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Improving Breast Imaging Quality Standards 2 Improving Interpretive Performance in Mammography Breast cancer is a significant cause of morbidity and mortality in the United States. Until it can be prevented, the best approach to the control of breast cancer includes mammography screening for early detection. Mammography, however, is not a perfect test, due to the complex architecture of the breast tissue being imaged, the variability of the cancers that may be present, and the technical limitations of the equipment and processing. The technical aspects of mammography are now less variable since the interim Mammography Quality Standards Act (MQSA) regulations went into effect in 1994. At this point, the focus is shifting to the quality of mammography interpretation. The available evidence indicates that interpretive performance is quite variable, but the ambiguities of human decision making, the complexities of clinical practice settings, and the rare occurrence of cancer make measurement, evaluation, and improvement of mammography interpretation a much more difficult task. The components of current MQSA regulations pertinent to interpretive performance include: (1) medical audit; (2) requirements related to training, including initial training and Continuing Medical Education (CME), and (3) interpretive volume, including initial and continuing experience (minimum of 960 mammograms/2 years for continuing experience). The purpose of this chapter is to explore current evidence on factors that affect the interpretive quality of mammography and to recommend ways to improve and ensure the quality of mammography interpretation. The primary questions that the Committee identified as currently relevant to interpretive performance include whether the current audit procedures are likely to ensure or improve the quality of interpretive performance, and whether any audit procedures applied to the current delivery of U.S. health care will allow for accurate and meaningful estimates of performance. In addition, the Committee questioned whether the current CME and volume requirements enhance performance. These issues will be described fully and the current state of research on these topics will be described in the sections that follow. The current state of knowledge about existing measures and standards is described first in order to define the terms needed to assess the medical audit requirement of MQSA. CURRENT STATE OF KNOWLEDGE REGARDING APPROPRIATE STANDARDS OR MEASURES Effectively measuring and analyzing interpretive performance in practice presents many challenges. For example, data must be gathered regarding whether a woman has breast cancer diagnosed within a specified timeframe after a mammogram and whether the finding(s) corresponds with the location in which the cancer is found. Other challenges include reaching agreement regarding the definition of positive and negative interpretation(s), standardizing the patient populations so that comparisons are meaningful, and deciding which measures are the most important reflection of an interpreting
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Improving Breast Imaging Quality Standards TABLE 2–1 Terms Used to Define Test Positivity/Negativity in BI-RADS 1st and 4th Editions ACR Category BI-RADS Assessment 1st Edition 4th Edition 0 Need additional imaging Need additional imaging evaluation and/or prior mammograms for comparison 1 Negative Negative 2 Benign finding Benign finding(s) 3 Probably benign Probably benign finding—short-interval follow-up suggested 4 Suspicious abnormality Suspicious abnormality—biopsy should be considered (4a, 4b, 4c may be included to reflect increasing suspicion) 5 Highly suggestive of malignancy Highly suggestive of malignancy—appropriate action should be taken 6 NA Known, biopsy-proven malignancy—appropriate action should be taken SOURCE: American College of Radiology (2003). physician’s skill. In this section, current well-established performance measures are reviewed and their strengths and weaknesses are discussed. These measures should be made separately for screening examinations (done for asymptomatic women) and diagnostic examinations (done for women with breast symptoms or prior abnormal screening mammograms) because of the inherent differences in these two populations and the pretest probability of disease (Dee and Sickles, 2001; American College of Radiology, 2003). However, for simplicity, in the discussion below “examinations” or “mammograms” are used without designating whether they are screening or diagnostic because the mechanics of the measures are similar in either case. Before describing the measures, it is important to clearly define a positive and negative test. The Breast Imaging Reporting and Data System (BI-RADS) was developed by the American College of Radiology (ACR), in collaboration with several federal government agencies and other professional societies in order to create a standardized and objective method of categorizing mammography results. The BI-RADS 4th Edition identifies the most commonly used and accepted definitions, which are based on a standard set of assessments first promulgated by the ACR in 1992 and modified slightly in 2003. Table 2–1 outlines terms used to define test positivity/negativity as found in the 1st and 4th editions of BI-RADS. The assessments are intended to be linked to specific recommendations for care, including continued routine screening (Category 1, 2), immediate additional imaging such as additional mammographic views and ultrasound or comparison with previous films (Category 0), short-interval (typically 6 months) follow-up (Category 3), or biopsy consideration (Category 4) and biopsy/surgical consult recommended (Category 5).
