Development of a Prognostic Survival Algorithm for Patients with Metastatic Spine Disease

Revolutionizing spinal interventions: a systematic review of artificial intelligence technology applications in contemporary surgery

Leveraging its ability to handle large and complex datasets, artificial intelligence can uncover subtle patterns and correlations that human observation may overlook. This is particularly valuable for understanding the intricate dynamics of spinal surgery and its multifaceted impacts on patient prognosis. This review aims to delineate the role of artificial intelligence in spinal surgery. A search of the PubMed database from 1992 to 2023 was conducted using relevant English publications related to the application of artificial intelligence in spinal surgery. The search strategy involved a combination of the following keywords: "Artificial neural network," "deep learning," "artificial intelligence," "spinal," "musculoskeletal," "lumbar," "vertebra," "disc," "cervical," "cord," "stenosis," "procedure," "operation," "surgery," "preoperative," "postoperative," and "operative." A total of 1,182 articles were retrieved. After a careful evaluation of abstracts, 90 articles were found to meet the inclusion criteria for this review. Our review highlights various applications of artificial neural networks in spinal disease management, including (1) assessing surgical indications, (2) assisting in surgical procedures, (3) preoperatively predicting surgical outcomes, and (4) estimating the occurrence of various surgical complications and adverse events. By utilizing these technologies, surgical outcomes can be improved, ultimately enhancing the quality of life for patients.

Assessment of survival prediction after surgery in spinal metastases patients using the Global Spine Study Tumor Group (GSTSG) risk calculator; an external validation from a tertiary cancer hospital

PURPOSE

We aim to validate the Global Spine Tumor Study Group (GSTSG) score compared to previous prognostic scoring systems in spinal metastasis.

METHODS

We conducted a retrospective study from January 2013 to December 2022. The survival prediction was compared between the GSTSG, Tomita Score, Revised Tokuhashi Score, and Skeletal Oncology Research Group (SORG) Nomogram. Single-variable factors associated with survival rate were analyzed using univariate Cox regression and multivariable Cox proportional hazard model. Receiver operating characteristic was used for external validity analysis at 3, 6, 12, and 24 months. The overall survival rate was reported using the Kaplan-Meier survival curve.

RESULTS

248 spinal metastasis patients were included. The mean age was 59.23 ± 12.55 years. The mean duration of follow-up time was 470.29 ± 441.98 days. The external validity of GSTSG was the highest at all follow-up times (sufficiently accurate AUC > 0.7), which was about the same as SORG at 3 months (both AUC of GSTSG and SORG = 0.76) and higher than modified Tokuhashi and Tomita score at 12 months (AUC of GSTSG = 0.78, SORG = 0.71, Tomita = 0.64, and modified Tokuhashi = 0.61, respectively).

CONCLUSION

From our study, the Multivariate Cox regression analysis indicates that the significant factors related to survival rate are regular analgesic use of weak opioids, lung metastasis, and previous chemotherapy. Compared to other traditional spinal metastases prognostic scoring systems, GSTSG shows the highest AUC for external validity in all follow-up times up to 24 months.

Which Prognostic Model Best Predicts Poor Prognosis in Patients with Spinal Metastases? A Comparative Analysis of 8 Scoring Systems

BACKGROUND

Existing scoring system's comparative effectiveness in identifying patients with poor prognosis (i.e., <6 months survival) has not been thoroughly explored.

METHODS

We compared the predictive performance of 8 prognostic scoring systems (Tomita, modified Tokuhashi, modified Bauer, Rades, Oncological Spinal Prognostic Index, Lei, New England Spinal Metastasis Score, and the skeletal oncology research group [SORG] nomogram) with the area under the curve (AUC) from receiver operating characteristic curves and evaluated the predictive accuracy for 6-month survival across different primary tumor origins, and 1-month survival. Logistic regression was used to identify factors associated with 6-month survival.

RESULTS

Six hundred forty one patients with spinal metastasis treated between 1994 and 2022 were included. The SORG nomogram showed best performance with low discriminative power in predicting 6-month survival (AUC [95% confidence interval {CI}]: 0.664 [0.584-0.744]). Logistic regression analysis identified significant factors influencing 6-month survival, including primary cancer type in Lei's classification, preoperative Frankel grades C and D, or grades A and B compared with grade E, preoperative white blood cell, preoperative albumin, and preoperative chemotherapy. For 1-month survival predictions, both the SORG nomogram (AUC [95% CI]: 0.750 [0.648-0.851]) and modified Tokuhashi score (AUC [95% CI]: 0.667 [0.552-0.781]) showed significance, albeit with moderate to low discriminative power.

CONCLUSIONS

This study shows that most scoring systems have low discriminative power, with only the SORG nomogram having moderate power for predicting poor prognosis. Recent and future advances in treatment, laboratory markers, and our understanding of tumor biology should be incorporated into prognostic models to improve their accuracy.

Preoperative prediction of early mortality after surgery for spinal metastases

OBJECTIVE

The objective of this study was to provide a convenient preoperative prediction of the risk of early postoperative mortality.

MATERIALS AND METHODS

This retrospective study included patients who underwent surgery for spinal metastasis at our hospital between 2009 and 2021. Preoperative blood test data of all patients were collected, and the survival time was calculated by dividing the blood data. A multivariate analysis was conducted using a Cox proportional hazards model to identify prognostic factors.

RESULTS

The study population included 83 patients (average: 64.5 years), 22 of whom died within 3 months. The most common lesion was the thoracic spine, and incomplete paralysis was observed in 57 patients. The surgical methods included posterior implant fixation (n = 17), posterior decompression (n = 31), and posterior decompression with fixation (n = 35). In the univariate analysis, the presence of abnormal values was significantly associated with postoperative survival in six preoperative blood collection items (hemoglobin, C-reactive protein, albumin, white blood cell, gamma-glutamyl transpeptidase, and lactate dehydrogenase). In a multivariate analysis, four test items (hemoglobin, C-reactive protein, white blood cell, and lactate dehydrogenase) were identified as independent prognostic factors.Comparing cases with ≥3 abnormal values among the above four items (high-risk group; n = 23) and those with ≤2 (low-risk group; n = 60), there was a significant difference in survival time. In addition, it was possible to predict cases of early death within 3 months after surgery with 73% sensitivity and 89% specificity.

CONCLUSIONS

The study showed that four preoperative blood test abnormalities (hemoglobin, C-reactive protein white blood cell, and lactate dehydrogenase) indicated the possibility of early death within 3 months after surgery.

Factors Affecting the Outcome of Spine Metastases: A Single-Center Evaluation in Surgically Treated Patients

BACKGROUND

The estimation of survival is extremely important for metastatic disease in the spine. The aim of this study was to determine the factors affecting the outcome of patients with spinal metastasis, primarily the character of neurologic deficit and the histopathology of the tumor.

METHOD

A retrospective examination was made of 158 patients with spinal metastasis who were followed up in our clinic between 2010 and 2020 and underwent surgical intervention. The patients were examined in respect of demographic characteristics, the primary tumor, comorbidities, preoperative-postoperative visual aAnalog scale scores, preoperative-postoperative neurologic examinations and neurologic deficit if present and ambulation status, postoperative survival duration, tumor localization, characteristics of the surgeries, complications, the Karnofsky Performance Scale, revised Tokuhashi, and Tomita scores.

RESULTS

Spinal metastasis was seen more frequently in males (72.8% male, 27.8% female). Male gender, multiple level involvement, intradural localization, and Karnofsky Performance Scale <70 were seen to cause a shorter survival time. Patients with a primary focus of hematologic malignancy, breast cancer, and lymphoma had a longer survival. The revised Tokuhashi and Tomita scores were observed to be successful in the prediction of survival. A decrease in postoperative visual analog scale score had a positive effect on functional survival. The absence of preoperative neurological deficit and the patient's ability for preoperative and postoperative ambulation affected survival positively. The overall survival period decreased in patients who were mobilized in the early postoperative period but became nonmobile in the late period, and in those who were nonmobile in both the early and late postoperative periods.

CONCLUSIONS

The neurologic and ambulatory status, the Tomita and Tokuhashi scores, intradural localization, and gender are the factors with a significant effect on prognosis.

Developmental and Validation of Machine Learning Model for Prediction Complication after Cervical Spine Metastases Surgery

STUDY DESIGN

This is a retrospective cohort study utilizing machine learning to predict postoperative complications in cervical spine metastases surgery.

OBJECTIVES

The main objective is to develop a machine learning model that accurately predicts complications following cervical spine metastases surgery.

SUMMARY OF BACKGROUND DATA

Cervical spine metastases surgery can enhance quality of life but carries a risk of complications influenced by various factors. Existing scoring systems may not include all predictive factors. Machine learning offers the potential for a more accurate predictive model by analyzing a broader range of variables.

METHODS

Data from January 2012 to December 2020 were retrospectively collected from medical databases. Predictive models were developed using Gradient Boosting, Logistic Regression, and Decision Tree Classifier algorithms. Variables included patient demographics, disease characteristics, and laboratory investigations. SMOTE was used to balance the dataset, and the models were assessed using AUC, F1-score, precision, recall, and SHAP values.

RESULTS

The study included 72 patients, with a 29.17% postoperative complication rate. The Gradient Boosting model had the best performance with an AUC of 0.94, indicating excellent predictive capability. Albumin level, platelet count, and tumor histology were identified as top predictors of complications.

