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Background: Identification of patients with high-risk of experiencing inability to walk after surgery is important for surgeons to make therapeutic strategies for patients with metastatic spinal disease. However, there is a lack of clinical tool to assess postoperative ambulatory status for those patients. The emergence of artificial intelligence (AI) brings a promising opportunity to develop accurate prediction models., Methods: This study collected 455 patients with metastatic spinal disease who underwent posterior decompressive surgery at three tertiary medical institutions. Of these, 220 patients were collected from one medical institution to form the model derivation cohort, while 89 and 146 patients were collected from two other medical institutions to form the external validation cohorts 1 and 2, respectively. Patients in the model derivation cohort were used to develop and internally validate models. To establish the interactive AI platform, machine learning techniques were used to develop prediction models, including logistic regression (LR), decision tree (DT), random forest (RF), extreme gradient boosting machine (eXGBM), support vector machine (SVM), and neural network (NN). Furthermore, to enhance the resilience of the study's model, an ensemble machine learning approach was employed using a soft-voting method by combining the results of the above six algorithms. A scoring system incorporating 10 evaluation metrics was used to comprehensively assess the prediction performance of the developed models. The scoring system had a total score of 0 to 60, with higher scores denoting better prediction performance. An interactive AI platform was further deployed via Streamlit. The prediction performance was compared between medical experts and the AI platform in assessing the risk of experiencing postoperative inability to walk among patients with metastatic spinal disease., Results: Among all developed models, the ensemble model outperformed the six other models with the highest score of 57, followed by the eXGBM model (54), SVM model (50), and NN model (50). The ensemble model had the best performance in accuracy and calibration slope, and the second-best performance in precise, recall, specificity, area under the curve (AUC), Brier score, and log loss. The scores of the LR model, RF model, and DT model were 39, 46, and 26, respectively. External validation demonstrated that the ensemble model had an AUC value of 0.873 (95% CI: 0.809-0.936) in the external validation cohort 1 and 0.924 (95% CI: 0.890-0.959) in the external validation cohort 2. In the new ensemble machine learning model excluding the feature of the number of comorbidities, the AUC value was still as high as 0.916 (95% CI: 0.863-0.969). In addition, the AUC values of the new model were 0.880 (95% CI: 0.819-0.940) in the external validation cohort 1 and 0.922 (95% CI: 0.887-0.958) in the external validation cohort 2, indicating favorable generalization of the model. The interactive AI platform was further deployed online based on the final machine learning model, and it was available at https://postoperativeambulatory-izpdr6gsxxwhitr8fubutd.streamlit.app/ . By using the AI platform, researchers were able to obtain the individual predicted risk of postoperative inability to walk, gain insights into the key factors influencing the outcome, and find the stratified therapeutic recommendations. The AUC value obtained from the AI platform was significantly higher than the average AUC value achieved by the medical experts ( P <0.001), denoting that the AI platform obviously outperformed the individual medical experts., Conclusions: The study successfully develops and validates an interactive AI platform for evaluating the risk of postoperative loss of ambulatory ability in patients with metastatic spinal disease. This AI platform has the potential to serve as a valuable model for guiding healthcare professionals in implementing surgical plans and ultimately enhancing patient outcomes., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)