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Improving Breast Imaging Quality Standards Based on these assessments and recommendations, definitions of a positive mammography interpretation have also been suggested by the ACR BI-RADS Committee, as follows: Screening Mammography: Positive test=Category 0, 4, 5 Negative test=Category 1, 2 Diagnostic Mammography: Positive test=Category 4, 5, 6 Negative test=Category 1, 2, 3 MQSA regulations, in contrast, define a positive mammogram as one that has an overall assessment of findings that is either “suspicious” or “highly suggestive of malignancy.” BI-RADS also now allows a single overall final assessment for the combined mammography and ultrasound imaging. Facilities that perform ultrasound online, at the time of diagnostic evaluation for an abnormal mammogram or palpable mass, will not have outcome statistics comparable to facilities where mammograms are reported without including the ultrasound evaluation. For example, a patient with a palpable finding may go to a facility and be found to have a negative mammogram and positive ultrasound, and the assessment will be reported as positive. While there has been much improvement in mammography reporting since the adoption of BI-RADS, there is still inter- and intraobserver variability in how this reporting system is used (Kerlikowske et al., 1998). Some variability in calculated performance measures can, therefore, be attributed to variance among interpreting physicians on what constitutes an abnormal mammogram. Moreover, though the intent is clear, the linkage between assessment and recommendations is not always maintained in clinical practice. Indeed, Food and Drug Administration (FDA) rules require use of the overall assessments listed in Table 2–1, but the recommendations associated with each category are not mandated or inspected by FDA. Thus, considerable variability in recommendations exists. For example, 38 percent of women with “probably benign” assessments had recommendations for immediate additional imaging in one national evaluation (Taplin et al., 2002). Some analyses include Category 3 assessments associated with recommendations for performance of additional imaging as positive tests (Barlow et al., 2002). In addition, some women with mammograms interpreted as Category 1 or 2 have received recommendations for biopsy/surgical consult due to a physical finding not seen on the mammogram because mammography cannot rule out cancer (Poplack et al., 2000). Therefore, these standard definitions serve as a starting point, but in practice, adaptations may be needed to accommodate the reality of clinical care. It is also important to define what constitutes “cancer.” In the context of mammography practice, the gold standard source for breast cancer diagnosis is tissue from the breast, obtained through needle sampling or open biopsy. This tissue sample then leads to the identification of invasive carcinoma or noninvasive ductal carcinoma in situ (DCIS). Breast cancers are labeled invasive because the cells are invading surrounding normal tissue. Invasive cancers account for most (80 percent) of breast cancers found at the time of screening in the United States. DCIS is included as a cancer diagnosis primarily because standard treatment for DCIS currently entails complete excision, similar to invasive cancers. Approximately 20 percent of breast cancer diagnoses are DCIS (Ernster et al.,
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Improving Breast Imaging Quality Standards TABLE 2–2 Possible Results for a Screening Test Cancer Outcome + − Test + TP—True positive FP—False positive Result − FN—False negative TN—True negative 2002). Lobular carcinoma in situ (LCIS) also is occasionally reported in the tissue, but should not be counted as cancer because it is not currently treated. Interpretive performance can also vary as a function of the time since the prior mammogram (Yankaskas et al., 2005). Recognizing that differences exist among screening guidelines regarding the appropriate screening interval (annual recommended by the American Cancer Society [ACS] and the American College of Obstetricians and Gynecologists [ACOG], every 1 to 2 years recommended by the U.S. Preventative Services Task Force [USPSTF]) (U.S. Preventive Services Task Force, 2002; Smith and D’Orsi, 2004; Smith et al., 2005), the specification of the period of follow-up after a mammogram is needed to observe women for the occurrence of cancer and calculate performance indices that can be compared in a meaningful way. With the above definitions, it is possible to identify several measures of interpretive performance. The measures of performance available to assess interpreting physician’s interpretation all build from a basic 2×2 table of test result and cancer outcome as noted in Table 2–2. A one-year interval should be used to calculate the performance indices so that they are comparable. Standard definitions of these measures are well summarized in the ACR BI-RADS 4th Edition, and are