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Purpose:
To estimate cumulative radiation exposure and lifetime attributable risk (LAR) of radiation-induced cancer from computed tomographic (CT) scanning of adult patients at a tertiary care academic medical center.
Materials and Methods:
This HIPAA-compliant study was approved by the institutional review board with waiver of informed consent. The cohort comprised 31 462 patients who underwent diagnostic CT in 2007 and had undergone 190 712 CT examinations over the prior 22 years. Each patient’s cumulative CT radiation exposure was estimated by summing typical CT effective doses, and the Biological Effects of Ionizing Radiation (BEIR) VII methodology was used to estimate LAR on the basis of sex and age at each exposure. Billing ICD9 codes and electronic order entry information were used to stratify patients with LAR greater than 1%.
Results:
Thirty-three percent of patients underwent five or more lifetime CT examinations, and 5% underwent between 22 and 132 examinations. Fifteen percent received estimated cumulative effective doses of more than 100 mSv, and 4% received between 250 and 1375 mSv. Associated LAR had mean and maximum values of 0.3% and 12% for cancer incidence and 0.2% and 6.8% for cancer mortality, respectively. CT exposures were estimated to produce 0.7% of total expected baseline cancer incidence and 1% of total cancer mortality. Seven percent of the cohort had estimated LAR greater than 1%, of which 40% had either no malignancy history or a cancer history without evidence of residual disease.
Conclusion:
Cumulative CT radiation exposure added incrementally to baseline cancer risk in the cohort. While most patients accrue low radiation-induced cancer risks, a subgroup is potentially at higher risk due to recurrent CT imaging. 娀 RSNA, 2009
1
From the Department of Radiology and Center for Evidence Based Imaging (A.S., P.F.B., K.P.A., L.M.P., R.H., R.K.), Brigham and Women’s Hospital, 75 Francis St, Boston, MA 02115; and Harvard Medical School, Boston, Mass (A.S., P.F.B., K.P.A., L.M.P., R.D.N., R.K.). From the 2008 RSNA Annual Meeting. Received July 26, 2008; revision requested August 28; revision received September 30; accepted October 24; final version accepted November 3. Address correspondence to A.S. (e-mail:
[email protected] ). 姝 RSNA, 2009
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䡲 MEDICAL PHYSICS
Aaron Sodickson, MD, PhD Pieter F. Baeyens, MD Katherine P. Andriole, PhD Luciano M. Prevedello, MD Richard D. Nawfel, MS Richard Hanson Ramin Khorasani, MD, MPH
ORIGINAL RESEARCH
Recurrent CT, Cumulative Radiation Exposure, and Associated Radiation-induced Cancer Risks from CT of Adults1
MEDICAL PHYSICS: Cumulative Radiation Exposure and Cancer Risks from CT
A
ttention has recently focused on the potential risks of radiation-induced carcinogenesis from diagnostic radiology (1,2), with particular emphasis on computed tomography (CT). CT utilization has grown rapidly as a consequence of its recognized clinical value in nearly all areas of medicine, a trend enabled by technologic advances and widespread availability. An estimated 62 million CT examinations were performed in the United States in 2006, and while CT represents only 15% of imaging procedures, it accounts for approximately half of the collective medical radiation dose owing to the relatively high dose per examination (3). Rising utilization has also heightened concern that patients may accrue large cumulative doses from recurrent CT imaging. Wiest et al (4) reported that in 2001, 30% of their patients had more than three CT examinations in their medical histories, 7% had more than five examinations, and 4% had more than nine examinations. Specific patient populations with chronic conditions or recurrent symptoms have been found to have high rates of repeat imaging, including those with Crohn disease (5) and renal colic (6,7).
