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Researchers Construct Prognostic Nomogram for Use in Patients With Bladder Cancer and Lung Metastasis

By: Joshua Swore, PhD
Posted: Wednesday, September 20, 2023

A new prognostic nomogram may improve clinicians’ ability to select appropriate therapy and assess survival outcomes for patients with bladder cancer who have lung metastasis, according to research published in the European Journal of Medical Research. The independent predictive factors of outcomes on which this nomogram is based include patient age at diagnosis, primary site of the tumor, histology, surgery of the primary site, additional chemotherapy, as well as bone and liver metastases.

“The overall survival rate of [patients with bladder cancer] with metastases remains quite low despite multiple therapeutic modalities,” said Liang Liu et al, of Baoding No. 1 Central Hospital, China. “For this reason, it is essential to construct prognostic models for overall survival of [patients with bladder cancer] with lung metastasis, as identifying patients with evaluated poor survival outcomes may guide enhanced therapeutics and improve prognosis.”

The retrospective study included 506 patients with bladder cancer who had lung metastasis. Patients were identified through the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database. Data included clinicopathologic, demographic, survival outcome, and both primary and metastatic tumor information. For model development, patients were randomly placed on an approximate 2:1 basis into training and validation sets.

The authors used multivariate Cox regression analysis to identify patient age, primary site, histologic type, surgery history, chemotherapy, and metastatic location as primary factors impacting patients’ overall survival. These factors allowed the researchers to achieve an acceptable accuracy to predict the survival at 1 and 3 years for patients in the validation set. The nomogram optimized the receiver operating characteristics curve, with an area under the curve of 0.766 and 0.717, respectively. The C-index, a score to summarize how well a risk score describes the probability of sequential events, was 0.699 and 0.747 in the training and validation sets.

Disclosure: The study authors reported no conflicts of interest.

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