Amantadine as an Aid to Extubation in Severe Acute Brain Injury: A Case Series.

For a subset of patients with severe acute brain injury (SABI) undergoing invasive mechanical ventilation, the primary barrier to successful extubation is depressed mental status. Amantadine is a neurostimulant that has been demonstrated to increase arousal and improve functional outcomes in patients with SABI. In this case series, we describe 5 patients with SABI and invasive mechanical ventilation who received amantadine as an agent to improve mental status to allow extubation. The primary barrier to extubation for all patients was depressed mental status. Median age was 77 (range 32 to 82). Primary diagnoses were ischemic stroke (n = 1), subdural hemorrhage (n = 2), intracerebral hemorrhage (n = 1), and traumatic brain injury (n = 1). Median Glasgow Coma Score was 7T prior to administration of amantadine and 10T on the day after amantadine was initiated, with improvements in eye-opening and motor response. Four patients displayed improvement in arousal and attention and were successfully extubated 1 to 4 days after initiation of amantadine (median 2 days). The fifth patient only displayed marginal improvement in mental status after starting amantadine, but was ultimately able to be extubated 7 days later. Amantadine may improve the likelihood of or reduce the time to successful extubation in patients with SABI.

Imaging-Based Deep Graph Neural Networks for Survival Analysis in Early Stage Lung Cancer Using CT: A Multicenter Study.

Background: Lung cancer is the leading cause of cancer-related mortality, and accurate prediction of patient survival can aid treatment planning and potentially improve outcomes. In this study, we proposed an automated system capable of lung segmentation and survival prediction using graph convolution neural network (GCN) with CT data in non-small cell lung cancer (NSCLC) patients. Methods: In this retrospective study, we segmented 10 parts of the lung CT images and built individual lung graphs as inputs to train a GCN model to predict 5-year overall survival. A Cox proportional-hazard model, a set of machine learning (ML) models, a convolutional neural network based on tumor (Tumor-CNN), and the current TNM staging system were used as comparison. Findings: A total of 1,705 patients (main cohort) and 125 patients (external validation cohort) with lung cancer (stages I and II) were included. The GCN model was significantly predictive of 5-year overall survival with an AUC of 0.732 (p < 0.0001). The model stratified patients into low- and high-risk groups, which were associated with overall survival (HR = 5.41; 95% CI:, 2.32-10.14; p < 0.0001). On external validation dataset, our GCN model achieved the AUC score of 0.678 (95% CI: 0.564-0.792; p < 0.0001). Interpretation: The proposed GCN model outperformed all ML, Tumor-CNN, and TNM staging models. This study demonstrated the value of utilizing medical imaging graph structure data, resulting in a robust and effective model for the prediction of survival in early-stage lung cancer.

Machine learning application for the prediction of SARS-CoV-2 infection using blood tests and chest radiograph.

Triaging and prioritising patients for RT-PCR test had been essential in the management of COVID-19 in resource-scarce countries. In this study, we applied machine learning (ML) to the task of detection of SARS-CoV-2 infection using basic laboratory markers. We performed the statistical analysis and trained an ML model on a retrospective cohort of 5148 patients from 24 hospitals in Hong Kong to classify COVID-19 and other aetiology of pneumonia. We validated the model on three temporal validation sets from different waves of infection in Hong Kong. For predicting SARS-CoV-2 infection, the ML model achieved high AUCs and specificity but low sensitivity in all three validation sets (AUC: 89.9-95.8%; Sensitivity: 55.5-77.8%; Specificity: 91.5-98.3%). When used in adjunction with radiologist interpretations of chest radiographs, the sensitivity was over 90% while keeping moderate specificity. Our study showed that machine learning model based on readily available laboratory markers could achieve high accuracy in predicting SARS-CoV-2 infection.

Detection of COVID-19 Using Deep Learning Algorithms on Chest Radiographs.

