The SORG nomogram accurately predicts 3- and 12-months survival for operable spine metastatic disease: External validation

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.

Survival in Patients With Spinal Metastatic Disease Treated Nonoperatively With Radiotherapy: Are the SORG-ML Algorithms Relevant?

SUMMARY OF BACKGROUND DATA

The SORG-ML algorithms for survival in spinal metastatic disease were developed in patients who underwent surgery and were externally validated for patients managed operatively.

OBJECTIVE

To externally validate the SORG-ML algorithms for survival in spinal metastatic disease in patients managed nonoperatively with radiation.

STUDY DESIGN

Retrospective cohort.

METHODS

The performance of the SORG-ML algorithms was assessed by discrimination [receiver operating curves and area under the receiver operating curve (AUC)], calibration (calibration plots), decision curve analysis, and overall performance (Brier score). The primary outcomes were 90-day and 1-year mortality.

RESULTS

Overall, 2074 adult patients underwent radiation for spinal metastatic disease and 29% (n=521) and 59% (n=917) had 90-day and 1-year mortality, respectively. On complete case analysis (n=415), the AUC was 0.76 (95% CI: 0.71-0.80) and 0.78 (95% CI: 0.73-0.83) for 90-day and 1-year mortality with fair calibration and positive net benefit confirmed by the decision curve analysis. With multiple imputation (n=2074), the AUC was 0.85 (95% CI: 0.83-0.87) and 0.87 (95% CI: 0.85-0.89) for 90-day and 1-year mortality with fair calibration and positive net benefit confirmed by the decision curve analysis.

CONCLUSION

The SORG-ML algorithms for survival in spinal metastatic disease generalize well to patients managed nonoperatively with radiation.

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.

Advances in Surgery for Metastatic Disease of the Spine: An Update for Oncologists

STUDY DESIGN

Narrative review.

OBJECTIVE

Metastatic spine disease is an increasingly common clinical challenge that requires individualised multidisciplinary care from spine surgeons and oncologists. In this article, the authors describe the recent surgical advances in patients presenting with spinal metastases.

METHODS

We present an overview of the presentation, assessment, and management of spinal metastases from the perspective of the spine surgeon, highlighting advances in surgical technology and techniques, to facilitate multidisciplinary care for this complex patient group. Neither institutional review board approval nor patient consent was needed for this review.

RESULTS

Advances in radiotherapy delivery and systemic therapy (including immunotherapy and targeted therapy) have refined operative indications for decompression of neural structures and spinal stabilisation, while advances in surgical technology and technique enable these goals to be achieved with reduced morbidity. Formulating individualised management strategies that optimise outcome, while meeting patient goals and expectations, requires a comprehensive understanding of the factors important to patient management.

CONCLUSION

Spinal metastases require prompt diagnosis and expert management by a multidisciplinary team. Improvements in systemic, radiation, and surgical therapies have broadened operative indications and increased operative candidacy, and future advances are likely to continue this trend.

Hematologic Malignancies Influence the Accuracy of Prediction of Survival in Patients With Solid Tumor Spinal Metastases Undergoing Surgery

PURPOSE

There is no consensus on how to identify patients with multiple-level spinal metastases who would benefit from surgery. Previous studies have revealed that patients with hematologic malignancies have a significantly longer median survival time than those with solid tumor spinal metastases. We aimed to compare predictors and survival data between patients with spinal metastases, including hematologic malignancies (all-malignancies group), with only those with nonhematologic malignancies (nonhematologic malignancies group).

MATERIALS AND METHODS

This single-center retrospective study included all patients age >18 years who underwent surgery to treat spinal metastases in our department from 2008 to 2018. The patients' baseline characteristics, treatment modalities, and laboratory results were analyzed. Survival was calculated from the date of surgery to the date of confirmed death. Cox regression analysis was used to identify independent predictors of survival.

RESULTS

The study cohort comprised 186 patients with a mean age of 57.1 ± 13.4 years, 101 of whom were male and 18 of whom had hematologic malignancies. The median survival time was 201 days in the all-malignancies group and 168 days in the nonhematologic malignancies group. Independent predictors of survival differed between the two groups. Eastern Cooperative Oncology Group status and response to preoperative chemotherapy were identified as independent factors in both groups. However, radiosensitivity and CNS metastases were identified only in the all-malignancies group, and tumor growth potential, albumin status, and number of vertebrae were identified only in the nonhematologic malignancies group. The receiver operating characteristics were comparable in the two groups: 0.75 in the all-malignancies group and 0.77 in the nonhematologic malignancies group.

CONCLUSION

Longer survival in patients with hematologic malignancies influences the overall prediction of survival. Tumor-specific prognostic factors may improve the prediction of survival in patients with spinal metastases.

Can Survival Scoring Systems for Spinal Metastases be Used to Predict Postoperative Neurologic Recovery? A Retrospective Study on 204 Patients With Thoracolumbar Metastases Treated at a Tertiary Center

STUDY DESIGN

Retrospective Cohort Study.

OBJECTIVE

Scoring systems for metastatic disease of the spine are used to select patients for surgical treatment based on survival estimation, but it is unknown whether they can be used to predict the outcome of surgery. This study aims to investigate the association between two widely used prognostic scores and the neurologic function after surgery.

METHODS

Retrospective analysis of 204 patients with thoracolumbar metastases treated with decompressive surgery at Karolinska University Hospital (2001-2020). Modified Bauer and Tokuhashi scores were categorized based on surgical indication, and post-operative neurological function was assessed using the Frankel scale at two different post-surgery intervals.

RESULTS

Modified Bauer scores ≥2 yielded higher late follow-up Frankel scores (3.9 ± 1.1) than scores <2 (3.5 ± 1.1), P = .03. Modified Tokuhashi scores ≥9 correlated with higher Frankel scores (4.5 ± .9) than scores <9 (3.5 ± 1.1), P < .0001. Both scoring systems positively predicted neurological outcomes at late follow-up, with odds ratios of 1.6 (P = .03) for Bauer and 9.2 (P < .0001) for Tokuhashi. However, only Tokuhashi predicted ambulatory function at late follow-up (P < .0001), demonstrating its utility in prognosticating post-surgical mobility.

CONCLUSION

Higher modified Bauer and Tokuhashi scores were associated with better neurologic function at last follow-up, as well as greater likelihood of being able to walk again. The Tokuhashi score was found to be more accurate than the modified Bauer score in predicting the neurological outcome after surgery.

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.

Development of Machine-Learning Models to Predict Ambulation Outcomes Following Spinal Metastasis Surgery

STUDY DESIGN

Retrospective cohort study.

