Herpes encephalitis masquerading as tumor

MRI-Based Machine Learning Fusion Models to Distinguish Encephalitis and Gliomas

This paper aims to compare the performance of the classical machine learning (CML) model and the deep learning (DL) model, and to assess the effectiveness of utilizing fusion radiomics from both CML and DL in distinguishing encephalitis from glioma in atypical cases. We analysed the axial FLAIR images of preoperative MRI in 116 patients pathologically confirmed as gliomas and clinically diagnosed with encephalitis. The 3 CML models (logistic regression (LR), support vector machine (SVM) and multi-layer perceptron (MLP)), 3 DL models (DenseNet 121, ResNet 50 and ResNet 18) and a deep learning radiomic (DLR) model were established, respectively. The area under the receiver operating curve (AUC) and sensitivity, specificity, accuracy, negative predictive value (NPV) and positive predictive value (PPV) were calculated for the training and validation sets. In addition, a deep learning radiomic nomogram (DLRN) and a web calculator were designed as a tool to aid clinical decision-making. The best DL model (ResNet50) consistently outperformed the best CML model (LR). The DLR model had the best predictive performance, with AUC, sensitivity, specificity, accuracy, NPV and PPV of 0.879, 0.929, 0.800, 0.875, 0.867 and 0.889 in the validation sets, respectively. Calibration curve of DLR model shows good agreement between prediction and observation, and the decision curve analysis (DCA) indicated that the DLR model had higher overall net benefit than the other two models (ResNet50 and LR). Meanwhile, the DLRN and web calculator can provide dynamic assessments. Machine learning (ML) models have the potential to non-invasively differentiate between encephalitis and glioma in atypical cases. Furthermore, combining DL and CML techniques could enhance the performance of the ML models.

Differentiation of Glioma Mimicking Encephalitis and Encephalitis Using Multiparametric MR-Based Deep Learning

Background

Computational aid for diagnosis based on convolutional neural network (CNN) is promising to improve clinical diagnostic performance. Therefore, we applied pretrained CNN models in multiparametric magnetic resonance (MR) images to classify glioma mimicking encephalitis and encephalitis.

Methods

A data set containing 3064 MRI brain images from 164 patients with a final diagnosis of glioma (n = 56) and encephalitis (n = 108) patients and divided into training and testing sets. We applied three MRI modalities [fluid attenuated inversion recovery (FLAIR), contrast enhanced-T1 weighted imaging (CE-T1WI) and T2 weighted imaging (T2WI)] as the input data to build three pretrained deep CNN models (Alexnet, ResNet-50, and Inception-v3), and then compared their classification performance with radiologists' diagnostic performance. These models were evaluated by using the area under the receiver operator characteristic curve (AUC) of a five-fold cross-validation and the accuracy, sensitivity, specificity were analyzed.

Results

The three pretrained CNN models all had AUC values over 0.9 with excellent performance. The highest classification accuracy of 97.57% was achieved by the Inception-v3 model based on the T2WI data. In addition, Inception-v3 performed statistically significantly better than the Alexnet architecture (p<0.05). For Inception-v3 and ResNet-50 models, T2WI offered the highest accuracy, followed by CE-T1WI and FLAIR. The performance of Inception-v3 and ResNet-50 had a significant difference with radiologists (p<0.05), but there was no significant difference between the results of the Alexnet and those of a more experienced radiologist (p >0.05).

Conclusions

The pretrained CNN models can automatically and accurately classify these two diseases and further help to improving clinical diagnostic performance.

Case Report of Immuno-Oncotherapy (IO) Provoked Encephalitis Mimicking Brain Metastasis in a Patient with History of Traumatic Brain Injury

BACKGROUND

Immuno-oncotherapy (IO) has revolutionized systemic cancer care but remains experimental in brain tumors. IO treatment risks multiorgan autoimmune inflammatory responses that limit its use. The central nervous system (CNS) is an immune-specialized compartment with restricted cellular access, thus fewer cases are reported for immune-mediated encephalitis. Interestingly, patients with history of blood-brain barrier compromise are potentially at higher risk for immune cell trafficking to the CNS.

CASE DESCRIPTION

We report the first case, to our knowledge, of a 70-year-old man with clear cell renal cell carcinoma with pulmonary metastases treated with lung irradiation, nephrectomy, and chemotherapy prior to switching to single-agent nivolumab IO. The patient presented with new-onset generalized tonic-clonic seizure and left visual field-cut. Review of patient history revealed remote traumatic brain injury (TBI). Brain imaging noted a solid-enhancing right occipital mass that was presumed metastasis versus lymphoma. Cerebrospinal fluid cytology was negative for malignancy but concerning for lymphoproliferative process not determined to be malignant. The patient started steroids and anti-epileptic therapy. After negative systemic cancer re-staging, IO was discontinued and steroids were initiated with demonstrated patient clinical improvement.

CONCLUSIONS

We concluded the diagnosis of immune-mediated encephalitis secondary to IO with collection of reactive T-cells within the area of encephalomalacia. The area of encephalomalacia from prior TBI served to compartmentalize the reactive lymphocytes, giving the appearance of a mass. Taken together, new onset seizure in patients on IO might signal encephalitis and CNS metastatic mimicry should be considered in patients with a prior history of TBI and encephalomalacia.

HSV-1 Encephalitis: High Index of Clinical Suspicion, Prompt Diagnosis, and Early Therapeutic Intervention Are the Triptych of Success-Report of Two Cases and Comprehensive Review of the Literature

Herpes Simplex Virus (HSV) encephalitis is an acute infectious disease of the Central Nervous System (CNS), usually affecting the limbic structures, the median temporal cortex, and the orbitofrontal regions. Its annual incidence has significantly increased over the last 20 years and the mortality rate is 7%, if early diagnosed and treated, and 70%, if left untreated, while it is associated with high rates of morbidity. It should be noted that even when Cerebrospinal fluid (CSF) analysis seems normal, imaging studies are not specific and HSV Polymerase Chain Reaction (PCR) test is negative; the clinician should be more aggressive, if clinical presentation is indicative for HSV encephalitis, by administrating acyclovir early after patient's admission. The latter may be a vital intervention for the patient, modifying the patient's clinical course. Through the presentation of two cases of HSV-1 encephalitis that we managed in our department over the last 1 year and after systematic and comprehensive research of the relevant literature, we aim at showing the crucial role of medical history and physical examination, along with the high index of clinical suspicion, in order to make promptly the diagnosis and administer timely intravenous acyclovir, limiting the possibility of complications during the disease's course.