Nomogram for prediction of hearing rehabilitation outcome in children with congenital sensorineural hearing loss after cochlear implantation.

Background: Reliable predictors for rehabilitation outcomes in patients with congenital sensorineural hearing loss (CSNHL) after cochlear implantation (CI) are lacking. The purchase of this study was to develop a nomogram based on clinical characteristics and neuroimaging features to predict the outcome in children with CSNHL after CI. Methods: Children with CSNHL prior to CI surgery and children with normal hearing were enrolled into the study. Clinical data, high resolution computed tomography (HRCT) for ototemporal bone, conventional brain MRI for structural analysis and brain resting-state fMRI (rs-fMRI) for the power spectrum assessment were assessed. A nomogram combining both clinical and imaging data was constructed using multivariate logistic regression analysis. Model performance was evaluated and validated using bootstrap resampling. Results: The final cohort consisted of 72 children with CSNHL (41 children with poor outcome and 31 children with good outcome) and 32 healthy controls. The white matter lesion from structural assessment and six power spectrum parameters from rs-fMRI, including Power4, Power13, Power14, Power19, Power23 and Power25 were used to build the nomogram. The area under the receiver operating characteristic (ROC) curve of the nomogram obtained using the bootstrapping method was 0.812 (95 % CI = 0.772-0.836). The calibration curve showed no statistical difference between the predicted value and the actual value, indicating a robust performance of the nomogram. The clinical decision analysis curve showed a high clinical value of this model. Conclusions: The nomogram constructed with clinical data, and neuroimaging features encompassing ototemporal bone measurements, white matter lesion values from structural brain MRI and power spectrum data from rs-fMRI showed a robust performance in predicting outcome of hearing rehabilitation in children with CSNHL after CI.

Nomogram incorporating preoperative MRI-VASARI features for differentiating intracranial extraventricular ependymoma from glioblastoma.

Background: Intracranial extraventricular ependymoma (IEE) and glioblastoma (GBM) may have similar imaging findings but different prognosis. This study aimed to develop and validate a nomogram based on magnetic resonance imaging (MRI) Visually AcceSAble Rembrandt Images (VASARI) features for preoperatively differentiating IEE from GBM. Methods: The clinical data and the MRI-VASARI features of patients with confirmed IEE (n=114) and confirmed GBM (n=258) in a multicenter cohort were retrospectively analyzed. Predictive models for differentiating IEE from GBM were built using a multivariate logistic regression method. A nomogram was generated and the performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. Results: The predictors identified in this study consisted of six VASARI features and four clinical features. Compared with the individual models, the combined model incorporating clinical and VASARI features had the highest area under the curve (AUC) value [training set: 0.99, 95% confidence interval (CI): 0.98-1.00; validation set: 0.97, 95% CI: 0.94-1.00] in comparison to the clinical model. The nomogram was well calibrated with significant clinical benefit according to the calibration curve and decision curve analyses. Conclusions: The nomogram combining clinical and MRI-VASARI characteristics was robust for differentiating IEE from GBM preoperatively and may potentially assist in diagnosis and treatment of brain tumors.

Neuroimaging features of primary central nervous system post-transplantation lymphoproliferative disorder following hematopoietic stem cell transplant in patients with ß-thalassemia: a case series and review of literature.

PURPOSE: Primary central nervous system post-transplantation lymphoproliferative disorder (PCNS-PTLD) is a rare but serious complication of hematopoietic stem cell transplantation (HSCT) in patients with severe ß-thalassemia. This study aimed to assess the clinical presentation, pathological characteristics, neuroimaging findings, and treatment strategies in patients with ß-thalassemia who developed PCNS-PTLD and to compare a case series from our transplant center to reported cases from literature. METHODS: We retrospectively reviewed our hospital database and identified four cases of pathologically confirmed PCNS-PTLD without a history of systemic PTLD in patients with severe ß-thalassemia after HSCT. We also performed a relevant literature review on PCNS-PTLD. RESULTS: The median time from transplantation to diagnosis of PCNS-PTLD was 5.5 months. Intracerebral lesions were usually multiple involving both supratentorial and infratentorial regions with homogeneous or rim enhancement. All patients had pathologically confirmed PCNS-PTLD with three patients having diffuse large B-cell lymphoma and the fourth patient having plasmacytic hyperplasia. There was low response to treatment with a median survival of 83 days. CONCLUSION: PCNS-PTLD should be considered in the differential diagnosis of patients with ß-thalassemia who had an intracranial lesion on neuroimaging after HSCT. CRITICAL RELEVANCE STATEMENT: This case series with a comprehensive review of neuroimaging and clinical characteristics of children with primary central nervous system post-transplantation lymphoproliferative disorder should advance our understanding and improve management of this rare yet severe complication following transplant for ß-thalassemia. KEY POINTS: • We assessed clinical presentation, treatment strategies, and neuroimaging characteristics of PCNS-PTLD in patients with ß-thalassemia after transplantation. • Patients with ß-thalassemia may have post-transplantation lymphoproliferative disorder presenting as brain lesions on neuroimaging. • Neuroimaging findings of the brain lesions are helpful for prompt diagnosis and proper management.

