Radiomics; A Potential Next "Omics" in Psychiatric Disorders; An Introduction.

Psychiatric disorders remain one of the most debilitating conditions; however, most patients are never diagnosed and do not seek treatment. Despite its massive burden on modern society and the health system, many hurdles prevent proper diagnosis and management of these disorders. The diagnosis is primarily based on clinical symptoms, and efforts to find appropriate biomarkers have not been practical. Through the past years, researchers have put a tremendous effort into finding biomarkers in "omics" fields: genomics, transcriptomics, proteomics, metabolomics, and epigenomics. This article reviews the evolving field of radiomics and its role in diagnosing psychiatric disorders as the sixth potential "omics." The first section of this paper elaborates on the definition of radiomics and its potential to provide a detailed structural study of the brain. Following that, we have provided the latest promising results of this novel approach in a broad range of psychiatric disorders. Radiomics fits well within the concept of psychoradiology. Besides volumetric analysis, radiomics takes advantage of many other features. This technique may open a new field in psychiatry for diagnosing and classifying psychiatric disorders and treatment response prediction in the era of precision and personalized medicine. The initial results are encouraging, but radiomics in psychiatry is still in its infancy. Despite the extensive burden of psychiatric disorders, there are very few published studies in this field, with small patient populations. The lack of prospective multi-centric studies and heterogeneity of studies in design are the significant barriers against the clinical adaptation of radiomics in psychoradiology.

Radiomics Outperforms Clinical and Radiologic Signs in Predicting Spontaneous Basal Ganglia Hematoma Expansion: A Pilot Study.

Prediction of the hematoma expansion (HE) of spontaneous basal ganglia hematoma (SBH) from the first non-contrast CT can result in better management, which has the potential of improving outcomes. This study has been designed to compare the performance of "Radiomics analysis," "radiology signs," and "clinical-laboratory data" for this task. We retrospectively reviewed the electronic medical records for clinical, demographic, and laboratory data in patients with SBH. CT images were reviewed for the presence of radiologic signs, including black-hole, blend, swirl, satellite, and island signs. Radiomic features from the SBH on the first brain CT were extracted, and the most predictive features were selected. Different machine learning models were developed based on clinical, laboratory, and radiology signs and selected Radiomic features to predict hematoma expansion (HE). The dataset used for this analysis included 116 patients with SBH. Among different models and different thresholds to define hematoma expansion (10%, 20%, 25%, 33%, 40%, and 50% volume enlargement thresholds), the Random Forest based on 10 selected Radiomic features achieved the best performance (for 25% hematoma enlargement) with an area under the curve (AUC) of 0.9 on the training dataset and 0.89 on the test dataset. The models based on clinical-laboratory and radiology signs had low performance (AUCs about 0.5-0.6).

Magnetic Resonance Imaging (MRI) Findings in COVID-19 Associated Encephalitis.

We conducted this study to investigate the scope of the MRI neuroimaging manifestations in COVID-19-associated encephalitis. From January 2020 to September 2021, patients with clinical diagnosis of COVID-19-associated encephalitis, as well as concomitant abnormal imaging findings on brain MRI, were included. Two board-certified neuro-radiologists reviewed these selected brain MR images, and further discerned the abnormal imaging findings. 39 patients with the clinical diagnosis of encephalitis as well as abnormal MRI findings were included. Most (87%) of these patients were managed in ICU, and 79% had to be intubated-ventilated. 15 (38%) patients died from the disease, while the rest were discharged from the hospital. On MRI, FLAIR hyperintensities in the insular cortex were the most common finding, seen in 38% of the patients. Micro-hemorrhages on the SWI images were equally common, also seen in 38% patients. FLAIR hyperintensities in the medial temporal lobes were seen in 30%, while FLAIR hyperintensities in the posterior fossa were evident in 20%. FLAIR hyperintensities in basal ganglia and thalami were seen in 15%. Confluent FLAIR hyperintensities in deep and periventricular white matter, not explained by microvascular angiopathy, were detected in 7% of cases. Cortical-based FLAIR hyperintensities in 7%, and FLAIR hyperintensity in the splenium of the corpus callosum in 7% of patients. Finally, isolated FLAIR hyperintensity around the third ventricle was noted in 2% of patients.

Radiomics for differentiation of the posterior fossa pilocytic astrocytoma versus hemangioblastomas in adults. A pilot study.

