Asian Spine Journal最新文献

Study design: Retrospective study.

Purpose: To compare and correlate technetium-99m methylene diphosphonate uptake between benign and metastatic bone lesions using semiquantitative analysis of maximum standard uptake value (SUVmax) and mean Hounsfield unit (HU) in single-photon emission computed tomography-computed tomography (SPECT-CT).

Overview of literature: Qualitative interpretation of metastatic bone lesions in breast cancer on bone scintigraphy is often complicated by coexisting benign lesions.

Methods: In total, 185 lesions were identified on bone and SPECT-CT scans from 32 patients. Lesions were classified as metastatic (109 sclerotic lesions) and benign (76 lesions) morphologically on low-dose CT. Semiquantitative analysis using SUVmax and mean HU was performed on the lesions and compared. To discriminate benign and metastatic lesions, the correlation between SUVmax and mean HU was determined using the intraclass correlation coefficients.

Results: The SUVmax was higher in metastatic lesions (20.66±14.36) but lower in benign lesions (10.18±12.79) (p<0.001). The mean HU was lower in metastatic lesions (166.62±202.02) but higher in benign lesions (517.65±192.8) (p<0.001). A weak negative correlation was found between the SUVmax and the mean HU for benign lesions, and a weak positive correlation was noted between the SUVmax and the mean HU on malignant lesions with no statistical significance (p=0.394 and 0.312, respectively). The cutoff values obtained were 10.8 for SUVmax (82.6% sensitivity and 84.2% specificity) and 240.86 for the mean HU (98.7% sensitivity and 88.1% specificity) in differentiating benign from malignant bone lesions.

Conclusions: Semiquantitative assessment using SUVmax and HU can complement qualitative analysis. Metastatic lesions had higher SUVmax but lower mean HU than benign lesions, whereas benign lesions demonstrated higher mean HU but lower SUVmax. A weak correlation was found between the SUVmax and the mean HU on malignant and benign lesions. Cutoff values of 10.8 for the SUVmax and 240.86 for the mean HU may differentiate bone metastases from benign lesions.

Study design: A retrospective study.

Purpose: This study aimed to develop machine-learning algorithms for predicting survival in patients who underwent surgery for spinal metastasis.

Overview of literature: This study develops machine-learning models to predict postoperative survival in spinal metastasis patients, filling the gaps of traditional prognostic systems. Utilizing data from 389 patients, the study highlights XGBoost and CatBoost algorithms̓ effectiveness for 90, 180, and 365-day survival predictions, with preoperative serum albumin as a key predictor. These models offer a promising approach for enhancing clinical decision-making and personalized patient care.

Methods: A registry of patients who underwent surgery (instrumentation, decompression, or fusion) for spinal metastases between 2004 and 2018 was used. The outcome measure was survival at postoperative days 90, 180, and 365. Preoperative variables were used to develop machine-learning algorithms to predict survival chance in each period. The performance of the algorithms was measured using the area under the receiver operating characteristic curve (AUC).

Results: A total of 389 patients were identified, with 90-, 180-, and 365-day mortality rates of 18%, 41%, and 45% postoperatively, respectively. The XGBoost algorithm showed the best performance for predicting 180-day and 365-day survival (AUCs of 0.744 and 0.693, respectively). The CatBoost algorithm demonstrated the best performance for predicting 90-day survival (AUC of 0.758). Serum albumin had the highest positive correlation with survival after surgery.

Conclusions: These machine-learning algorithms showed promising results in predicting survival in patients who underwent spinal palliative surgery for spinal metastasis, which may assist surgeons in choosing appropriate treatment and increasing awareness of mortality-related factors before surgery.

Study design: Retrospective single-center study.

Purpose: This study aimed to examine the factors associated with the self-image domain of the Scoliosis Research Society-22 revised (SRS-22r) in patients who underwent corrective surgery for adult idiopathic scoliosis (AdIS).

Overview of literature: Adult spinal deformity (ASD) can be classified into AdIS and de novo scoliosis. However, no studies have investigated the effect of different ASD pathologies on self-image.

