Paez, Rafael; Rowe, Dianna J.; Deppen, Stephen A.; Grogan, Eric L.; Kaizer, Alexander M.; Bornhop, Darryl J.; Kussrow, Amanda K.; Barõn, Anna E.; Maldonado, Fabien; Kammer, Michael N. (2025). Assessing the clinical utility of biomarkers using the intervention probability curve (IPC). Cancer Biomarkers, 42(1), CBM230054. https://doi.org/10.3233/CBM-230054
Before new medical tests, or biomarkers, are used in clinics, it is important to understand how useful they are for guiding patient care. One way to do this is to see how a test might change which patients are assigned to different treatment groups, but traditional methods have some limitations. To address this, researchers developed the intervention probability curve (IPC), which models how likely a doctor is to choose a particular treatment based on a patient’s estimated risk of disease.
In this study, the IPC was used to evaluate a new biomarker for suspected lung cancer, using data from the National Lung Screening Trial. The analysis estimated how the biomarker would affect decisions about interventions, such as biopsies or surgeries. The results suggested that 8% of patients with non-cancerous nodules could avoid unnecessary invasive procedures, while patients with actual cancer nodules would almost always still receive appropriate care (only 0.1% change).
Compared with traditional methods, the IPC provides a more detailed and continuous view of how a biomarker could influence clinical decisions. This approach shows that the IPC can be a valuable tool for assessing the potential impact of new biomarkers before they are implemented in everyday clinical practice.
Figure 1.
Population-based assessment of changes in intervention probability. While the mean of the distributions is similar, the spread of distributions shows the change in probability is more tightly clustered around zero in the cancer population than the change in probability.