CONCLUSIONS

The Gradient Boosting machine learning model showed superior performance in predicting postoperative complications in cervical spine metastases surgery. With continuous data updating and model training, machine learning can become a vital tool in clinical decision-making, potentially improving patient outcomes.

LEVEL OF EVIDENCE

Level III.

Impact of perioperative hemoglobin-related parameters on clinical outcomes in patients with spinal metastases: identifying key markers for blood management

PURPOSE

Patients with spinal metastases undergoing surgical treatment face challenges related to preoperative anemia, intraoperative blood loss, and frailty, emphasizing the significance of perioperative blood management. This retrospective analysis aimed to assess the correlation between hemoglobin-related parameters and outcomes, identifying key markers to aid in blood management.

METHODS

A retrospective review was performed to identify patients who underwent surgical treatment for spinal metastases. Hb-related parameters, including baseline Hb, postoperative nadir Hb, predischarge Hb, postoperative nadir Hb drift, and predischarge Hb drift (both in absolute values and percentages) were subjected to univariate and multivariate analyses. These analyses were conducted in conjunction with other established variables to identify independent markers predicting patient outcomes. The outcomes of interest were postoperative short-term (6-week) mortality, long-term (1-year) mortality, and postoperative 30-day morbidity.

RESULTS

A total of 289 patients were included. Our study demonstrated that predischarge Hb (OR 0.62, 95% CI 0.44-0.88, P = 0.007) was an independent prognostic factor of short-term mortality, while baseline Hb (OR 0.76, 95% CI 0.66-0.88, P < 0.001) was identified as an independent prognostic factor of long-term mortality. Additionally, nadir Hb drift (OR 0.82, 95% CI 0.70-0.97, P = 0.023) was found to be an independent prognostic factor for postoperative 30-day morbidity.

CONCLUSIONS

This study demonstrated that predischarge Hb, baseline Hb, and nadir Hb drift are prognostic factors for outcomes. These findings provide a foundation for precise blood management strategies. It is crucial to consider Hb-related parameters appropriately, and prospective intervention studies addressing these markers should be conducted in the future.

Artificial Intelligence in Detection, Management, and Prognosis of Bone Metastasis: A Systematic Review

BACKGROUND

Metastasis commonly occur in the bone tissue. Artificial intelligence (AI) has become increasingly prevalent in the medical sector as support in decision-making, diagnosis, and treatment processes. The objective of this systematic review was to assess the reliability of AI systems in clinical, radiological, and pathological aspects of bone metastases.

METHODS

We included studies that evaluated the use of AI applications in patients affected by bone metastases. Two reviewers performed a digital search on 31 December 2023 on PubMed, Scopus, and Cochrane library and extracted authors, AI method, interest area, main modalities used, and main objectives from the included studies.

RESULTS

We included 59 studies that analyzed the contribution of computational intelligence in diagnosing or forecasting outcomes in patients with bone metastasis. Six studies were specific for spine metastasis. The study involved nuclear medicine (44.1%), clinical research (28.8%), radiology (20.4%), or molecular biology (6.8%). When a primary tumor was reported, prostate cancer was the most common, followed by lung, breast, and kidney.

CONCLUSIONS

Appropriately trained AI models may be very useful in merging information to achieve an overall improved diagnostic accuracy and treatment for metastasis in the bone. Nevertheless, there are still concerns with the use of AI systems in medical settings. Ethical considerations and legal issues must be addressed to facilitate the safe and regulated adoption of AI technologies. The limitations of the study comprise a stronger emphasis on early detection rather than tumor management and prognosis as well as a high heterogeneity for type of tumor, AI technology and radiological techniques, pathology, or laboratory samples involved.

Comparison of Classically and Machine Learning Generated Survival Prediction Models for Patients With Spinal Metastasis - A meta-Analysis of Two Recently Developed Algorithms

STUDY DESIGN

A systemic review and a meta-analysis. We also provided a retrospective cohort for validation in this study.

OBJECTIVE

(1) Using a meta-analysis to determine the pooled discriminatory ability of The Skeletal Oncology Research Group (SORG) classical algorithm (CA) and machine learning algorithms (MLA); and (2) test the hypothesis that SORG-CA has less variability in performance than SORG-MLA in non-American validation cohorts as SORG-CA does not incorporates regional-specific variables such as body mass index as input.

METHODS

After data extraction from the included studies, logit-transformation was applied for extracted AUCs for further analysis. The discriminatory abilities of both algorithms were directly compared by their logit (AUC)s. Further subgroup analysis by region (America vs non-America) was also conducted by comparing the corresponding logit (AUC).

RESULTS

The pooled logit (AUC)s of 90-day SORG-CA was .82 (95% confidence interval [CI], .53-.11), 1-year SORG-CA was 1.11 (95% CI, .74-1.48), 90-day SORG-MLA was 1.36 (95% CI, 1.09-1.63), and 1-year SORG-MLA was 1.57 (95% CI, 1.17-1.98). All the algorithms performed better in United States than in Taiwan (P < .001). The performance of SORG-CA was less influenced by a non-American cohort than SORG-MLA.

CONCLUSION

These observations might highlight the importance of incorporating region-specific variables into existing models to make them generalizable to racially or geographically distinct regions.

Machine Learning in Spine Oncology: A Narrative Review

STUDY DESIGN

Narrative Review.

OBJECTIVE

Machine learning (ML) is one of the latest advancements in artificial intelligence used in medicine and surgery with the potential to significantly impact the way physicians diagnose, prognose, and treat spine tumors. In the realm of spine oncology, ML is utilized to analyze and interpret medical imaging and classify tumors with incredible accuracy. The authors present a narrative review that specifically addresses the use of machine learning in spine oncology.

METHODS

This study was conducted in accordance with the Preferred Reporting Items of Systematic Reviews and Meta-Analysis (PRISMA) methodology. A systematic review of the literature in the PubMed, EMBASE, Web of Science, Scopus, and Cochrane Library databases since inception was performed to present all clinical studies with the search terms '[[Machine Learning] OR [Artificial Intelligence]] AND [[Spine Oncology] OR [Spine Cancer]]'. Data included studies that were extracted and included algorithms, training and test size, outcomes reported. Studies were separated based on the type of tumor investigated using the machine learning algorithms into primary, metastatic, both, and intradural. A minimum of 2 independent reviewers conducted the study appraisal, data abstraction, and quality assessments of the studies.

RESULTS

Forty-five studies met inclusion criteria out of 480 references screened from the initial search results. Studies were grouped by metastatic, primary, and intradural tumors. The majority of ML studies relevant to spine oncology focused on utilizing a mixture of clinical and imaging features to risk stratify mortality and frailty. Overall, these studies showed that ML is a helpful tool in tumor detection, differentiation, segmentation, predicting survival, predicting readmission rates of patients with either primary, metastatic, or intradural spine tumors.

CONCLUSION

Specialized neural networks and deep learning algorithms have shown to be highly effective at predicting malignant probability and aid in diagnosis. ML algorithms can predict the risk of tumor recurrence or progression based on imaging and clinical features. Additionally, ML can optimize treatment planning, such as predicting radiotherapy dose distribution to the tumor and surrounding normal tissue or in surgical resection planning. It has the potential to significantly enhance the accuracy and efficiency of health care delivery, leading to improved patient outcomes.

Development and internal validation of machine-learning models for predicting survival in patients who underwent surgery for spinal metastases

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.

Unveiling the natural history of paralysis in metastatic cervical spinal tumor: An experimental study

Introduction

Despite the relatively low prevalence of metastatic cervical spinal tumor, these entities give rise to more profound complications than thoracic and lumbar spinal tumor. However, it is regrettable that experimental investigation has thus far shown a dearth of attention to metastatic cervical spinal tumor.

Research question

What is the conceptualization and realization of quadriparesis resulting from metastatic cervical spinal tumor?

Material and methods

Using Fischer 344 rats as the experimental cohort, this study orchestrated the engraftment of tumor cells procured from the 13762 MAT B III cell line (RRID: CVCL_3475), which represents mammary adenocarcinoma. These cells were engrafted into the vertebrae of the cervical spine. A comprehensive inquiry encompassing behavioral assessments, histological evaluations, and microangiographic analyses conducted after the aforementioned cellular transplantation was subsequently pursued.

Results

The incidence of cervical paralysis was 61.1%. Notably, the evolution of paralysis was unfurled by two distinctive temporal phases within its natural history. A meticulous histological examination facilitated delineation of the tumor's posterior expansion within the spinal canal. Simultaneously, the tumor exhibited anterior and lateral encroachment on the spinal cord, inducing compression from all sides. Augmented by microangiographic investigations, conspicuous attenuation of stained blood vessels within the affected anterior horn and funiculus of the spinal cord was observed.

Discussion and conclusion

The pathological advancement of paralysis stemming from metastatic cervical spinal tumor is now apprehended to unfurl through a biphasic phase. The initial phase is characterized by gradual unfurling spanning several days, juxtaposed against the second phase marked by swift and accelerated progression.

