A nomogram statistical model may play a major role in predicting the early death of patients with metastatic renal cell cancer (mRCC), which can aid clinicians in individualized therapies, according to the authors of a study published in Frontiers in Surgery.
“mRCC is usually considered to have a poor prognosis, which has a high risk for early death (3 months or less after diagnosis),” the researchers wrote. “The nomogram can integrate relevant factors to predict the individual oncologic prognosis.”
Practical Nomogram Based on 11 Significant Risk Factors
The study team accessed the Surveillance, Epidemiology, and End Results (SEER) database, consisting of 6,005 patients diagnosed with mRCC between 2010 and 2015. Patients were randomly divided into primary and validation cohorts, with a 7:3 ratio. An X-tile analysis was used to identify optimal cut-off point regarding age at diagnosis and tumor size. The researchers used univariate and multivariate logistic regression models to define independent risk factors contributing to early death.
“A practical nomogram was constructed and then verified by using calibration plots, receiver operating characteristics curve, and decision curve analysis (DCA),” the authors wrote. The nomogram was based on 11 significant risk factors, including age, grade, histologic type, N-stage, metastatic sites (brain, bone, liver, and lung), and treatments (chemotherapy, radiation, and surgery).
Nomogram Useful for Formulating Targeted Treatment Strategies
Of total patients, 1,816 experienced early death; among them, 1,687 died of mRCC. The model’s effectiveness, discrimination, and clinical practicality were proved by the area under the curve value, calibration plots, and DCA, respectively.
The researchers acknowledge several limitations in the study. First, some relative factors, such as the comorbidities and performance status and the Fürhman classification, were not considered in the nomogram. “Secondly, our study was retrospective and potential selection bias may adversely affect the conclusion,” the authors wrote. “Thirdly, the detailed information on chemotherapy and specific surgical procedures were lacking in the SEER database. Finally, although our model was validated internally, it is necessary to conduct external verification.”
In the future, the researchers would like to combine their study results with other research data to better predict the early death of patients with mRCC. “This nomogram is conducive for surgeons to formulate targeted treatment strategies and improve survival outcomes for patients with mRCC,” the study authors wrote.
