古矢 丈雄

STUDY DESIGN: Retrospective analysis of data collected across multiple centers. OBJECTIVE: To develop machine learning models for predicting neurological outcomes one month postoperatively in patients with metastatic spinal tumors undergoing surgery, and to identify key factors influencing neurological recovery. SUMMARY OF BACKGROUND DATA: The increasing prevalence of spinal metastases has led to a growing need for surgical intervention to address mechanical instability and neurological deficits. Predicting postoperative neurological status, as assessed by the Frankel classification, can provide valuable insights for surgical planning and patient counseling. Traditional prognostic models have shown limitations in capturing the complexity of neurological recovery patterns. METHODS: We analyzed data from 244 patients who underwent spinal surgery for metastatic disease across 38 institutions. The primary outcome was functional ambulation, defined as Frankel grades D or E at one month postoperatively. Four machine learning algorithms (Random Forest, XGBoost, LightGBM, and CatBoost) were used to build predictive models. Feature selection employed the Boruta algorithm and Variance Inflation Factor analysis to reduce multicollinearity. RESULTS: Among the 244 patients, the proportion of ambulatory patients (Frankel grades D or E) increased from 36.8% preoperatively to 63.1% at one month postoperatively. The Random Forest model achieved the highest area under the receiver operating characteristic curve (AUC-ROC) of 0.8516, followed by XGBoost (0.8351), CatBoost (0.8331), and LightGBM (0.8098). SHapley Additive exPlanations analysis identified preoperative Frankel classification, transfer ability, inflammatory markers (C-reactive protein, white blood cell-lymphocyte), and surgical timing as the most important predictors of postoperative outcomes. CONCLUSIONS: Machine learning models showed strong predictive performance in assessing postoperative neurological status for patients with metastatic spinal tumors. Key factors including preoperative neurological function, functional ability, and inflammation markers significantly influenced outcomes. These findings could inform surgical decision-making and help set realistic postoperative expectations while potentially improving patient care through more accurate outcome prediction.

PURPOSE: Cervical spine injuries in the elderly (defined as individuals aged 65 years and older) are increasing, often resulting from falls and minor trauma. Prognosis varies widely, influenced by multiple factors. This study aimed to develop a deep-learning-based predictive model for post-injury outcomes. METHODS: This study analyzed a nationwide dataset from the Japan Association of Spine Surgeons with Ambition, comprising 1512 elderly patients (aged 65 years and older) with cervical spine injuries from 2010 to 2020. Deep learning predictive models were constructed for residence, mobility, and the American Spinal Injury Association Impairment Scale (AIS). The model's performance was compared with that of a traditional statistical analysis. RESULTS: The deep-learning model predicted the residence and AIS outcomes with varying accuracies. The highest accuracy was observed in predicting residence one year post-injury. The model also identified that the AIS score at discharge was significantly predicted by upper extremity trauma, mobility, and elbow extension strength. The deep learning model highlighted factors, such as upper extremity trauma, that were not considered significant in the traditional statistical analysis. CONCLUSION: Our deep learning-based model offers a novel method for predicting outcomes following cervical spine injuries in the elderly population. The model is highly accurate and provides additional insights into potential prognostic factors. Such models can improve patient care and individualize future interventions.

OBJECTIVE: Instrumentation surgery in combination with radiotherapy (RT) is one of the key management strategies for patients with spinal metastases. However, the use of materials can affect the RT dose delivered to the tumor site and surrounding tissues, as well as hinder optimal postoperative tumor evaluation. The association of the preoperative Spine Instability Neoplastic Score (SINS) with the need for spinal stabilization and life expectancy are unclear. This multicenter prospective study aimed to investigate the current situation and make recommendations regarding the choice of surgical procedure based on the preoperative SINS and prospectively collected postoperative patient-reported outcomes (PROs). METHODS: The study prospectively included 317 patients with spinal metastases who underwent palliative surgery and had a minimum follow-up period of 6 months. The survey items included SINS, patient background, and clinical data including surgical procedure, history of RT, prognosis, and PROs (i.e., the visual analog scale score, Faces Scale, Barthel Index, Vitality Index, and 5-level EQ-5D health survey) at baseline, and at 1 and 6 months after surgery. The association of preoperative SINS with life expectancy, PROs, and surgical procedures was examined using statistical analysis. RESULTS: Preoperative SINS (three categories) had no association with life expectancy. All PROs evaluated in the study improved up to 6 months after surgery. Pain categories (visual analog scale score and/or Faces Scale) at baseline were correlated with preoperative SINS. As many as 90.9% of enrolled patients underwent fusion surgery, and even in SINS 0-6 cases, implants were used in 64.3% of patients. Postoperative RT was performed in 42.9% of the patients. However, prospective assessments of PROs showed no significant difference between surgical procedures (with and without fusion) in patients with SINS 0-9. In addition, no cases required conversion from noninstrumentation surgery to fusion surgery. CONCLUSIONS: Although the choice of surgical procedure should be made on a case-by-case basis on the NOMS (neurological, oncological, mechanical, and systemic) framework, careful consideration is required to determine whether spinal stabilization is needed in patients with SINS ≤ 9, considering the patient's background and the plan for postoperative adjuvant therapy.