Predictive Modeling for Spinal Metastatic Disease

Spinal metastasis is exceedingly common in patients with cancer and its prevalence is expected to increase. Surgical management of symptomatic spinal metastasis is indicated for pain relief, preservation or restoration of neurologic function, and mechanical stability. The overall prognosis is a major driver of treatment decisions; however, clinicians' ability to accurately predict survival is limited. In this narrative review, we first discuss the NOMS decision framework used to guide decision making in the treatment of patients with spinal metastasis. Given that decision making hinges on prognosis, multiple scoring systems have been developed over the last three decades to predict survival in patients with spinal metastasis; these systems have largely been developed using expert opinions or regression modeling. Although these tools have provided significant advances in our ability to predict prognosis, their utility is limited by the relative lack of patient-specific survival probability. Machine learning models have been developed in recent years to close this gap. Employing a greater number of features compared to models developed with conventional statistics, machine learning algorithms have been reported to predict 30-day, 6-week, 90-day, and 1-year mortality in spinal metastatic disease with excellent discrimination. These models are well calibrated and have been externally validated with domestic and international independent cohorts. Despite hypothesized and realized limitations, the role of machine learning methodology in predicting outcomes in spinal metastatic disease is likely to grow.

Post-operative complications affect survival in surgically treated metastatic spinal cord compression

PURPOSE

The prevalence of metastatic epidural spinal cord compression (MESCC) is increasing globally due to advancements in cancer diagnosis and treatment. Whilst surgery can benefit specific patients, the complication rate can reach up to 34%, with limited reporting on their impact in the literature. This study aims to analyse the influence of major complications on the survival of surgically treated MESCC patients.

METHODS

Consecutive MESCC patients undergoing surgery and meeting inclusion criteria were selected. Survival duration from decompressive surgery to death was recorded. Perioperative factors influencing survival were documented and analysed. Kaplan-Meier survival analysis at one year compared these factors. Univariate and multivariate Cox proportional hazard regression analyses were performed. Additionally, univariate analysis compared complicated and uncomplicated groups.

RESULTS

Seventy-five patients were analysed. Median survival for this cohort was 229 days (95% CI 174-365). Surgical complications, low patient performance, and rapid primary tumour growth were significant perioperative variables for survival in multivariate analyses (p < 0.001, p = 0.003, and p = 0.02, respectively) with a hazard ratio of 3.2, 3.6, and 2.1, respectively. Univariate analysis showed no variables associated with complication occurrence.

CONCLUSION

In this cohort, major surgical complications, patient performance, and primary tumour growth rate were found to be independent factors affecting one year survival. Thus, prioritizing complication prevention and appropriate patient selection is crucial for optimizing survival in this population.

Opportunistic CT for Prediction of Adverse Postoperative Events in Patients with Spinal Metastases

The purpose of this study was to assess the value of body composition measures obtained from opportunistic abdominal computed tomography (CT) in order to predict hospital length of stay (LOS), 30-day postoperative complications, and reoperations in patients undergoing surgery for spinal metastases. 196 patients underwent CT of the abdomen within three months of surgery for spinal metastases. Automated body composition segmentation and quantifications of the cross-sectional areas (CSA) of abdominal visceral and subcutaneous adipose tissue and abdominal skeletal muscle was performed. From this, 31% (61) of patients had postoperative complications within 30 days, and 16% (31) of patients underwent reoperation. Lower muscle CSA was associated with increased postoperative complications within 30 days (OR [95% CI] = 0.99 [0.98-0.99], p = 0.03). Through multivariate analysis, it was found that lower muscle CSA was also associated with an increased postoperative complication rate after controlling for the albumin, ASIA score, previous systemic therapy, and thoracic metastases (OR [95% CI] = 0.99 [0.98-0.99], p = 0.047). LOS and reoperations were not associated with any body composition measures. Low muscle mass may serve as a biomarker for the prediction of complications in patients with spinal metastases. The routine assessment of muscle mass on opportunistic CTs may help to predict outcomes in these patients.

Prediction of Survival Prognosis for Spinal Metastasis From Cancer of Unknown Primary: Derivation and Validation of a Nomogram Model

STUDY DESIGN

Retrospective and prospective cohort study.

OBJECTIVES

Survival estimation is necessary in the decision-making process for treatment in patients with spinal metastasis from cancer of unknown primary (SMCUP). We aimed to develop a novel survival prediction system and compare its accuracy with that of existing survival models.

METHODS

A retrospective derivation cohort of 268 patients and a prospective validation cohort of 105 patients with SMCUP were performed. Univariate and multivariable survival analysis were used to generate independently prognostic variables. A nomogram model for survival prediction was established by integrating these independent predictors based on the size of the significant variables' β regression coefficient. Then, the model was subjected to bootstrap validation with calibration curves and concordance index (C-index). Finally, predictive accuracy was compared with Tomita, revised Tokuhashi and SORG score by the receiver-operating characteristic (ROC) curve.

RESULTS

The survival prediction model included six independent prognostic factors, including pathology (P < .001), visceral metastases (P < .001), Frankel score (P < .001), weight loss (P = .005), hemoglobin (P = .001) and serum tumor markers (P < .001). Calibration curve of the model showed good agreement between predicted and actual mortality risk in 6-, 12-, and 24-month estimation in derivation and validation cohorts. The C-index was .775 in the derivation cohort and .771 in the validation cohort. ROC curve analysis showed that the current model had the best accuracy for SMCUP survival estimation amongst 4 models.

CONCLUSIONS

The novel nomogram system can be applied in survival prediction for SMCUP patients, and furtherly be used to give individualized therapeutic suggestions based on patients' prognosis.

Preoperative assessment and surgical indications: Separation surgery

Neurological compression occurs in 10%-20% of patients who develop spinal metastases. In the last decade, the evolution of oncological diagnostic and medical techniques, the change from conventional external radiation to radiosurgery and the new surgical instruments have meant that the treatment of these patients must be indicated in a personalized manner and by consensus, multidisciplinary way, in specific commissions. Today, the biological state of the patient, the presence of mechanical instability, the neurological assessment and degree of epidural compression, as well as the best prognostic categorization of the tumor, are established as decision factors prior to the indication of surgical treatment, treatment that has passed from a cytoreductive concept to that of a spinal cord release from tumor in order to ensure safe radiosurgery.

[Translated article] Preoperative assessment and surgical indications: Separation surgery

Neurological compression occurs in 10%-20% of patients who develop spinal metastases. In the last decade, the evolution of oncological diagnostic and medical techniques, the change from conventional external radiation to radiosurgery and the new surgical instruments have meant that the treatment of these patients must be indicated in a personalized manner and by consensus, multidisciplinary way, in specific commissions. Today, the biological state of the patient, the presence of mechanical instability, the neurological assessment and degree of epidural compression, as well as the best prognostic categorization of the tumor, are established as decision factors prior to the indication of surgical treatment, treatment that has passed from a cytoreductive concept to that of a spinal cord release from tumor in order to ensure safe radiosurgery.

Determining patients with spinal metastases suitable for surgical intervention: A cost-effective analysis

BACKGROUND

Both nonoperative and operative treatments for spinal metastasis are expensive interventions. Patients' expected 3-month survival is believed to be a key factor to determine the most suitable treatment. However, to the best of our knowledge, no previous study lends support to the hypothesis. We sought to determine the cost-effectiveness of operative and nonoperative interventions, stratified by patients' predicted probability of 3-month survival.

METHODS

A Markov model with four defined health states was used to estimate the quality-adjusted life years (QALYs) and costs for operative intervention with postoperative radiotherapy and radiotherapy alone (palliative low-dose external beam radiotherapy) of spine metastases. Transition probabilities for the model, including the risks of mortality and functional deterioration, were obtained from secondary and our institutional data. Willingness to pay thresholds were prespecified at $100,000 and $150,000. The analyses were censored after 5-year simulation from a health system perspective and discounted outcomes at 3% per year. Sensitivity analyses were conducted to test the robustness of the study design.

RESULTS

The incremental cost-effectiveness ratios were $140,907 per QALY for patients with a 3-month survival probability >50%, $3,178,510 per QALY for patients with a 3-month survival probability <50%, and $168,385 per QALY for patients with independent ambulatory and 3-month survival probability >50%.

CONCLUSIONS

This study emphasizes the need to choose patients carefully and estimate preoperative survival for those with spinal metastases. In addition to reaffirming previous research regarding the influence of ambulatory status on cost-effectiveness, our study goes a step further by highlighting that operative intervention with postoperative radiotherapy could be more cost-effective than radiotherapy alone for patients with a better survival outlook. Accurate survival prediction tools and larger future studies could offer more detailed insights for clinical decisions.

Assessing the Performance of Prognostic Scores in Patients with Spinal Metastases from Lung Cancer Undergoing Non-surgical Treatment

STUDY DESIGN

Retrospective study.

PURPOSE

The purpose of this study was to see how well the Tomita score, revised Tokuhashi score, modified Bauer score, Van der Linden score, classic Skeletal Oncology Research Group (SORG) algorithm, SORG nomogram, and New England Spinal Metastasis Score (NESMS) predicted 3-month, 6-month, and 1-year survival of non-surgical lung cancer spinal metastases.

OVERVIEW OF LITERATURE

There has been no study assessing the performance of prognostic scores for non-surgical lung cancer spinal metastases.

