Development and validation of a prognostic nomogram for the overall survival of patients living with spinal metastases

Prognostic factors and novel prediction models for overall survival of patients with submandibular gland cancer: A population-based retrospective cohort study

Background

Accurately predicting the survival rate of submandibular gland cancer (SGC) is of significant importance for guiding treatment decision-making and improving patient outcomes. This study was aimed to identify the independent prognostic factors of overall survival (OS) in SGC patients, and develop novel prediction models to aid clinicians in predicting the survival probability.

Materials and methods

Patients diagnosed with primary SGC after the year 2010 were extracted from SEER database and then randomly allocated into training and test samples in a 7:3 ratio. Uni- and multi-variable COX analyses were employed using the training sample to ascertain independent prognostic factors for OS. Subsequently, graphic and online dynamic nomograms were established basing on the independent prognostic factors. We utilized C-index, calibration curve, receiver operating characteristic (ROC) curve, and area under ROC curve (AUC) value to evaluate the discrimination capacity and the consistency between predicted and actual survival.

Results

A total of 527 SGC patients were included (369 assigned to training group and 158 assigned to test group). The multivariable COX analysis showed that age, sex, marital status, tumor histology, summary stage, metastases to bone, and tumor size were independently associated with OS. Novel graphical and online dynamic (URL: https://yangxg1209.shinyapps.io/overall_survival_submandibular_gland_tumor/) nomograms were established. The C-indices (training: 0.77, 95%CI 0.71-0.84; test: 0.77, 95%CI 0.68-0.85) indicate favorable discrimination ability of the model, and the calibration curves demonstrated favorable consistency between the predicted and actual survival rates.

Conclusions

Our study identified the independent prognostic factors influencing OS in patients with SGC, and successfully established and validated novel nomograms, which provide accurate prediction of survival rates and allows for personalized risk assessment.

Development and validation of a prognostic nomogram for the renal relapse of lupus nephritis

OBJECTIVES

This study aims to assess the risk of relapse after complete remission (CR) and partial remission (PR), and to develop a prognostic nomogram predicting the probability in lupus nephritis (LN) patients.

METHODS

Data from patients with LN who had been in remission were collected as a training cohort. The prognostic factors were analyzed using the univariable and multivariable Cox model for the training group. A nomogram was then developed using significant predictors in multivariable analysis. Both discrimination and calibration were assessed by bootstrapping with 100 resamples.

RESULTS

A total of 247 participants were enrolled, including 108 in the relapse group and 139 in the no relapse group. In multivariate Cox analysis, Systemic Lupus Erythematosus Disease Activity Index (SLEDAI), erythrocyte sedimentation rate (ESR), complement 1q (C1q), and antiphospholipid (aPL), anti-Sm antibody were found to be significant for predicting relapse rates. The prognostic nomogram including the aforementioned factors effectively predicted 1- and 3-year probability of flare-free. Moreover, a favorable consistency between the predicted and actual survival probabilities was demonstrated using calibration curves.

CONCLUSIONS

High SLEDAI, ESR, and positive aPL, anti-Sm antibody are potential risk factors for LN flare, while high C1q can reduce its recurrence. The visualized model we established can help predict the relapse risk of LN and aid clinical decision-making for individual patients.

A nomogram prediction model for the TP53mut subtype in endometrial cancer based on preoperative noninvasive parameters

BACKGROUND

The molecular subtypes of endometrial carcinoma are significantly correlated with survival outcomes and can guide surgical methods and postoperative adjuvant therapy. Among them, the TP53mut subtype has the worst prognosis and can only be determined by detection after surgery. Therefore, identifying preoperative noninvasive clinical parameters for early prediction of the TP53mut subtype would provide important guidance in choosing the appropriate surgical method and early warning for clinicians. Our study aimed to establish a model for the early prediction of the TP53mut subtype by using preoperative noninvasive parameters of endometrial cancer and screen out potential TP53mut patients.

METHODS

Information and pathological specimens of 376 patients who underwent surgery for FIGO stage I-IV endometrial cancer in the Department of Gynecology, Peking University Cancer Hospital, from June 2011 to July 2020 were collected, and 178 cases were finally included in the study as the training dataset (part A). Thirty-six cases from January 2022 to March 2023 were collected as the validation dataset (part B). Molecular subtyping was performed using a one-stop next-generation sequencing (NGS) approach. Compared with the TP53mut subtype, the POLE EDM, MSI-H and TP53 wild-type subtypes were defined as non-TP53mut subtypes. Univariate Cox regression analysis and multivariate logistic analysis were performed to determine the preoperative clinical parameters associated with the TP53mut subtype. A nomogram prediction model was established using preoperative noninvasive parameters, and its efficacy in predicting TP53mut subtype and survival outcomes was verified.