Advances in Knowledge 䡲 Individualized cumulative radiation dose and associated cancer risk estimates may be performed by using administrative databases and incorporating patient demographics and Biological Effects of Ionizing Radiation (BEIR) VII risk estimation techniques. 䡲 While most patients accrue low radiation-induced cancer risks from cumulative CT exposures, incremental risks are estimated to exceed 1% above baseline in 7% of scanned patients. 䡲 While patients at higher risk from cumulative CT generally have complex medical problems, approximately 40% have no history of malignancy or have a cancer history with no evidence of active disease. 176
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Much prior work investigating radiation-induced cancer risks from diagnostic imaging has focused on imaging of particular organs (5–9) or on populations of particular concern. The greatest emphasis to date has been placed on pediatric patients, owing both to their higher dose for a fixed set of imaging parameters and to their higher cancer risk per unit dose compared with adult populations (10–12). This type of risk estimation to large populations is vital when considering issues of overall imaging utilization, appropriateness, and optimal technique selection. However, methods have not been well developed in the United States to track an individual patient’s cumulative exposure and to estimate that patient’s associated radiation-induced cancer risk. Among other recommendations for controlling radiation exposures, the recent American College of Radiology white paper calls for development of “a surveillance mechanism to identify patients with high cumulative radiation doses due to repeat imaging” (1). Identification of this subgroup would also be useful for future development of pointof-care computerized decision support tools designed to help physicians reduce patient-specific cumulative radiation doses and associated cancer risks. The purpose of this study was to estimate cumulative radiation exposure and lifetime attributable risk (LAR) of radiation-induced cancer from CT scanning of adult patients at a tertiary care academic medical center. We hypothesized that patients with potentially high cumulative radiation risks due to recurrent CT imaging can be identified by
Implications for Patient Care 䡲 Patient-specific cumulative radiation risk estimates can inform risk-benefit decisions in balance with an individual’s clinical presentation and the anticipated benefits of recurrent imaging. 䡲 Identification of patients with the highest rates of recurrent imaging may help to focus radiation protection efforts where they are most needed.
extracting information from administrative databases and applying relevant patient demographics (sex and age at the time of exposure) and standard riskestimation techniques.
Materials and Methods Study Design and Setting This retrospective cohort study was performed at a 752-bed adult urban tertiary academic medical center and its associated outpatient cancer center. Institutional review board approval was obtained for this Health Insurance Portability and Accountability Act– compliant study, with informed consent waived for retrospective medical records review. Cohort Selection The cohort included all patients who underwent diagnostic CT from January 1, 2007, through December 31, 2007, in any care setting (inpatient, outpatient, or emergency department). Data Collection and Analysis We searched the radiology information system (RIS) database (IDXRad version 9.9.7; IDX, Burlington, Vt [a subsidiary of GE Healthcare]) for all patients who underwent at least one CT examination in 2007. For each individual in the cohort, we extracted all diagnostic CT codes (excluding interventional CT pro-
Published online 10.1148/radiol.2511081296 Radiology 2009; 251:175–184 Abbreviations: BEIR ⫽ Biological Effects of Ionizing Radiation LAR ⫽ lifetime attributable risk RIS ⫽ radiology information system Author contributions: Guarantors of integrity of entire study, A.S., P.F.B.; study concepts/study design or data acquisition or data analysis/interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; approval of final version of submitted manuscript, all authors; literature research, A.S., R.D.N.; clinical studies, P.F.B., R.D.N.; statistical analysis, A.S., R.H.; and manuscript editing, all authors Authors stated no financial relationship to disclose.
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cedures) in the RIS database from the full 21.8-year institutional imaging record between May 28, 1986, and March 10, 2008. Sex and date of birth were obtained, and exposure ages were calculated as the difference between each examination completion date and the date of birth. Access 2000 (Microsoft, Seattle, Wash) was used for database management and calculations, and Excel 2004 (Microsoft) was used for descriptive statistics and graphs. CT examination counts.—We eliminated all examination codes not corresponding to a unique radiation exposure, such as those corresponding to intravenous contrast agent administration or multiplanar reformations. Unique CT examinations were tallied by combining examination codes through the following logic rules: Concurrently performed abdomen and pelvis codes were counted as a single abdomen-pelvis examination, while a pelvis code performed on its own constituted a unique examination. Thoracic spine codes were counted when performed alone, but not when performed concurrently with chest CT, in which case they were considered reformations (without additional radiation exposure). Lumbar spine codes were treated similarly based on concurrent abdominal CT. Lower extremity venogram codes were not counted separately when there was a concurrent CT pulmonary angiography examination. All other CT codes were counted as a single examination. A chest, abdomen, and pelvis scan was thus counted as two examinations— one of the chest, and one of the abdomen-pelvis. CT effective dose estimates.—Each examination code was assigned an effective dose according to its region of anatomic coverage (Table 1), regardless of examination date or scanner model. While CT effective doses may be highly dependent on patient size, imaging parameters, and scanner technology, Table 1 reflects current typical adult doses at our institution, determined from internal surveys of dose-length product for our most frequent examination types, with conversion of dose-length