PURPOSE

This study aimed to develop machine-learning algorithms to predict ambulation outcomes following surgery for spinal metastasis.

OVERVIEW OF LITERATURE

Postoperative ambulation status following spinal metastasis surgery is currently difficult to predict. The improved ability to predict this important postoperative outcome would facilitate management decision-making and help in determining realistic treatment goals.

METHODS

This retrospective study included patients who underwent spinal metastasis at a university-based medical center in Thailand between January 2009 and November 2021. Collected data included preoperative parameters and ambulatory status 90 and 180 days following surgery. Thirteen machine-learning algorithms, namely, artificial neural network, logistic regression, CatBoost classifier, linear discriminant analysis, extreme gradient boosting, extra trees classifier, random forest classifier, gradient boosting classifier, light gradient boosting machine, naïve Bayes, K-neighbor classifier, Ada boost classifier, and decision tree classifier were developed to predict ambulatory status 90 and 180 days following surgery. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and F1-score.

RESULTS

In total, 167 patients were enrolled. The number of patients classified as ambulatory 90 and 180 days following surgery was 140 (81.9%) and 137 (82.0%), respectively. The extreme gradient boosting algorithm was found to most accurately predict 180-day ambulatory outcome (AUC, 0.85; F1-score, 0.90), and the decision tree algorithm most accurately predicted 90-day ambulatory outcome (AUC, 0.94; F1-score, 0.88).

CONCLUSIONS

Machine-learning algorithms were effective in predicting ambulatory status following surgery for spinal metastasis. Based on our data, the extreme gradient boosting and decision tree best predicted postoperative ambulatory status 180 and 90 days after spinal metastasis surgery, respectively.

The Impact of the COVID-19 Pandemic on the Presentation of Patients With Spinal Metastases

STUDY DESIGN

Retrospective review.

OBJECTIVE

The aim was to determine if preoperative spinal instability neoplastic scores (SINSs) and Tokuhashi prognostication scores differed in patients receiving surgical care before and during the coronavirus disease-2019 (COVID-19) pandemic.

SUMMARY OF BACKGROUND DATA

The COVID-19 pandemic has caused delays in scheduling nonemergent surgeries. Delay in presentation and/or surgical treatment for oncology patients with metastatic spinal disease could result in progression of the disease, which can complicate surgical care and worsen patient outcomes.

MATERIALS AND METHODS

Retrospective review of electronic medical records between March 1, 2019 and March 1, 2021 at a tertiary medical center was performed to identify patients who underwent surgery for metastatic spine disease. Primary spinal tumors were excluded. Patients were separated into two groups base on their surgery date: before the COVID-19 pandemic (March 1, 2019-February 29, 2020) and during the COVID-19 pandemic (March 1, 2020-March 1, 2021). Primary outcomes included SINS and Tokuhashi scores. A variety of statistical tests were performed to compare the groups.

RESULTS

Fifty-two patients who underwent surgery before the COVID-19 pandemic were compared to 41 patients who underwent surgery during the COVID-19 pandemic. There was a significant difference between the before and during groups with respect to SINS (9.31±2.39 vs . 11.00±2.74, P =0.002) and Tokuhashi scores (9.27±2.35 vs . 7.88±2.85, P =0.012). Linear regression demonstrated time of surgery (before or during COVID-19 restrictions) was a significant predictor of SINS (β=1.55, 95% CI: 0.42-2.62, P =0.005) and Tokuhashi scores (β=-1.41, 95% CI: -2.49 to -0.34, P =0.010).

CONCLUSIONS

Patients with metastatic spinal disease who underwent surgery during the COVID-19 pandemic had higher SINS, lower Tokuhashi scores and similar Skeletal Oncology Research Group scores compared to patients who underwent surgery before the pandemic. This suggests the pandemic has impacted the instability of disease at presentation in patients with spinal metastases, but has not impacted surgical prognosis, as there were no differences in Skeletal Oncology Research Group scores and the difference in Tokuhashi scores is most likely not clinically significant.

The NOMS approach to metastatic tumors: Integrating new technologies to improve outcomes

Treatment paradigms for patients with spine metastases have evolved significantly over the past two decades. The most transformative change to these paradigms has been the integration of spinal stereotactic radiosurgery (sSRS). sSRS allows for the delivery of tumoricidal radiation doses with sparing of nearby organs at risk, particularly the spinal cord. Evidence supports the safety and efficacy of radiosurgery as it currently offers durable local tumor control with low complication rates even for tumors previously considered radioresistant to conventional external beam radiation therapy. The role for surgical intervention remains consistent, but a trend has been observed toward less aggressive, often minimally invasive techniques. Using modern technologies and improved instrumentation, surgical outcomes continue to improve with reduced morbidity. Additionally, targeted agents such as biologics and checkpoint inhibitors have revolutionized cancer care by improving both local control and patient survival. These advances have brought forth a need for new prognostication tools and a more critical review of long-term outcomes. The complex nature of current treatment schemes necessitates a multidisciplinary approach including surgeons, medical oncologists, radiation oncologists, interventionalists and pain specialists. This review recapitulates the current state-of-the-art, evidence-based data on the treatment of spinal metastases and integrates these data into a decision framework, NOMS, which is based on four sentinel pillars of decision making in metastatic spine tumors: Neurological status, Oncologic tumor behavior, Mechanical stability, and Systemic disease burden and medical co-morbidities.

The NOMS approach to metastatic tumors: Integrating new technologies to improve outcomes

Treatment paradigms for patients with spine metastases have evolved significantly over the past two decades. The most transformative change to these paradigms has been the integration of spinal stereotactic radiosurgery (sSRS). sSRS allows for the delivery of tumoricidal radiation doses with sparing of nearby organs at risk, particularly the spinal cord. Evidence supports the safety and efficacy of radiosurgery as it currently offers durable local tumor control with low complication rates even for tumors previously considered radioresistant to conventional external beam radiation therapy. The role for surgical intervention remains consistent, but a trend has been observed toward less aggressive, often minimally invasive techniques. Using modern technologies and improved instrumentation, surgical outcomes continue to improve with reduced morbidity. Additionally, targeted agents such as biologics and checkpoint inhibitors have revolutionized cancer care by improving both local control and patient survival. These advances have brought forth a need for new prognostication tools and a more critical review of long-term outcomes. The complex nature of current treatment schemes necessitates a multidisciplinary approach including surgeons, medical oncologists, radiation oncologists, interventionalists and pain specialists. This review recapitulates the current state-of-the-art, evidence-based data on the treatment of spinal metastases and integrates these data into a decision framework, NOMS, which is based on four sentinel pillars of decision making in metastatic spine tumors: neurological status, Oocologic tumor behavior, mechanical stability and systemic disease burden and medical co-morbidities.

