Analysis of Four Scoring Systems for the Prognosis of Patients with Metastasis of the Vertebral Column

Prognostic Factors Associated With Overall Survival in Breast Cancer Patients With Metastatic Spinal Disease

Introduction The spine is the third most frequent site of metastasis, after the lungs and liver, in breast cancer patients. The current treatment modality is based on the prognosis calculated according to multiple clinical features; therefore, multiple scores have been developed to make the therapeutic decision; however, there are no specific scores to take an adequate therapeutic approach in the treatment of vertebral metastases due to breast cancer. The aim of the study is to identify the prognostic factors associated with survival in breast cancer patients with spinal metastatic disease. Methods A retrospective cohort study was carried out at the National Cancerology Institute (INCAN) in Mexico City from January 2011 to December 2017. To this extent, 56 consecutive cases of patients with breast cancer were included. Multiple demographic, laboratory, and clinical variables were taken into account for the survival calculation. Kaplan-Meier graphs and log-rank tests were performed to observe significant differences by subgroups in survival, and Cox regression was used for multivariate analysis. Results Concerning the survival analysis, the patients who presented extra-spinal metastases, an unstable spine, and Frankel grade C had a statistically significantly worse prognosis. In the multivariate analysis, the variables included extra-spinal metastases, age >50 years, spinal instability, serum alkaline phosphatase, and CA 15.3 serum levels, finding statistical significance with a p=0.015. Conclusion Prognostic factors associated with shorter overall survival in breast cancer patients with metastatic spinal disease were the presence of extra-spinal metastases and spinal instability. Additionally, the use of the Tomita and Tokuhashi scores for patients with breast cancer and spinal metastases is not justified at present. The study should be continued with a larger population to decrease biases and obtain a more homogeneous sample, as well as to obtain a personalized score to determine a more efficient treatment for these patients.

Multiple Myeloma Spinal Lesion Care: Management of a Primary Bone Malignancy Rather Than a Spinal Metastasis

OBJECTIVE

Multiple myeloma (MM) is too often wrongly categorized as a spinal metastasis (SpM), although it is distinguishable from SpM in many aspects, such as its earlier natural history at the time of diagnosis, its increased overall survival (OS), and its response to therapeutic modalities. The characterization of these 2 different spine lesions remains a main challenge.

METHODS

This study compares 2 consecutive prospective oncologic populations of patients with spine lesions: 361 patients treated for MM spine lesions and 660 patients treated for SpM between January 2014 and 2017.

RESULTS

The mean time between the tumor/MM diagnosis and spine lesions was respectively 0.3 (standard deviation [SD] 4.1) and 35.1 months (SD 21.2) for the MM and SpM groups. The median OS for the MM group was 59.6 months (SD 6.0) versus 13.5 months (SD 1.3) for the SpM group (P < 0.0001). Regardless of Eastern Cooperative Oncology Group (ECOG) performance status, patients with MM always have a significantly better median OS than do patients with SpM: ECOG 0, 75.3 versus 38.7 months; ECOG 1, 74.3 versus 24.7 months; ECOG 2, 34.6 versus 8.1 months; ECOG 3, 13.5 versus 3.2 months and ECOG 4, 7.3 versus 1.3 months (P < 0.0001). The patients with MM had more diffuse spinal involvement (mean, 7.8 lesions; SD 4.7) than did patients with SpM (mean, 3.9; SD 3.5) (P < 0.0001).

CONCLUSIONS

MM must be considered as a primary bone tumor, not as SpM. The strategic position of the spine in the natural course of cancer (i.e., nurturing cradle of birth for MM vs. systemic metastases spreading for SpM) explains the differences in OS and outcome.

A Novel Prognostication System for Spinal Metastasis Patients Based on Network Science and Correlation Analysis

AIMS

During the progress of oncological diseases, there is an increased probability that spinal metastases may develop, requiring personalised treatment options. Risk calculator systems aim to provide assistance in the therapeutic decision-making process by estimating survival chances. The predictive ability of such calculators can be improved, thereby optimising the choice of personalised therapy. The aim of this research was to create a new risk assessment system and show a method with which other centres can develop their own local score.