RESULTS

The TP53mut subtype was identified in 12.4% of the part A and 13.9% of the part B. Multivariate logistic regression analysis showed that HDL-C/LDL-C level, CA125 level, and cervical or lower uterine involvement were independent influencing factors associated with the TP53mut subtype (p = 0.016, 0.047, <0.001). A TP53mut prognostic model (TPMM) was constructed based on the factors identified in the multivariate analysis, namely, TPMM = -1.385 × HDL-C/LDL-C + 1.068 × CA125 + 1.89 × CI or LUI, with an AUC = 0.768 (95% CI, 0.642 to 0.893) in the part A. The AUC of TPMM for predicting TP53mut subtype in the part B was 0.781(95% CI, 0.581 to 0.980). The progression-free survival (PFS) and overall survival (OS) of patients with the TP53mut subtype were significantly worse than those of patients with the non-TP53mut subtype, as predicted by the model in the part A.

CONCLUSIONS

TP53mut prediction model (TPMM) had good diagnostic accuracy, and survival analysis showed the model can identify patients with different prognostic risk.

Development of a Dynamic Nomogram for Predicting the Probability of Satisfactory Recovery after 6 Months for Cervical Traumatic Spinal Cord Injury

OBJECTIVE

Cervical traumatic spinal cord injury (CTSCI) is a seriously disabling disease that severely affects the physical and mental health of patients and imposes a huge economic burden on patients and their families. Accurate identification of the prognosis of CTSCI patients helps clinicians to design individualized treatment plans for patients. For this purpose, a dynamic nomogram was developed to predict the recovery of CTSCI patients after 6 months.

METHODS

We retrospectively included 475 patients with CTSCI in our institution between March 2013 and January 2022. The outcome variable of the current study was a satisfactory recovery of patients with CTSCI at 6 months. Univariate analyses and univariate logistic regression analyses were used to assess the factors affecting the prognosis of patients with CTSCI. Subsequently, variables (P < 0.05) were included in the multivariate logistic regression analysis to evaluate these factors further. Eventually, a nomogram model was constructed according to these independent risk factors. The concordance index (C-index) and the calibration curve were utilized to assess the model's predictive ability. The discriminating capacity of the prediction model was measured by the receiver operating characteristic (ROC) area under the curve (AUC). One hundred nine patients were randomly selected from 475 patients to serve as the center's internal validation test cohort.

RESULTS

The multivariate logistic regression model further screened out six independent factors that impact the recovery of patients with CTSCI. Including admission to the American Spinal Injury Association Impairment Scale (AIS) grade, the length of high signal in the spinal cord, maximum spinal cord compression (MSCC), spinal segment fractured, admission time, and hormonal therapy within 8 h after injury. A nomogram prediction model was developed based on the six independent factors above. In the training cohort, the AUC of the nomogram that included these predictors was 0.879, while in the test cohort, it was 0.824. The nomogram C-index incorporating these predictors was 0.872 in the training cohort and 0.813 in the test cohort, while the calibration curves for both cohorts also indicated good consistency. Furthermore, this nomogram was converted into a Web-based calculator, which provided individual probabilities of recovery to be generated for individuals with CTSCI after 6 months and displayed in a graphical format.

CONCLUSION

The nomogram, including ASIA grade, the length of high signal in the spinal cord, MSCC, spinal segment fractured, admission time, and hormonal therapy within 8 h after injury, is a promising model to predict the probability of content recovery in patients with CTSCI. This nomogram assists clinicians in stratifying patients with CTSCI, enhancing evidence-based decision-making, and individualizing the most appropriate treatment.