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.

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.

Performance assessment and external validation of specific thresholds of total psoas muscle cross-sectional area as predictors of mortality in oncologic spine surgery for spinal metastases

PURPOSE

The purpose of this study was to assess the utility of low muscle mass (LMM) in predicting 90-day and 12-month mortality after spinal tumor surgery.

METHODS

We identified 115 patients operated on for spinal metastases between April 2012 and August 2022 who had available perioperative abdominal or lumbar spine CT scans and minimum 90-day follow-up. LMM was defined as a total psoas muscle cross-sectional area (TPA) at the L4 pedicle level less than 10.5 cm² for men and less than 7.2 cm² for women based on previously reported thresholds. A secondary analysis was performed by analyzing TPA as a continuous variable. The primary endpoint was 90-day mortality, and the secondary endpoint was 12-month mortality. Multivariate logistic regression analyses were performed.

RESULTS

The 90-day mortality was 19% for patients without and 42% for patients with LMM (p = 0.010). After multivariate analysis, LMM was not independently associated with increased odds of 90-day mortality (odds ratio 2.16 [95% confidence interval 0.62 to 7.50]; p = 0.223). The 12-month mortality was 45% for patients without and 71% for patients with LMM (p = 0.024). After multivariate analysis, LMM was not independently associated with increased odds of 12-month mortality (OR 1.64 [95% CI 0.46 to 5.86]; p = 0.442). The secondary analysis showed no independent association between TPA and 90-day or 12-month mortality.

CONCLUSION

Patients with LMM had higher rates of 90-day and 12-month mortality in our study, but this was not independent of other parameters such as performance status, hypoalbuminemia, or primary cancer type.

Prognostic Factors of Bone Metastases From Colorectal Cancer in the Era of Targeted Therapy

BACKGROUND

Various prognostic factors have been reported for bone metastases from different primary tumor sites. However, bone metastases from colorectal cancer are very rare, and the prognostic factors have not been investigated in detail.

OBJECTIVE

This study aimed to identify prognostic factors of bone metastases from colorectal cancer.

DESIGN

This is a retrospective cohort study using data from a prospectively collected database.

SETTINGS

This study was conducted at a single tertiary care cancer center in Japan.

PATIENTS

Patients who developed bone metastases from colorectal cancer during the study period among all patients who received initial treatment for colorectal cancer at our hospital between 2005 and 2016 (n = 4538) were included.

MAIN OUTCOME MEASURES

Overall survival after diagnosis of bone metastases from colorectal cancer was the main outcome measure.

RESULTS

Ninety-four patients developed bone metastases during the study period. The 5-year overall survival rate was 11.0%. Multivariable analysis identified the following independent risk factors associated with poor prognosis: ≥70 years of age at diagnosis of bone metastases (HR, 2.48; 95% CI, 1.24-4.95; p < 0.01), curative surgery not performed as initial treatment (HR, 2.54; 95% CI, 1.24-5.19; p = 0.01), multiple bone metastases (HR, 2.44; 95% CI, 1.30-4.57; p < 0.01), albumin level <3.7 g/dL (HR, 3.80; 95% CI, 1.95-7.39; p < 0.01), CEA ≥30 ng/mL (HR, 1.94; 95% CI, 1.09-3.46; p = 0.02), and less than 3 chemotherapy options remaining at diagnosis of bone metastases (HR, 2.83; 95% CI, 1.51-5.30; p < 0.01). The median survival times for patients with 0-2, 3, and 4-6 risk factors were 25.0, 8.8, and 4.3 months, respectively.

LIMITATIONS

The main limitation is the single-center, retrospective design of this study.

CONCLUSIONS

Our results may facilitate multidisciplinary decision-making in patients with bone metastases from colorectal cancer. See Video Abstract at http://links.lww.com/DCR/B930 .

FACTORES PRONSTICOS DE LAS METSTASIS SEAS DEL CNCER COLORRECTAL EN LA ERA DE LA TERAPIA DIRIGIDA

ANTECEDENTES:Se han reportado varios factores pronósticos para las metástasis óseas de diferentes sitios de tumores primarios. Sin embargo, las metástasis óseas del cáncer colorrectal son muy raras y los factores pronósticos no se han investigado en detalle.OBJETIVO:Identificar los factores pronósticos de las metástasis óseas del cáncer colorrectal.DISEÑO:Estudio de cohorte retrospectivo utilizando datos de una base de datos recolectada prospectivamente.ENTORNO CLINICO:Un solo centro oncológico de atención terciaria en Japón.PACIENTES:Se seleccionaron pacientes que desarrollaron metástasis óseas de cáncer colorrectal durante el período de estudio entre todos los pacientes que recibieron tratamiento inicial para el cáncer colorrectal en nuestro hospital entre 2005 y 2016 (n = 4538).MEDIDA DE RESULTADO PRINCIPAL:Supervivencia general después del diagnóstico de metástasis óseas por cáncer colorrectal.RESULTADOS:Noventa y cuatro pacientes desarrollaron metástasis óseas, lo que representa el 2,0% de todos los pacientes con cáncer colorrectal que comenzaron el tratamiento durante el período de estudio. La tasa de supervivencia global a 5 años fue del 11,0 %. El análisis multivariable identificó los siguientes factores de riesgo independientes asociados con mal pronóstico: edad ≥70 años al momento del diagnóstico de metástasis óseas (hazard ratio 2,48, CI del 95 % 1,24-4,95, p < 0,01), cirugía curativa no realizada como tratamiento inicial (hazard ratio 2,54, CI 95 % 1,24-5,19, p = 0,01), metástasis óseas múltiples (hazard ratio 2,44, CI del 95 % 1,30-4,57, p < 0,01), nivel de albúmina <3,7 g/dL (hazard ratio 3,80, CI del 95 % 1,95 -7,39, p < 0,01), antígeno carcinoembrionario ≥30 ng/mL (hazard ratio 1,94, CI del 95 % 1,09-3,46, p = 0,02) y menos de 3 opciones de quimioterapia restantes al momento del diagnóstico de metástasis óseas (hazard ratio 2,83, 95 % CI 1,51-5,30, p < 0,01). La mediana de los tiempos de supervivencia para los pacientes con 0-2, 3 y 4-6 factores de riesgo fue de 25,0, 8,8 y 4,3 meses, respectivamente.LIMITACIONES:Diseño retrospectivo de un solo centro.CONCLUSIÓN:Nuestros resultados pueden facilitar la toma de decisiones multidisciplinares en pacientes con metástasis óseas de cáncer colorrectal. Consulte Video Resumen en http://links.lww.com/DCR/B930 . (Traducción- Dr. Francisco M. Abarca-Rendon ).

