To characterize the imaging features of patients with pathologically confirmed intraosseous schwannoma (IOS), institutional pathology and imaging databases were searched for IOS cases over a period of 17 years. A musculoskeletal radiologist evaluated all imaging studies. Additionally, a literature search was performed to identify IOS cases that had imaging findings of at least two modalities. Six patients (one female, five males, mean age of 50 ± 14 years) with IOS were identified, with all lesions localized to the lumbosacral region. Radiographic imaging was available in four patients, while all patients underwent CT and MR imaging. Radiographs depicted lytic lesions, and CT depicted heterogeneous expansile lesions with centrally hypodense areas and peripheral sclerosis. All cases involved extra-osseous extension, producing a mass effect on adjacent soft tissues and nerve roots. On MRI, the neoplasms displayed iso- to- slightly- low signal intensity on T1-weighted images and hyperintense signal intensity on T2-weighted images with heterogeneous enhancement. The literature review resulted in 102 IOS cases, which to the best of our knowledge, is the largest review on IOS, and the imaging findings of the previously published cases were the same as our cases. IOSs are rare benign neoplasms that should be considered in the differential diagnosis of well-defined expansile lytic lesions with sclerotic borders. This is particularly important in middle-aged adults with mandibular, sacral, or vertebral body mass.

Artificial intelligence (AI) has attracted increasing attention as a tool for the detection and management of several medical conditions. Multiple myeloma (MM), a malignancy characterized by uncontrolled proliferation of plasma cells, is one of the most common hematologic malignancies, which relies on imaging for diagnosis and management. We aimed to review the current literature and trends in AI research of MM imaging. This study was performed according to the PRISMA guidelines. Three main concepts were used in the search algorithm, including “artificial intelligence” in “radiologic examinations” of patients with “multiple myeloma”. The algorithm was used to search the PubMed, Embase, and Web of Science databases. Articles were screened based on the inclusion and exclusion criteria. In the end, we used the checklist for Artificial Intelligence in Medical Imaging (CLAIM) criteria to evaluate the manuscripts. We provided the percentage of studies that were compliant with each criterion as a measure of the quality of AI research on MM. The initial search yielded 977 results. After reviewing them, 14 final studies were selected. The studies used a wide array of imaging modalities. Radiomics analysis and segmentation tasks were the most popular studies (10/14 studies). The common purposes of radiomics studies included the differentiation of MM bone lesions from other lesions and the prediction of relapse. The goal of the segmentation studies was to develop algorithms for the automatic segmentation of important structures in MM. Dice score was the most common assessment tool in segmentation studies, which ranged from 0.80 to 0.97. These studies show that imaging is a valuable data source for medical AI models and plays an even greater role in the management of MM.

A heterogeneous group of tumors can affect the posteromedial chest wall. They form diverse groups of benign and malignant (primary or secondary) pathologies that can arise from different chest wall structures, i.e., fat, muscular, vascular, osseous, or neurogenic tissues. Chest radiography is very nonspecific for the characterization of chest wall lesions. The modality of choice for the initial assessment of the chest wall lesions is computed tomography (CT). More advanced cross-sectional modalities such as magnetic resonance imaging (MRI) and positron emission tomography (PET) with fluorodeoxyglucose are usually used for further characterization, staging, treatment response, and assessment of recurrence. A systematic approach based on age, clinical history, and radiologic findings is required for correct diagnosis. It is essential for radiologists to be familiar with the spectrum of lesions that might affect the posteromedial chest wall and their characteristic imaging features. Although the imaging findings of these tumors can be nonspecific, cross-sectional imaging helps to limit the differential diagnosis and determine the further diagnostic investigation (e.g., image-guided biopsy). Specific imaging findings, e.g., location, mineralization, enhancement pattern, and local invasion, occasionally allow a particular diagnosis. This article reviews the posteromedial chest wall anatomy and different pathologies. We provide a combination of location and imaging features of each pathology. We will also explore the role of imaging and its strengths and limitations for diagnosing posteromedial chest wall lesions.

Background: Cutaneous manifestations of systemic diseases are diverse and sometimes precede more serious diseases and symptomatology. Similarly, radiologic imaging plays a key role in early diagnosis and determination of the extent of systemic involvement. Simultaneous awareness of skin and imaging manifestations can help the radiologist to narrow down differential diagnosis even if imaging findings are nonspecific. Aims: To improve diagnostic accuracy and patient care, it is important that clinicians and radiologists be familiar with both cutaneous and radiologic features of various systemic disorders. This article reviews cutaneous manifestations and imaging findings of commonly encountered systemic diseases. Conclusions: Familiarity with the most disease-specific skin lesions help the radiologist pinpoint a specific diagnosis and consequently, in preventing unnecessary invasive workups and contributing to improved patient care.