Deep Learning Body Region Classification of MRI and CT Examinations.

This study demonstrates the high performance of deep learning in identification of body regions covering the entire human body from magnetic resonance (MR) and computed tomography (CT) axial images across diverse acquisition protocols and modality manufacturers. Pixel-based analysis of anatomy contained in image sets can provide accurate anatomic labeling. For this purpose, a convolutional neural network (CNN)-based classifier was developed to identify body regions in CT and MRI studies. Seventeen CT (18 MRI) body regions covering the entire human body were defined for the classification task. Three retrospective datasets were built for the AI model training, validation, and testing, with a balanced distribution of studies per body region. The test datasets originated from a different healthcare network than the train and validation datasets. Sensitivity and specificity of the classifier was evaluated for patient age, patient sex, institution, scanner manufacturer, contrast, slice thickness, MRI sequence, and CT kernel. The data included a retrospective cohort of 2891 anonymized CT cases (training, 1804 studies; validation, 602 studies; test, 485 studies) and 3339 anonymized MRI cases (training, 1911 studies; validation, 636 studies; test, 792 studies). Twenty-seven institutions from primary care hospitals, community hospitals, and imaging centers contributed to the test datasets. The data included cases of all sexes in equal proportions and subjects aged from 18 years old to + 90 years old. Image-level weighted sensitivity of 92.5% (92.1-92.8) for CT and 92.3% (92.0-92.5) for MRI and weighted specificity of 99.4% (99.4-99.5) for CT and 99.2% (99.1-99.2) for MRI were achieved. Deep learning models can classify CT and MR images by body region including lower and upper extremities with high accuracy.

Data Sharing of Imaging in an Evolving Health Care World: Report of the ACR Data Sharing Workgroup, Part 2: Annotation, Curation, and Contracting.

A core principle of ethical data sharing is maintaining the security and anonymity of the data, and care must be taken to ensure medical records and images cannot be reidentified to be traced back to patients or misconstrued as a breach in the trust between health care providers and patients. Once those principles have been observed, those seeking to share data must take the appropriate steps to curate the data in a way that organizes the clinically relevant information so as to be useful to the data sharing party, assesses the ensuing value of the data set and its annotations, and informs the data sharing contracts that will govern use of the data. Embarking on a data sharing partnership engenders a host of ethical, practical, technical, legal, and commercial challenges that require a thoughtful, considered approach. In 2019 the ACR convened a Data Sharing Workgroup to develop philosophies around best practices in the sharing of health information. This is Part 2 of a Report on the workgroup's efforts in exploring these issues.

Data Sharing of Imaging in an Evolving Health Care World: Report of the ACR Data Sharing Workgroup, Part 1: Data Ethics of Privacy, Consent, and Anonymization.

Radiology is at the forefront of the artificial intelligence transformation of health care across multiple areas, from patient selection to study acquisition to image interpretation. Needing large data sets to develop and train these algorithms, developers enter contractual data sharing agreements involving data derived from health records, usually with postacquisition curation and annotation. In 2019 the ACR convened a Data Sharing Workgroup to develop philosophies around best practices in the sharing of health information. The workgroup identified five broad domains of activity important to collaboration using patient data: privacy, informed consent, standardization of data elements, vendor contracts, and data valuation. This is Part 1 of a Report on the workgroup's efforts in exploring these issues.

Impingement of lesser trochanter on ischium as a potential cause for hip pain.

The bony anatomy of the hip leads to a limited array of impingement syndromes, more frequently resulting from abnormal contact between the femoral neck and acetabulum. We report an unusual case of osseous impingement between the lesser trochanter and ischium, with involvement of the intervening quadratus femoris muscle. While the prevalence and etiology of this finding is unclear, it may represent a cause for hip pain.

Radiofrequency ablation treatment in proximity to the gallbladder without subsequent acute cholecystitis.

Initial reports have suggested that proximity of liver tumors to the gallbladder may increase the risk for cholecystitis after radiofrequency ablation. A colon adenocarcinoma metastasis to the liver in contact with the gallbladder was successfully treated with radiofrequency ablation without subsequent cholecystitis.

Percutaneous radiofrequency ablation of hepatocellular carcinoma in the presence of portal vein thrombosis.

Portal vein thrombosis (PVT) may not be an absolute contraindication for hepatic radiofrequency ablation (RFA). Although the data are sparse, PVT is commonly considered a contraindication to RFA. PVT has actually been described as a complication following RFA. RFA was used to treat a 3.9 x 2.9 cm primary hepatocellular carcinoma (HCC) in a patient with concomitant PVT without complication. RFA can be safely performed in this setting but further studies could clarify this issue.

Radiofrequency ablation for cancer-associated pain.

Many treatment options are available for the management of cancer pain including drugs, local excision, radiation, brachytherapy, and nerve blocks. Percutaneous radiofrequency ablation has been used to treat painful neurologic and bone lesions and thus could potentially be used to treat cancer pain in other sites. Two superficial subcutaneous metastatic nodules were treated with percutaneous radiofrequency ablation. The patient received significant pain relief and improved quality of life.