Background Context: Intraoperative blood loss is a significant concern in patients with metastatic spinal disease. Early identification of patients at high risk of experiencing massive intraoperative blood loss is crucial as it allows for the development of appropriate surgical plans and facilitates timely interventions. However, accurate prediction of intraoperative blood loss remains limited based on prior studies., Purpose: The purpose of this study was to develop and validate a web-based artificial intelligence (AI) model to predict massive intraoperative blood loss during surgery for metastatic spinal disease., Study Design/setting: An observational cohort study., Patient Sample: Two hundred seventy-six patients with metastatic spinal tumors undergoing decompressive surgery from two hospitals were included for analysis. Of these, 200 patients were assigned to the derivation cohort for model development and internal validation, while the remaining 76 were allocated to the external validation cohort., Outcome Measures: The primary outcome was massive intraoperative blood loss defined as an estimated blood loss of 2,500 cc or more., Methods: Data on patients' demographics, tumor conditions, oncological therapies, surgical strategies, and laboratory examinations were collected in the derivation cohort. SMOTETomek resampling (which is a combination of Synthetic Minority Oversampling Technique and Tomek Links Undersampling) was performed to balance the classes of the dataset and obtain an expanded dataset. The patients were randomly divided into two groups in a proportion of 7:3, with the most used for model development and the remaining for internal validation. External validation was performed in another cohort of 76 patients with metastatic spinal tumors undergoing decompressive surgery from a teaching hospital. The logistic regression (LR) model, and five machine learning models, including K-Nearest Neighbor (KNN), Decision Tree (DT), XGBoosting Machine (XGBM), Random Forest (RF), and Support Vector Machine (SVM), were used to develop prediction models. Model prediction performance was evaluated using area under the curve (AUC), recall, specificity, F1 score, Brier score, and log loss. A scoring system incorporating 10 evaluation metrics was developed to comprehensively evaluate the prediction performance., Results: The incidence of massive intraoperative blood loss was 23.50% (47/200). The model features were comprised of five clinical variables, including tumor type, smoking status, Eastern Cooperative Oncology Group (ECOG) score, surgical process, and preoperative platelet level. The XGBM model performed the best in AUC (0.857 [95% CI: 0.827, 0.877]), accuracy (0.771), recall (0.854), F1 score (0.787), Brier score (0.150), and log loss (0.461), and the RF model ranked second in AUC (0.826 [95% CI: 0.793, 0.861]) and precise (0.705), whereas the AUC of the LR model was only 0.710 (95% CI: 0.665, 0.771), the accuracy was 0.627, the recall was 0.610, and the F1 score was 0.617. According to the scoring system, the XGBM model obtained the highest total score of 55, which signifies the best predictive performance among the evaluated models. External validation showed that the AUC of the XGBM model was also up to 0.809 (95% CI: 0.778, 0.860) and the accuracy was 0.733. The XGBM model, was further deployed online, and can be freely accessed at https://starxueshu-massivebloodloss-main-iudy71.streamlit.app/., Conclusions: The XGBM model may be a useful AI tool to assess the risk of intraoperative blood loss in patients with metastatic spinal disease undergoing decompressive surgery., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: This study aimed to evaluate the perioperative safety and efficacy of the Mini-open and trans-tubular approach in patients with spinal metastases who underwent decompression surgery., Methods: 37 consecutive patients with spinal metastases who underwent decompression surgery through a Mini-open or trans-tubular approach were retrospectively reviewed between June 2017 and June 2022. Thirty-four patients were included in this study. 19 underwent decompression surgery through the Mini-open approach, and 15 underwent the Trans-tubular approach. T-test and chi-square test were used to evaluate the difference between baseline data and primary and secondary outcomes., Results: Baseline characteristics did not differ significantly between Trans-tubular and Mini-open groups except for the Ambulatory status (P < 0.001). There was no significant difference in blood loss between the two groups (P = 0.061). Operative time, intraoperative blood transfusion, intraoperative complication (dural tear), and postoperative hospitalization were comparable in the two groups (P > 0.05). The trans-tubular group had significantly less amount of postoperative drainage (133.5 ± 30.9 ml vs. 364.5 ± 64.2 ml, p = 0.003), and the time of drainage (3.1 ± 0.2 days vs. 4.6 ± 0.5 days, p = 0.019) compared with Mini-open group (P < 0.05). Sub-group analysis showed that for patients with hypo-vascular tumors, the Trans-tubular group had significantly less blood loss than the Mini-open group (951.1 ± 171.7 ml vs. 1599.1 ± 105.7 ml, P = 0.026)., Conclusions: Decompression through Mini-open or Trans-tubular was safe and effective for patients with spinal metastases. The trans-tubular approach might be more suitable for patients with hypo-vascular tumors., (© 2023. The Author(s).)

Background: The study aimed to identify the risk factors of cement leakage following percutaneous vertebroplasty for spinal metastases., Methods: 230 consecutive patients with 530 vertebrae were retrospectively reviewed. Characteristics including age, primary cancer, location, pathological fracture, the integrity of the posterior wall, and the volume of bone cement were considered as potential risk factors. Cement leakage was evaluated by postoperative imaging examination and classified into three subtypes with different potential sequelae: spinal canal leakage, intravascular leakage around vertebrae, intradiscal and paravertebral leakage. Univariate and multivariate analyses were used to assess the risk factors., Results: Leakage was detected in 185 vertebrae (34.9%), 18.3% for intradiscal and paravertebral, 13.2% for intravascular around vertebrae, and 7.0% for spinal canal. Multivariate analysis showed that incomplete posterior wall (P = 0.001) and breast cancer (P = 0.015) were strong predictive factors for spinal canal leakage, incomplete posterior wall (P = 0.024) was for intravascular leakage around vertebrae, thoracic (P = 0.010) and pathological fracture (P = 0.000) were for intradiscal and paravertebral leakage., Conclusions: Our findings suggest that cement leakage is common following percutaneous vertebroplasty for spinal metastases. The incomplete posterior wall is an unfavourable factor for intravascular leakage around vertebrae. Vertebrae with incomplete posterior wall and breast cancer metastases are more likely to develop spinal canal leakage., Competing Interests: Conflict of interest The authors declare that they have no conflict of interest., (Copyright © 2020 The Japanese Orthopaedic Association. Published by Elsevier B.V. All rights reserved.)