product to effective dose by commonly used conversion factors (13). They are within the range of published estimates (14–18). The same logic rules described above to define unique CT examinations were used to assign a relevant effective dose estimate only for examination codes representing a true radiation exposure. To avoid overestimating dose, multiple-pass scans were assigned the same effective dose values as those with a
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single pass through the relevant body region. Extremity scans were assigned zero dose due to the nonspecific nature of our extremity examination codes and the widely variable exposures to different extremities. For the same reason, CT interventions were not included in the study. Risk estimation from effective doses.—We used the Biological Effects of Ionizing Radiation (BEIR) VII methodology (19) to estimate the lifetime attrib-
Table 1 CT Effective Dose Estimates Based on Anatomic Coverage Region Covered Anatomy Head, face Cervical spine, neck Chest, pulmonary embolus, thoracic spine Abdomen alone (no pelvis) Pelvis alone (no abdomen) Abdomen and pelvis, lumbar spine Extremity
Assigned Effective Dose per CT Examination (mSv) 2 2 8 7.5 7.5 15 0
Figure 1
Figure 1: BEIR VII radiation-induced cancer risk estimation as a function of age and sex. Data extracted from tables 12D-1 and 12D-2 of BEIR VII (19). For a standardized U.S. population, BEIR VII predicts one excess cancer per 1000 patients receiving a 10-mSv exposure (thick horizontal line), approximately half of which are expected to be fatal. Interpolation between BEIR VII data points (E) was performed with exponential interpolation up to age 30 years, linear interpolation above age 30 years. A LAR of zero was used for age at exposure greater than 80 years.
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utable risk (LAR) of radiation-induced cancer incidence and mortality for each CT exposure on the basis of the patient’s sex and age at exposure (Fig 1). BEIR VII data points were interpolated to the nearest integer age of exposure by using exponential interpolation for ages younger than 30 years, and linear interpolation for ages older than 30 years. No extrapolation was performed for ages of more than 80 years, resulting in zero assigned LAR
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for all exposures after age 80 years (a small proportion of the total examination count). Cumulative examination counts, effective dose estimates, and LAR were obtained by summing these values over each study in the patient’s history. Clinical classification of high-risk patients.—We used billing and electronic order entry data to explore the clinical context of patients receiving the highest esti-
Table 2 Patient Demographics in the Cohort Sex Female Male Both
No. of Patients
Minimum Age (y)
Mean Age (y)
Maximum Age (y)
Standard Deviation
17603 13859 31462
11 16 11
56.5 57.4 56.9
108 101 108
17.5 17.4 17.5
Note.—Cohort of all patients undergoing a diagnostic CT examination in 2007.
Figure 2
Figure 2: Distribution of anatomic locations for the 190 712 CT examinations captured over the 22-year study period in the cohort of 31 463 patients. Extrem ⫽ Extremities.
Table 3 Summary Data for the Distributions in Figures 3–5 Parameter Median Mean 99th Percentile Maximum
178
Cumulative CT Examination Count
Cumulative Effective Dose (mSv)
LAR of Cancer Incidence (%)
LAR of Cancer Mortality (%)
3 6.1 38 132
24 54.3 399 1375
0.13 0.3 2.7 12.0
0.08 0.2 1.6 6.7
mated levels of cancer risk from CT exposures (LAR of cancer incidence ⬎ 1%). For each of these patients, we collected all ICD9 codes recorded in our RIS database (IDXRad; IDX) to bill for any radiology study performed between November 5, 1999, and September 9, 2008. Each patient was classified as having a malignancy history if any of his or her billing ICD9 codes included the malignant neoplasm categories 140–208. Those patients with presumed metastatic disease were identified by the ICD9 categories 197–198, which include all secondary neoplasms to organs other than lymph nodes. The malignancy subgroup was further stratified by using data from our radiology electronic order entry system (Percipio; Medicalis, Kitchener, Ontario, Canada), as ICD9 codes do not contain information about the disease status of a patient’s malignancy. Beginning in March 2007, a structured entry field was added to our order entry system so that the ordering physician can indicate a patient’s malignancy type and specify the clinical status of their disease through selection of the field “history of malignancy (no evidence of disease)” or “active malignancy (under/planning for treatment).” Each patient’s most recent structured order entry field recorded between March 30, 2007, and September 09, 2008, was used to subclassify the patients with LAR greater than 1% by disease status and malignancy type. The electronic medical records of 25 patients with no malignancy billing ICD9 code (hereafter, “non-cancer patients”) were examined to lend insight into some of the clinical scenarios leading to high levels of recurrent CT imaging, as classification according to ICD9 codes or structured order entry fields cannot fully capture the clinical complexity of this group. Summary clinical vignettes are presented for the five non-cancer patients with the highest estimated LAR and for 20 others randomly selected from the group with LAR greater than 1%.
