Projected Effects of Radiation-Induced Cancers on Life Expectancy in Patients Undergoing CT Surveillance for Limited-Stage Hodgkin Lymphoma: A Markov Model.

OBJECTIVE: Patients with limited-stage Hodgkin lymphoma (HL) undergo frequent posttreatment surveillance CT examinations, raising concerns about the cumulative magnitude of radiation exposure. The purpose of this study was to project radiation-induced cancer risks relative to competing risks of HL and account for the differential timing of each. MATERIALS AND METHODS: We adapted a previously developed Markov model to project lifetime mortality risks and life expectancy losses due to HL versus radiation-induced cancers in HL patients undergoing surveillance CT. In the base case, we modeled 35-year-old men and women undergoing seven CT examinations of the chest, abdomen, and pelvis over 5 years. Radiation-induced cancer risks and deaths for 17 organ systems were modeled using an organ-specific approach, accounting for specific anatomy exposed at CT. Cohorts of 20-, 50-, and 65-year-old men and women were evaluated in secondary analyses. Markov chain Monte Carlo methods were used to estimate the uncertainty of radiation risk projections. RESULTS: For 35-year-old adults, we projected 3324/100,000 (men) and 3345/100,000 (women) deaths from recurrent lymphoma and 245/100,000 (men, 95% uncertainty interval [UI]: 121-369) and 317/100,000 (women, 95% UI: 202-432) radiation-induced cancer deaths. Discrepancies in life expectancy losses between HL (428 days in men, 482 days in women) and radiation-induced cancers (11.6 days in men, [95% UI: 5.7-17.5], 15.6 days in women [95% UI: 9.8-21.4]) were proportionately greater because of the delayed timing of radiation-induced cancers relative to recurrent HL. Deaths and life expectancy losses from radiation-induced cancers were highest in the youngest cohorts. CONCLUSION: Given the low rate of radiation-induced cancer deaths associated with CT surveillance, modest CT benefits would justify its use in patients with limited-stage HL.

Microsimulation model of CT versus MRI surveillance of Bosniak IIF renal cystic lesions: should effects of radiation exposure affect selection of imaging strategy?

OBJECTIVE: The objective of this study was to quantify the effects of radiation-induced cancer risks in patients with Bosniak category IIF lesions undergoing CT versus MRI surveillance. MATERIALS AND METHODS: We developed a Markov-Monte Carlo model to determine life expectancy losses attributable to radiation-induced cancers in hypothetical patients undergoing CT versus MRI surveillance of Bosniak IIF lesions. Our model tracked hypothetical patients as they underwent imaging surveillance for up to 5 years, accounting for potential lesion progression and treatment. Estimates of radiation-induced cancer mortality were generated using a published organ-specific radiation-risk model based on Biological Effects of Ionizing Radiation VII methods. The model also incorporated surgical mortality and renal cancer-specific mortality. Our primary outcome was life expectancy loss attributable to radiation-induced cancers. A sensitivity analysis was performed to assess the stability of the results with variability in key parameters. RESULTS: The mean number of examinations per patient was 6.3. In the base case, assuming 13 mSv per multiphase CT examination, 64-year-old men experienced an average life expectancy decrease of 5.5 days attributable to radiation-induced cancers from CT; 64-year-old women experienced a corresponding life expectancy loss of 6.9 days. The results were most sensitive to patient age: Life expectancy loss attributable to radiation-induced cancers increased to 21.6 days in 20-year-old women and 20.0 days in 20-year-old men. Varied assumptions of each modality's (CT vs MRI) depiction of lesion complexity also impacted life expectancy losses. CONCLUSION: Microsimulation modeling shows that radiation-induced cancer risks from CT surveillance for Bosniak IIF lesions minimally affect life expectancy. However, as progressively younger patients are considered, increasing radiation risks merit stronger consideration of MRI surveillance.

Patients with testicular cancer undergoing CT surveillance demonstrate a pitfall of radiation-induced cancer risk estimates: the timing paradox.

PURPOSE: To demonstrate a limitation of lifetime radiation-induced cancer risk metrics in the setting of testicular cancer surveillance-in particular, their failure to capture the delayed timing of radiation-induced cancers over the course of a patient's lifetime. MATERIALS AND METHODS: Institutional review board approval was obtained for the use of computed tomographic (CT) dosimetry data in this study. Informed consent was waived. This study was HIPAA compliant. A Markov model was developed to project outcomes in patients with testicular cancer who were undergoing CT surveillance in the decade after orchiectomy. To quantify effects of early versus delayed risks, life expectancy losses and lifetime mortality risks due to testicular cancer were compared with life expectancy losses and lifetime mortality risks due to radiation-induced cancers from CT. Projections of life expectancy loss, unlike lifetime risk estimates, account for the timing of risks over the course of a lifetime, which enabled evaluation of the described limitation of lifetime risk estimates. Markov chain Monte Carlo methods were used to estimate the uncertainty of the results. RESULTS: As an example of evidence yielded, 33-year-old men with stage I seminoma who were undergoing CT surveillance were projected to incur a slightly higher lifetime mortality risk from testicular cancer (598 per 100 000; 95% uncertainty interval [UI]: 302, 894) than from radiation-induced cancers (505 per 100 000; 95% UI: 280, 730). However, life expectancy loss attributable to testicular cancer (83 days; 95% UI: 42, 124) was more than three times greater than life expectancy loss attributable to radiation-induced cancers (24 days; 95% UI: 13, 35). Trends were consistent across modeled scenarios. CONCLUSION: Lifetime radiation risk estimates, when used for decision making, may overemphasize radiation-induced cancer risks relative to short-term health risks.

Journal club: How radiation exposure histories influence physician imaging decisions: a multicenter radiologist survey study.

OBJECTIVE: The purpose of this article is to evaluate the influence of patient radiation exposure histories on radiologists' imaging decisions. MATERIALS AND METHODS: We conducted a physician survey study in three academic medical centers. Radiologists were asked to make an imaging recommendation for a hypothetical patient with a history of multiple CT scans. We queried radiologists' decision making, evaluating whether they incorporated cancer risks from previous imaging, reported acceptance (or rejection) of the linear no-threshold model, and understood linear no-threshold model implications in this setting. Consistency between radiologists' decisions and their linear no-threshold model beliefs was evaluated; those acting in accordance with the linear no-threshold model were expected to disregard previously incurred cancer risks. A Fisher exact test was used to verify the generalizability of results across institutions and training levels (residents, fellows, and attending physicians). RESULTS: Fifty-six percent (322/578) of radiologists completed the survey. Most (92% [295/322]) incorporated risks from the patient's exposure history during decision making. Most (61% [196/322]) also reported acceptance of the linear no-threshold model. Fewer (25% [79/322]) rejected the linear no-threshold model; 15% (47/322) could not judge. Among radiologists reporting linear no-threshold model acceptance or rejection, the minority (36% [98/275]) made decisions that were consistent with their linear no-threshold model beliefs. This finding was not statistically different across institutions (p = 0.070) or training levels (p = 0.183). Few radiologists (4% [13/322]) had an accurate understanding of linear no-threshold model implications. CONCLUSION: Most radiologists, when faced with patient exposure histories, make decisions that contradict their self-reported acceptance of the linear no-threshold model and the linear no-threshold model itself. These findings underscore a need for educational initiatives.