Magnetic resonance imaging findings of intracranial extraventricular ependymoma: A retrospective multi-center cohort study of 114 cases.

BACKGROUND: Intracranial extraventricular ependymoma (IEE) is an ependymoma located in the brain parenchyma outside the ventricles. IEE has overlapping clinical and imaging characteristics with glioblastoma multiforme (GBM) but different treatment strategy and prognosis. Therefore, an accurate preoperative diagnosis is necessary for optimizing therapy for IEE. METHODS: A retrospective multicenter cohort of IEE and GBM was identified. MR imaging characteristics assessed with the Visually Accessible Rembrandt Images (VASARI) feature set and clinicopathological findings were recorded. Independent predictors for IEE were identified using multivariate logistic regression, which was used to construct a diagnostic score for differentiating IEE from GBM. RESULTS: Compared to GBM, IEE tended to occur in younger patients. Multivariate logistic regression analysis identified seven independent predictors for IEE. Among them, 3 predictors including tumor necrosis rate (F7), age, and tumor-enhancing margin thickness (F11), demonstrated higher diagnostic performance with an Area Under Curve (AUC) of more than 70% in distinguishing IEE from GBM. The AUC was 0.85, 0.78, and 0.70, with sensitivity of 92.98%, 72.81%, and 96.49%, and specificity of 65.50%, 73.64%, and 43.41%, for F7, age, and F11, respectively. CONCLUSION: We identified specific MR imaging features such as tumor necrosis and thickness of enhancing tumor margins that could help to differentiate IEE from GBM. Our study results should be helpful to assist in diagnosis and clinical management of this rare brain tumor.

Brain iron content and cognitive function in patients with ß-thalassemia.

Patients with ß-thalassemia (ß-TM) may have brain iron overload from long-term blood transfusions, ineffective erythropoiesis, and increased intestinal iron absorption, leading to cognitive impairment. Brain magnetic resonance imaging (MRI) methods such as the transverse relaxation rate, susceptibility-weighted imaging, and quantitative susceptibility mapping can provide quantitative, in vivo measurements of brain iron. This review assessed these MRI methods for brain iron quantification and the measurements for cognitive function in patients with ß-TM. We aimed to identify the neural correlates of cognitive impairment, which should help to evaluate therapies for improving cognition and quality of life in patients with ß-TM.

MRI-based radiomics models for the early prediction of radiation-induced temporal lobe injury in nasopharyngeal carcinoma.

Objective: This study was conducted to develop and validate a radiomics-clinics combined model-based magnetic resonance imaging (MRI) radiomics and clinical features for the early prediction of radiation-induced temporal lobe injury (RTLI) in patients with nasopharyngeal carcinoma (NPC). Methods: This retrospective study was conducted using data from 130 patients with NPC (80 patients with and 50 patients without RTLI) who received radiotherapy. Cases were assigned randomly to training (n = 91) and testing (n = 39) datasets. Data on 168 medial temporal lobe texture features were extracted from T1WI, T2WI, and T1WI-CE MRI sequences obtained at the end of radiotherapy courses. Clinics, radiomics, and radiomics-clinics combined models (based on selected radiomics signatures and clinical factors) were constructed using machine learning software. Univariate logistic regression analysis was performed to identify independent clinical factors. The area under the ROC curve (AUC) was performed to evaluate the performance of three models. A nomogram, decision curves, and calibration curves were used to assess the performance of the combined model. Results: Six texture features and three independent clinical factors associated significantly with RTLI were used to build the combined model. The AUCs for the combined and radiomics models were 0.962 [95% confidence interval (CI), 0.9306-0.9939] and 0.904 (95% CI, 0.8431-0.9651), respectively, for the training cohort and 0.947 (95% CI, 0.8841-1.0000) and 0.891 (95% CI, 0.7903-0.9930), respectively, for the testing cohort. All of these values exceeded those for the clinics model (AUC = 0.809 and 0.713 for the training and testing cohorts, respectively). Decision curve analysis showed that the combined model had a good corrective effect. Conclusion: The radiomics-clinics combined model developed in this study showed good performance for predicting RTLI in patients with NPC.

Performance of Pretreatment MRI-Based Radiomics in Recombinant Human Endostatin Plus Concurrent Chemoradiotherapy Response Prediction in Nasopharyngeal Carcinoma: A Retrospective Study.