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.

Assessment of Spinal Metastases Surgery Risk Stratification Tools in Breast Cancer by Molecular Subtype

BACKGROUND

Breast cancer molecular features and modern therapies are not included in spine metastasis prediction algorithms.

OBJECTIVE

To examine molecular differences and the impact of postoperative systemic therapy to improve prognosis prediction for spinal metastases surgery and aid surgical decision making.

METHODS

This is a retrospective multi-institutional study of patients who underwent spine surgery for symptomatic breast cancer spine metastases from 2008 to 2021 at the Massachusetts General Hospital and Brigham and Women's Hospital. We studied overall survival, stratified by breast cancer molecular subtype, and calculated hazard ratios (HRs) adjusting for demographics, tumor characteristics, treatments, and laboratory values. We tested the performance of established models (Tokuhashi, Bauer, Skeletal Oncology Research Group, New England Spinal Metastases Score) to predict and compare all-cause.

RESULTS

A total of 98 patients surgically treated for breast cancer spine metastases were identified (100% female sex; median age, 56 years [IQR, 36-84 years]). The 1-year probabilities of survival for hormone receptor positive, hormone receptor positive/human epidermal growth factor receptor 2+, human epidermal growth factor receptor 2+, and triple-negative breast cancer were 63% (45 of 71), 83% (10 of 12), 0% (0 of 3), and 12% (1 of 8), respectively ( P < .001). Patients with triple-negative breast cancer had a higher proportion of visceral metastases, brain metastases, and poor physical activity at baseline. Postoperative chemotherapy and endocrine therapy were associated with prolonged survival. The Skeletal Oncology Research Group prognostic model had the highest discrimination (area under the receiver operating characteristic, 0.77 [95% CI, 0.73-0.81]). The performance of all prognostic scores improved when preoperative molecular data and postoperative systemic treatment plans was considered.

CONCLUSION

Spine metastases risk tools were able to predict prognosis at a significantly higher degree after accounting for molecular features which guide treatment response.

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.

Fifty-year history of the evolution of spinal metastatic disease management

Spine metastases are a significant source of morbidity in oncology. Treatment of these spine metastases largely remains palliative, but advances over the past 50 years have improved the effectiveness of interventions for preserving functional status and obtaining local control while minimizing morbidity. While the field began with conventional external beam radiation as the primary treatment modality, a series of paradigm shifts and technological advances in the 2000s led to a change in treatment patterns. These advances allowed for an increased role of surgical decompression of neural elements, a shift in the stereotactic capabilities of radiation oncologists, and an improved understanding of the radiobiology of metastatic disease. The result was improved local control while minimizing treatment morbidity. These advances fit within the larger framework of metastatic spine tumor management known as the Neurologic, Oncologic, Mechanical, and Systemic disease decision framework. This dynamic framework takes into account the neurological function of the patient, the radiobiology of their tumor, their degree of mechanical instability, and their systemic disease control and treatment options to help determine appropriate interventions based on the individual patient. Herein, we describe the 50-year evolution of metastatic spine tumor management and the impact of various advances on the field.

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.

Spinal Tumors: Diagnosis and Treatment

Tumors that present in or around the spine can be challenging to diagnose and treat. A proper workup involves a complete history and physical examination, appropriate staging studies, appropriate imaging of the entire spine, and a tissue biopsy. The biopsy defines the lesion and guides treatment, but in some rare instances, rapid neurological decline may lead to urgent or emergent surgery before it can be analyzed. "Enneking-appropriate" margins should remain the goal for primary tumors while adequate debulking/separation/stabilization are often the goals in metastatic disease. Primary tumors of the spine are rare and often complex tumors to operate on-achieving Enneking-appropriate margins provides the greatest chance of survival while decreasing the chance of local recurrence. Metastatic tumors of the spine are increasingly more common, and timing of surgery must be considered within the greater framework of the patient and the patient's disease, deficits, stability, and other treatments available. The specific tumor type will dictate what other multidisciplinary approaches are available, allowing for chemotherapy and radiation as needed.

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.

Prolonged durability of extensive contiguous spinal metastasis stabilization in non-small cell lung cancer patients receiving targeted therapy: two case reports and a literature review

Contiguous spinal metastasis poses a challenge for spine surgeons. In patients with a short remaining life expectancy, surgery may be discouraged. However, in select cases, surgery may be inevitable to eliminate pain and improve the patient’s quality of life. Additionally, with advancements in systemic cancer therapy, the efficacy and duration of tumor control have improved significantly. Consequently, a patient’s life expectancy may be difficult to estimate with existing prognostic scores. Because patients may achieve prolonged survival, spinal metastasis surgery could greatly benefit a patient’s quality of life. In this report, we present the details of two patients with non-small lung cancer with contiguous spinal metastasis who underwent spinal surgery for their metastatic disease. After surgery and targeted therapy with epidermal growth factor tyrosine kinase inhibitors (EGFR TKI), the patients attained substantial healing of their previously lytic spines and achieved prolonged survival of up to 42 months. With modern systemic therapy for lung cancer, the treatment of spinal metastatic disease can achieve decent outcomes, even in poor surgical candidates.

Spinal metastasis: narrative reviews of the current evidence and treatment modalities

The treatment for spinal metastasis has evolved significantly during the past decade. An advancement in systemic therapy has led to a prolonged overall survival in cancer patients, thus increasing the incidence of spinal metastasis. In addition, with the improved treatment armamentarium, the prediction of patient survival using traditional prognostic models may have limitations and these require the incorporation of some novel parameters to improve their prognostic accuracy. The development of minimally-invasive spinal procedures and minimal access surgical techniques have facilitated a quicker patient recovery and return to systemic treatment. These modern interventions help to alleviate pain and improve quality of life, even in candidates with a relatively short life expectancy. Radiotherapy may be considered in non-surgical candidates or as adjuvant therapy for improving local tumour control. Stereotactic radiosurgery has facilitated this even in radioresistant tumours and may even replace surgery in radiosensitive malignancies. This narrative review summarizes the current evidence leading to the paradigm shifts in the modern treatment of spinal metastasis.