Metabolic risk score as a predictor in a nomogram for assessing myometrial invasion for endometrial cancer

The purpose of the present study was to investigate the predictive value of metabolic syndrome in evaluating myometrial invasion (MI) in patients with endometrial cancer (EC). The study retrospectively included patients with EC who were diagnosed between January 2006 and December 2020 at the Department of Gynecology of Nanjing First Hospital (Nanjing, China). The metabolic risk score (MRS) was calculated using multiple metabolic indicators. Univariate and multivariate logistic regression analyses were performed to determine significant predictive factors for MI. A nomogram was then constructed based on the independent risk factors identified. A calibration curve, a receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the effectiveness of the nomogram. A total of 549 patients were randomly assigned to a training or validation cohort, with a 2:1 ratio. Data was then gathered on significant predictors of MI in the training cohort, including MRS [odds ratio (OR), 1.06; 95% confidence interval (CI), 1.01-1.11; P=0.023], histological type (OR, 1.98; 95% CI, 1.11-3.53; P=0.023), lymph node metastasis (OR, 3.15; 95% CI, 1.61-6.15; P<0.001) and tumor grade (grade 2: OR, 1.71; 95% CI, 1.23-2.39; P=0.002; Grade 3: OR, 2.10; 95% CI, 1.53-2.88; P<0.001). Multivariate analysis indicated that MRS was an independent risk factor for MI in both cohorts. A nomogram was generated to predict a patient's probability of MI based on the four independent risk factors. ROC curve analysis showed that, compared with the clinical model (model 1), the combined model with MRS (model 2) significantly improved the diagnostic accuracy of MI in patients with EC (area under the curve in model 1 vs. model 2: 0.737 vs. 0.828 in the training cohort and 0.713 vs. 0.759 in the validation cohort). Calibration plots showed that the training and validation cohorts were well calibrated. DCA showed that a net benefit is obtained from the application of the nomogram. Overall, the present study developed and validated a MRS-based nomogram predicting MI in patients with EC preoperatively. The establishment of this model may promote the use of precision medicine and targeted therapy in EC and has the potential to improve the prognosis of patients affected by EC.

A population-based study of synchronous distant metastases and prognosis in patients with PDAC at initial diagnosis

Objective

Cancer of the pancreas is a life-threatening condition and has a high distant metastasis (DM) rate of over 50% at diagnosis. Therefore, this study aimed to determine whether patterns of distant metastases correlated with prognosis in pancreatic ductal adenocarcinoma (PDAC) with metastatic spread, and build a novel nomogram capable of predicting the 6, 12, 18-month survival rate with high accuracy.

Methods

We analyzed data from the Surveillance, Epidemiology, and End Results (SEER) database for cases of PDAC with DM. Kaplan-Meier analysis, log-rank tests and Cox-regression proportional hazards model were used to assess the impact of site and number of DM on the cancer-specific survival (CSS) and over survival (OS). A total of 2709 patients with DM were randomly assigned to the training group and validation group in a 7:3 ratio. A nomogram was constructed by the dependent risk factors which were determined by multivariate Cox-regression analysis. An assessment of the discrimination and ability of the prediction model was made by measuring AUC, C-index, calibration curve and decision curve analysis (DCA). In addition, we collected 98 patients with distant metastases at the time of initial diagnosis from Ningbo University Affiliated LiHuili Hospital to verify the efficacy of the prediction model.

Results

There was a highest incidence of liver metastases from pancreatic cancer (2387,74.36%), followed by lung (625,19.47%), bone (190,5.92%), and brain (8,0.25%). The prognosis of liver metastases differed from that of lung metastases, and the presence of multiple organ metastases was associated with poorer prognosis. According to univariate and multivariate Cox-regression analyses, seven factors (i.e., diagnosis age, tumor location, grade of tumor differentiation, T-stage, receipt of surgery, receipt of chemotherapy status, presence of multiple organ metastases) were included in our nomogram model. In internal and external validation, the ROC curves, C-index, calibration curves and DCA were calculated, which confirmed that this nomogram can precisely predict prognosis of PDAC with DM.

Conclusion

Metastatic PDAC patients with liver metastases tended to have a worse prognosis than those with lung metastases. The number of DM had significant effect on the overall survival rate of metastatic PDAC. This study had a high prediction accuracy, which was helpful clinicians to analyze the prognosis of PDAC with DM and implement individualized diagnosis and treatment.

Bisphosphonates May Reduce Intraoperative Blood Loss in Surgery for Metastatic Spinal Disease: A Retrospective Cohort Study

Purpose

This study was undertaken to investigate the relationship between bisphosphonate use and intraoperative blood loss following surgery for metastatic spinal disease.

Methods

We retrospectively analyzed cancer patients who were treated by metastatic spinal tumor surgery at our institution. Recorded data included intraoperative blood loss, timing and duration of bisphosphonate use, and other important confounding factors. We showed the results of crude model, minimally adjusted model, and fully adjusted model to fully observe the effects of bisphosphonates under different adjustment strategies. The timing and duration of bisphosphonate exposure were assessed and statistical results were tested to identify a trend.

Results

A total of 467 patients were treated by metastatic spinal tumor surgery, with or without bisphosphonate treatments. In all adjustment strategies, intraoperative blood loss was lower in patients using bisphosphonates than in patients without bisphosphonate treatments. In the fully adjusted model, the effect size, confidence interval, and p value were -246.4, -447.0 to -45.8, and 0.017, respectively. In terms of duration, all three models showed the same duration-response relationship: a longer duration of bisphosphonate use accurately predicted a smaller amount of blood loss (p for trend <0.001). We observed an interaction between operative time and bisphosphonate use, the effect size in the bottom tertile group was significantly smaller than that in the other two groups.

Conclusion

We found that the preoperative use of bisphosphonates could reduce the amount of intraoperative blood loss during metastatic spinal tumor surgery, especially for surgery with longer operative time.

A nomogram prediction model for lymph node metastasis in endometrial cancer patients

Background

This study aimed to explore the risk factors for lymph node metastasis (LNM) in patients with endometrial cancer (EC) and develop a clinically useful nomogram based on clinicopathological parameters to predict it.

Methods

Clinical information of patients who underwent staging surgery for EC was abstracted from Qilu Hospital of Shandong University from January 1st, 2005 to June 31st, 2019. Parameters including patient-related, tumor-related, and preoperative hematologic examination-related were analyzed by univariate and multivariate logistic regression to determine the correlation with LNM. A nomogram based on the multivariate results was constructed and underwent internal and external validation to predict the probability of LNM.

Results

The overall data from the 1517 patients who met the inclusion criteria were analyzed. 105(6.29%) patients had LNM. According the univariate analysis and multivariate logistic regression analysis, LVSI is the most predictive factor for LNM, patients with positive LVSI had 13.156-fold increased risk for LNM (95%CI:6.834–25.324; P < 0.001). The nomogram was constructed and incorporated valuable parameters including histological type, histological grade, depth of myometrial invasion, LVSI, cervical involvement, parametrial involvement, and HGB levels from training set. The nomogram was cross-validated internally by the 1000 bootstrap sample and showed good discrimination accuracy. The c-index for internal and external validation of the nomogram are 0.916(95%CI:0.849–0.982) and 0.873(95%CI:0.776–0.970), respectively.

Conclusions

We developed and validated a 7-variable nomogram with a high concordance probability to predict the risk of LNM in patients with EC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08466-4.

Nomogram for Predicting the Postoperative Venous Thromboembolism in Spinal Metastasis Tumor: A Multicenter Retrospective Study

Introduction

Venous thromboembolism can be divided into deep vein thrombosis and pulmonary embolism. These diseases are a major factor affecting the clinical prognosis of patients and can lead to the death of these patients. Unfortunately, the literature on the risk factors of venous thromboembolism after surgery for spine metastatic bone lesions are rare, and no predictive model has been established.

Methods

We retrospectively analyzed 411 cancer patients who underwent metastatic spinal tumor surgery at our institution between 2009 and 2019. The outcome variable of the current study is venous thromboembolism that occurred within 90 days of surgery. In order to identify the risk factors for venous thromboembolism, a univariate logistic regression analysis was performed first, and then variables significant at the P value less than 0.2 were included in a multivariate logistic regression analysis. Finally, a nomogram model was established using the independent risk factors.

Results

In the multivariate logistic regression model, four independent risk factors for venous thromboembolism were further screened out, including preoperative Frankel score (OR=2.68, 95% CI 1.78-4.04, P=0.001), blood transfusion (OR=3.11, 95% CI 1.61-6.02, P=0.041), Charlson comorbidity index (OR=2.01, 95% CI 1.27-3.17, P=0.013; OR=2.29, 95% CI 1.25-4.20, P=0.017), and operative time (OR=1.36, 95% CI 1.14-1.63, P=0.001). On the basis of the four independent influencing factors screened out by multivariate logistic regression model, a nomogram prediction model was established. Both training sample and validation sample showed that the predicted probability of the nomogram had a strong correlation with the actual situation.

Conclusion

The prediction model for postoperative VTE developed by our team provides clinicians with a simple method that can be used to calculate the VTE risk of patients at the bedside, and can help clinicians make evidence-based judgments on when to use intervention measures. In clinical practice, the simplicity of this predictive model has great practical value.

