External validation of a genitourinary cancer-specific prognostic scoring system to predict survival for patients with bone metastasis (modified B-FOM scoring model): Comparison with other scoring models in terms of accuracy.

ABSTRACT:

Objective

We previously developed genitourinary (GU) cancer-specific scoring system for prediction of survival in patients with bone metastasis (the Bone-Fujimoto-Owari-Miyake [B-FOM] scoring model) based on five prognostic factors: the type of primary tumor (prostate cancer (PCa) vs renal cell carcinoma (RCC) and PCa vs urothelial carcinoma (UC)), poor performance status (PS), visceral metastasis, high Glasgow-prognostic score (GPS), elevated neutrophil-to-lymphocyte ratio (NLR). The aim of this study was to externally validate and further improve the performance of the B-FOM score.

Methods

The external validation cohort comprised 309 patients with GU cancer with bone metastasis from multiple institutions. Clinical factors were analyzed using Kaplan-Meier method and COX regression hazard model. Performance of a modified B-FOM score was compared to that of other scoring models by the Kaplan-Meier method and the area under the curve (AUC) of receiver operating characteristic curves.

Results

The median follow-up period of development and validation cohort were 25 and 17 months, respectively. Kaplan-Meier curve demonstrated that the type of primary tumor (RCC and UC vs PCa), poor PS, presence of visceral metastasis, high GPS, elevated NLR were significantly associated with shorter cancer-specific survival. Risk groups were successfully stratified by the modified B-FOM score classification. Moreover, the AUC of the modified B-FOM scoring model for predicting mortality at 6, 12, and 24 months were 0.895, 0.856, and 0.815, respectively, which were the highest among evaluated models.

Conclusions

The B-FOM scoring model is a simple and accurate prediction tool. By using this scoring model at the time of the diagnosis of bone metastasis in patients with GU cancers, an individualized optimal treatment strategy can be selected.

SUBMITTER: Owari T 

PROVIDER: S-EPMC7770480 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature