Gallbladder adenocarcinoma is an uncommon and serious disease. The primary disease grows rapidly with local invasion into the liver and with distant spread to lymph nodes. It is often detected late, due to which management can be challenging. Despite routine use of computed tomography (CT) and ultrasonography (US) for detection, magnetic resonance imaging (MRI) is often considered for a detailed assessment of the anatomic behavior of these tumors. We share three cases where 18-FDG PET/CT played a role in management thereof.
Imaging Considerations
18F-FDG PET/CT Imaging of Gallbladder Adenocarcinoma – A Pictorial Review
Diagnostic yield of FDG PET/CT, MRI, and CSF cytology in nonbiopsiable Neurolymphomatosis as a heralding feature of Diffuse B-cell Lymphoma recurrence.
Neurolymphomatosis (NL) is a rare condition associated with lymphomas in which various structures of the nervous system are infiltrated by malignant lymphocytes. Rarely, it may be the presenting feature of recurrence of lymphoma otherwise deemed to be in remission. It is crucial, as is the case with all types of nodal or visceral involvement of lymphoma, to identify the disease early and initiate treatment with chemotherapy and/or radiation therapy. Positron emission tomography-computed tomography (PET-CT) has been shown to be a sensitive modality for staging, restaging, biopsy guidance, therapy response assessment, and surveillance for recurrence of lymphoma. Magnetic resonance imaging (MRI) is another useful imaging modality, which, along with PET/CT, compliment cerebrospinal spinal fluid (CSF) cytology and electromyography (EMG) in the diagnosis of NL. Performing nerve biopsies to confirm neurolymphomatosis can be challenging and with associated morbidity. The case presented herein illustrates the practical usefulness of these tests in detecting NL as a heralding feature of lymphoma recurrence, especially in the absence of histopathologic correlation.
Quantitative Imaging Analysis of FDG PET/CT Imaging for Detection of Central Neurolymphomatosis in a Case of Recurrent Diffuse B-Cell Lymphoma
Neurolymphomatosis (NL) is a rare disease characterized by malignant lymphocytes infiltrating various structures of the nervous system. It typically manifests as a neuropathy involving the peripheral nerves, nerve roots, plexuses, or cranial nerves. It often presents as a complication of lymphoma, but it can be the presenting feature of recurrent lymphoma. It is essential to identify and initiate treatment early with chemotherapy and/or radiation therapy in all cases of nodal or visceral (including neural) involvement with lymphoma. There are various diagnostic tests that can be used for its detection, such as cerebrospinal spinal fluid (CSF) cytology, electromyography (EMG), magnetic resonance imaging (MRI), and positron-emission tomography/computed tomography (PET/CT). FDG-PET/CT is the standard of care in lymphoma staging, restaging, and therapy response assessment, but has an inherent limitation in the detection of disease involvement in the central nervous system. While that is mostly true for visual assessment, there are quantitative methods to measure variation in the metabolic activity in the brain, which in turn helps detect the occurrence of neurolymphomatosis.
Multimodality Imaging for Malignant transformation assessment in Neurofibromatosis type 1
Imaging, Endoscopic and Genetic Assessment of Marfan Syndrome Presenting with Sigmoid Volvulus: A Case Review
The Marfan syndrome (MFS) is a pleiotropic, autosomal dominant disorder of connective tissue with highly variable clinical manifestations. It primarily involves the skeletal, cardiovascular, and ocular systems; however, gastrointestinal complications are rare. Herein, we describe the case of a 31-year-old male who initially presented with acute abdominal pain for one day. His imaging features revealed a dilated sigmoid colon, consistent with sigmoid volvulus that was immediately decompressed. Surgical resection was recommended to treat the sigmoid volvulus. Preceding the treatment, the patient underwent an extensive workup, including an echocardiography that revealed aortic root dilatation. His clinical history, physical exam, and echocardiographic findings raised the suspicion for MFS. Subsequently, the diagnosis of MFS was confirmed on genetic testing. This is a case that highlights the multidisciplinary (clinical, radiological, endoscopic, molecular/genetic) approach to diagnose a patient with MFS who presented with symptomatic sigmoid volvulus. As this presentation may be a harbinger of more severe manifestations of MFS, it is important to identify it as such in order to accomodate for timely management.