Results Cohort Characteristics The cohort included 31 462 patients, of whom 56% were female. Further demoradiology.rsnajnls.org ▪ Radiology: Volume 251: Number 1—April 2009
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graphic data are presented in Table 2. Over the 22-year study period, these patients had a total of 255 464 CT examination codes (after excluding 101 528 that did not represent additional radiation exposure). These radiation-related examination codes combined to 190 712 unique CT examinations by using the counting methodology described above, distributed according to anatomy as in Figure 2.
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Figure 3
Cumulative CT Survey Results Table 3 contains summary distribution data for per-patient CT examination counts, estimated cumulative effective doses, and associated radiation-induced cancer risks. Cumulative CT Examination Counts Figure 3 includes histogram data of the cumulative examination counts per patient over the 22-year study period and contains a long right tail. Thirty-three percent of patients underwent more than five CT examinations, 5% underwent more than 22 examinations, and 1% underwent more than 38 examinations.
Figure 3: Histogram of total number of CT examinations per patient, over the 22-year study period in the cohort of 31 463 patients. The inset includes an expanded y-axis to display the right tail and contains the top percentile of per-patient examination counts; 33% of patients underwent more than five lifetime CT scans, 5% underwent more than 22 scans, and 1% underwent more than 38 scans.
Figure 4
Estimated Cumulative Effective Doses Figure 4 demonstrates the distribution of estimated cumulative CT effective doses over the 22-year study period. There is a long right tail, with 15% of patients receiving over 100 mSv, 4% receiving over 250 mSv, and 1% receiving over 399 mSv. Estimated Cumulative Radiation-induced Cancer Risks Incorporating patient sex and the age at each exposure allows calculation of BEIR VII radiation-induced cancer risks, displayed in Figure 5 for both cancer incidence and cancer mortality. For a standardized U.S. population, BEIR VII (19) predicts a baseline cancer incidence of 42% and cancer mortality of 20%. LAR represents the estimated cancer risk above these baseline rates, so that a LAR of 5%, for example, represents a predicted increase in cancer incidence to 47% from the baseline rate of 42%. LAR values may be converted to a percentage of the total expected cancers, so
Figure 4: Histogram of cumulative CT effective dose per patient, over the 22-year study period in the cohort of 31 463 patients. The inset includes an expanded y-axis to display the right tail and contains the top percentile of cumulative exposures; 15% of patients received over 100 mSv, 4% over 250 mSv, 1% over 399 mSv.
that the LAR of 5%, for example, equates to: 5/(5 ⫹ 42) ⫽ 11% of total cancer incidence.
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Our estimates of CT-induced cancer incidence predict that 7% of patients exceed a LAR of 1% and 1% exceed a LAR of 179
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2.7%. For cancer mortality, 3% of patients exceed a LAR of 1%, and 1% exceed a LAR of 1.6%.
Estimated Cumulative Risks to the Cohort In a cohort of this size, baseline cancer rates predict 13 214 cancers, including
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6292 fatal cancers. In comparison, our LAR calculations (Table 3) predict that CT imaging of this cohort will produce 98 additional radiation-induced cancers, including 62 fatal cancers. In the cohort as a whole, this represents 0.7% of expected cancer incidence and 1% of
Figure 5
Figure 5: Histogram of LAR of radiation-induced cancer incidence (green) and mortality (red) above baseline, over the 22-year study period in the cohort of 31 463 patients. The inset includes an expanded y-axis to display the right tail and contains the top percentile of LAR incidence (and the top 0.3% of LAR mortality). For cancer incidence, 7.3% of patients exceed a LAR of 1% above baseline and 1% exceed a LAR of 2.6%. For cancer mortality, 3.4% of patients exceed a LAR of 1%, and 1% exceed a LAR of 1.6%.