highlighted here along with some of the strengths and weaknesses of each measure. Separation of the data of screening from diagnostic indications for mammography is absolutely essential if performance measures are to be meaningful. Sensitivity Sensitivity refers to the ability of a test to find a cancer when it is present [TP/(TP+FN)]. The challenge with this measure is determining whether a cancer has been diagnosed, particularly if a woman was given a negative mammogram interpretation. Those women are not necessarily seen back in the same facility for their next examination. Therefore it is not possible to know with certainty whether they have cancer or not. This problem is called verification bias. Because only those women sent to biopsy within a facility have their true cancer status known, verification bias may lead to an overestimation of sensitivity (Zheng et al., 2005). Relatively complete ascertainment of cancer cases can be expected only if a mammography facility is able to link its examinations to those breast cancer cases compiled in a regional tumor registry, and this is practical only for a very small minority (likely fewer than 5 percent) of mammography facilities in the United States.
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Improving Breast Imaging Quality Standards Because the ultimate purpose of screening is to reduce disease-specific mortality by detecting and treating early-stage cancers, the sensitivity of mammography is important. However, sensitivity is affected by many factors, including whether it is a first (prevalent1) mammogram or subsequent (incident) mammogram, the distribution of patient ages and tumor sizes in the population of women being screened by the interpreting physician, the length of time since prior mammograms, the density of the breast tissue among women with cancer, and the number of women with cancer found by an interpreting physician (Carney et al., 2003; Yankaskas et al., 2005). Most screening populations have between 2 and 10 cancers per 1,000 women screened, and among women undergoing periodic screening on a regular basis, the cancer incidence rate is 2 to 4 per 1,000 (American College of Radiology, 2003). Under current MQSA regulations, a single interpreting physician must interpret 960 mammograms over 2 years to maintain accreditation. If he or she is reading only screening (and not any diagnostic) mammograms, he or she may, on average, see two to four women with cancer per year. Estimating sensitivity among such a small set of cancers affects the reliability of the measures. Random variation will be large for some measures, making comparisons among interpreting physicians very difficult, even if the interpreting physician has complete knowledge regarding the cancer status of all the women examined. Because most interpreting physicians do not have that complete information (no linkage to regional tumor registry) or the volumes to create stable estimates, measurement of sensitivity will be of very limited use for individual interpreting physicians in practice. Specificity Specificity is the ability of the test to determine that a disease is absent when a patient is disease-free [TN/(TN+FP)]. Because most screened women (990 to 998 per 1,000) are disease free, this number will be quite high even if a poorly performing interpreting physician gives nearly every woman a negative interpretation. But interpreting physicians must interpret some mammograms as positive in order to find cancers, so false-positive examinations occur. Estimates of the cumulative risk of a false-positive mammogram over a 10-year period of annual mammography vary between 20 and 50 percent (Elmore et al., 1998; Hofvind et al., 2004), and the risk of a negative invasive procedure may be as high as 6 percent (Hofvind et al., 2004). High specificity of a test is therefore important to limit the harms done to healthy women as a result of screening. Although one study of nearly 500 U.S. women without a history of breast cancer found that 63 percent thought 500 or more false-positive mammograms per life saved was reasonable (Schwartz et al., 2000), the cost and anxiety associated with false-positive mammograms can be substantial. Studies have shown that anxiety usually diminishes soon after the episode, but in some women anxiety can endure, and in one study anxiety was greater prior to the next screening mammogram for women who had undergone biopsy on the previous occasion of screening compared with women who had normal test results (Brett and Austoker, 2001). One study has shown that immediate interpretation of mammograms was associated with reduced levels of anxiety (Barton et al., 2004). 1 The prevalent screen refers to the first time a woman undergoes a screening test. Incident screens refer to subsequent screening tests performed at regular intervals. One useful index of screening mammography performance is that the number of cancers per 1,000 women identified by prevalent screens should be at least two times higher than by incident screens.