PURPOSE: Both pilocytic astrocytoma (PA) and hemangioblastoma (HB) are common primary neoplasms of the posterior fossa with similar radiological manifestations. This study was conducted to evaluate the role of Radiomics in differentiating these two conditions in adults. MATERIALS AND METHODS: After a retrospective search of our institutional imaging archive, adult patients with a known diagnosis of PA or HB were included. We reviewed each patient's most recent preoperative brain magnetic resonance imaging (MRI). The solid enhancing nodule of each lesion on post-contrast T1 sequence was manually segmented. Multiple Radiomics features were then extracted from each nodule using the Pyradiomics library. Subsequently, the most predictive features were identified by feature selection models. Following this, different machine learning (ML) models were constructed based on these selected features to classify lesions as PA or HB. Finally, we evaluated the performance of each model by leave-one-out cross-validation. RESULTS: With inclusion and exclusion criteria, 34 enhancing PA nodules and 39 HB nodules were selected. A total of 115 features were extracted from each enhancing nodule. Twelve characteristics were detected as most predictive of histopathological diagnosis. Among various ML models, the neural network had the best performance in differentiating these two conditions with an AUC of 0.9 and an accuracy of 82%. CONCLUSIONS: In this retrospective study, Radiomics MRI techniques demonstrated high performance in distinguishing adult posterior fossa PA from HB. Future development of Radiomics models may advance presurgical diagnosis of these two conditions when added to routine clinical practice and thus improve patient management.

Imaging spectrum of amyloid-related imaging abnormalities associated with aducanumab immunotherapy.

Alzheimer's Disease (AD) is a leading cause of morbidity. Management of AD has traditionally been aimed at symptom relief rather than disease modification. Recently, AD research has begun to shift focus towards disease-modifying therapies that can alter the progression of AD. In this context, a class of immunotherapy agents known as monoclonal antibodies target diverse cerebral amyloid-beta (Aß) epitopes to inhibit disease progression. Aducanumab was authorized by the US Food and Drug Administration (FDA) to treat AD on June 7, 2021. Aducanumab has shown promising clinical and biomarker efficacy but is associated with amyloid-related imaging abnormalities (ARIA). Neuroradiologists play a critical role in diagnosing ARIA, necessitating familiarity with this condition. This pictorial review will appraise the radiologic presentation of ARIA in patients on aducanumab.

The Role of Machine Learning and Radiomics for Treatment Response Prediction in Idiopathic Normal Pressure Hydrocephalus.

Introduction Ventricular shunting remains the standard of care for patients with idiopathic normal pressure hydrocephalus (iNPH); however, not all patients benefit from the shunting. Prediction of response in advance can result in improved patient selection for ventricular shunting. This study aims to develop a machine learning predictive model for treatment response after shunt placement using the clinical and radiomics features. Methods In this retrospective pilot study, the medical records of iNPH patients who underwent ventricular shunting were evaluated. In each patient, the "idiopathic normal pressure hydrocephalus grading scale" (iNPHGS) and a "Modified Rankin Scale" were calculated before and after surgery. The subsequent treatment response was calculated as the difference between the iNPHGS scores before and after surgery. iNPHGS score reduction of two or more than two were considered as treatment response. The presurgical MRI scans were evaluated by radiologists, the ventricular systems were segmented on the T2-weighted images, and the radiomics features were extracted from the segmented ventricular system. Using Orange data mining open-source platform, different machine learning models were then developed based on the presurgical clinical features and the selected radiomics features to predict treatment response after shunt placement. Results After the implementation of the inclusion criteria, 78 patients were included in this study. One hundred twenty radiomics features were extracted, and the 12 best predictive radiomics features were selected. Using only clinical data (iNPHGS and Modified Rankin Scale), the random forest model achieved the best performance in treatment prediction with an area under the curve (AUC) of 0.71. Adding the Radiomics analysis to the clinical data improved the prediction performance, with the support vector machine (SVM) achieving the highest rank in treatment prediction with an AUC of 0.8. Adding age and sex to the analysis did not improve the prediction. Conclusion Using machine learning models for treatment response prediction in patients with iNPH is feasible with acceptable accuracy. Adding the Radiomics analysis to the clinical features can further improve the predictive performance. SVM is likely the best model for this task.

Mimics of perineural tumor spread in the head and neck.

Perineural spread (PNS) is an important potential complication of head and neck malignancy, as it is associated with decreased survival and a higher risk of local recurrence and metastasis. There are many review articles focused on the imaging findings of PNS. However, a false-positive diagnosis of PNS can be just as harmful to the patient as an overlooked case. In this manuscript, we delineate and classify various imaging mimics of PNS. Mimics can be divided into the following categories: normal variants (including vascular structures and failed fat suppression), infections, inflammatory disease (including granulomatous disease and demyelination), neoplasms, and post-traumatic/surgical changes. Knowledge of potential mimics of PNS will prevent false-positive imaging interpretation, and enable appropriate oncologic management.

Emerging Applications of Radiomics in Neurological Disorders: A Review.

Radiomics has achieved significant momentum in radiology research and can reveal image information invisible to radiologists' eyes. Radiomics first evolved for oncologic imaging. Oncologic applications (histopathology, tumor grading, gene mutation analysis, patient survival, and treatment response prediction) of radiomics are widespread. However, it is not limited to oncologic analysis, and any digital medical images can benefit from radiomics analysis. This article reviews the current literature on radiomics in non-oncologic, neurological disorders including ischemic strokes, hemorrhagic stroke, cerebral aneurysms, and demyelinating disorders.

The Role of Dual-Phase FDG PET/CT in the Diagnosis and Follow-Up of Brain Tumors.