PURPOSE: To evaluate the performance of a deep learning (DL) algorithm for the detection of COVID-19 on chest radiographs (CXR). MATERIALS AND METHODS: In this retrospective study, a DL model was trained on 112,120 CXR images with 14 labeled classifiers (ChestX-ray14) and fine-tuned using initial CXR on hospital admission of 509 patients, who had undergone COVID-19 reverse transcriptase-polymerase chain reaction (RT-PCR). The test set consisted of a CXR on presentation of 248 individuals suspected of COVID-19 pneumonia between February 16 and March 3, 2020 from 4 centers (72 RT-PCR positives and 176 RT-PCR negatives). The CXR were independently reviewed by 3 radiologists and using the DL algorithm. Diagnostic performance was compared with radiologists' performance and was assessed by area under the receiver operating characteristics (AUC). RESULTS: The median age of the subjects in the test set was 61 (interquartile range: 39 to 79) years (51% male). The DL algorithm achieved an AUC of 0.81, sensitivity of 0.85, and specificity of 0.72 in detecting COVID-19 using RT-PCR as the reference standard. On subgroup analyses, the model achieved an AUC of 0.79, sensitivity of 0.80, and specificity of 0.74 in detecting COVID-19 in patients presented with fever or respiratory systems and an AUC of 0.87, sensitivity of 0.85, and specificity of 0.81 in distinguishing COVID-19 from other forms of pneumonia. The algorithm significantly outperforms human readers (P<0.001 using DeLong test) with higher sensitivity (P=0.01 using McNemar test). CONCLUSIONS: A DL algorithm (COV19NET) for the detection of COVID-19 on chest radiographs can potentially be an effective tool in triaging patients, particularly in resource-stretched health-care systems.

Percutaneous palliative care interventions in the cancer patient.

Patients with incurable cancer face lots of problems before they expire. Complications resulting from cancer or its treatment are a significant determinant of the quality of life of cancer patients. It is of outmost importance to make use of all treatment options in order to improve their survival and quality of life. Nowadays, available are a number of Interventional Radiology procedures that are minimally invasive, can be performed under local anesthesia or conscious sedation and are indispensable in helping patient through the cancer disease process. These techniques can be broadly classified into drainage of fluid collections, decompression and relief of obstruction, pain and tumor burden reduction, vascular and gastrointestinal tract access as well as hemostasis. The purpose of this article is to describe the basic concepts of minimally invasive techniques applied as palliative care therapies in the cancer patients. Controversies concerning techniques and products and the need for patient-centered tailored approaches will be discussed.

Optic neuropathy as the first sign of central nervous system relapse in acute myeloid leukaemia: MRI findings and its diagnostic challenge.

We describe the case of a 22-year-old man who presented with right eye visual impairment and oral mucositis. MRI revealed findings compatible with right optic neuritis. Herpes simplex virus 1 was detected in oral swab. He has a previous history of acute myeloid leukaemia (AML) and was in clinical remission. Initial investigations for possible relapse of AML with central nervous system (CNS) involvement were negative. Treatment for HSV-related optic neuritis was initiated but the patient's vision deteriorated. Repeat MRI revealed right optic nerve infarct, new left optic nerve abnormality and new leptomeningeal enhancement in the brain. Repeated cerebrospinal fluid analysis confirmed CNS relapse of AML. Despite prompt initiation of pulse steroid and high dose intrathecal cytarabine followed by cranial irradiation, the patient never regained his vision. We present a case of unexpected optic neuropathy as first and sole presentation of CNS relapse of AML.

Ultrasound-MRI Fusion for Targeted Biopsy of Myopathies.

OBJECTIVE. The purpose of this article is to describe the use of ultrasound-MRI fusion imaging to guide precise and targeted muscle biopsy in patients with suspected myopathies. CONCLUSION. Ultrasound-MRI fusion-guided muscle biopsy allows targeted sampling of tissues with active inflammatory changes and facilitates diagnosis of myopathies.

Opportunistic screening for osteoporosis in abdominal computed tomography for Chinese population.