Figure 6
Figure 6: Chart shows classification according to billing ICD9 code for the 2298 patients with an estimated LAR of cancer incidence greater than 1%. Patients with malignancy have an ICD9 code within categories 140 – 208. Patients with secondary malignancy have an ICD9 code in categories 197–198. 180
cancer mortality. However, further examination of the 315 patients in the top percentile of cumulative LAR (inset box, Fig 5) reveals estimated LARs of cancer incidence ranging from 2.7% to 12% above the 42% baseline rate, which equates to 6%–22% of these particular patients’ total expected cancer incidence.
Disease Classification in Frequently Imaged Patients A total of 2298 patients (7.3%) had estimated LAR of cancer incidence exceeding 1% above baseline. Figure 6 stratifies these patients according to billing ICD9 codes: 85% had at least one ICD9 code indicating a malignancy history, and 40% had an ICD9 code indicating metastasis. Since structured electronic order entry fields pertaining to malignancy became available in March 2008, they were selected by the ordering physician for 1547 of the patients with a LAR greater than 1% (79% of those with billing ICD9 malignancy codes). Within this group, 469 (30%) were classified with the field “history of malignancy (no evidence of disease)” and 1078 (70%) with “known active malignancy (under/ planning for treatment).” Figure 7 further classifies these patients by malignancy type. Assuming this group represents a statistically random sample of the full ICD9 malignancy group, this ratio of active to dormant disease would predict 584 cancer patients without evidence of disease, or 25% of the cohort with LAR greater than 1%. There were 350 patients (15%) with estimated LAR greater than 1% who did not have a malignancy history according to billing ICD9 codes. Figure 8 plots the distribution of repeat studies per patient according to anatomic region of imaging. Only 12% of these patients had all of their repeat imaging of the same anatomic region, with the remainder undergoing a variety of study types, often for varied clinical indications. Table 4 contains database results for 25 sample patients, including the anatomic distributions of their CT studies (in the four most common categories), and brief medical histories abstracted from their
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electronic medical records. Patients 1–5 are the non-cancer patients with the highest estimated LAR, and patients 6 –25 are representative patients with intermediate levels of LAR above 1%. These patients have complex and varied medical histories, and undergo recurrent imaging for a broad range of chronic conditions and symptoms.
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Figure 7
Discussion We found high rates of recurrent CT imaging, exceeding those of Wiest et al (4), with 33% of patients having undergone more than five CT examinations, and 5% of patients having undergone at least 22 CT examinations in our administrative database over the 22-year study period. Fifteen percent of the cohort had accrued cumulative CT effective doses in excess of 100 mSv, the dose realm in which there is convincing epidemiologic evidence of increased cancer risk (20). Brenner and Hall (2) have recently predicted that 1.5%–2.0% of all U.S. population cancers may be caused by CT radiation exposure. The BEIR VII risk model predicts that approximately 0.7% of our cohort’s lifetime cancers may be caused by CT, though our estimate includes only past exposures at a single institution in a purely adult population. Fortunately, the great majority of patients accrue low radiation-related cancer risks. However, the patients who are most frequently imaged have cumulative risks far greater than the typical patient. The top percentile of our cohort have estimated LARs of cancer incidence in excess of 2.7% (above the baseline 42% cancer rate), equating to 6% or more of their total expected cancer incidence. Not surprisingly, the patients who undergo large amounts of recurrent imaging generally have substantial underlying disease. Eighty-five percent of the patients with estimated LAR greater than 1% have a malignancy diagnosis. However, 40% of the group with LAR greater than 1% either have no malignancy history or are assessed by their treating physicians as having no ongoing evidence of active ma-
Figure 7: Distribution of malignancy types according to electronic order entry structured text fields, for the 1547 patients with available data. Thirty percent of patients were classified as having no evidence of disease, while 70% were classified as having active malignancy. Some categories (especially gastrointestinal [GI ] and genitourinary [GU]) contain several cancer subtypes, as 77 distinct structured text fields were collapsed into these seven categories for display.
