Bukowiec, Lainey G

ABSTRACT Background and Objectives Non‐rhabdomyosarcoma soft tissue sarcomas (NRSTS) account for less than 4% of pediatric cancers. Treatment and outcomes have been described in adults, but pediatric data are lacking. The purpose of this study is to review our institution's outcomes of extremity NRSTS in pediatric patients. Methods Ninety‐eight (56 male: 42 female) pediatric patients with NRSTS of the extremity at our institution were reviewed. Mean age at diagnosis was 11 ± 5 years, and mean follow‐up was 12 ± 10 years. The most common diagnosis was synovial sarcoma (27%), and 60% were high‐grade. Results Treatment included surgical resection in all patients, with 33% receiving radiation and 37% receiving systemic therapy. 20‐year local recurrence‐ (LRFS) and metastatic disease‐free survival (MDFS) were 91% and 91%, respectively, with 92% disease specific survival (DSS). High grade tumors were associated with lower LRFS ( p = 0.02) and DSS ( p = 0.03). Tumor size < 5 cm demonstrated improved MDFS ( p = 0.03), and stage 3 tumors showed worse MDFS ( p < 0.01). Conclusions Treatment of pediatric NRSTS was variable, with large, deep, high‐grade tumors more likely to receive neoadjuvant and adjuvant treatments. Multidisciplinary treatment was associated with high rates of local control and durable survival. This study provides an extended follow‐up of pediatric patients to identify late complications.

Purpose Lower extremity spasticity can cause pain and limit function. This study examined the safety and efficacy of hyperselective neurectomy (HSN) of the gastrocnemius-soleus complex (GSC) in addressing spastic equinus deformities in pediatric patients. Methods Pediatric patients that underwent HSN of the GSC by a single surgeon were retrospectively reviewed. Preoperative and postoperative Modified Ashworth Score and passive ankle dorsiflexion (°) were compared using Wilcoxon signed-rank test and Student's paired two-tailed t-test, respectively. Results Ten lower extremities in seven patients with a mean age of 10 years were reviewed. Mean follow-up was 15 months. All extremities demonstrated improvement in spasticity. All exhibited improved passive ankle dorsiflexion range of motion with the knee in extension (mean change +17°, SD = 8°, p = 0.000). Five patients demonstrated improved passive dorsiflexion with the knee in flexion while two remained unchanged from preoperative assessment (mean change +13°, SD = 9°, p = 0.002). One complication, wound dehiscence, was noted and managed conservatively. No patients reported loss of strength or sensation. Four patients underwent subsequent staged surgery on the ipsilateral extremity for concomitant contractures. Conclusion HSN of the GSC is safe in pediatric patients and effectively reduces spastic equinus deformity and improves ankle passive dorsiflexion in short-term follow-up.

As artificial intelligence (AI) advances in healthcare, encompassing robust applications for the diagnosis and prognostication of musculoskeletal diseases, clinicians must increasingly understand the implications of machine learning and deep learning in their practice. This review article explores computer vision algorithms and patient-specific, multimodal prediction models; provides a simple framework to guide discussion on the limitations of AI model development; and introduces the field of generative AI.

Abstract Morphological hip abnormalities (MHAs) significantly influence lifelong prognosis of the hip, contributing to early-onset osteoarthritis and impaired functionality. Developmental dysplasia of the hip (DDH) and femoroacetabular impingement (FAI) represent key pathologies, resulting from insufficient or excessive femoral head coverage, respectively. These abnormalities alter hip biomechanics, leading to structural damage, pain, and accelerated joint degeneration. Advances in genetic research have illuminated the interplay between genetics and mechanical loading in shaping hip morphology. Genes associated with osteoarthritis, DDH, and FAI include COL1A1, MMP13, and IL-6. Genes associated with FAI and osteoarthritis include ADAMTS4. Genes associated with DDH and osteoarthritis include FRZB, CX3CR1, ASPN, DKK1, PDRG1, GDF5, UQCC1, and TGF-β1. The mechanisms linking morphological derangements to symptomatic osteoarthritis remain incompletely understood. Multimodal approaches integrating imaging, biomechanics, and genetics may uncover distinct disease subtypes, enabling personalized interventions. Early detection of MHAs is critical in preventing early-onset osteoarthritis. Incorporating advanced imaging techniques, such as statistical shape modelling, can enhance the understanding of complex 3D hip morphologies and their progression to osteoarthritis. Future research should explore the genetic underpinnings of other morphologic hip conditions, including Slipped Capital Femoral Epiphysis and Legg–Calvé–Perthes disease, to refine preventive and therapeutic strategies. A comprehensive approach combining genetics, imaging, and clinical insights holds promise for mitigating the lifelong impact of MHAs.

Abstract Deep learning is revolutionizing medical imaging analysis by enabling the classification of various pathoanatomical conditions at scale. Unfortunately, there have been a limited number of accurate and efficient machine learning (ML) algorithms that have been developed for the diagnostic workup of morphological hip pathologies, including developmental dysplasia of the hip and femoroacetabular impingement. The current study reports on the performance of a novel ML model with YOLOv5 and ConvNeXt-Tiny architecture in predicting the morphological features of these conditions, including cam deformity, ischial spine sign, dysplastic appearance, and other abnormalities. The model achieved 78.0% accuracy for detecting cam deformity, 87.2% for ischial spine sign, 76.6% for dysplasia, and 71.6% for all abnormalities combined. The model achieved an Area under the Receiver Operating Curve of 0.89 for ischial spine sign, 0.80 for cam deformity, 0.80 for dysplasia, and 0.81 for all abnormalities combined. Inter-rater agreement among surgeons, assessed using Gwet’s AC1, was substantial for dysplasia (0.83) and all abnormalities (0.88), and moderate for ischial spine sign (0.75) and cam deformity (0.61).