Background: Blood loss in posterior surgery patients with thoracolumbar metastasis posed a significant challenge to surgeons. This study aimed to explore the risk factors of blood loss in posterior surgery for patients with thoracolumbar metastasis., Methods: One hundred forty-two patients were retrospectively reviewed. Their baseline characteristics were recorded. The Gross equation was used to calculate blood loss on a surgical day. Multivariate linear regression was used to analyze the risk factors., Results: Mean blood loss of 142 patients were 2055 ± 94 ml. Hypervascular primary tumor (kidney, thyroid and liver) (P = 0.017), wide or marginal excision (en-bloc: P = 0.001), metastasis at the lumbar spine (P = 0.033), and the presence of extraosseous tumor mass (P = 0.012) were independent risk factors of blood loss in the posterior surgery. Sub-analysis showed that wide or marginal excision (en-bloc: P < 0.001) and metastasis at lumbar spine (P = 0.007) were associated with blood loss for patients with non-hyper vascular primary tumors. Wide or marginal excision (piece-meal: P = 0.014) and the presence of an extraosseous tumor mass (P = 0.034) were associated with blood loss for patients with hypervascular primary tumors., Conclusion: Hypervascular primary tumor (kidney, thyroid, and liver) was an independent risk factor of blood loss in the posterior surgery. The presence of extraosseous tumor mass and wide or marginal excision (piece-meal) were independent risk factors for patients with hypervascular primary tumors. Metastasis at the lumbar spine and wide or marginal excision (en-bloc) were independent risk factors for patients with non-hyper vascular primary tumors., (© 2021. The Author(s).)

Background: Cement leakage into venous blood posed significant challenge to surgeons. The aim of the study was to create a Peking University First Hospital Score (PUFHS) which could evaluate the probability of vascular cement leakage among spine metastases patients following percutaneous vertebroplasty., Methods: The study retrospectively enrolled 272 spine metastases patients treated with percutaneous vertebroplasty. We randomly extracted all enrolled patients as the training or validation group and baseline characteristic comparison was assessed between the two groups. Creation of the PUFHS was performed in the training group and validation of the PUFHS was performed in the validation group., Results: Of all the 272 patients, the total number of included vertebrae was 632 and the median treated levels were 2 per patient. Vascular cement leakage occurred in 26.47% (72/272) of patients. The baseline characteristics were comparable between the two groups (P > 0.05). Three risk predictors (primary cancer types, number of treated vertebrae levels, and vertebrae collapse) were included in the PUFHS. The area under the receiver operating characteristic curve (AUROC) of the PUFHS was 0.71 in the training group and 0.69 in the validation group. The corresponding correct classification rates were 73.0 and 70.1%, respectively. The calibration slope was 0.78 (95% confidence interval[CI]: 0.45-1.10) in the training group and 1.10 (95% CI: 0.73-1.46) in the validation group. The corresponding intercepts were 0.06 (95% CI: - 0.04-0.17) and - 0.0079 (95% CI: - 0.11-0.092), respectively., Conclusions: Vascular cement leakage is common among spine metastases after percutaneous vertebroplasty. The PUFHS can calculate the probability of vascular cement leakage, which can be a useful tool to inform surgeons about vascular cement leakage risk in advance.

Various survival scoring systems have been developed to help surgeons select the best candidates for appropriate therapies in patients with metastatic spinal disease. This study aims to discuss the current status and future directions of scoring systems for the prediction of survival prognosis in these patients. The search terms "spine metastases," "metastatic spinal disease," and "metastatic spinal cord compression" were combined with "survival prognosis," "scoring system," and "score" to elicit relevant literatures in PubMed and Embase databases. As a result, 159 articles were selected from PubMed, and 246 articles were extracted from Embase. After reviewing each article, we carefully included and analyzed 74 articles about the development and evaluation of scoring systems for predicting survival prognosis in spine metastases. In this review, those scoring systems were stratified into the historic scoring systems and the modern scoring systems on the basis of the proposed time. The historic scoring systems, including the original/revised Tokuhashi scoring system, the Bauer scoring system, the Tomita scoring system, and the Linden scoring system, and the modern scoring systems, such as the Lei scoring system, the Bartels scoring system, the Mizumoto scoring system, the Bollen scoring system, the Rades scoring system, Oswestry Spinal Risk Index, and the Choi risk calculator, were introduced and discussed in this review. Besides, the clinical effectiveness and pitfalls of the existing systems and the future directions of the next generation of scoring systems were also addressed and discussed. We recommended these scoring systems as preferable reference tools to help doctors to select surgical candidates. In patients with long-term life expectancy, radical surgery, such as wide or marginal excision, can be considered in patients with neurological deficits, spine instability, or severe back pain. Besides, with the advancement and improvement of medical technologies, surgical procedures are changing, which can affect surgical indications such as vertebroplasty, minimal invasive surgery, and percutaneous stabilization, which can also be used in patients with spine instability or severe back pain, and do not require much recovery; hence, they can even be used in patients with relative short-term life expectancy. However, the decision about the treatment of patients with metastatic spinal disease is so complicated and should never rely on prognostic scores alone. The final therapeutic decision should be made by interdisciplinary corporations of oncologists, radiologists, and spinal surgeons. Besides, individual intentions should be respected.