METHODS

Data analysis was carried out to identify the variables that had a significant impact on survival. For all patients with spinal metastasis from lung cancer who received non-surgical treatment, the Tomita score, revised Tokuhashi score, modified Bauer score, Van der Linden score, classic SORG algorithm, SORG nomogram, and NESMS were calculated. The performance of the scoring systems was assessed by using receiver operating characteristic (ROC) curves at 3 months, 6 months, and 12 months. The predictive accuracy of the scoring systems was quantified using the area under the ROC curve (AUC).

RESULTS

A total of 127 patients are included in the present study. The median survival of the population study was 5.3 months (95% confidence interval [CI], 3.7-9.6 months). Low hemoglobin was associated with shorter survival (hazard ratio [HR], 1.49; 95% CI, 1.00-2.23; p =0.049), while targeted therapy after spinal metastasis was associated with longer survival (HR, 0.34; 95% CI, 0.21-0.51; p <0.001). In the multivariate analysis, targeted therapy was independently associated with longer survival (HR, 0.3; 95% CI, 0.17-0.5; p <0.001). The AUC of the time-dependent ROC curves for the above prognostic scores revealed all of them performed poorly (AUC <0.7).

CONCLUSIONS

The seven scoring systems investigated are ineffective at predicting survival in patients with spinal metastasis from lung cancer who are treated non-surgically.

Predicting 30-day mortality after surgery for metastatic disease of the spine: the H2-FAILS score

PURPOSE

Scoring systems for metastatic spine disease focus on predicting long- to medium-term mortality or a combination of perioperative morbidity and mortality. However, accurate prediction of perioperative mortality alone may be the most important factor when considering surgical intervention. We aimed to develop and evaluate a new tool, the H2-FAILS score, to predict 30-day mortality after surgery for metastatic spine disease.

METHODS

Using the National Surgical Quality Improvement Program database, we identified 1195 adults who underwent surgery for metastatic spine disease from 2010 to 2018. Incidence of 30-day mortality was 8.7% (n = 104). Independent predictors of 30-day mortality were used to derive the H2-FAILS score. H2-FAILS is an acronym for: Heart failure (2 points), Functional dependence, Albumin deficiency, International normalized ratio elevation, Leukocytosis, and Smoking (1 point each). Discrimination was assessed using area under the receiver operating characteristic curve (AUC). The H2-FAILS score was compared with the American Society of Anesthesiologists Physical Status Classification (ASA Class), the 5-item modified Frailty Index (mFI-5), and the New England Spinal Metastasis Score (NESMS). Internal validation was performed using bootstrapping. Alpha = 0.05.

RESULTS

Predicted 30-day mortality was 1.8% for an H2-FAILS score of 0 and 78% for a score of 6. AUC of the H2-FAILS was 0.77 (95% confidence interval: 0.72-0.81), which was higher than the mFI-5 (AUC 0.58, p < 0.001), ASA Class (AUC 0.63, p < 0.001), and NESMS (AUC 0.70, p = 0.004). Internal validation showed an optimism-corrected AUC of 0.76.

CONCLUSIONS

The H2-FAILS score accurately predicts 30-day mortality after surgery for spinal metastasis.

LEVEL OF EVIDENCE

Prognostic level III.

Risk Factors for Poor Outcome after Palliative Surgery for Metastatic Spinal Tumors

Palliative surgery is performed to improve the quality of life of patients with spinal metastases. However, it is sometimes difficult to achieve the expected results because the patient's condition, and risk factors related to poor outcomes have not been well elucidated. This study aimed to evaluate the functional outcomes and investigate the risk factors for poor outcomes after palliative surgery for spinal metastasis. We retrospectively reviewed the records of 117 consecutive patients who underwent palliative surgery for spinal metastases. Neurological and ambulatory statuses were evaluated pre- and post-operatively. Poor outcomes were defined as no improvement or deterioration in functional status or early mortality, and the related risk factors were analyzed using multivariate logistic regression analysis. The results showed neurological improvement in 48% and ambulatory improvement in 70% of the patients with preoperative impairment, whereas 18% of the patients showed poor outcomes. In the multivariate analysis, low hemoglobin levels and low revised Tokuhashi scores were identified as risk factors for poor outcomes. The present results suggest that anemia and low revised Tokuhashi scores are related not only to life expectancy but also to functional recovery after surgery. Treatment options should be carefully selected for the patients with these factors.

Predictive factors for respiratory failure and in-hospital mortality after surgery for spinal metastasis

PURPOSE

Spinal metastasis surgeries carry substantial risk of complications. PRF is among complications that significantly increase mortality rate and length of hospital stay. The risk factor of PRF after spinal metastasis surgery has not been investigated. This study aims to identify the predictors of postoperative respiratory failure (PRF) and in-hospital death after spinal metastasis surgery.

METHODS

We retrospectively reviewed consecutive patients with spinal metastasis surgically treated between 2008 and 2018. PRF was defined as mechanical ventilator dependence > 48 h postoperatively (MVD) or unplanned postoperative intubation (UPI). Collected data include demographics, laboratory data, radiographic and operative data, and postoperative complications. Stepwise logistic regression analysis was used to determine predictors independently associated with PRFs and in-hospital death.

RESULTS

This study included 236 patients (average age 57 ± 14 years, 126 males). MVD and UPI occurred in 13 (5.5%) patients and 13 (5.5%) patients, respectively. During admission, 14 (5.9%) patients had died postoperatively. Multivariate logistic regression analysis revealed significant predictors of MVD included intraoperative blood loss > 2000 mL (odds ratio [OR] 12.28, 95% confidence interval [CI] 2.88-52.36), surgery involving cervical spine (OR 9.58, 95% CI 1.94-47.25), and ASA classification ≥ 4 (OR 6.59, 95% CI 1.85-23.42). The predictive factors of UPI included postoperative sepsis (OR 20.48, 95% CI 3.47-120.86), central nervous system (CNS) metastasis (OR 10.21, 95% CI 1.42-73.18), lung metastasis (OR 7.18, 95% CI 1.09-47.4), and postoperative pulmonary complications (OR 6.85, 95% CI 1.44-32.52). The predictive factors of in-hospital death included postoperative sepsis (OR 13.15, 95% CI 2.92-59.26), CNS metastasis (OR 10.55, 95% CI 1.54-72.05), and postoperative pulmonary complications (OR 9.87, 95% CI 2.35-41.45).

CONCLUSION

PRFs and in-hospital death are not uncommon after spinal metastasis surgery. Predictive factors for PRFs included preoperative comorbidities, intraoperative massive blood loss, and postoperative complications. Identification of risk factors may help guide therapeutic decision-making and patient counseling.

Multidisciplinary Approach to Spinal Metastases and Metastatic Spinal Cord Compression—A New Integrative Flowchart for Patient Management

Metastatic spine disease (MSD) and metastatic spinal cord compression (MSCC) are major causes of permanent neurological damage and long-term disability for cancer patients. The development of MSD is pathophysiologically framed by a cooperative interaction between general mechanisms of bone growth and specific mechanisms of spinal metastases (SM) expansion. SM most commonly affects the thoracic spine, even though multiple segments may be affected concomitantly. The great majority of SM are extradural, while intradural-extramedullary and intramedullary metastases are less frequently seen. The management of patients with SM is particularly complex and challenging, with multiple factors—such as the spinal stability status, primary tumor radio and chemosensitivity, cancer biological burden, patient performance status and comorbidities, and patient’s oncological prognosis—influencing the clinical decision-making process. Different frameworks were developed in order to systematize and support this process. A multidisciplinary, personalized approach, enriched by the expertise of each involved specialty, is crucial. We reviewed the most recent evidence and proposed an updated algorithmic approach to patients with MSD according to the clinical scenario of each patient. A flowchart-based approach offers an evidence-based management of MSD, providing a valuable clinical decision tool in a context of high uncertainty and quick-acting need.

Evaluation of different scoring systems for spinal metastases based on a Chinese cohort

INTRODUCTIONS

The spine is one of the most common sites of metastasis for malignancies. This study aimed to compare the predictive performance of seven commonly used prognostic scoring systems for surgically treated spine metastases. It is expected to assist surgeons in selecting appropriate scoring systems to support clinical decision-making and better inform patients.

METHODS

We performed a retrospective study involving 268 surgically treated patients with spine metastases between 2017 and 2020 at a single regional oncology center in China. The revised Tokuhashi, Tomita, modified Bauer, revised Katagiri, van der Linden, Skeletal Oncology Research Group (SORG) nomogram, and SORG machine-learning (ML) scoring systems were externally validated. The area under the curve (AUC) of the receiver operating characteristic curve was used to evaluate sensitivity and specificity at different postoperative time points. The actual survival time was compared with the reference survival time provided in the original publication.

RESULTS

In the present study, the median survival was 16.6 months. The SORG ML scoring system demonstrated the highest accuracy in predicting 90-day (AUC: 0.743) and 1-year survival (AUC: 0.787). The revised Katagiri demonstrated the highest accuracy (AUC: 0.761) in predicting 180-day survival. The revised Katagiri demonstrated the highest accuracy (AUC: 0.779) in predicting 2-year survival. Based on this series, the actual life expectancy was underestimated compared with the original reference survival time.

CONCLUSIONS

None of the scoring systems can perform optimally at all time points and for all pathology types, and the reference survival times provided in the original study need to be updated. A cautious awareness of the underestimation by these models is of paramount importance in relation to current patients.