Value-Based Assessment of Radiology Reporting Using Radiologist-Referring Physician Two-Way Feedback System—a Design Thinking-Based Approach
In the era of value-based healthcare, many aspects of medical care are being measured and assessed to improve quality and reduce costs. Radiology adds enormously to health care costs and is under pressure to adopt a more efficient system that incorporates essential metrics to assess its value and impact on outcomes. Most current systems tie radiologists’ incentives and evaluations to RVU-based productivity metrics and peer-review-based quality metrics. In a new potential model, a radiologist’s performance will have to increasingly depend on a number of parameters that define “value,” beginning with peer review metrics that include referrer satisfaction and feedback from radiologists to the referring physician that evaluates the potency and validity of clinical information provided for a given study. These new dimensions of value measurement will directly impact the cascade of further medical management. We share our continued experience with this project that had two components: RESP (Referrer Evaluation System Pilot) and FRACI (Feedback from Radiologist Addressing Confounding Issues), which were introduced to the clinical radiology workflow in order to capture referrer-based and radiologist-based feedback on radiology reporting. We also share our insight into the principles of design thinking as applied in its planning and execution.
Technical Challenges in the Clinical Application of Radiomics
Radiomics is a quantitative approach to medical image analysis targeted at deciphering the morphologic and functional features of a lesion. Radiomic methods can be applied across various malignant conditions to identify tumor phenotype characteristics in the images that correlate with their likelihood of survival, as well as their association with the underlying biology. Identifying this set of characteristic features, called tumor signature, holds tremendous value in predicting the behavior and progression of cancer, which in turn has the potential to predict its response to various therapeutic options. We discuss the technical challenges encountered in the application of radiomics, in terms of methodology, workflow integration, and user experience, that need to be addressed to harness its true potential.
Computer-Assisted Learning Applications in Health Educational Informatics: A Review.
Computer-assisted learning (CAL) as a health informatics application is a useful tool for medical students in the era of expansive knowledge bases and the increasing need for and the consumption of automated and interactive systems. As the scope and breadth of medical knowledge expand, the need for additional learning outside of lecture hours is becoming increasingly important. CAL can be an impactful adjunct to conventional methods that currently exist in the halls of learning. There is an increasing body of literature that suggests that CAL should be a commonplace and the recommended method of learning for medical students. Factors such as technical issues that hinder the performance of CAL are also evaluated. We conclude by encouraging the use of CAL by medical students as a highly beneficial method of learning that complements and enhances lectures and provides intuitive, interactive modulation of a self-paced curriculum based on the individual’s academic abilities.
Differential diagnosis of mild cognitive impairment and Alzheimer’s disease using structural MRI cortical thickness, hippocampal shape, hippocampal texture, and volumetry
This paper presents a brain T1-weighted structural magnetic resonance imaging (MRI) biomarker that combines several individual MRI biomarkers (cortical thickness measurements, volumetric measurements, hippocampal shape, and hippocampal texture). The method was developed, trained, and evaluated using two publicly available reference datasets: a standardized dataset from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the imaging arm of the Australian Imaging Biomarkers and Lifestyle flagship study of ageing (AIBL). In addition, the method was evaluated by participation in the Computer-Aided Diagnosis of Dementia (CADDementia) challenge. Cross-validation using ADNI and AIBL data resulted in a multi-class classification accuracy of 62.7% for the discrimination of healthy normal controls (NC), subjects with mild cognitive impairment (MCI), and patients with Alzheimer’s disease (AD). This performance generalized to the CADDementia challenge where the method, trained using the ADNI and AIBL data, achieved a classification accuracy 63.0%. The obtained classification accuracy resulted in a first place in the challenge, and the method was significantly better (McNemar’s test) than the bottom 24 methods out of the total of 29 methods contributed by 15 different teams in the challenge. The method was further investigated with learning curve and feature selection experiments using ADNI and AIBL data. The learning curve experiments suggested that neither more training data nor a more complex classifier would have improved the obtained results. The feature selection experiment showed that both common and uncommon individual MRI biomarkers contributed to the performance; hippocampal volume, ventricular volume, hippocampal texture, and parietal lobe thickness were the most important. This study highlights the need for both subtle, localized measurements and global measurements in order to discriminate NC, MCI, and AD simultaneously based on a single structural MRI scan. It is likely that additional non-structural MRI features are needed to further improve the obtained performance, especially to improve the discrimination between NC and MCI.