The central role of receiver operating characteristic (ROC) curves in evaluating tests for the early detection of cancer.

Cancer screening involves early detection coupled with early intervention. For cancer screening to reduce cancer mortality, the cancer must be detected sufficiently early and the early intervention must be effective. For cancer screening to minimize harms, the cancers detected early should, to the extent possible, exclude cancers that would not cause medical problems in a person’s lifetime (a phenomenon known as overdiagnosis). Detection of overdiagnosed cancers leads to unnecessary work-ups, biopsies, and treatment. The gold standard for evaluating benefits and harms of cancer screening is a randomized controlled trial (1). Recently, with enthusiasm over advances in biomarker and imaging technology, early detection of cancer has taken a more prominent role in cancer research. Although the performance evaluation of early detection tests is only one component in evaluating cancer screening (and good performance of an early detection test does not necessarily translate into a reduction in cancer mortality), it is important for cancer researchers to understand how best to evaluate the performance of early detection tests. Researchers need good methods for identifying those early detection tests that are most promising for further investigation in a randomized trial in which the early detection test triggers early intervention (2,3). Researchers also need good methods for identifying factors or making comparisons that could improve the performance of existing early detection tests (4). An important and useful technique for evaluating the performance of diagnostic medical tests is the receiver operating characteristic (ROC) curve (5). However, for evaluating early detection tests of cancer, ROC curves have been either underused or not always used in the best manner. This commentary explains why ROC curves should be the primary method for evaluating the performance of early detection tests of cancer. It also discusses special considerations in applying ROC curves in particular applications.