Excessive lordosis is a common finding and may produce mechanical pressure that causes repetitive strains of the interspinous ligament with subsequent degeneration and collapse. Baastrup’s disease (kissing spine syndrome) is a term referring to close approximation of adjacent spinous processes due to degenerative changes of the spine. Baastrup’s disease usually affects the lumbar spine, with L4-L5 being the most commonly affected level. There is higher occurrence at ages over 70 and no gender predilection. Symptoms include back pain with midline distribution that worsens during extension, is relieved during flexion and is exaggerated upon finger pressure at the level of interest. Diagnosis rests on clinical examination and imaging studies. The hallmark of imaging findings is the close approximation and contact of adjacent spinous processes, with all the subsequent findings including oedema, cystic lesions, sclerosis, flattening and enlargement of the articulating surfaces, bursitis and occasionally epidural cysts or midline epidural fibrotic masses. Proposed therapies include conservative treatment, percutaneous infiltrations or surgical therapies such as excision of the bursa or osteotomy. The purpose of this study is to illustrate the spectrum of imaging findings in Baastrup’s disease and to emphasise upon including the syndrome in the list of potential causes of low-back pain. Teaching Points • Baastrup’s disease refers to close approximation of adjacent spinous processes. • Diagnosis of Baastrup’s disease is verified with clinical examination and imaging studies. • Contact of adjacent spinous processes results in oedema, sclerosis, flattening and enlargement. • Proposed therapies include conservative treatment, percutaneous infiltrations or surgical therapies.

Artificial Intelligence (AI) has emerged as a transformative force in orthopedic oncology, offering significant advances in the diagnosis, classification, and prediction of treatment response for primary malignant bone tumors (PBT). Through machine learning and deep learning techniques, AI leverages computational algorithms and large datasets to enhance medical imaging interpretation and support clinical decision-making. The integration of radiomics with AI enables the extraction of quantitative features from medical images, allowing for precise tumor characterization and the development of personalized therapeutic strategies. Notably, convolutional neural networks have demonstrated exceptional capabilities in pattern recognition, significantly improving tumor detection, segmentation, and differentiation. This narrative review synthesizes the evolving applications of AI in PBTs, focusing on early tumor detection, imaging analysis, therapy response prediction, and histological classification. AI-driven radiomics and predictive models have yielded promising results in assessing chemotherapy efficacy, optimizing preoperative imaging, and predicting treatment outcomes, thereby advancing the field of precision medicine. Innovative segmentation techniques and multimodal imaging models have further enhanced healthcare efficiency by reducing physician workload and improving diagnostic accuracy. Despite these advancements, challenges remain. The rarity of PBTs limits the availability of robust, high-quality datasets for model development and validation, while the lack of standardized imaging protocols complicates reproducibility. Ethical considerations, including data privacy and the interpretability of complex AI algorithms, also warrant careful attention. Future research should prioritize multicenter collaborations, external validation of AI models, and the integration of explainable AI systems into clinical practice. Addressing these challenges will unlock AI’s full potential to revolutionize PBT management, ultimately improving patient outcomes and advancing personalized care.

Most musculoskeletal tumors are hyperintense on T2-weighted images. However, some T2-weighted hypointense tumors and tumor-like lesions are encountered in everyday clinical practice. We explore the spectrum of such T2 hypointense tumors and tumor mimickers that can arise from (1) the bones, presenting as diffuse processes or focal lesions; (2) the joints including diffuse or focal synovial disorders, loose bodies, or substance depositions; and (3) soft tissues, comprising T2 hypointense tumors and tumor mimickers (those that contain abundant fibrous tissue, mineralization, or hemosiderin deposits). Appropriate magnetic resonance imaging sequence selection is required to identify and characterize these lesions confidently when imaging musculoskeletal tumors.

This study reports the reliability of the juvenile idiopathic arthritis magnetic resonance imaging scoring system (JAMRIS-SIJ). The study comprised of eight raters—two rheumatologists and six radiologists—and 30 coronal T1 and Short-Tau Inversion Recovery (STIR) MRI scans of patients with enthesitis-related juvenile spondylarthritis. The median age of patients was 15 years with a mean disease duration of 5 years and 22 (73.3%) of the sample were boys. The inter-rater agreement of scores for each of the JAMRIS-SIJ items was calculated using a two-way random effect, absolute agreement, and single rater intraclass correlation coefficient (ICC 2.1). The ICC was interpreted together with kurtosis, since the ICC is also affected by the distribution of scores in the sample. The eight-rater, single measure inter-rater ICC (and kurtosis) values for JAMRIS-SIJ inflammation and damage components were the following: bone marrow edema (BME), 0.76 (1.2); joint space inflammation, 0.60 (1.8); capsulitis, 0.58 (9.2); enthesitis, 0.20 (0.1); ankylosis, 0.89 (35); sclerosis, 0.53 (4.6); erosion, 0.50 (6.5); fat lesion, 0.40 (21); backfill, 0.38 (38). The inter-rater reliability for BME and ankylosis scores was good and met the a priori set ICC threshold, whereas for the other items it was variable and below the selected threshold. Future directives should focus on refinement of the scores, definitions, and methods of interpretation prior to validation of the JAMRIS-SIJ through the assessment of its measurement properties.