Figure 8
Figure 8: Distribution of repeat studies per patient for 350 patients with estimated LAR greater than 1% who have no cancer history according to billing ICD9 codes. The curves plot the percentage of patients for whom more than X% of studies were of the same type, stratified by the dominant anatomy imaged for the patient. Abdominopelvic CT is the most common source of repeat CT in these patients. Most patients undergo CT of multiple types, with only 12% of patients having all of their repeat imaging of the same anatomy.
lignancy. In these patients, and in cancer patients whose disease subsequently responds to treatment, the risks of cumula-
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tive radiation exposure must be considered in balance with the anticipated benefits of recurrent imaging. 181
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Limitations and Underlying Controversies Cohort setting.—Our study of a single adult tertiary care institution reflects the combined CT ordering and utilization practices of all our practitioners but may not be generalizable to other institutions with different patient mixes or different provider attitudes toward CT imaging. Data capture.—We likely underestimated cumulative examination counts and doses for many patients, as we could not capture data on CT studies performed before our 22-year records or those performed at other institutions. Furthermore, we focused solely on diagnostic CT, which represents approximately half of the collective popu-
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lation dose (3), excluding interventional radiology, nuclear medicine, fluoroscopy, and radiography studies. Dosimetry.—CT radiation doses depend on scanner technology and imaging parameters used and may vary with patient size (21–23). We did not adjust dose estimates for the particular scanner type or date of examination. This type of universal dosimetry estimation has inherent limitations in a group of disparate patients. Different dosimetry estimation methods would not affect the cumulative examination count results, but might alter the shape and scale of the cumulative dose distribution. In theory, even for the same absorbed organ doses, the calculated ef-
fective dose would vary in patients of different ages because of age-dependent differences in the radiation sensitivities of their tissues. For this and other reasons, prior publications have opposed the use of effective dose estimates for individual risk assessment, instead preferring to sum the effects of organ-specific absorbed doses (10,14,24,25). This would indeed be the optimal approach if all organ doses were known, but in their absence, would require expansion to a larger look-up table of typical organspecific absorbed doses, ideally as a function of patient size, CT scanner, and CT protocol or imaging parameters used, if such information were available in an accessible database.
Table 4 Sample Patient Detail in Non-Cancer Patients
Total
No. of Examinations AbdomenPelvis Chest Head
Spine
Abstracted Medical History
5.3 3.1 2.2
70 81 35
69 27 21
0 24 10
1 21 1
0 1 0
3.9 3.8 3.6 2.7 2.5 2.4 2.2 2.0 1.9
2.5 2.0 2.0 1.4 1.4 1.3 1.1 1.2 1.1
88 28 36 37 38 16 45 37 48
20 26 32 7 11 15 2 19 3
20 1 2 22 10 1 5 2 21
35 1 1 4 12 0 36 12 5
7 0 0 3 0 0 0 0 0
13/F/55
1.9
1.2
37
11
5
18
1
14/F/81
1.7
1.4
40
30
8
0
0
15/F/62 16/F/49 17/F/53 18/F/58 19/F/57
1.6 1.5 1.4 1.4 1.3
1.1 0.9 0.9 0.9 0.9
29 17 13 18 19
11 9 4 12 10
10 1 0 2 4
5 3 0 1 4
0 2 8 3 0
20/F/56 21/F/58 22/F/31 23/F/47
1.2 1.1 1.1 1.1
0.8 0.7 0.5 0.6
24 25 9 21
6 5 5 6
11 9 1 1
2 5 2 12
0 2 0 1
24/M/55 25/M/53
1.1 1.0
0.6 0.6
19 36
8 6
7 4
2 19
1 5
Recurrent pyelonephritis, lithotripsy, stone extraction, ulcerative colitis End-stage renal disease, hemodialysis, lupus Chronic pancreatitis and Whipple operation, ventral hernias, mesh infections, empyema Lupus, osteogenesis imperfecta, seizures, stroke Crohn, hemicolectomy, chronic abdominal pain, heart transplant Necrotizing pancreatitis, enterocutaneous fistula Cystic fibrosis, lung transplants Lupus, asthma, seizures, pulmonary embolus, pulmonary hypertension Gastric bypass, small bowel obstructions Acute disseminated encephalomyelitis, ventriculoperitoneal shunt Renal transplant, hemodialysis, abdominal abscess Recurrent chest pain, sickle- thalassemia, emphysema, pulmonary embolus End-stage renal disease, human immunodeficiency virus, ruptured middle cerebral artery aneurysm Perforated diverticulitis, congestive heart failure, pulmonary hypertension Bronchioloitis obliterans organizing pneumonia, perinephric hematoma Diverticulitis, colectomy, ventral hernia repair, recurrent abdominal pain Lumbar fusion, back pain Diverticulitis, trauma Sarcoidosis, pulmonary hypertension, intraductal papillary mucinous neoplasm Endocarditis, aortic valve replacement, hepatitis C Chronic obstructive pulmonary disease, chronic pancreatitis Flank pain, asthma, migraines Perforated appendicitis and complications, stroke, ventriculoperioneal shunt Type A aortic dissection repair Alcoholic, frequent trauma
LAR Incidence (%)
LAR Mortality (%)
1/F/45 2/F/49 3/F/34
9.6 5.2 4.4
4/F/58 5/F/46 6/M/33 7/M/27 8/F/42 9/F/34 10/F/24 11/M/54 12/F/44