Validation of Traditional Prognosis Scoring Systems and Skeletal Oncology Research Group Nomogram for Predicting Survival of Spinal Metastasis Patients Undergoing Surgery

BACKGROUND

Many scoring systems that predict overall patient survival are based on clinical parameters and primary tumor type. To date, no consensus exists regarding which scoring system has the greatest predictive survival accuracy, especially when applied to specific primary tumors. Additionally, such scores usually fail to include modern treatment modalities, which influence patient survival. This study aimed to evaluate both the overall predictive accuracy of such scoring systems and the predictive accuracy based on the primary tumor.

METHODS

A retrospective review on spinal metastasis patients who were aged more than 18 years and underwent surgical treatment was conducted between October 2008 and August 2018. Patients were scored based on data before the time of surgery. A survival probability was calculated for each patient using the given scoring systems. The predictive ability of each scoring system was assessed using receiver operating characteristic analysis at postoperative time points; area under the curve was then calculated to quantify predictive accuracy.

RESULTS

A total of 186 patients were included in this analysis: 101 (54.3%) were men and the mean age was 57.1 years. Primary tumors were lung in 37 (20%), breast in 26 (14%), prostate in 20 (10.8%), hematologic malignancy in 18 (9.7%), thyroid in 10 (5.4%), gastrointestinal tumor in 25 (13.4%), and others in 40 (21.5%). The primary tumor was unidentified in 10 patients (5.3%). The overall survival was 201 days. For survival prediction, the Skeletal Oncology Research Group (SORG) nomogram showed the highest performance when compared to other prognosis scores in all tumor metastasis but a lower performance to predict survival with lung cancer. The revised Katagiri score demonstrated acceptable performance to predict death for breast cancer metastasis. The Tomita and revised Tokuhashi scores revealed acceptable performance in lung cancer metastasis. The New England Spinal Metastasis Score showed acceptable performance for predicting death in prostate cancer metastasis. SORG nomogram demonstrated acceptable performance for predicting death in hematologic malignancy metastasis at all time points.

CONCLUSIONS

The results of this study demonstrated inconsistent predictive performance among the prediction models for the specific primary tumor types. The SORG nomogram revealed the highest predictive performance when compared to previous survival prediction models.

The predictive ability of routinely collected laboratory markers for surgically treated spinal metastases: a retrospective single institution study

PURPOSE

We aimed to identify effective routinely collected laboratory biomarkers for predicting postoperative outcomes in surgically treated spinal metastases and attempted to establish an effective prediction model.

METHODS

This study included 268 patients with spinal metastases surgically treated at a single institution. We evaluated patient laboratory biomarkers to determine trends to predict survival. The markers included white blood cell (WBC) count, platelet count, neutrophil count, lymphocyte count, hemoglobin, albumin, alkaline phosphatase, creatinine, total bilirubin, calcium, international normalized ratio (INR), platelet-to-lymphocyte ratio (PLR), and neutrophil-to-lymphocyte ratio (NLR). A nomogram based on laboratory markers was established to predict postoperative 90-day and 1-year survival. The discrimination and calibration were validated using concordance index (C-index), area under curves (AUC) from receiver operating characteristic curves, and calibration curves. Another 47 patients were used as a validation group to test the accuracy of the nomogram. The prediction accuracy of the nomogram was compared to Tomita, revised Tokuhashi, modified Bauer, and Skeletal Oncology Research Group machine-learning (SORG ML).

RESULTS

WBC, lymphocyte count, albumin, and creatinine were shown to be the independent prognostic factors. The four predictive laboratory markers and primary tumor, were incorporated into the nomogram to predict the 90-day and 1-year survival probability. The nomogram performed good with a C-index of 0.706 (0.702-0.710). For predicting 90-day survival, the AUC in the training group and the validation group was 0.740 (0.660-0.819) and 0.795 (0.568-1.000), respectively. For predicting 1-year survival, the AUC in the training group and the validation group was 0.765 (0.709-0.822) and 0.712 (0.547-0.877), respectively. Our nomogram seems to have better predictive accuracy than Tomita, revised Tokuhashi, and modified Bauer, alongside comparable prediction ability to SORG ML.

CONCLUSIONS

Our study confirmed that routinely collected laboratory markers are closely associated with the prognosis of spinal metastases. A nomogram based on primary tumor, WBC, lymphocyte count, albumin, and creatinine, could accurately predict postoperative survival for patients with spinal metastases.

Posterior Endoscopic Cervical Decompression in Metastatic Cervical Spine Tumors: An Alternative to Palliative Surgery

Metastatic spinal cord compression of the cervical spine is a well-known consequence of cancer that generally manifests as an oncological emergency. This study presents and describes an alternative to the minimally invasive posterior full-endoscopic approach for direct decompression and tumor debulking from the metastasis of hepatocellular carcinoma (HCC) in the cervical spine. A 54-year-old man presented with progressive cervical radiculopathy that had persisted for 3 months. The underlying disease was HCC. Radiographic examination revealed evidence of metastatic spinal cord compression with an epidural mass at the C4-C5 levels, which compressed the C4-C5 spinal cord without bony destruction. The modified Tomita score was 6 to 8 points based on palliative surgery. A posterior full-endoscopic approach to remove the tumor from the metastasis of HCC in the cervical spine was done. A postoperative radiographic study revealed adequate tumor mass resection and spinal decompression. The patient was extremely satisfied with this alternative treatment and achieved complete neurologic recovery at 1 month and no recurrent symptoms at the 6-month follow-up. The technique of posterior full-endoscopic decompression of cervical metastasis causing unilateral radiculopathy, presented in this study, is feasible. This surgical intervention seems to be optional minimally invasive and acts as an alternative to palliative surgery.

Survival Rate after Palliative Surgery Alone for Symptomatic Spinal Metastases: A Prospective Cohort Study

The effect of spine surgery for symptomatic spinal metastases (SSM) on patient prognosis remains unclear. This study aimed to reveal the prognosis of patients with SSM after spine surgery. One hundred twenty-two patients with SSM were enrolled in this prospective cohort study. The patients who received chemotherapy after enrollment were excluded. The decision of surgery depended on patient's willingness; the final cohort comprised 31 and 24 patients in the surgery and non-surgery groups, respectively. The patients were evaluated by their performance status (PS), activities of daily living (ADL) and ambulatory status. Survival was evaluated by the Kaplan-Meier method. The PS, ADL and ambulation were significantly improved in the surgery group compared to non-surgery group. The median survival was significantly longer in the surgery group (5.17 months, 95% confidence interval (CI) 3.27 to 7.07) than in the non-surgery group (2.23 months, 95% CI 2.03 to 2.43; p = 0.003). Furthermore, the patients with a better PS, ADL and ambulatory status had a significantly longer survival. Surgery improved the PS, ADL, ambulation and survival of patients with SSM. In the management of SSM, spine surgery is not only palliative but may also prolong survival.

SORG algorithm to predict 3- and 12-month survival in metastatic spinal disease: a cross-sectional population-based retrospective study

PURPOSE

In this study, we wished to compare statistically the novel SORG algorithm in predicting survival in spine metastatic disease versus currently used methods.

METHODS

We recruited 40 patients with spinal metastatic disease who were operated at Geneva University Hospitals by the Neurosurgery or Orthopedic teams between the years of 2015 and 2020. We did an ROC analysis in order to determine the accuracy of the SORG ML algorithm and nomogram versus the Tokuhashi original and revised scores.

RESULTS

The analysis of data of our independent cohort shows a clear advantage in terms of predictive ability of the SORG ML algorithm and nomogram in comparison with the Tokuhashi scores. The SORG ML had an AUC of 0.87 for 90 days and 0.85 for 1 year. The SORG nomogram showed a predictive ability at 90 days and 1 year with AUCs of 0.87 and 0.76 respectively. These results showed excellent discriminative ability as compared with the Tokuhashi original score which achieved AUCs of 0.70 and 0.69 and the Tokuhashi revised score which had AUCs of 0.65 and 0.71 for 3 months and 1 year respectively.

CONCLUSION

The predictive ability of the SORG ML algorithm and nomogram was superior to currently used preoperative survival estimation scores for spinal metastatic disease.

Optimization of Tokuhashi Scoring System to Improve Survival Prediction in Patients with Spinal Metastases

Introduction: Predicting survival time for patients with spinal metastases is important in treatment choice. Generally speaking, six months is a landmark cutoff point. Revised Tokuhashi score (RTS), the most widely used scoring system, lost its accuracy in predicting 6-month survival, gradually. Therefore, a more precise scoring system is urgently needed. Objective: The aim of this study is to create a new scoring system with a higher accuracy in predicting 6-month survival based on the previously used RTS. Methods: Data of 171 patients were examined to determine factors that affect prognosis (reference group), and the remaining (validation group) were examined to validate the reliability of a new score, adjusted Tokuhashi score (ATS). We compared their discriminatory abilities of the prediction models using area under receiver operating characteristic curve (AUC). Results: Target therapy and the Z score of BMI (Z-BMI), which adjusted to the patients’ sex and age, were additional independent prognostic factors. Patients with target therapy use are awarded 4 points. The Z score of BMI could be added directly to yield ATS. The AUCs were 0.760 for ATS and 0.636 for RTS in the validation group. Conclusion: Appropriate target therapy use can prolong patients’ survival. Z-BMI which might reflect nutritional status is another important influencing factor. With the optimization, surgeons could choose a more individualized treatment for patients.

