Mesenchymal chondrosarcoma (MCS) is a rare malignant variant of chondrosarcoma with a high tendency of recurrence and metastasis. Intradural extramedullary spinal MCS is exceedingly rare and usually found in pediatric patients. Herein, we present an elderly patient with primary intradural extramedullary spinal MCS. Relevant literatures are reviewed to disclose characteristics of intradural extramedullary spinal MCS. A 64-year-old female presented with urinary difficulty and tightness of upper back preceding progressive weakness of right lower extremity. Magnetic resonance imaging revealed an intradural extramedullary tumor at the level of 3rd thoracic vertebra. This patient underwent total tumor resection and then received adjuvant radiotherapy. Histopathological examination showed that the tumor composed of spindle and round cells with high nucleocytoplasmic ratio accompanied by scattered eosinophilic chondroid matrix. Along with immunohistochemical findings and the existence of HEY1-NCOA2 fusion transcript, the diagnosis of MCS was confirmed. Neurologic deficit recovered nearly completely after surgery. No evidence of local recurrence or distant metastasis was found 5 years after treatments. Including the current case, a total of 18 cases have been reported in the literature with only one case with local recurrence and one case of mortality. The current case was the eldest patient diagnosed with primary intraspinal MCS in the literature. MCS rarely appears in the intradural space of the spine. In contrast to classic MCS, treatment outcome of primary intradural extramedullary spinal MCS is usually excellent as total tumor resection is commonly achievable. Adjuvant radiotherapy may reduce local recurrence and chemotherapy may be associated with fewer recurrences especially for unresectable tumors.

Patient-specific instrumentation (PSI)-assisted high tibial osteotomy (HTO) represents a significant advancement in the surgical management of varus knee deformity and medial compartment osteoarthritis. This chapter provides an in-depth, technique-oriented guide covering the full PSI-HTO workflow – from three-dimensional (3D) computed tomography (CT)-based deformity analysis and virtual surgical planning, through computer-aided design (CAD)/computer-aided manufacturing (CAM) guide fabrication, to intraoperative execution, bone gap management, fixation, and rehabilitation. The historical evolution, evidence-based patient selection, biomechanical rationale, comparative performance of PSI against conventional and navigation-assisted techniques, complication management, bone graft selection, long-term survivorship, return-to-sport outcomes, and future directions, including artificial intelligence (AI) and robotic assistance, are all discussed. More than 20 PubMed-indexed references support the evidence base.

Background: Adolescent idiopathic scoliosis (AIS) affects approximately 0.5–5.2% of adolescents. Only a minority of curves progress, and surgery is generally reserved for curves exceeding 45–50°. Selection of the lowest instrumented vertebra (LIV) remains contested and directly influences postoperative coronal balance, distal adding-on (DA) and revision risk. Aim: This narrative review synthesizes the biomechanical principles governing LIV selection, compares traditional anatomical landmarks with contemporary prediction formulas and machine learning (ML) tools, and defines what these tools can and cannot currently deliver at the bedside. Methods: A structured but deliberately non-systematic search of PubMed/MEDLINE, EMBASE and the Cochrane Library (January 2000–December 2024) was supplemented by purposive inclusion of seminal pre-2000 work. No PRISMA-compliant screening, risk-of-bias appraisal or quantitative synthesis was undertaken, and the article is therefore reported as a narrative review. Results: Traditional landmarks—end, neutral, stable, last touched (LTV) and last substantially touched vertebra (LSTV)—are compared; fusion at or distal to the LTV/LSTV lowers DA incidence, yet 8–28% of patients still develop DA. Prediction formulas estimate the residual lumbar curve to within approximately 6°, but each was derived within a single surgical team and none nominates a vertebral level. A composite index combining (LIV− Neutral vertebra) and (LIV− Stable vertebra) reported sensitivity 100% and specificity 92–94% in its derivation cohort, without external validation. Hypercorrection of the main thoracic curve (>53%) combined with postoperative LIV tilt < 10° is associated with DA. Machine learning models predict three-dimensional alignment within 5–7°, yet every published model predicts an outcome; none has shown that acting on a model-derived recommendation prevents DA. Conclusions: LIV selection is best framed as a multidimensional judgment integrating coronal balance, vertebral body rotation, sagittal alignment, skeletal maturity, lumbopelvic morphology and non-radiographic factors such as paraspinal muscle quality, bracing and rehabilitation. Prediction models and artificial intelligence remain hypothesis-generating: external validation and prospective outcome trials are prerequisites before they can be considered practice-changing.