Establishment and validation of nomogram model for survival predicting in patients with spinal metastases secondary to lung cancer

OBJECTIVES

To evaluate the prognostic effect of pre-treatment factors in patients with spinal metastases secondary to lung cancer, and establish a novel predicting nomogram for predicting the survival probability.

METHODS

A total of 209 patients operated for spinal metastases from lung cancer were consecutively enrolled, and divided into the training and validation samples with a ratio of 7:3, for model establishing and validating, respectively. Basing on the training sample, univariate and multivariate COX proportional hazard models were used for identifying the prognostic effect of pre-treatment factors, following which significant prognostic factors would be listed as items in nomogram to calculate the survival probabilities at 3, 6, 12 and 18 months. Then, the C-indexes and the calibration curves would be figured out to evaluate the discrimination ability and accuracy of the model both for the training and validation samples.

RESULTS

In the multivariate COX analysis, the gender, smoking history, location of spinal metastasis, visceral metastasis, Karnofsky performance status (KPS), adjuvant therapy, lymphocyte percentage and globulin were found to be significantly associated with the overall survival, and a novel nomogram was generated basing on these independent predictors. The C-indexes for the training and validation samples were 0.761 and 0.732, respectively. Favorable consistencies between the predicted and actual survival rates were demonstrated both in the internal and external validations.

DISCUSSION

Pre-treatment characteristics, including gender, smoking history, location of spinal metastasis, visceral metastasis, KPS, adjuvant therapy, percentage of lymphocyte, and serum globulin level, were identified to be significantly associated with overall survival of patients living with spinal metastases derived from lung cancer, and a user-friendly nomogram was established using these independent predictors.

Predicting postoperative quality-of-life outcomes in patients with metastatic spine disease: who benefits?

Symptomatic spinal metastasis occurs in around 10% of all cancer patients, 5%-10% of whom will require operative management. While postoperative survival has been extensively evaluated, postoperative health-related quality-of-life (HRQOL) outcomes have remained relatively understudied. Available tools that measure HRQOL are heterogeneous and may emphasize different aspects of HRQOL. The authors of this paper recommend the use of the EQ-5D and Spine Oncology Study Group Outcomes Questionnaire (SOSGOQ), given their extensive validation, to capture the QOL effects of systemic disease and spine metastases. Recent studies have identified preoperative QOL, baseline functional status, and neurological function as potential predictors of postoperative QOL outcomes, but heterogeneity across studies limits the ability to derive meaningful conclusions from the data. Future development of a valid and reliable prognostic model will likely require the application of a standardized protocol in the context of a multicenter study design.

Development and Internal Validation of a Nomogram to Predict Post-Stroke Fatigue After Discharge

OBJECTIVES

We aimed to develop and validate a nomogram for the individualized prediction of the risk of post-stroke fatigue (PSF) after discharge.

MATERIALS AND METHODS

Fatigue was measured using the Fatigue Assessment Scale. Multivariable logistic regression analysis was applied to build a prediction model incorporating the feature selected in the least absolute shrinkage and selection operator regression model. Discrimination, calibration, and clinical usefulness of the predictive model were assessed using the C-index, calibration plot, and decision curve analysis. Internal validation was conducted using bootstrapping validation. Finally, a web application was developed to facilitate the use of the nomogram.

RESULTS

We developed a nomogram based on 95 stroke patients. The predictors included in the nomogram were sex, pre-stroke sarcopenia, acute phase fatigue, dysphagia, and depression. The model displayed good discrimination, with a C-index of 0.801 (95% confidence interval: 0.700-0.902) and good calibration. A high C-index value of 0.762 could still be reached in the interval validation. Decision curve analysis showed that the risk of PSF after discharge was clinically useful when the intervention was decided at the PSF risk possibility threshold of 10% to 90%.

CONCLUSION

This nomogram could be conveniently used to provide an individual, visual, and precise prediction of the risk probability of PSF after being discharged home. Thus, as an aid in decision-making, physicians and other healthcare professionals can use this predictive method to provide early intervention or a discharge plan for stroke patients during the hospitalization period.

A Novel Prediction Tool for Overall Survival of Patients Living with Spinal Metastatic Disease

OBJECTIVE

To identify the significant prognostic factors for overall survival in patients with spinal metastases and to establish an online widget for predicting survival with an interactive visual approach.