This study assessed the possibility of diagnosing and excluding osteoporosis with routine abdominal CT scans in a Chinese population who underwent both DXA and CT for unrelated reasons. Statistical correlation was made between the HU measured of the spine on CT and various parameters on DXA. Diagnostic cutoff points in terms of HU were established for the diagnosis (≤ 136 HU) and exclusion (≥ 175 HU) of osteoporosis on sagittal reformatted images. There was excellent positive and negative predictive value for the DXA-defined diagnostic subgroups and were also comparable with previous studies in Caucasian populations. The authors exhort radiologists to report these incidental findings to facilitate early detection and treatment of osteoporosis in unsuspecting patients to prevent fractures and related complications. PURPOSE: The suspicion for osteoporosis can be raised in diagnostic computed tomography of the abdomen performed for other indications. We derived cutoff thresholds for the attenuation value of the lumbar spinal vertebrae (L1-5) in Hounsfield units (HU) in a Chinese patient population to facilitate implementation of opportunistic screening in radiologists. METHODS: We included 109 Chinese patients who concomitantly underwent abdominal CT and dual X-ray absorptiometry (DXA) within 6 months between July 2014 and July 2017 at a university hospital in Hong Kong. Images were retrospectively reviewed on sagittal reformats, and region-of-interest (ROI) markers were placed on the anterior portion of each of the L1-L5 vertebra to measure the HU. The mean values of CT HU were then compared with the bone mineral density (BMD) and T-score obtained by DXA. Receiver operator characteristic (ROC) curves were generated to determine diagnostic cutoff thresholds and their sensitivity and specificity values. RESULTS: The mean CT HU differed significantly (p < 0.01) for the three DXA-defined BMD categories of osteoporosis (97 HU), of osteopenia (135 HU), and of normal individuals (230 HU). There was good correlation between the mean CT HU and BMD and T-score (Pearson coefficient of 0.62 and 0.61, respectively, p < 0.001). The optimal cutoff point for exclusion of osteoporosis or osteopenia was HU ≥ 175 with negative predictive value as 98.9% and with area under curve (AUC) of ROC curve as 0.97. The optimal cutoff point for diagnosis of osteoporosis was HU ≤ 136 with positive predictive value as 81.2% and with AUC of ROC curve as 0.86. CONCLUSION: This is the first study on osteoporosis diagnosis with routine CT abdominal scans in Chinese population. The cutoff values were comparable with previous studies in Caucasian populations suggesting generalizability. Radiologists should consider routinely reporting these opportunistic findings to facilitate early detection and treatment of osteoporosis to prevent fractures and related complications.

Extrinsic pulmonary artery compression mimicking acute pulmonary embolism.

Right ventricular strain patterns on electrocardiogram such as right axis derivation and S1Q3T3 are well known for their diagnostic value in cases of acute pulmonary embolism. Nonetheless, these changes are not pathognomonic. We report a patient with electrocardiographic evidence of right ventricular strain secondary to an unusual etiology.

Fixing a fractured arthrodesed hip with rapid prototype templating and minimal invasive plate osteosynthesis.

CASE: We present an elderly lady with an intertrochanteric fracture of a previously fused hip. A 3D printed model of her pelvis and femur was used for implant templating before surgery. Minimal invasive fixation was performed with a spanning reversed distal femur locking plate without the need for removal of the previous implant. Multiple long locking screws were placed in the supra-acetabular region. The patient had union in 4 months, return to function and no complication. CONCLUSION: The technique allowed us to optimize implant selection and insert screws safely at difficult trajectories using minimal invasive surgery.

Prostate gland development and adrenal tumor in a female with congenital adrenal hyperplasia: a case report and review from radiology perspective.

We describe a case of a female with simple virilizing congenital adrenal hyperplasia (CAH) reared as a male diagnosed at the late age of 64. Computed Tomography (CT) demonstrated a large adrenal mass, bilateral diffuse adrenal enlargement, female pelvic organs as well as a clearly visualized prostate gland. This is to the best of our knowledge the first case of such a sizable prostate gland in a female CAH patient documented on CT. We review the literature regarding aspects where radiologists may encounter CAH and the finding of presence of a prostate gland in female CAH patients.