OBJECTIVE. FDG PET/CT of brain tumors is limited by background activity. Dual-phase FDG PET/CT can eliminate this limitation and allow discernment of viable tumors. Our aim was to assess the diagnostic capability of dual-phase FDG PET/CT qualitatively and quantitatively and to determine cutoff values for dual-phase FDG PET/CT in brain tumor imaging. MATERIALS AND METHODS. Retrospectively, 51 malignant brain tumors were evaluated with dual-phase FDG PET/CT in 32 patients. Acquisitions were performed 30 minutes (time 1) and 3 hours (time 2) after administration of 10 mCi (370 MBq) FDG and 6 hours of fasting. Two observers independently and qualitatively evaluated lesions. A weighted Cohen kappa was used to calculate interrater reliability and accuracy. Quantitatively, maximum standardized uptake value (SUVmax) was measured in the lesions, contralateral white matter (CWM), contralateral caudate nucleus head, and ipsilateral cerebellar cortex (CC). Lesion-to-CWM SUVmax, lesion-to-contralateral caudate nucleus head SUVmax, and lesion-to-ipsilateral CC SUVmax ratios at time 1 and time 2 were calculated. ROC analysis was used to determine optimum cutoff values, and AUC ratios were compared among quantitative parameters. Lesion outcome was determined by pathologic results (available in 15 lesions), lesion stability on serial MRI examinations (representing nonviable tumor), or decreased tumor size on serial MRI examinations after new treatment (representing viable tumor). RESULTS. Thirty-seven viable and 14 nonviable lesions were evaluated. Qualitatively, the diagnostic accuracy (first observer: κ = 0.45 to κ = 0.59; second observer: κ = 0.41 to κ = 0.66) and interrater reliability (at time 1: κ = 0.51; at time 2: κ = 0.83) improved with delayed imaging. AUC and ROC analysis showed comparably high sensitivity, specificity, and accuracy profiles for early and delayed dual-phase FDG PET/CT. Some of the proposed cutoff values were as follows: lesion SUVmax at time 1, 7.20 (sensitivity, 89.2%; specificity, 85.7%); lesion SUVmax at time 2, 7.80 (sensitivity, 97.3%; specificity, 71.4%); lesion-to-CWM SUVmax at time 1, 2.05 (sensitivity, 78.4%; specificity, 92.9%); and lesion-to-CWM SUVmax at time 2, 2.36 (sensitivity, 81.1%; specificity, 85.7%). CONCLUSION. Dual-phase FDG PET/CT improves lesion detection and diagnostic accuracy in malignant brain tumors.

AUTOMATED SEGMENTATION OF CORTICAL NECROSIS USING A WAVELET BASED ABNORMALITY DETECTION SYSTEM.

We propose an automated method to segment cortical necrosis from brain FLAIR-MR Images. Cortical necrosis are regions of dead brain tissue in the cortex caused by cerebrovascular disease (CVD). The accurate segmentation of these regions is difficult as their intensity patterns are similar to the adjoining cerebrospinal fluid (CSF). We generate a model of normal variation using MR scans of healthy controls. The model is based on the Jacobians of warps obtained by registering scans of normal subjects to a common coordinate system. For each patient scan a Jacobian is obtained by warping it to the same coordinate system. Large deviations between the model and subject-specific Jacobians are flagged as `abnormalities'. Abnormalities are segmented as cortical necrosis if they are in the cortex and have the intensity profile of CSF. We evaluate our method by using a set of 72 healthy subjects to model cortical variation.We use this model to successfully detect and segment cortical necrosis in a set of 37 patients with CVD. A comparison of the results with segmentations from two independent human experts shows that the overlap between our approach and either of the human experts is in the range of the overlap between the two human experts themselves.

Effect of rasanjana madhu ashchyotana in netra abhishyanda (mucopurulent conjunctivitis).

To evaluate the comparative efficacy of Ayurvedic formulation a Rasanjana Madhu (RM) eye drops and Honey Rose (HR) water eye drops in Netra Abhishyanda in mucopurulent conjunctivitis, the current study is planned. Total of 35 patients attending the outpatient department of Shalakya Tantra at R. G. G. Postgraduate Ayurvedic College, Paprola, Distt. Kangra, Himachal Pradesh with characteristic features of Netra Abhishyanda were selected for the present study. Twenty patients were given trial drug, i.e., RM eye drops, while 15 patients were given HR eye drops. Random sampling technique was adopted for the present study. The duration of the treatment was 7 days with 1 week follow-up. Patients receiving the trial group demonstrated reduction of redness, burning sensation, lacrimation, photophobia, foreign body sensation, discharge, and congestion, which were statistically significant with 93% patients cured or markedly improved category. Signs and symptoms stated above were also statistically reduced with HR eye drops, probably because of well-documented hygroscopic and bacteriocidal properties of honey. Based on the study, it can be concluded that, RM eye drops are very effective in the management of Netra Abhishyanda viz. Infective conjunctivitis.