Methods: This study enrolled 60 patients who underwent corrective surgery and were followed up for >2 years postoperatively. AdIS was defined as adolescent idiopathic scoliosis in patients who had no history of corrective surgery, had a primary thoracolumbar/lumbar (TL/L) curve, and were ≥30 years old at the time of surgery.

Results: The AdIS (n=23; mean age, 53.1 years) and de novo (n=37; mean age, 70.0 years) groups were significantly different in terms of the main thoracic and TL/L curves, sagittal vertical axis, thoracic kyphosis, and thoracolumbar kyphosis preoperatively. The scores in the self-image domain of the SRS-22r (before surgery/2 years after surgery [PO2Y]) were 2.2/4.4 and 2.3/3.7 in the AdIS and de novo groups, respectively, and PO2Y was significantly different between the two groups (p<0.001). Multivariate regression analysis revealed that AdIS was an independent factor associated with self-image at PO2Y (p=0.039).

Conclusions: AdIS, a spinal deformity pathology, was identified as a significant factor associated with the self-image domain of SRS-22r in patients who underwent corrective surgery. AdIS is not solely classified based on pathology but also differs in terms of the clinical aspect of self-image improvement following corrective surgery.

This review comprehensively examines the evolution and current state of interbody cage technology for lumbar interbody fusion (LIF). This review highlights the biomechanical and clinical implications of the transition from traditional static cage designs to advanced expandable variants for spinal surgery. The review begins by exploring the early developments in cage materials, highlighting the roles of titanium and polyetheretherketone in the advancement of LIF techniques. This review also discusses the strengths and limitations of these materials, leading to innovations in surface modifications and the introduction of novel materials, such as tantalum, as alternative materials. Advancements in three-dimensional printing and surface modification technologies form a significant part of this review, emphasizing the role of these technologies in enhancing the biomechanical compatibility and osseointegration of interbody cages. In addition, this review explores the increase in biodegradable and composite materials such as polylactic acid and polycaprolactone, addressing their potential to mitigate long-term implant-related complications. A critical evaluation of static and expandable cages is presented, including their respective clinical and radiological outcomes. While static cages have been a mainstay of LIF, expandable cages are noted for their adaptability to the patient's anatomy, reducing complications such as cage subsidence. However, this review highlights the ongoing debate and the lack of conclusive evidence regarding the superiority of either cage type in terms of clinical outcomes. Finally, this review proposes future directions for cage technology, focusing on the integration of bioactive substances and multifunctional coatings and the development of patient-specific implants. These advancements aim to further enhance the efficacy, safety, and personalized approach of spinal fusion surgeries. Moreover, this review offers a nuanced understanding of the evolving landscape of cage technology in LIF and provides insights into current practices and future possibilities in spinal surgery.

The purpose of this narrative review was to comprehensively elaborate the various components of artificial intelligence (AI), their applications in spine surgery, practical concerns, and future directions. Over the years, spine surgery has been continuously transformed in various aspects, including diagnostic strategies, surgical approaches, procedures, and instrumentation, to provide better-quality patient care. Surgeons have also augmented their surgical expertise with rapidly growing technological advancements. AI is an advancing field that has the potential to revolutionize many aspects of spine surgery. We performed a comprehensive narrative review of the various aspects of AI and machine learning in spine surgery. To elaborate on the current role of AI in spine surgery, a review of the literature was performed using PubMed and Google Scholar databases for articles published in English in the last 20 years. The initial search using the keywords "artificial intelligence" AND "spine," "machine learning" AND "spine," and "deep learning" AND "spine" extracted a total of 78, 60, and 37 articles and 11,500, 4,610, and 2,270 articles on PubMed and Google Scholar. After the initial screening and exclusion of unrelated articles, duplicates, and non-English articles, 405 articles were identified. After the second stage of screening, 93 articles were included in the review. Studies have shown that AI can be used to analyze patient data and provide personalized treatment recommendations in spine care. It also provides valuable insights for planning surgeries and assisting with precise surgical maneuvers and decisionmaking during the procedures. As more data become available and with further advancements, AI is likely to improve patient outcomes.