categorization including MYB/MYBL1 altered diffuse astrocytoma which is a newly recognized type of low-grade pediatric-type brain tumor. We discuss imaging features of two pediatric-type low-grade gliomas with MYB/MYBL1-mutation that encountered at our institution.

Imaging-based stratification of adult gliomas prognosticates survival and correlates with the 2021 WHO classification.

BACKGROUND: Because of the lack of global accessibility, delay, and cost-effectiveness of genetic testing, there is a clinical need for an imaging-based stratification of gliomas that can prognosticate survival and correlate with the 2021-WHO classification. METHODS: In this retrospective study, adult primary glioma patients with pre-surgery/pre-treatment MRI brain images having T2, FLAIR, T1, T1 post-contrast, DWI sequences, and survival information were included in TCIA training-dataset (n = 275) and independent validation-dataset (n = 200). A flowchart for imaging-based stratification of adult gliomas(IBGS) was created in consensus by three authors to encompass all adult glioma types. Diagnostic features used were T2-FLAIR mismatch sign, central necrosis with peripheral enhancement, diffusion restriction, and continuous cortex sign. Roman numerals (I, II, and III) denote IBGS types. Two independent teams of three and two radiologists, blinded to genetic, histology, and survival information, manually read MRI into three types based on the flowchart. Overall survival-analysis was done using age-adjusted Cox-regression analysis, which provided both hazard-ratio (HR) and area-under-curve (AUC) for each stratification system(IBGS and 2021-WHO). The sensitivity and specificity of each IBSG type were analyzed with cross-table to identify the corresponding 2021-WHO genotype. RESULTS: Imaging-based stratification was statistically significant in predicting survival in both datasets with good inter-observer agreement (age-adjusted Cox-regression, AUC > 0.5, k > 0.6, p < 0.001). IBGS type-I, type-II, and type-III gliomas had good specificity in identifying IDHmut 1p19q-codel oligodendroglioma (training - 97%, validation - 85%); IDHmut 1p19q non-codel astrocytoma (training - 80%, validation - 85.9%); and IDHwt glioblastoma (training - 76.5%, validation- 87.3%) respectively (p-value < 0.01). CONCLUSIONS: Imaging-based stratification of adult diffuse gliomas predicted patient survival and correlated well with 2021-WHO glioma classification.

The landscape of brain tumor mimics in neuro-oncology practice.

BACKGROUND AND OBJECTIVE: Differentiating neoplastic and non-neoplastic brain lesions is essential to make management recommendations and convey prognosis, but the distinction between brain tumors and their mimics in practice may prove challenging. The aim of this study is to provide the incidence of brain tumor mimics in the neuro-oncology setting and describe this patient subset. METHODS: Retrospective study of adult patients referred to the Division of Neuro-oncology for a presumed diagnosis of brain tumor from January 1, 2005 through December 31, 2017, who later satisfied the diagnosis of a non-neoplastic entity based on neuroimaging, clinical course, and/or histopathology evaluation. We classified tumor mimic entities according to clinical, radiologic, and laboratory characteristics that correlated with the diagnosis. RESULTS: The incidence of brain tumor mimics was 3.4% (132/3897). The etiologies of the non-neoplastic entities were vascular (35%), inflammatory non-demyelinating (26%), demyelinating (15%), cysts (10%), infectious (9%), and miscellaneous (5%). In our study, 38% of patients underwent biopsy to determine diagnosis, but in 26%, the biopsy was inconclusive. DISCUSSION: Brain tumor mimics represent a small but important subset of the neuro-oncology referrals. Vascular, inflammatory, and demyelinating etiologies represent two-thirds of cases. Recognizing the clinical, radiologic and laboratory characteristics of such entities may improve resource utilization and prevent unnecessary as well as potentially harmful diagnostic and therapeutic interventions.

Lymph Node Dissection: Principles and Postoperative Imaging.

The management of neck nodes in head and neck cancer is critical, given a markedly increased poor prognosis in patients with nodal metastasis. The surgical management of neck nodes has undergone radical changes secondary to a paradigm shift from curative surgery to nonsurgical organ and function-preserving options, such as radiation therapy. In the neck after treatment, radiologists should be familiar with imaging findings in various types of neck dissections and post-chemoradiation changes, along with signs of residual or recurrent disease. A multidisciplinary approach is essential with well-designed evidence-based surveillance imaging protocols and standardized reporting.

Comparison of diagnostic value of multidetector computed tomography and X-ray in the detection of body packing.

OBJECTIVE: Radiologists and other clinicians are facing an increasing number of illegal drug-related medical conditions. We aimed to draw attention to this growing global problem and to highlight some of the important points related to diagnosis and follow-up of body packing. We compare the diagnostic performance of unenhanced multidetector CT (MDCT) and abdomen X-ray for the detection of drug-filled packets. MATERIALS AND METHODS: Sixty-seven suspects, who underwent both CT and X-ray examinations, have been included in the study. All MDCT and X-ray images were independently and retrospectively reviewed by two observers with different degrees of experience in abdomen imaging. Fifty-two of them were identified as body packers finally. Interobserver agreement, sensitivity, specificity, positive and negative predictive value were calculated. RESULTS: Two types of packets with different characteristics were identified in all body packers. Type 1 packets (solid-state drug) were found in 41 patients and type 2 packets (liquid cocaine) in 11 patients. All statistical analyses concern the detection of any packets. That is, the whole evaluation has been performed per patient. Sensitivity/specificity values of type 1 and type 2 packets for MDCT were 100-98%/100-100% and 100-100%/100-100%, respectively. Besides, sensitivity/specificity values of type 1 and type 2 packets for X-ray were 93-90%/100-91% and 64-45%/73-71%, respectively. In addition, interobserver agreements for detection of any packets were excellent (κ=0.96) and good (κ=0.75) for interpretation of MDCT and X-ray, respectively. CONCLUSION: Unenhanced MDCT is a fast, accurate and easily used diagnostic tool with high sensitivity and specificity for the exact diagnosis of body packing.