Skip to main content

What Is the “Cancer Cliff” and Why Aren’t We Talking About It?

September 9, 2026

The “cancer cliff” describes a preventable gap between detecting a potentially malignant incidental finding on an imaging scan and completing the recommended follow-up care. Research shows that many patients never receive recommended surveillance or diagnostic testing. Closing this gap requires clear accountability, patient communication, tracking, escalation, and carefully governed AI to ensure that potentially important findings lead to appropriate care.

In healthcare, we talk about “closed-loop follow-up.” The phrase tells a complicated story about communication, process, and system performance, but it is relatively meaningless to a patient or someone who isn’t deeply embedded in the healthcare system. What we are really talking about may be better described as the “cancer cliff”: a scenario in which a potentially malignant finding is incidentally reported on imaging, but the patient falls through the cracks before timely characterization, surveillance, diagnosis, or treatment.

What is an incidental finding?

“Incidentally” is a key term. The American College of Radiology defines an incidental finding as a “lesion or abnormality discovered during imaging tests (such as CT or MRI) performed for an unrelated reason.” In patient-facing language, an incidental finding is an unusual spot or change found on a scan, such as a CT or MRI, done for another reason. Not every incidental finding needs a second look, but when follow-up is recommended, many patients do not receive it.

The gap between an incidental finding and follow-up care is what comprises the cancer cliff: a preventable disconnect between the detection or reporting of a potentially malignant incidental imaging finding and completion of the clinically indicated diagnostic or surveillance pathway. When that pathway breaks down, it can increase the risk of delayed cancer diagnosis, stage progression, avoidable morbidity, greater treatment burden, or death.

How prevalent are incidental findings?

The prevalence of incidental findings depends on what clinicians are looking at, how they are looking at it, and the clinical context. An older systematic review estimated a mean prevalence of 23.6% across imaging and 31.1% for CT, while mean follow-up was only 64.5%. More recent studies show substantial variation by setting and workflow, with published adherence rates for radiology follow-up recommendations ranging from 29% to 77%.

Pulmonary nodules are among the best-documented incidental findings when it comes to the risk of missed follow-up care. One emergency-department CT pulmonary angiography cohort found that only 29% of nodules requiring surveillance received follow-up. When the nodule appeared only in the findings section of the radiology report, follow-up was 0%. Care setting also matters. Emergency and transitional settings are particularly vulnerable to missed follow-up. In a cohort of indeterminate abdominal findings, 36.6% lacked follow-up within the same health system at one year, rising to 60.2% among emergency-department patients compared with 28.8% among outpatients.

The takeaway is that both the likelihood of identifying an incidental lesion or abnormality and the risk of not receiving follow-up care vary considerably. Complexity, communication, care setting, system design, and departmental interdependency all contribute to whether and when follow-up takes place. This is not simply a problem of whether an individual clinician remembers to order another test; it is a systems problem that spans detection, communication, accountability, and completion of care.

What does a missed finding mean for cancer care?

When follow-up care does happen, it is clinically productive. Diagnoses are established in about 45% of followed actionable findings, with cancer reported in up to approximately 5%. At the same time, research directly linking missed incidental findings to later-stage cancer diagnosis and mortality risk is limited, so it is important not to overstate what the evidence shows. What we do know is that stage-specific cancer survival differs dramatically, and a large meta-analysis found a 6% to 8% increase in mortality risk for every four-week delay in cancer surgery.

The true impact of delayed follow-up is difficult to tease out from the research. Studies are heterogeneous and can demonstrate a “waiting-time paradox,” in which sicker patients receive expedited care but still have worse outcomes. Some cancers and lesions are highly time-sensitive, while others progress more slowly, making it difficult to quantify the harm associated with each month of delayed incidental-finding follow-up. That uncertainty, however, does not eliminate the risk created when a clinically indicated follow-up recommendation is never completed.

We also know that unresolved incidental findings can affect patients even when they do not ultimately result in a cancer diagnosis. In a multicenter pulmonary-nodule survey, 26% of patients reported clinically significant distress, 78% worried about the lesion’s cause, and 73% worried about cancer. Later-stage cancers also consistently cost more to treat. Across 17 cancers in Medicare data, first-year stage IV costs were 1.6 to 7.7 times stage I costs, and those excess costs persisted through five years.

How do we close the cancer cliff?

A high-performing incidental-finding program should use standardization, redundancy, visibility, escalation, and continuous learning to reduce the risk of missed follow-up. That starts with high-volume, high-harm findings such as pulmonary nodules, suspicious renal masses, pancreatic lesions, liver masses, adrenal masses with concerning features, and unexpected lymphadenopathy. Health systems also need reliable detection methods that can identify follow-up recommendations regardless of where they appear in a radiology report.

Recommendations themselves must be actionable. They should clearly identify the lesion, appropriate modality, recommended interval, risk, and evidence basis. When that information is missing or unclear, there should be a process for identifying the omission and resolving it. Just as importantly, accountability needs to be explicit: the ordering clinician should own the initial action until responsibility is clearly transferred and accepted, while a centralized program can provide surveillance of the process and escalation when follow-up does not occur.

Patients also need to be part of the process. Providers and patients should receive plain-language notifications, direct contact for higher-risk findings, acknowledgment of receipt, and accessible scheduling support. Health systems can make it easier to convert recommendations into action through streamlined orders and referrals, preauthorized protocols, and navigator-facilitated escalation.

Tracking must extend beyond whether a follow-up test was ordered. A true closed-loop process should determine whether the test was completed, resulted, reviewed, and ultimately led to a documented clinical disposition. When the process stalls, escalation pathways should extend to backup clinicians, navigators, and safety officers, with clear procedures for patients who cannot be reached or who receive care outside the health system.

Finally, health systems should learn from misses and near misses, including differences by care setting and patient population, and continually recalibrate the technologies used to identify and prioritize findings. Natural language processing and AI can help identify recommendations buried in radiology reports and surface patients who may otherwise be lost, but technology alone cannot solve an accountability problem. These tools need clearly defined workflows, oversight, escalation pathways, and governance.

Falling off the “cancer cliff” means that a potentially curable cancer signal can be visible or reported, yet no reliable system ensures that the patient reaches diagnosis. Incidental imaging creates a substantial reservoir of actionable findings, and depending on setting and workflow, approximately 15% to more than 50% of recommended follow-up may remain incomplete or undocumented. Not every open loop represents a missed cancer, but every open loop represents unresolved clinical risk.

As imaging becomes more sophisticated and AI makes it possible to detect even more abnormalities, health systems will get better at finding potential problems. Finding them, however, is only half the job. Closed-loop reporting, explicit accountability, tracking, navigation, patient communication, and carefully governed AI can help ensure that a finding leads to appropriate care — and that fewer patients fall off an avoidable cancer cliff.