Prognostic significance of lab data and performance comparison by validating survival prediction models for patients with spinal metastases after radiotherapy

BACKGROUND AND PURPOSE

Well-performing survival prediction models (SPMs) help patients and healthcare professionals to choose treatment aligning with prognosis. This retrospective study aims to investigate the prognostic impacts of laboratory data and to compare the performances of Metastases location, Elderly, Tumor primary, Sex, Sickness/comorbidity, and Site of radiotherapy (METSSS) model, New England Spinal Metastasis Score (NESMS), and Skeletal Oncology Research Group machine learning algorithm (SORG-MLA) for spinal metastases (SM).

MATERIALS AND METHODS

From 2010 to 2018, patients who received radiotherapy (RT) for SM at a tertiary center were enrolled and the data were retrospectively collected. Multivariate logistic and Cox-proportional-hazard regression analyses were used to assess the association between laboratory values and survival. The area under receiver-operating characteristics curve (AUROC), calibration analysis, Brier score, and decision curve analysis were used to evaluate the performance of SPMs.

RESULTS

A total of 2786 patients were included for analysis. The 90-day and 1-year survival rates after RT were 70.4% and 35.7%, respectively. Higher albumin, hemoglobin, or lymphocyte count were associated with better survival, while higher alkaline phosphatase, white blood cell count, neutrophil count, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, or international normalized ratio were associated with poor prognosis. SORG-MLA has the best discrimination (AUROC 90-day, 0.78; 1-year 0.76), best calibrations, and the lowest Brier score (90-day 0.16; 1-year 0.18). The decision curve of SORG-MLA is above the other two competing models with threshold probabilities from 0.1 to 0.8.

CONCLUSION

Laboratory data are of prognostic significance in survival prediction after RT for SM. Machine learning-based model SORG-MLA outperforms statistical regression-based model METSSS model and NESMS in survival predictions.

Surgical Efficacy and Prognosis of 54 Cases of Spinal Metastases from Breast Cancer

OBJECTIVE

To analyze the efficacy and complications of spinal metastasis surgery for breast cancer; to understand the survival and the influencing factors; and to verify the predictive ability of the currently used spinal metastasis cancer survival prediction scoring system on 1 year postoperative survival.

METHODS

A retrospective study was conducted of 54 patients with spinal metastases from breast cancer who underwent open surgery after multidisciplinary consultation in our hospital from January 2017 to October 2020. Patient demographic-related variables, breast cancer-related variables, spinal disorder-related variables, and treatment-related variables were collected. Survival curves were plotted using the Kaplan-Meier method, 1-way tests were performed using the log-rank method for factors that might affect prognosis, and candidate variables were included in the Cox model for multifactor analysis. The Tomita score, modified Tokuhashi score, modified Bauer score, and modified Katagiri score were examined by plotting the subject operating characteristic curve and calculating the area under the curve. The area under the curve was used to test the predictive ability of the SORG (Skeletal Oncology Research Group) original version, SORG line graph version, and SORG Web version for 1-year postoperative survival in patients with spinal metastases from breast cancer.

RESULTS

The average age was 51.3 ± 8.6 years in 54 patients. Twenty-one patients underwent vertebral body debulking surgery, 32 patients underwent palliative canal decompression, and 1 patient underwent vertebral en bloc resection, with an operative time of 229.3 ± 87.6 minutes and intraoperative bleeding of 1018.1 ± 931.1 mL. Postoperatively, the patient experienced significant pain relief and gradual recovery from nerve injury. Major surgical complications included cerebrospinal fluid leakage, secondary spinal cord injury, spinal tumor progression, and broken fixation. The mean survival was 32.2 months, including a 6-month survival of 90.7%, a 1-year survival of 77.8%, and a 2-year survival of 60.3%. Univariate analysis showed that preoperation with neurologic deficits, hormone-insensitive type, with brain metastases were potential risk factors for poor prognosis. Multifactorial analysis showed that hormone-insensitive type and concomitant brain metastasis were independent risk factors associated with poor prognosis. The SORG Web version had good ability to predict 1-year postoperative survival in patients with spinal metastases from breast cancer.

CONCLUSIONS

Spinal metastasis from breast cancer has good surgical efficacy, low postoperative recurrence rate, and relatively long survival after surgery. Patients with hormone-insensitive type, with brain metastasis, have a poor prognosis, and SORG Web version can predict patients' 1-year survival more accurately.

Preoperative embolization in surgical treatment of spinal metastases originating from non-hypervascular primary tumors: a propensity score matched study using 495 patients

BACKGROUND CONTEXT

Preoperative embolization (PE) reduces intraoperative blood loss during surgery for spinal metastases of hypervascular primary tumors such as thyroid and renal cell tumors. However, most spinal metastases originate from primary breast, prostate, and lung tumors and it remains unclear whether these and other spinal metastases benefit from PE.

PURPOSE

To assess the (1) efficacy of PE on the amount of intraoperative blood loss and safety in patients with spinal metastases originating from non-hypervascular primary tumors, and (2) secondary outcomes including perioperative allogeneic blood transfusion, anesthesia time, hospitalization, postoperative complication within 30 days, reoperation, 90-day mortality, and 1-year mortality.

STUDY DESIGN

Retrospective propensity-score matched, case-control study at 2 academic tertiary medical centers.

PATIENT SAMPLE

Patients 18 years of age or older undergoing surgery for spinal metastases originating from primary non-thyroid, non-renal cell, and non-hepatocellular tumors between January 1, 2002 and December 31, 2016 were included.

OUTCOME MEASURES

The primary outcomes were estimated amount of intraoperative blood loss and complications attributable to PE, such as neurologic injury, wound infection, thrombosis, or dissection. The secondary outcomes included perioperative allogeneic blood transfusion, anesthesia time, hospitalization, postoperative complication within 30 days, reoperation, 90-day mortality, and 1-year mortality.

METHODS

In total, 495 patients were identified, of which 54 (11%) underwent PE. After propensity score matching on 21 variables, including primary tumor, number of spinal levels, and surgical treatment, 53 non-PE patients were matched to 53 PE patients. Matching was adequate measured by comparing the matched variables, testing the standardized mean differences (<0.25), and inspecting Kernel density plots. The degree of embolization was noted to be complete, until stasis, or successful in 43 (80%) patients.

RESULTS

Intraoperative blood loss did not differ between both groups with a median blood loss in liters of 0.6 (IQR, 0.4-1.2) for non-PE patients and 0.9 (IQR, 0.6-1.2) for PE patients (p=.32). No complications occurred during embolization or the time between embolization and surgery. No differences were found in terms of the secondary outcomes.

CONCLUSIONS

Our data suggest that, although no complications occurred and the embolization procedure can be considered safe, patients with non-hypervascular spinal metastases might not benefit from PE. A larger, prospective study could confirm or refute these study findings and aid in elucidating a subset of spinal metastases that might benefit from PE.

Predicting Mortality in Elderly Spine Trauma Patients

STUDY DESIGN

Retrospective analysis on prospectively collected data.

OBJECTIVE

The aim of this study was to construct a clinical prediction model for 90-day mortality in elderly patients with traumatic spine injuries.

SUMMARY OF BACKGROUND DATA

Spine trauma in the elderly population is increasing. Comparing elderly spine trauma patients to younger patients with similar injuries proves challenging due to the extensive comorbidities and frailty found in the elderly. There is a paucity of evidence to predict survival of elderly patients following traumatic spinal injuries.

METHODS

All patients 65+ with spine trauma presenting to a level I trauma center from 2010 to 2019 were reviewed from a prospectively maintained trauma registry. Retrospective chart review was performed to record injury, frailty scores, comorbidities, presence of spinal cord injury, imaging evidence of sarcopenia and osteopenia, mortality, and complications. We preselected 13 variables for our multivariable logistic regression model: hypotension on admission, gender, marital status, age, max Abbreviated Injury Scale, Modified Frailty Index, surgical treatment, hematocrit, white blood count, spinal cord injury, closed head injury, injury level and presence of high energy mechanism. The performance of the prediction model was evaluated using a concordance index and calibration plot. The model was internally validated via bootstrap approach.

RESULTS

Over the 9-year period, 1746 patients met inclusion criteria; 359 (20.6%) patients died within 90 days after presenting with spine trauma. The most important predictors for 90-day mortality were age, hypotension, closed head injury, max Abbreviated Injury Scale and hematocrit. There was an optimism-corrected C-index of 0.77. A calculator was created to predict a personalized mortality risk.

CONCLUSION

The incidence of spine trauma in elderly patients continues to increase. Previous publications described preexisting conditions that imply increased mortality, but ours is the first to develop a predictive calculator. Prospective research is planned to externally validate this model to better determine its predictive value and utility in the clinical setting.

