Although the standard mode of classification and reporting of cancer—Tumor, Node, Metastases (TNM) classification—is efficient in categorizing cancer, it has limited prognostic capabilities. Newly developed statistical models for helping provide individualized predictions have been quickly accepted for use in renal cell carcinoma (RCC) treatment. However, the retrospective nature of the models development has not been questioned, notes Dr. Correa.

For a study published in the Journal of Clinical Oncology, Dr, Correa and colleagues sought to validate the most commonly used RCC models using contemporary and prospectively collected data. The study was a collaboration with the ASSURE trial, the first study to assess the benefit of targeted therapy in patients with fully resected intermediate- and high-risk localized kidney cancer. The study authors analyzed eight models (University of California at Los Angeles Integrated Staging System [UISS]; Stage, Size, Grade, and Necrosis [SSIGN]; Leibovich; Kattan; Memorial Sloan Kettering Cancer Center [MSKCC]; Yaycioglu; Karakiewicz; and Cindolo) based on their use in clinical trial design, popularity, and previous validation by independent cohorts. These models were validated based on discrimination and calibration.

“All the validated models demonstrated a significant decline in their predictive ability compared with their published estimates,” says Dr. Correa. “More importantly, these ‘personalized’ models did not provide a significant prediction benefit compared with the standard TNM staging. Additionally, all models demonstrated a persistent degradation of their predictive ability after 2 years.” Among the models, SSIGN performed best, while UISS performed worst.

The findings should give urologists and oncologists pause, according to Dr. Correa. The largest RCC trials rely on the prediction models to confirm findings, but this study indicates that the models are not as accurate as estimated. “I believe that with the increasing availability of high-quality clinical trial data, we should strive to leverage the data to develop increasingly accurate prognostic models that will not be limited to retrospective data collection,” explains Dr. Correa. “It is clear in RCC that tumor-centric models only provide a small glimpse into the biology of RCC recurrence and that we need better tools to more accurately predict late recurrences.”

References

Predicting Renal Cancer Recurrence: Defining Limitations of Existing Prognostic Models With Prospective Trial-Based Validation
https://ascopubs.org/doi/abs/10.1200/JCO.19.00107