PURPOSE: To investigate the capability of an Magnetic resonance imaging (MRI) radiomics model based on pretreatment texture features in predicting the short-term efficacy of recombinant human endostatin (RHES) plus concurrent chemoradiotherapy (CCRT) for nasopharyngeal carcinoma (NPC). METHODS: We retrospectively enrolled 65 patients newly diagnosed as having NPC and treated with RHES + CCRT. A total of 144 texture features were extracted from the MRI before RHES + CCRT treatment of all the NPC patients. The maximum relevance minimum redundancy (mRMR) method was used to remove redundant, irrelevant texture features, and calculate the Rad score of the primary tumor. Multivariable logistic regression was used to select the most predictive features subset, and prediction models were constructed. The performance of the 3 models in predicting the early response of RHES + CCRT for NPC was explored. RESULTS: The diagnostic efficiency of combined model and radiomics model in distinguishing between the effective and the ineffective groups of patients was found to be moderate. The area under the ROC curve (AUC) of the combined model and radiomics model was 0.74 (95% confidence interval [CI]: 0.62-0.86) and 0.71 (95% CI: 0.58-0.84), respectively, with both being higher than the AUC of the clinics model (0.63, 95% CI: 0.49-0.78). Compared with the radiomics model, the combined model showed marginally improved diagnostic performance in predicting RHES + CCRT treatment response. The accuracy of combined model and radiomics model for RHES + CCRT response assessment in NPC were higher than those of the clinics model (0.723, 0.723 vs 0.677). CONCLUSION: The pretreatment MRI-based radiomics may be a noninvasive and effective method for the prediction of RHES + CCRT early response in patients with NPC.

Spirometra mansoni sparganosis identified by metagenomic next-generation sequencing: a case report.

A 30-year-old male patient had a cyst on the left hip and progressive enlargement for more than 2 months. Combined blood tests, magnetic resonance imaging, and pathology findings, cysticercosis infection was suspected. However, the treatment for cysticercosis was ineffective. We conducted a metagenomic next-generation sequencing (mNGS) analysis on the formalin-fixed, paraffin-embedded specimen of the patient's surgically excised tissue, and the results suggested Spirometra mansoni, mNGS was further confirmed by polymerase chain reaction and phylogenetic analysis of cytochrome c oxidase subunit 1 (cox1) gene. Based on these results, we found that mNGS provided a better method of diagnosing parasitic infections.

Intelligent Diagnosis and Analysis of Brain Lymphoma Based on DSC Imaging Features.

Currently, DSC has been extensively studied in the diagnosis, differential diagnosis, and prognosis evaluation of brain lymphoma, but it has not obtained a uniform standard. By combining DSC imaging features, this study investigated the imaging features and diagnostic value of several types of tumors such as primary brain lymphoma. At the same time, this study obtained data from brain lymphoma patients by data collection and set up different groups to conduct experimental studies to explore the correlation between IVIMMRI perfusion parameters and DSC perfusion parameters in brain lymphoma. Through experimental research, it can be seen that the combination of two perfusion imaging techniques can more fully reflect the blood flow properties of the lesion, which is beneficial to determine the nature of the lesion.

Imaging characteristics of cerebral extraventricular neurocytoma with pathological correlation.

PURPOSE: Extraventricular neurocytoma (EVN) is an exceedingly rare tumor. In this study, we sought to characterize the imaging and pathological features of this uncommon tumor. METHODS: Retrospective review of 18 patients (9 male; 9 female) with pathologically confirmed EVN treated at a single center between 2005 and 2017. RESULTS: All patients had a solitary lesion. Sixteen lesions were found in hemispheres. The greatest tumor diameter ranged from 2.6 to 8 cm. The lesions were generally solid with cystic components; the solid portion appeared isodense or hyperdense on CT, isointense to hypointense on T1WI, and slightly hyperintense on T2WI. Heterogeneous hyperintensity interspersed with isointense or hypointense areas suggestive of hemorrhage, calcification or vascular flow voids were seen on T2WI. Heterogeneous enhancement was noted in 17 lesions; no enhancement was observed in one lesion. Cystic components were observed in 13 lesions; 9 of these showed characteristic perilesional cysts (9/13). Mild to moderate peritumoral edema (15/18), calcification (4/16), intratumoral hemorrhage (11/18) and vascular flow voids (10/16) were observed in some lesions. Pathologically, tumor cells showed round nucleus and fine neuropil matrix. Foci of calcification in the solid portion of the tumor were seen in five cases. Microcystic changes were observed in almost all cases. Some lesions exhibited positive staining for synaptophysin (Syn) (15/16) and neuronal nuclei (NeuN) (7/8). MIB-1 was determined for 10 patients; seven of these had an MIB-1 ≥ 3. These six patients experienced recurrence; four of them relapsed twice. CONCLUSIONS: EVNs occur as single intracranial solid mass with cystic components (especially peripherally located cysts); solid portion exhibits slight hyperintensity or heterogeneous signal intensity. Mild to moderate peritumoral edema, calcification, intratumoral hemorrhage and vascular flow voids were characteristic features of extraventricular neurocytoma. Positive staining for synaptophysin and neuronal nuclei confirmed the diagnosis. A combination of atypical pathologic features and atypical radiologic features should be considered for prognostic assessment.