The Palliative Care in the Metastatic Spinal Tumors. A Systematic Review on the Radiotherapy and Surgical Perspective

Spine represents the most common site for metastatic disease involvement. Due to the close relationship between the spinal cord and critical structures, therapeutical management of metastatic spinal cord disease remains challenging. Spinal localization can lead to neurological sequelae, which can significantly affect the quality of life in patients with a limited life expectancy. The authors conducted a systematic literature review according to PRISMA guidelines in order to determine the impact of the most updated palliative care on spinal metastases. The initial literature search retrieved 2526 articles, manually screened based on detailed exclusion criteria. Finally, 65 studies met the inclusion criteria and were finally included in the systematic review. In the wide scenario of palliative care, nowadays, recent medical or surgical treatments represent valuable options for ameliorating pain and improving patients QoL in such this condition.

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.

International external validation of the SORG machine learning algorithms for predicting 90-day and one-year survival of patients with spine metastases using a Taiwanese cohort

BACKGROUND CONTEXT

Accurately predicting the survival of patients with spinal metastases is important for guiding surgical intervention. The SORG machine-learning (ML) algorithm for the 90-day and one-year mortality of patients with metastatic cancer to the spine has been multiply validated, with a high degree of accuracy in both internal and external validation studies. However, prior external validations were conducted using patient groups located on the east coast of the United States, representing a generally homogeneous population. The aim of this study was to externally validate the SORG algorithms with a Taiwanese population.

STUDY DESIGN/SETTING

Retrospective study at a single tertiary care center in Taiwan PATIENT SAMPLE: Four hundred and twenty-seven patients who underwent surgery for metastatic spine disease from November 1, 2010 to December 31, 2018 OUTCOME MEASURES: 90-day and one-year mortality METHODS: The baseline characteristics of our validation cohort were compared with those of the previously published developmental and external validation cohorts. Discrimination (c-statistic and receiver operating curve), calibration (calibration plot, intercept, and slope), overall performance (Brier score), and decision curve analysis were used to assess the performance of the SORG ML algorithms in this cohort.

RESULTS

Ninety-day and one-year mortality rates were 110 of 427 (26%) and 256 of 427 (60%), respectively. The external validation cohort and the developmental cohort differed in body mass index (BMI), preoperative performance status, American Spinal Injury Association impairment scale, primary tumor histology and in several laboratory measurements. The SORG ML algorithm for 90-day and 1-year mortality demonstrated a high level of discriminative ability (c-statistics of 0.73 [95% confidence interval [CI], 0.67-0.78] and 0.74 [95% CI, 0.69-0.79]), overall performance, and had a positive net benefit throughout the range of threshold probabilities in decision curve analysis. The algorithm for 1-year mortality had a calibration intercept of 0.08, representing a good calibration. However, the 90-day mortality algorithm underestimated mortality for the lowest predicted probabilities, with an overall intercept of 0.81.

CONCLUSIONS

The SORG algorithms for predicting 90-day and 1-year mortality in patients with spinal metastatic disease generally performed well on international external validation in a predominately Taiwanese population. However, 90-day mortality was underestimated in this group. Whether this inconsistency was due to different primary tumor characteristics, body mass index, selection bias or other factors remains unclear, and may be better understood with further validative works that utilize international and/or diverse populations.

Spinal metastases 2021: a review of the current state of the art and future directions

Spinal metastases are an increasing societal health burden secondary to improvements in systemic therapy. Estimates indicate that 100,000 or more people have symptomatic spine metastases requiring management. While open surgery and external beam radiotherapy have been the pillars of treatment, there is growing interest in more minimally invasive technologies (eg separation surgery) and non-operative interventions (eg percutaneous cementoplasty, stereotactic radiosurgery). The great expansion of these alternatives to open surgery and the prevalence of adjuvant therapeutic options means that treatment decision making is now complex and reliant upon multidisciplinary collaboration. To help facilitate construction of care plans that meet patient goals and expectations, clinical decision aids and prognostic scores have been developed. These have been shown to have superior predictive value relative to more classic prediction models and may become an increasingly important aspect of the clinical practice of spinal oncology. Here we overview current therapeutic advances in the management of spine metastases and highlight emerging areas for research. Given the rapid advancements in surgical technologies and adjuvants, the field is likely to undergo further transformative changes in the coming decade.

Prognostic scores for survival as decisional support for surgery in spinal metastases: a performance assessment systematic review

PURPOSE

To review the evidence on the relative prognostic performance of the available prognostic scores for survival in spinal metastatic surgery in order to provide a recommendation for use in clinical practice.

METHODS

A systematic review of comparative external validation studies assessing the performance of prognostic scores for survival in independent cohorts was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. Eligible studies were identified through Medline and Embase until May 2021. Studies were included when they compared at least four survival scoring systems in surgical or mixed cohorts across all primary tumor types. Predictive performance was assessed based on discrimination and calibration for 3-month, 1-year and overall survival, and generalizability was assessed based on the characteristics of the development cohort and external validation cohorts. Risk of bias and concern regarding applicability were assessed based on the 'Prediction model study Risk Of Bias Assessment Tool' (PROBAST).

RESULTS

Twelve studies fulfilled the inclusion criteria and covered 17 scoring systems across 5.130 patients. Several scores suffer from suboptimal development and validation. The SORG Nomogram, developed in a large surgical cohort, showed good discrimination on 3-month and 1-year survival, good calibration and was superior in direct comparison with low risk of bias and low concern regarding applicability. Machine learning algorithms are promising as they perform equally well in direct comparison. Tokuhashi, Tomita and other traditional risk scores showed suboptimal performance.

CONCLUSION

The SORG Nomogram and machine learning algorithms outline superior performance in survival prediction for surgery in spinal metastases. Further improvement by comparative validation in large multicenter, prospective cohorts can still be obtained. Given the heterogeneity of spinal metastases, superior methodology of development and validation is key in improving future machine learning systems.

Utility of prediction model score: a proposed tool to standardize the performance and generalizability of clinical predictive models based on systematic review

OBJECTIVE

The objective of this study was to evaluate the characteristics and performance of current prediction models in the fields of spine metastasis and degenerative spine disease to create a scoring system that allows direct comparison of the prediction models.

METHODS

A systematic search of PubMed and Embase was performed to identify relevant studies that included either the proposal of a prediction model or an external validation of a previously proposed prediction model with 1-year outcomes. Characteristics of the original study and discriminative performance of external validations were then assigned points based on thresholds from the overall cohort.

RESULTS

Nine prediction models were included in the spine metastasis category, while 6 prediction models were included in the degenerative spine category. After assigning the proposed utility of prediction model score to the spine metastasis prediction models, only 1 reached the grade of excellent, while 2 were graded as good, 3 as fair, and 3 as poor. Of the 6 included degenerative spine models, 1 reached the excellent grade, while 3 studies were graded as good, 1 as fair, and 1 as poor.