Treatment of Metastatic Spinal Disease; what the Radiologist needs to know

Advancements in technology and multidisciplinary management have revolutionized the treatment of spinal metastases. Imaging plays a pivotal role in determining the treatment course for spinal metastases. This article aims to review the relevant imaging findings in spinal metastases from the perspective of the treating clinician, describe the various treatment options, and discuss factors influencing choice for each available treatment option. Cases that once required radical surgical resection or low-dose conventional external beam radiation therapy, or both, are now being managed with separation surgery, spine stereotactic radiosurgery/stereotactic body radiation therapy, or both, with decreased morbidity, improved local control, and more durable pain control. The primary focus in determining treatment choice is now on tumor control outcomes, treatment-related morbidity, and quality of life.

Identifying new predictive factors for survival after surgery for spinal metastases: an exploratory in-depth retrospective analysis

OBJECTIVES

In selected patients with symptomatic spinal metastasis from solid tumors, surgery improves quality of life. Since selection is key, inaccurate survival prognostication may result in poor decisions and outcomes. However, most prognostic scores suffer from suboptimal external validation and subsequent mediocre performance. This warrants the ongoing search for factors that better capture the oncological status. This exploratory study aims to identify new preoperative variables that predict survival.

METHODS

A retrospective analysis was conducted on 62 patients from a tertiary care referral center who underwent debulking and/or reconstruction surgery for spinal metastases between 2006 and 2018, and in whom detailed clinical, oncological, surgical and biochemical variables were collected. Univariate and multivariate analyses were performed for overall survival.

RESULTS

Median survival was 13.2 months. Multivariate analysis for overall survival identified that a higher number of organs with metastases, a shorter time to progression on the last line of systemic therapy before surgery (TTPbs), low serum albumin, high alkaline phosphatase, high C-reactive peptide (CRP), presence of brain metastasis and the index spinal level located in the cervical region were independently associated with shorter survival.

CONCLUSION

We confirmed previously known predictors and identified CRP and TTPbs as new variables that were strongly associated with survival. The latter variable may replace primary tumor type, as improved cancer treatments make the primary tumor type less relevant as a predictor. This study is exploratory and its findings need to be validated, preferably in large prospective multicenter studies that are aiming at improving existing models.

Prognostic Value of Preclinical Markers after Radiotherapy of Metastatic Spinal Cord Compression-An Additional Analysis of Patients from Two Prospective Trials

For optimal personalization of treatment for metastatic spinal cord compression (MSCC), the patient’s survival prognosis should be considered. Estimation of survival can be facilitated by prognostic factors. This study investigated the prognostic value of pre-treatment preclinical markers, namely hemoglobin, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lactate dehydrogenase (LDH), and c-reactive protein (CRP), in 190 patients from two prospective trials who had poor or intermediate survival prognoses and were irradiated for MSCC with motor deficits. In addition, clinical factors including radiation regimen, age, gender, tumor type, interval from tumor diagnosis to MSCC, number of affected vertebrae, visceral metastases, other bone metastases, time developing motor deficits, ambulatory status, sensory function, and sphincter function were evaluated. On univariate analyses, NLR (p = 0.033), LDH (p < 0.001), CRP (p < 0.001), tumor type (p < 0.001), pre-radiotherapy ambulatory status (p < 0.001), and sphincter function (p = 0.011) were significant. In the subsequent Cox regression analysis, LDH (p = 0.007), CRP (p = 0.047), tumor type (p = 0.003), and ambulatory status (p = 0.010) maintained significance. In addition to clinical factors, preclinical markers may help in estimating the survival of patients irradiated for MSCC. Additional prospective trials are warranted.

A Natural History of Patients Treated Operatively and Nonoperatively for Spinal Metastases Over 2 Years Following Treatment: Survival and Functional Outcomes

STUDY DESIGN

Prospective observational study.

OBJECTIVE

We present the natural history, including survival and function, among participants in the prospective observational study of spinal metastases treatment investigation.

SUMMARY OF BACKGROUND DATA

Surgical treatment has been touted as a means to preserve functional independence, quality of life, and survival. Nearly all prior investigations have been limited by retrospective design and relatively short-periods of post-treatment surveillance.

METHODS

This natural history study was conducted using the records of patients who were enrolled in the prospective observational study of spinal metastases treatment study (2017-2019). Eligible participants were 18 or older and presenting for treatment of spinal metastatic disease. Patients were followed at predetermined intervals (1, 3, 6, 12, and 24-mo) following treatment. We conducted cox proportional hazard regression analysis adjusting for confounders including age, biologic sex, number of comorbidities, type of metastatic lesion, neurologic symptoms at presentation, number of metastases involving the vertebral body, vertebral body collapse, New England Spinal Metastasis Score (NESMS) at presentation, and treatment strategy.

RESULTS

We included 202 patients. Twenty-three percent of the population had died by 3 months following treatment initiation, 51% by 1 year, and 70% at 2 years. There was no significant difference in survival between patients treated operatively and nonoperatively (P = 0.16). No significant difference in HRQL between groups was appreciated beyond 3 months following treatment initiation. NESMS at presentation (scores of 0 [HR 5.61; 95% CI 2.83, 11.13] and 1 [HR 3.00; 95% CI 1.60, 5.63]) was significantly associated with mortality.

CONCLUSION

We found that patients treated operatively and nonoperatively for spinal metastases benefitted from treatment in terms of HRQL. Two-year mortality for the cohort as a whole was 70%. When prognosticating survival, the NESMS appears to be an effective utility, particularly among patients with scores of 0 or 1.Level of Evidence: 2.

Body composition predictors of mortality on computed tomography in patients with spinal metastases undergoing surgical treatment

BACKGROUND CONTEXT

Although survival of patients with spinal metastases has improved over the last decades due to advances in multi-modal therapy, there are currently no reliable predictors of mortality. Body composition measurements obtained using computed tomography (CT) have been recently proposed as biomarkers for survival in patients with and without cancer. Patients with cancer routinely undergo CT for staging or surveillance of therapy. Body composition assessed using opportunistic CTs might be used to determine survival in patients with spinal metastases.

PURPOSE

The purpose of this study was to determine the value of body composition measures obtained on opportunistic abdomen CTs to predict 90-day and 1-year mortality in patients with spinal metastases undergoing surgery. We hypothesized that low muscle and abdominal fat mass were positive predictors of mortality.

STUDY DESIGN

Retrospective study at a single tertiary care center in the United States.

PATIENT SAMPLE

This retrospective study included 196 patients between 2001 and 2016 that were 18 years of age or older, underwent surgical treatment for spinal metastases, and had a preoperative CT of the abdomen within three months prior to surgery.

OUTCOME MEASURES

Ninety-day and 1-year mortality by any cause.

METHODS

Quantification of cross-sectional areas (CSA) and CT attenuation of abdominal subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and paraspinous and abdominal skeletal muscle were performed on CT images at the level of L4 using an in-house automated algorithm. Sarcopenia was determined by total muscle CSA (cm²) divided by height squared (m²) with cutoff values of <52.4 cm²/m² for men and <38.5 cm²/m² for women. Bivariate and multivariate Cox proportional-hazard analyses were used to determine the associations between body compositions and 90-day and 1-year mortality.

RESULTS

The median age was 62 years (interquartile range=53-70). The mortality rate for 90-day was 24% and 1-year 54%. The presence of sarcopenia was associated with an increased 1-year mortality rate of 66% compared with a 1-year mortality rate of 41% in patients without sarcopenia (hazard ratio, 1.68; 95% confidence interval, 1.08-2.61; p=.02) after adjusting for various clinical factors including primary tumor type, ECOG performance status, additional metastases, neurology status, and systemic therapy. Additional analysis showed an association between sarcopenia and increased 1-year mortality when controlling for the prognostic modified Bauer score (HR, 1.58; 95%CI, 1.04-2.40; p=.03). Abdominal fat CSAs or muscle attenuation were not independently associated with mortality.

CONCLUSIONS

The presence of sarcopenia is associated with an increased risk of 1-year mortality for patients surgically treated for spinal metastases. Sarcopenia retained an independent association with mortality when controlling for the prognostic modified Bauer score. This implies that body composition measurements such as sarcopenia could serve as novel biomarkers for prediction of mortality and may supplement other existing prognostic tools to improve shared decision making for patients with spinal metastases that are contemplating surgical treatment.

Characteristics of Patients Undergoing Surgical Treatment for Spinal Metastases From Colorectal Cancer: A Comparison With Non-Small Cell Lung Cancer

STUDY DESIGN

This was a retrospective study.

OBJECTIVE

This study aimed to investigate the prognosis and characteristics of patients undergoing surgical treatment for colorectal cancer (CRC) spinal metastasis. To better understand the characteristics of such patients, their results were compared with those with spinal metastasis from non-small cell lung cancer (NSCLC), as the prognosis of these patients is well-studied.

SUMMARY OF BACKGROUND DATA

CRC commonly metastasizes to the liver or lung, while spinal metastases occur infrequently. The literature contains very few studies evaluating the prognosis of patients with spinal metastases from CRC.

MATERIALS AND METHODS

A consecutive 155 patients who underwent surgical treatment for spinal metastases from CRC (n=35) or NSCLC (n=120) between 2010 and 2018 were included in this study. Data were collected throughout the disease course, including those concerning all cancer-related treatments for both the primary cancer and spinal metastasis. Categorical variables were divided into patient, tumor, and treatment factors, and postoperative survival times were compared between the CRC and NSCLC groups.