METHODS

Patients operated for spinal metastases between 2010 and 2018 were retrospectively enrolled and were randomly divided into training and validation samples with a ratio of 7:3. Patients' characteristics were analyzed with univariate and multivariate Cox analyses to identify independent prognostic factors basing on the training sample. A shiny web tool was developed by transforming the fitted multivariable Cox model into a visual interface. Time-dependent area under the curve plot and calibration curve were generated to assess the discrimination ability and consistency of the novel model, both for the training and validation samples.

RESULTS

A total of 265 consecutive patients were finally included, with 185 in the training sample and 80 in the validation sample. The primary tumor types, lesion site of metastasis, visceral metastasis, Frankel grade, operation category, number of surgical segments, and the preoperative percentage of lymphocyte were demonstrated to be significantly associated with overall survival. A novel shiny model (https://yang1209xg.shinyapps.io/predictspinalmetastasis/) that could provide predicted survival curve and median survival time was established, with favorable discrimination ability and consistency between predicted and actual survival both in internal and external data, according to time-dependent area under the curve plots and calibration curves.

CONCLUSIONS

A user-friendly shiny app with favorable discrimination ability and consistency was released online for predicting the survival of patients with spinal metastases. A continuous survival curve and the predicted median survival time are available to guide the treatment planning.

Predictive Model and Risk Factors for Case Fatality of COVID-19: A Cohort of 21,392 Cases in Hubei, China

An increasing number of patients are being killed by coronavirus disease 2019 (COVID-19), however, risk factors for the fatality of COVID-19 remain unclear. A total of 21,392 COVID-19 cases were recruited in the Hubei Province of China between December 2019 and February 2020, and followed up until March 18, 2020. We adopted Cox regression models to investigate the risk factors for case fatality and predicted the death probability under specific combinations of key predictors. Among the 21,392 patients, 1,020 (4.77%) died of COVID-19. Multivariable analyses showed that factors, including age (≥60 versus <45 years, hazard ratio [HR] = 7.32; 95% confidence interval [CI], 5.42, 9.89), sex (male versus female, HR = 1.31; 95% CI, 1.15, 1.50), severity of the disease (critical versus mild, HR = 39.98; 95% CI, 29.52, 48.86), comorbidity (HR = 1.40; 95% CI, 1.23, 1.60), highest body temperature (>39°C versus <39°C, HR = 1.28; 95% CI, 1.09, 1.49), white blood cell counts (>10 × 10⁹/L versus (4–10) × 10⁹/L, HR = 1.69; 95% CI, 1.35, 2.13), and lymphocyte counts (<0.8 × 10⁹/L versus (0.8–4) × 10⁹/L, HR = 1.26; 95% CI, 1.06, 1.50) were significantly associated with case fatality of COVID-19 patients. Individuals of an older age, who were male, with comorbidities, and had a critical illness had the highest death probability, with 21%, 36%, 46%, and 54% within 1–4 weeks after the symptom onset. Risk factors, including demographic characteristics, clinical symptoms, and laboratory factors were confirmed to be important determinants of fatality of COVID-19. Our predictive model can provide scientific evidence for a more rational, evidence-driven allocation of scarce medical resources to reduce the fatality of COVID-19.

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21,392 COVID-19 patients constituted one of the largest cohort studies to date

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Elderly male patients with critical illness and comorbidities had higher death rate

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The death probability increased with time, which was evident for critically ill patients

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The highest death probability within 1 month can reach 54% by the predictive model

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The predictive model could guide the allocation of medical resources

Percutaneous vertebral augmentation procedures in the management of spinal metastases

Bone metastasis is a common complication of cancer, and bone is the third most common metastatic site following the lung and liver. Among the various bones, spine is the most common site of metastatic tumors. The treatment goals of patients with spinal metastases are mostly palliative, with the aim of reducing pain and improving quality of life. The treatment of spinal metastases has made significant progress over the past few decades. Each new technology has tried to solve the shortcomings of its predecessors. Currently, there are no mature algorithms or specific techniques that have proven to be the best for spinal metastases, and the treatment method often relies on operator and institutional preferences or biases in some cases. Percutaneous vertebral augmentation has unique value in the management of spinal metastases, understanding its indications, surgical techniques, uses, advantages and complications is critical to providing optimal patient care. We believe that the application of percutaneous vertebral augmentation alone or combined with other techniques can achieve optimal pain relief and functional improvement in the patients with spinal metastases.