CONCLUSIONS

As interest in utilizing predictive analytics in spine surgery increases, there is a concomitant increase in the number of published prediction models that differ in methodology and performance. Prior to applying these models to patient care, these models must be evaluated. To begin addressing this issue, the authors proposed a grading system that compares these models based on various metrics related to their original design as well as internal and external validation. Ultimately, this may hopefully aid clinicians in determining the relative validity and usability of a given model.

Comprehensive surgical treatment strategy for spinal metastases

The management of patients with spinal metastases (SM) requires a multidisciplinary team of specialists involved in oncological care. Surgical management has evolved significantly over the recent years, which warrants reevaluation of its role in the oncological treatment concept. Any patient with a SM was screened for study inclusion. We report baseline characteristics, surgical procedures, complication rates, functional status and outcome of a large consecutive cohort undergoing surgical treatment according to an algorithm. 667 patients underwent 989 surgeries with a mean age of 65 years (min/max 20–94) between 2007 and 2018. The primary cancers mostly originated from the prostate (21.7%), breast (15.9%) and lung (10.0%). Surgical treatment consisted of dorsoventral stabilization in 69.5%, decompression without instrumentation in 12.5% and kyphoplasty in 18.0%. Overall survival reached 18.4 months (95% CI 9.8–26.9) and the median KPS increased by 10 within hospital stay. Surgical management of SMs should generally represent the first step of a conclusive treatment algorithm. The need to preserve long-term symptom control and biomechanical stability requires a surgical strategy currently not supported by level I evidence.

To operate, or not to operate? Narrative review of the role of survival predictors in patient selection for operative management of patients with metastatic spine disease

Accurate prediction of patient survival is an essential component of the preoperative evaluation of patients with spinal metastases. Over the past quarter of a century, a number of predictors have been developed, although none have been accurate enough to be instituted as a staple of clinical practice. However, recently more comprehensive survival calculators have been published that make use of larger data sets and machine learning to predict postoperative survival among patients with spine metastases. Given the glut of calculators that have been published, the authors sought to perform a narrative review of the current literature, highlighting existing calculators along with the strengths and weaknesses of each. In doing so, they identify two "generations" of scoring systems-a first generation based on a priori factor weighting and a second generation comprising predictive tools that are developed using advanced statistical modeling and are focused on clinical deployment. In spite of recent advances, the authors found that most predictors have only a moderate ability to explain variation in patient survival. Second-generation models have a greater prognostic accuracy relative to first-generation scoring systems, but most still require external validation. Given this, it seems that there are two outstanding goals for these survival predictors, foremost being external validation of current calculators in multicenter prospective cohorts, as the majority have been developed from, and internally validated within, the same single-institution data sets. Lastly, current predictors should be modified to incorporate advances in targeted systemic therapy and radiotherapy, which have been heretofore largely ignored.

The importance of multidisciplinary care for spine metastases: initial tumor management

Spine metastases are very common in cancer patients often requiring urgent assessment and the initiation of therapy. Treatment paradigms have changed exponentially over the past decade with the evolution and integration of stereotactic body radiotherapy, minimally invasive spine techniques, and systemic options including biologics and checkpoint inhibitors. These advances necessitate multidisciplinary assessments and interventions to optimize outcomes. The NOMS framework provides a mechanism for all practitioners to evaluate the 4 sentinel assessments required to make decisions in patients with spine metastases: Neurologic, Oncologic, Mechanical Stability, and Systemic disease. The NOMS framework is continuously updated with the integration of newer technologies and evidence-based medicine as they become available. This paper presents the current iteration of NOMS with a focus on the role of medical and neuro-oncologists in the assessment and treatment of metastatic spine tumors.

Predicting tumor-specific survival in patients with spinal metastatic renal cell carcinoma: which scoring system is most accurate?

OBJECTIVE

Although several prognostic scores for spinal metastatic disease have been developed in the past 2 decades, the applicability and validity of these models to specific cancer types are not yet clear. Most of the data used for model formation are from small population sets and have not been updated or externally validated to assess their performance. Developing predictive models is clinically relevant as prognostic assessment is crucial to optimal decision-making, particularly the decision for or against spine surgery. In this study, the authors investigated the performance of various spinal metastatic disease risk models in predicting prognosis for spine surgery to treat metastatic renal cell carcinoma (RCC).

METHODS

Data of patients who underwent surgery for RCC metastatic to the spine at 2 tertiary centers between 2010 and 2019 were retrospectively retrieved. The authors determined the prognostic value associated with the following scoring systems: the Tomita score, original and revised Tokuhashi scores, original and modified Bauer scores, Katagiri score, the Skeletal Oncology Research Group (SORG) classic algorithm and nomogram, and the New England Spinal Metastasis Score (NESMS). Regression analysis of patient variables in association with 1-year survival after surgery was assessed using Cox proportional hazard models. Calibration and time-dependent discrimination analysis were tested to quantify the accuracy of each scoring system at 3 months, 6 months, and 1 year.

RESULTS

A total of 86 metastatic RCC patients were included (median age 64 years [range 29-84 years]; 63 males [73.26%]). The 1-year survival rate was 72%. The 1-year survival group had a good performance status (Karnofsky Performance Scale [KPS] score 80%-100%) and an albumin level > 3.5 g/dL (p < 0.05). Multivariable-adjusted Cox regression analysis showed that poor performance status (KPS score < 70%), neurological deficit (Frankel grade A-D), and hypoalbuminemia (< 3.5 g/dL) were associated with a higher risk of death before 1 year (p < 0.05). The SORG nomogram, SORG classic, original Tokuhashi, and original Bauer demonstrated fair performance (0.7 < area under the curve < 0.8). The NESMS differentiates survival among the prognostic categories with the highest accuracy (area under the curve > 0.8).

CONCLUSIONS

The present study shows that the most cited and commonly used scoring systems have a fair performance predicting survival for patients undergoing spine surgery for metastatic RCC. The NESMS had the best performance at predicting 1-year survival after surgery.