MATERIALS AND METHODS

We created a database by retrospectively processing 454 patients. The prognostic factors were selected via a network science-based correlation analysis that maximises Uno's C-index, keeping only a small number of predictors. To validate the new system, we calculated the D-statistic, the Integrated Discrimination Index, made a five-fold cross-validation and also calculated the integrated time-dependent Brier score.

RESULTS

As a result of multivariate Cox analysis, we found five independent prognostic factors suitable for the design of the risk calculator. This new system has a better predictive ability compared with six other well-known systems with an average C-index of 0.706 at 10 years (95% confidence interval 0.679-0.733).

CONCLUSIONS

An accurate estimation of the life expectancy of cancer patients is essential for the implementation of personalised medicine. The training performance of our system is encouraging, indicating the benefit of a network science-based visualisation step. We believe that in order to further improve the prediction ability, it is necessary to systematise previously 'unknown' factors (e.g. radiological morphology).

Predicting survival of patients with spinal metastatic disease using PathFx 3.0 - A validation study of 668 patients in Sweden

Introduction

PathFx is a computer-based prediction model for estimating survival of patients with bone metastasis. The model has been validated in several studies, but this is the first validation using exclusively patients with spinal metastases.

Research question

Is PathFx 3.0 a tool useful for predicting survival for patients with spinal metastatic disease?

Material and methods

668 patients (67% male, median age 67 years) presenting with spinal metastases at two university hospitals in Sweden 1991-2014 were included. Of those, the majority (82%, n ​= ​551) underwent surgery. Data on all patients was analyzed with PathFx version 3.0, generating a probability of survival at 1, 3, 6, 12, 18 and 24 months. The predictions were compared to real survival data and the precision in estimation was evaluated with Receiver-Operating Characteristic curve (ROC) analysis where the Area Under Curve (AUC) was calculated. Brier score and decision curve analyses were also assessed.

Results

The AUC for 1-, 3-, 6- and 12 months survival predictions were 0.64 (95% CI 0.5-0.71), 0.71 (95% CI 0.67-0.75), 0.70 (95% CI 0.66-0.77) and 0.74 (95% CI 0.70-0.78). For 18- and 24 months survival the AUC were 0.74 (95% CI 0.69-0.78) and 0.76 (95% CI 0.72-0.81). The Brier scores were all 0.23 or lower depending on the estimated survival time.

Discussion and conclusion

PathFx 3.0 is a reasonably reliable tool for predicting survival in patients with spinal metastatic disease. As the PathFx computer model can be updated to reflect advancements in oncology, we suggest this type of model, rather than rigid point-based scoring systems, to be used for estimating survival in patients with metastatic spinal disease in the future.

Are spine metastasis survival scoring systems outdated and do they underestimate life expectancy? Caution in surgical recommendation guidance

OBJECTIVE

Survival scoring systems for spine metastasis (SPM) were designed to help surgical practice. The authors sought to validate the prognostic accuracy of the main preoperative scoring systems for SPM.

METHODS

It was hypothesized that true patient survival in SPM was better than that predicted using prognosis scores. To investigate this hypothesis, the authors designed a French national retrospective study of a prospectively collected multicenter database involving 739 patients treated for SPM between 2014 and 2017.

RESULTS

In this series, the median survival time for all patients from an SPM diagnosis was 17.03 ± 1.5 months. Sensitivity and specificity were estimated using the area under the curve (AUC). The AUC of Tomita's prognosis score was the lowest and poorest (0.4 ± 0.023, range 0.35-0.44), whereas the AUC of the Tokuhashi score was the highest (0.825). The Lei score presented an AUC of 0.686 ± 0.022 (range 0.64-0.7), and the Rades score showed a weaker AUC (0.583 ± 0.020, range 0.54-0.63). Differences among AUCs were all statistically significant (p < 0.001). The modified Bauer score and the Rades score had the highest rate of agreement in predicting survival, with a weighted Cohen's kappa of 0.54 and 0.41, respectively, indicating a moderate agreement. The revised Tokuhashi and Lei scores had a fair rate of agreement (weighted Cohen's kappa = 0.24 and 0.22, respectively). The van der Linden and Tomita scores demonstrated the worst performance, with only a "slight" rate of agreement (weighted Cohen's kappa = 0.19 and 0.16, respectively) between what was predicted and the actual survival.

