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

Abstract

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.

The Impact of Adjunct Medical Therapy on Survival after Spine Metastasis: A Systematic Review and Pooled Data Analysis

“…The current literature is limited in providing safety, efficacy, and overall response rate estimates for many of these new treatment agents [1], reinforcing the need for more focused research on the support of adjunct medical therapy. Similarly, many studies in the literature discuss treatments and prognostics through the lens of algorithms that currently exist; the NOMS (neurologic, oncologic, mechanical stability, and systemic disease) framework and Tokuhashi score were developed over a decade ago and do not incorporate newer genomic data [8,61]. Future research should focus on adapting these scores, similar to the approach used by Cal et al [19] in their study on lung cancer metastases to the spine.…”

“…Adjusting the score to reflect population-specific characteristics could enhance its accuracy in identifying patients with spinal metastases who would benefit from surgical intervention [6]. With the emergence of tumor molecular sequencing and genetic profiling, current prognostic algorithms should reflect the significant advancements in targeted systemic therapy [7,8]. Chen et al [9] and Morgen et al [10] demonstrated that patients with spinal metastases from certain cancer types have improved prognoses due to rapid treatment advancements in immunotherapy.…”

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

“…The SORG ML algorithm has shown superior survival-predicting power in several recent series and studies and has been externally validated [ 91 , 92 , 93 ]. Additionally, the SORG nomogram seems to have the highest accuracy in retrospectively predicting 90-day and 1-year survival in surgically treated spinal metastatic patients among an array of different scoring systems, also including Tomita, modified Tokuhashi, modified Bauer, revised Katagiri, and van der Linden [ 94 ].…”