A Personalized Medicine Approach for the Management of Spinal Metastases with Cord Compression: Development of a Novel Clinical Prediction Model for Postoperative Survival and Quality of Life

Surgery should be considered for patients with metastatic epidural spinal cord compression (MESCC) with a life expectancy of ≥3 months. Given the heterogeneity of the clinical presentation and outcomes, clinical prognostic models (CPMs) can assist in tailoring a personalized medicine approach to optimize surgical decision-making. We aimed to develop and internally validate the first CPM of health-related quality of life (HRQoL) and a novel CPM to predict the survival of patients with MESCC treated surgically. Using data from 258 patients (AOSpine North America MESCC study and Nottingham MESCC registry), we created 1-year survival and HRQoL CPMs using a Cox model and logistic regression analysis with manual backward elimination. The outcome measure for HRQoL was the minimal clinical important difference in EuroQol 5-dimension questionnaire scores. Internal validation involved 200 bootstrap iterations, and calibration and discrimination were evaluated. Longer survival was associated with a higher SF-36 physical component score (hazard ratio [HR], 0.96). In contrast, primary tumor other than breast, thyroid, or prostate (unfavorable: HR, 2.57; other: HR, 1.20), organ metastasis (HR, 1.51), male sex (HR, 1.58), and preoperative radiotherapy (HR, 1.53) were not (c-statistic, 0.69; 95% confidence interval, 0.64-0.73). Karnofsky performance status <70% (odds ratio [OR], 2.50), living in North America (OR, 4.06), SF-36 physical component score (OR, 0.95) and SF-36 mental component score (OR, 0.96) were associated with the likelihood of achieving a minimal clinical important difference improvement in the EuroQol 5-Dimension Questionnaire score at 3 months (c-statistic, 0.74; 95% confidence interval, 0.68-0.79). The calibration for both CPMs was very good. We developed and internally validated the first CPMs of survival and HRQoL at 3 months postoperatively in patients with MESCC using the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) guidelines. A web-based calculator is available (available at: http://spine-met.com) to assist with clinical decision-making.

Fracture risk assessment and clinical decision making for patients with metastatic bone disease

Metastatic breast, prostate, lung, and other cancers often affect bone, causing pain, increasing fracture risk, and decreasing function. Management of metastatic bone disease (MBD) is clinically challenging when there is potential but uncertain risk of pathological fracture. Management of MBD has become a major focus within orthopedic oncology with respect to fracture and impending fracture care. If impending skeletal-related events (SREs), particularly pathologic fracture, could be predicted, increasing evidence suggests that prophylactic surgical treatment improves patient outcomes. However, current fracture risk assessment and radiographic metrics do not have high accuracy and have not been combined with relevant patient survival tools. This review first explores the prevalence, incidence, and morbidity of MBD and associated SREs for different cancer types. Strengths and limitations of current fracture risk scoring systems for spinal stability and long bone fracture are highlighted. More recent computed tomography (CT)-based structural rigidity analysis (CTRA) and finite element (FE) analysis methods offer advantages of increased specificity (true negative rate), but are limited in availability. Other fracture prediction approaches including parametric response mapping and positron emission tomography/computed tomography measures show early promise. Substantial new information to inform clinical decision-making includes measures of survival, clinical benefits, and economic analysis of prophylactic treatment compared to after-fracture stabilization. Areas of future research include use of big data and machine learning to predict SREs, greater access and refinement of CTRA/FE approaches, combination of clinical survival prediction tools with radiographically based fracture risk assessment, and net benefit analysis for fracture risk assessment and prophylactic treatment.

Prognostic factors for laryngeal sarcoma and nomogram development for prediction: a retrospective study based on SEER database

Background

Laryngeal sarcoma is an extremely rare malignant tumor of larynx and usually reported as case reports or small series. At present, there is no research based on big data about the prognostic factors affecting laryngeal sarcoma. Our study aimed to investigate the prognostic survival factors of laryngeal sarcoma and develop a comprehensive nomogram for predicting the survival of laryngeal sarcoma.

Methods

Data were obtained from the Surveillance, Epidemiology, and End Results (SEER) database to find patients diagnosed with laryngeal sarcoma from 1998 to 2016. The data were obtained using SEER Stat 8.3.5 software, collated, and analyzed by Excel 2016 software and SPSS (v25.0). Kaplan-Meier curves were used for survival analysis. The variables obtained by univariate analysis were introduced into the Cox proportional hazard model for multivariate analysis. The risk factors affecting the prognosis of laryngeal sarcoma were obtained (P<0.05 indicated statistical significance). The independent prognostic factors of laryngeal sarcoma were integrated and used to construct a nomogram.

Results

A total of 381 patients with laryngeal sarcoma were included. The median age of diagnosis was 67 years. The proportion of patients who had received surgical treatment was 62.73%, while 22.31% of patients had received no surgery. The 1-, 3-, 5-, and 10-year survival rates were 87%, 76%, 61%, and 45%, respectively. The median survival time was 102.35 months. Univariate analysis showed that increased age, primary site, pathology, pathological grade, and surgical treatment were significantly correlated with patient survival time and were risk factors for the patients' prognosis. Race, gender, and even lymph node metastasis were not significantly correlated with patient prognosis. The risk factors obtained from the univariate analysis were incorporated into the Cox risk model for multivariate analysis, the independent risk factors for prognosis of patients were: age (HR 1.569, 95% CI: 1.358–1.813, P<0.005), pathology (HR 0.834, 95% CI: 0.734–0.948, P<0.005), pathological grade (HR 1.433, 95% CI: 1.164–1.764, P<0.001), surgical treatment (HR 0.778, 95% CI: 0.696–0.870, P<0.000), primary site was excluded (P=0.092). We included all the risk factors from the multi-factor analysis to construct a nomogram, and the C-index value was 0.73, indicating that it was well-calibrated in the medium and long term.

Conclusions

Laryngeal sarcoma is a rare malignant tumor of the larynx, which most often affects people between the ages of 50 and 79 and males. Our study shows that age, pathology, pathological grade, surgical treatment may be the risk factors for patients’ prognosis. Based on this, we constructed a nomogram model with a prediction accuracy of 73% that could help clinicians make decisions on an individual basis.

Design of the prospective observational study of spinal metastasis treatment (POST)

BACKGROUND CONTEXT

There are several prognostic scores available that intend to inform decision-making for patients with spinal metastases. Many of these have not been found to reliably predict survival across the continuum of care. Recently, our group developed the New England Spinal Metastasis Score (NESMS). While the NESMS demonstrated many of the necessary attributes of a useful prediction tool, it has yet to be validated prospectively.

PURPOSE

To describe the prospective observational study of spinal metastasis treatment (POST). This investigation examined the performance of the NESMS, compared its predictive capacity with other scoring systems and determined its ability to identify patients who benefit the most from surgery.

STUDY DESIGN

Prospective observational study at two medical centers.

PATIENT SAMPLE

Patients age 18 and older with spinal metastases involving the spine.

OUTCOME MEASURES

Survival, post-treatment morbidity and health-related quality of life outcomes.