CONCLUSIONS

The use of prognostic scoring systems in the estimation of survival in patients with SPM has become obsolete and therefore underestimates survival. Surgical treatment decisions should no longer be based on survival estimations alone but must also take into account patient symptoms, spinal instability, and quality of life.

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.

Prognostic Significance of a Novel Score Model Based on Preoperative Indicators in Patients with Breast Cancer Spine Metastases (BCSM)

Background

Surgery remains the mainstay of treatment for breast cancer spinal metastasis (BCSM) to relieve symptoms and improve the quality of life of BCSM patients. Therefore, it is important to effectively predict the prognosis of patients to determine whether they can undergo surgical operation. However, the prevalent methods for prognosis evaluation lack specificity and sensitivity for indicated malignancies like breast cancer because they are built on a relatively small number of heterogeneous types of primary tumors. The aim of the present study was to explore a novel predictive model based on the clinical, pathological and blood parameters obtained from BCSM patients before they received surgical intervention.

Methods

Altogether, 144 patients were included in this study. Univariate and multivariate analyses were performed to investigate the significance of preoperative parameters and identify independent factors for prognostic prediction of BCSM. A nomogram for survival prediction was then established and validated. Time-dependent ROC (TDROC) curves were graphed to evaluate the accuracy of the novel model vs other scoring systems including Tomita Score, revised Tokuhashi Score, modified Bauer Score and New England Spinal Metastasis Score. P values <0.05 were considered statistically significant.

Results

Independent factors, including preoperative postmenopausal (P=0.034), visceral metastasIs (P=0.021), preoperative Frankel Score (P=0.001), estrogen receptor status (P=0.014), platelet-to-lymphocyte ratio (P=0.012), lymphocyte-monocyte ratio (P<0.001) and albumin-globulin ratio (P=0.017), were selected into the nomogram model with the C-index of 0.834 (95% CI, 0.789–0.890). TDROC curves showed that the Changzheng Hospital (CZ) Score system had the best performance and exhibited the largest IAUC value in comparison with the other scoring systems.

Conclusion

We constructed a nomogram model known as CZ Score based on the significant factors to predict the prognosis for BCSM patients. The result showed that CZ Score had a better value for prognostic evaluation and surgical decision-making as compared with the other scoring systems.

Research on the predicting power of the revised Tokuhashi system: how much time can surgery give to patients with short life expectancy?

Object

The primary treatment option for symptomatic metastatic spinal tumors is surgery. Prognostic systems are designed to assist in the establishment of the indication and the choice of surgical methodology. The best-known prognostic system is the revised Tokuhashi system, which has a predictive ability of about 60%. In our study, we are attempting to find the reason for its poor predictive ability, despite its proper separation ability.

Methods

We have designed a one-center-based retrospective clinical trial, by which we would like to test the feasibility and the inaccuracy of the revised Tokuhashi system. In our database, there are 329 patients who underwent surgery. Statistical analysis was performed.

Results

A significant increase in survival time was observed in the ‘conservative’ category. Earlier studies reported OS 0.15 at the 180-day control time, in contrast with our 0.38 OS value. The literature suggested supportive care for this category, but in our population, every patient underwent surgery. Our population passes the 0.15 OS value on day 475. We propose an adjustment of the Tokuhashi category scores. We observed significant success in resolving pain. Motor functions were improved or stabilized compared to changes in vegetative dysfunction.

Conclusion

According to our results, the Tokuhashi scoring system makes very conservative predictions and prefers non-surgical palliative or supportive care. Surgical treatment increases the life expectancy of patients in poor condition. We propose modifying the therapeutic options of the revised Tokuhashi system, taking into consideration modern spine surgery techniques.