Patient No./ Sex/Age (y)
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Better still would be to capture and archive dose parameters for every scan performed. CT dose index and doselength product are currently the best available and most commonly used CT dose descriptors but are not readily archivable, and as quality assurance measures of the exposure to a standarddiameter acrylic phantom, are inherently limited in predicting the actual dose to a specific patient (26). Ultimately, longitudinal dose monitoring efforts will benefit most from improved patient-specific dose estimates for each examination, archived in the Digital Imaging and Communications in Medicine (DICOM) header information or another institutional database. Cancer risk models.—Controversy persists about the shape of the doseresponse curve to describe cancer risks from low-dose radiation exposures, owing primarily to the epidemiologic challenges of detecting small incremental increases over the large baseline cancer rates. A number of studies have explored radiation-related cancer risks, both from the one-time acute exposures of the Life Span Study (LSS) cohort of Hiroshima and Nagasaki survivors (27) and from protracted occupational (28) or therapeutic radiation exposures. Drawing on numerous epidemiologic studies, the BEIR VII report (19) elaborates the most commonly used linear-no-threshold model of carcinogenesis estimation for low-dose radiation exposures. The report committee estimates subjective error bars of a factor of 2 in either direction, resulting from: (a) accuracy of the LSS dosimetry values, (b) risk transport from a Japanese to a U.S. population owing to differences in baseline cancer rates, and (c) uncertainty in the appropriate value of the dose and dose rate effectiveness factor to adjust for low doses or protracted exposures, as compared with the single acute exposure of the LSS cohort. This estimated factor of 2 uncertainty inherent in the BEIR VII model is expected to dominate the dosimetry limitations discussed. However, when attempting to apply these methods to predict individual patient risks, it is important to remember that the LAR pre-
dictions represent statistical risks and are based on an implicit assumption that a patient’s competing causes of mortality are similar to age- and sexmatched peers, without incorporating known diagnoses that might shorten a patient’s life. In the extreme example, a patient expected to die imminently of his or her underlying disease will not survive long enough to develop a radiation-induced cancer. Future methodology work is needed to incorporate underlying disease mortality into LAR calculations.
Summary and Recommendations Despite these controversies and any effects they might have on the shape or scale of the effective dose and LAR distributions, our study identifies a subgroup of patients who undergo large amounts of recurrent CT imaging and potentially high radiation-induced cancer risks, for whom further measures may be warranted to control subsequent exposures. A number of approaches may be used to reduce cumulative radiation risks to these patients (24,29,30). In broad terms, methods to reduce the dose of each examination include technical developments (such as automated tube current modulation, beam filtration, and adaptive collimation), imaging parameter selection (decreasing tube potential, tube current, or both), protocol modifications (reducing duplicate coverage regions and multiple-pass scanning), and utilization of standardized reference dose levels. Measures to reduce CT utilization include adoption of broadly applicable imaging algorithms and recommendations to use nonionizing imaging alternatives or no imaging at all. Yet ultimately a risk-benefit decision must be made at the level of the individual patient and should involve balancing the highly context-dependent benefits of imaging against the patient-specific cumulative risks. While the incremental risk is essentially the same from the first or the 50th CT scan (aside from effects of different exposure ages), the risks of an individual study should not be considered in isolation but should be
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viewed as part of the patient’s past (and predicted future) cumulative exposure. While all patients may benefit from universal dose reduction efforts and appropriateness review, there may also be a role for more aggressive radiation protection efforts in patients who have been frequently imaged in the past and are likely to continue to be frequently imaged in the future. As a first step, inspection of the CT history can give the ordering provider a sense of the general level of risk to the patient at hand. As a next step, we are developing real-time decision support tools to identify high-risk patients and provide cumulative exposure and risk estimates at the point of care. This may help to create the framework needed to educate referring physicians and better inform the risk-benefit decision at the individual patient level.
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radiology.rsnajnls.org ▪ Radiology: Volume 251: Number 1—April 2009