METHODS

The POST study assessed patients at baseline and at 1-month, 3-month, 6-month, and 12-month time-points. During the baseline assessment patient demographics, past medical history and assessment of co-morbidities, surgical history, primary tumor histology, and ambulatory status were recorded along with the designated treatment strategy (eg, operative or nonoperative). The NESMS and other predictive scores for each patient were calculated based on baseline data. Study-specific surveys administered at all time-points consisted of the EuroQuol 5-Dimension and Short-Form (SF)-12, Visual Analog Scale (VAS) for pain, and PROMIS assessment of global health.

RESULTS

Two hundred patients were enrolled in POST from 2017 to 2019. Patients were followed to one of the two predetermined study end-points (ie, mortality, or completion of the 12-month follow-up). Survival was considered the principle dependent variable. Post-treatment morbidity and health-related quality of life outcomes were considered secondarily. Analyses, by aim, relied on Cox proportional hazards regression, repeated measures logistic regression, propensity score matching and multivariable logistic regression.

CONCLUSION

The POST's findings are anticipated to provide evidence regarding the prognostic capabilities of the NESMS as well as that of other popular grading schemes for survival, post-treatment complications and physical as well as mental function.

Development of Machine Learning Algorithms for Prediction of 30-Day Mortality After Surgery for Spinal Metastasis

BACKGROUND

Preoperative prognostication of short-term postoperative mortality in patients with spinal metastatic disease can improve shared decision making around end-of-life care.

OBJECTIVE

To (1) develop machine learning algorithms for prediction of short-term mortality and (2) deploy these models in an open access web application.

METHODS

The American College of Surgeons, National Surgical Quality Improvement Program was used to identify patients that underwent operative intervention for metastatic disease. Four machine learning algorithms were developed, and the algorithm with the best performance across discrimination, calibration, and overall performance was integrated into an open access web application.

RESULTS

The 30-d mortality for the 1790 patients undergoing surgery for spinal metastatic disease was 8.49%. Preoperative factors used for prognostication were albumin, functional status, white blood cell count, hematocrit, alkaline phosphatase, spinal location (cervical, thoracic, lumbosacral), and severity of comorbid systemic disease (American Society of Anesthesiologist Class). In this population, machine learning algorithms developed to predict 30-d mortality performed well on discrimination (c-statistic), calibration (assessed by calibration slope and intercept), Brier score, and decision analysis. An open access web application was developed for the best performing model and this web application can be found here: https://sorg-apps.shinyapps.io/spinemets/.

CONCLUSION

Machine learning algorithms are promising for prediction of postoperative outcomes in spinal oncology and these algorithms can be integrated into clinically useful decision tools. As the volume of data in oncology continues to grow, creation of learning systems and deployment of these systems as accessible tools may significantly enhance prognostication and management.

Predictive Analytics in Spine Oncology Research: First Steps, Limitations, and Future Directions

The potential of big data analytics to improve the quality of care for patients with spine tumors is significant. At this moment, the application of big data analytics to oncology and spine surgery is at a nascent stage. As such, efforts are underway to advance data-driven oncologic care, improve patient outcomes, and guide clinical decision making. This is both relevant and critical in the practice of spine oncology as clinical decision making is often made in isolation looking at select variables deemed relevant by the physician. With rapidly evolving therapeutics in surgery, radiation, interventional radiology, and oncology, there is a need to better develop decision-making algorithms utilizing the vast data available for each patient. The challenges and limitations inherent to big data analyses are presented with an eye towards future directions.

Stereotactic body radiation therapy and minimally invasive surgery in the management of spinal metastases: a change in the paradigm

The main goal of treatment in spinal metastatic patients is local control of the disease, pain relief and the maintenance of ambulation. Traditionally, wide surgical resection of the tumour followed by adjuvant radiation and/or chemotherapy has been recommended. Currently, single-fraction or hypofractionated stereotactic body radiation therapy (SBRT) yields a one-year local control rate of over 95% with minimum morbidity, even for tumours previously considered radioresistant. In addition, by posterolateral and circumferential decompression and stabilisation of the spinal cord, it is feasible to create a 2 to 3 mm epidural margin between the dura mater and the tumour (separation surgery), enough to deliver safe and ablative doses of SBRT to the vertebrae. As these patients tend to be frail, such interventions should ideally be minimally invasive, thereby reducing surgical aggressiveness and helping to minimise the delay of any systemic therapies.

Prognostic models for spinal metastatic disease: evolution of methodologies, limitations, and future opportunities

Advances in cancer biology and therapy have increased survival of metastatic disease patients and, in turn, the rates of metastatic epidural spinal cord compression (MESCC). Surgery can improve patient quality of life, but accurate estimation of postoperative survival is critical for appropriate patient selection, multidisciplinary management, and shared decision making. Survival estimation on the basis of clinician judgement alone has been shown to be inaccurate and unreliable. Numerous prognostic scoring systems have been developed to address this need but the inputs to these models, the modeling methodologies, and the model outputs have evolved significantly over time. Here we discuss the available scoring systems, existing limitations, and future opportunities.

Predicting 90-Day and 1-Year Mortality in Spinal Metastatic Disease: Development and Internal Validation

BACKGROUND

Increasing prevalence of metastatic disease has been accompanied by increasing rates of surgical intervention. Current tools have poor to fair predictive performance for intermediate (90-d) and long-term (1-yr) mortality.

OBJECTIVE

To develop predictive algorithms for spinal metastatic disease at these time points and to provide patient-specific explanations of the predictions generated by these algorithms.

METHODS

Retrospective review was conducted at 2 large academic medical centers to identify patients undergoing initial operative management for spinal metastatic disease between January 2000 and December 2016. Five models (penalized logistic regression, random forest, stochastic gradient boosting, neural network, and support vector machine) were developed to predict 90-d and 1-yr mortality.

RESULTS

Overall, 732 patients were identified with 90-d and 1-yr mortality rates of 181 (25.1%) and 385 (54.3%), respectively. The stochastic gradient boosting algorithm had the best performance for 90-d mortality and 1-yr mortality. On global variable importance assessment, albumin, primary tumor histology, and performance status were the 3 most important predictors of 90-d mortality. The final models were incorporated into an open access web application able to provide predictions as well as patient-specific explanations of the results generated by the algorithms. The application can be found at https://sorg-apps.shinyapps.io/spinemetssurvival/

CONCLUSION

Preoperative estimation of 90-d and 1-yr mortality was achieved with assessment of more flexible modeling techniques such as machine learning. Integration of these models into applications and patient-centered explanations of predictions represent opportunities for incorporation into healthcare systems as decision tools in the future.