RESULTS

The mean interval from cancer diagnosis to spinal metastasis was significantly greater in CRC group (32.5 mo) than in NSCLC group (12.9 mo). Concurrent spinal metastasis was more common in NSCLC group than CRC group (45.0% vs. 17.2%; P=0.003). Visceral metastasis was found more frequently in CRC group than NSCLC group (77.1% vs. 42.5%; P<0.001). The proportion of patients undergoing postoperative systemic treatment was significantly higher in NSCLC group than CRC group (59.2% vs. 20.0%; P<0.001). Median survival time after spine surgery was 4.2 months in CRC group and 5.8 months in NSCLC group (P=0.015).

CONCLUSIONS

The prognosis after surgical treatment for CRC spinal metastasis was poor, and worse than that of NSCLC group. These results can be explained by the later development of spinal metastases and the limited chance of postoperative medical treatment in CRC spinal metastasis.

Artificial intelligence applied to musculoskeletal oncology: a systematic review

Developments in artificial intelligence have the potential to improve the care of patients with musculoskeletal tumors. We performed a systematic review of the published scientific literature to identify the current state of the art of artificial intelligence applied to musculoskeletal oncology, including both primary and metastatic tumors, and across the radiology, nuclear medicine, pathology, clinical research, and molecular biology literature. Through this search, we identified 252 primary research articles, of which 58 used deep learning and 194 used other machine learning techniques. Articles involving deep learning have mostly involved bone scintigraphy, histopathology, and radiologic imaging. Articles involving other machine learning techniques have mostly involved transcriptomic analyses, radiomics, and clinical outcome prediction models using medical records. These articles predominantly present proof-of-concept work, other than the automated bone scan index for bone metastasis quantification, which has translated to clinical workflows in some regions. We systematically review and discuss this literature, highlight opportunities for multidisciplinary collaboration, and identify potentially clinically useful topics with a relative paucity of research attention. Musculoskeletal oncology is an inherently multidisciplinary field, and future research will need to integrate and synthesize noisy siloed data from across clinical, imaging, and molecular datasets. Building the data infrastructure for collaboration will help to accelerate progress towards making artificial intelligence truly useful in musculoskeletal oncology.

Estimating survival and choosing treatment for spinal metastases: Do spine surgeons agree with each other?

Purpose

This study aimed to investigate spine surgeons’ ability to estimate survival in patients with spinal metastases and whether survival estimates influence treatment recommendations.

Methods

60 Spine surgeons were asked a survival estimate and treatment recommendation in 12 cases. Intraclass correlation coefficients and descriptive statistics were used to evaluate variability, accuracy and association of survival estimates with treatment recommendation.

Results

There was substantial variability in survival estimates amongst the spine surgeons. Survival was generally overestimated, and longer estimated survival seemed to lead to more invasive procedures.

Conclusions

Prognostic models to estimate survival may aid surgeons treating patients with spinal metastases.

Comparison of three predictive scoring systems for morbidity in oncological spine surgery

Estimating complications in oncological spine surgery is challenging. The objective of this study was to compare the accuracy of three scoring systems for predicting perioperative morbidity after surgery for spinal metastases. One-hundred and five patients who underwent surgery between 2013 and 2019 were included in this study. All patients had scores retrospectively calculated using the New England Spinal Metastasis Score (NESMS), Metastatic Spinal Tumor Frailty Index (MSTFI), and Anzuategui scoring systems. The main outcome measure was development of a medical complication (minor or major) within 30 days of surgery. The predictive ability for each system was assessed using receiver operating characteristic analysis and calculations of the area under the curve (AUC). The average age for all patients was 61 years and 61/105 patients (58.1%) were male. The most common primary tumor origins were hematologic (23.8%), prostate (16.2%), breast (14.3%), and lung (13.3%). The overall 30-day complication rate was 36.2% and the rate of major complications was 21.9%. Among all patients who underwent oncological spine surgery, the NESMS score had the highest AUC for 30-day overall (AUC 0.64; 95% CI, 0.53 - 0.75) and major morbidity (AUC 0.68; 95% CI, 0.54- 0.81) in our population. However, the accuracy did not meet the threshold for clinical utility. Future prospective validation of these systems in other populations is encouraged.

Analysis of Short-Term versus Long-Term Readmission-Free Survival After Metastatic Spine Tumor Surgery

BACKGROUND

Readmission-free survival (ReAFS) is a novel clinical and quality metric after metastatic spine tumor surgery (MSTS). We believe that factors influencing ReAFS after index MSTS vary based on time. We considered 2 time frames and defined short-term ReAFS as survival without an unplanned hospital readmission up to 90 days and long-term ReAFS as survival without unplanned hospital readmission up to 1 year after MSTS.

METHODS

We retrospectively analyzed 266 patients who underwent MSTS between 2005 and 2016. All relevant oncologic, surgical and follow-up data were collected. Multivariate logistic regression analysis was used to analyze prognostic factors associated with higher probability of short-term ReAFS and long-term ReAFS.

RESULTS

Multivariate analysis showed that Eastern Cooperative Oncology Group score ≤2 (P = 0.011), preoperative hemoglobin (Hb) level >12 g/dL (P = 0.008), ≤3 comorbidities (P = 0.052), shorter index length of stay ≤10 days (P = 0.007), and absence of neurologic/hematologic complications during index stay (P = 0.015) significantly increased the probability of short-term ReAFS, whereas preoperative Hb level >12 g/dL (P = 0.003) or tumor primaries with advanced treatment modalities such as breast (P = 0.012), hematologic (P = 0.006), prostate (P = 0.004), and renal/thyroid (P = 0.038) as opposed to aggressive lung tumor primaries were associated with significantly higher probability of long-term ReAFS.

CONCLUSIONS

Patient and treatment factors predominantly influence ReAFS up to 90 days, whereas primary tumor-related factors alongside general health influence ReAFS beyond 90 days after index MSTS. Awareness of these factors may help oncologists and surgeons optimize treatment planning. The clinical significance of this study will continue to evolve, because we have been witnessing over the past decade that patients are becoming more involved in both their general health and understanding the natural history of the diseases that affect them.

Wide range of applications for machine-learning prediction models in orthopedic surgical outcome: a systematic review

Background and purpose - Advancements in software and hardware have enabled the rise of clinical prediction models based on machine learning (ML) in orthopedic surgery. Given their growing popularity and their likely implementation in clinical practice we evaluated which outcomes these new models have focused on and what methodologies are being employed.Material and methods - We performed a systematic search in PubMed, Embase, and Cochrane Library for studies published up to June 18, 2020. Studies reporting on non-ML prediction models or non-orthopedic outcomes were excluded. After screening 7,138 studies, 59 studies reporting on 77 prediction models were included. We extracted data regarding outcome, study design, and reported performance metrics.Results - Of the 77 identified ML prediction models the most commonly reported outcome domain was medical management (17/77). Spinal surgery was the most commonly involved orthopedic subspecialty (28/77). The most frequently employed algorithm was neural networks (42/77). Median size of datasets was 5,507 (IQR 635-26,364). The median area under the curve (AUC) was 0.80 (IQR 0.73-0.86). Calibration was reported for 26 of the models and 14 provided decision-curve analysis.Interpretation - ML prediction models have been developed for a wide variety of topics in orthopedics. Topics regarding medical management were the most commonly studied. Heterogeneity between studies is based on study size, algorithm, and time-point of outcome. Calibration and decision-curve analysis were generally poorly reported.

Updated external validation of the SORG machine learning algorithms for prediction of ninety-day and one-year mortality after surgery for spinal metastasis

BACKGROUND CONTEXT

Surgical decompression and stabilization in the setting of spinal metastasis is performed to relieve pain and preserve functional status. These potential benefits must be weighed against the risks of perioperative morbidity and mortality. Accurate prediction of a patient's postoperative survival is a crucial component of patient counseling.

PURPOSE

To externally validate the SORG machine learning algorithms for prediction of 90-day and 1-year mortality after surgery for spinal metastasis.

STUDY DESIGN/SETTING

Retrospective, cohort study PATIENT SAMPLE: Patients 18 years or older at a tertiary care medical center treated surgically for spinal metastasis OUTCOME MEASURES: Mortality within 90 days of surgery, mortality within 1 year of surgery METHODS: This is a retrospective cohort study of 298 adult patients at a tertiary care medical center treated surgically for spinal metastasis between 2004 and 2020. Baseline characteristics of the validation cohort were compared to the derivation cohort for the SORG algorithms. The following metrics were used to assess the performance of the algorithms: discrimination, calibration, overall model performance, and decision curve analysis.

RESULTS

Sixty-one patients died within 90 days of surgery and 133 died within 1 year of surgery. The validation cohort differed significantly from the derivation cohort. The SORG algorithms for 90-day mortality and 1-year mortality performed excellently with respect to discrimination; the algorithm for 1-year mortality was well-calibrated. At both postoperative time points, the SORG algorithms showed greater net benefit than the default strategies of changing management for no patients or for all patients.

CONCLUSIONS

With an independent, contemporary, and geographically distinct population, we report successful external validation of SORG algorithms for preoperative risk prediction of 90-day and 1-year mortality after surgery for spinal metastasis. By providing accurate prediction of intermediate and long-term mortality risk, these externally validated algorithms may inform shared decision-making with patients in determining management of spinal metastatic disease.