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Improving Breast Imaging Quality Standards Like sensitivity, specificity is a difficult measure to obtain for most interpreting physicians because it requires knowing the cancer status of all women examined (linkage to a regional tumor registry). Because it is difficult to ascertain the status of all women who undergo mammography with respect to the presence or absence of cancer, it is important to be clear about who is being included in the measure and what the follow-up period is. This has led to three levels of false-positive measurement (Bassett et al., 1994): FP1: No known cancer within one year of a Category 0, 4, or 5 assessment (screening). FP2: No known cancer within one year of a Category 4 or 5 assessment (usually diagnostic). FP3: No known cancer within one year of a Category 4 or 5 assessment, for which biopsy was actually performed. If each of these measures is estimated for a year, they can also be called rates. The limitation in choosing only one of the three rates is that there is a trade-off between the accuracy of the measure and the insight it provides regarding an interpreting physician’s performance. Although FP3 involves the most accurate measure of cancer status, it reflects only indirectly on the interpreting physician’s choice to send women to biopsy. Interpreting physicians’ ability to make that choice, and to make the recall versus no-recall decision at screening, are important characteristics. The most accurate estimate of FP (FP3) is therefore not necessarily the measure that provides the best insight into the interpreting physician’s performance. Conversely, FP1 includes BI-RADS 0’s, a high percentage of which have a low index of suspicion. Furthermore, measuring FP1 involves knowing the cancer status of all women for whom additional imaging was recommended (defined in BI-RADS as Category 0—incomplete, needs additional imaging). This is challenging because results of the subsequent evaluation may not be available. Currently, MQSA does not require that Category 0 examinations be tracked to determine the final overall assessment. The Committee recommends that for women who need additional imaging, mammography facilities must attempt to track these cases until they resolve to a final assessment. Although studies indicate that some interpreting physicians inappropriately assign women who need additional imaging a Category 3 BI-RADS assessment (Poplack et al., 2000; Taplin et al., 2002), this practice should be discouraged, and all women needing additional imaging should be tracked. Positive Predictive Value (PPV) There are three positive predictive values (PPV) that can be measured in practice, derived from the three false-positive measures described above. Again, these different measures are used to accommodate the challenges of data collection in practice. For example, though an interpreting physician may recommend a biopsy, it may not be done, and therefore the true cancer status may not be known. Thus, one must clearly state which PPV or PPVs are being monitored (Bassett et al., 1994), as recommended by the ACR. PPV1: The proportion of all women with positive examinations (Category 0, 4, or 5) who are diagnosed with breast cancer [TP/(TP +FP1)].
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Improving Breast Imaging Quality Standards PPV2: The proportion of all women recommended for biopsy after mammography (Category 4 or 5) that are diagnosed with breast cancer [TP/(TP+FP2)]. PPV3: The proportion of all women biopsied due to the interpreting physician’s recommendation who are diagnosed with cancer at the time of biopsy [TP/(TP +FP3)]. MQSA requires that interpreting physicians have an established mechanism to ascertain the status of women referred for biopsy. With these data interpreting physicians can measure their PPV2, but it is still subject to verification bias because not all women recommended for biopsy will have it done and because ascertainment of procedures is never 100 percent. The limitation of PPV2 or PPV3 is that many more women are referred for additional imaging (8 percent) than biopsy (1.5 percent) (Taplin et al., 2002). An important skill in interpretation involves sorting who needs additional imaging versus biopsy; PPV2 and PPV3 do not account for this because they only focus on women referred for biopsy. The ACR recommends that interpreting physicians who choose to perform one of the two types of audits described in the BI-RADS atlas should track all women referred for additional imaging for their subsequent cancer status (PPV1) (American College of Radiology, 2003). Because measuring PPV1 may not be possible in the absence of an integrated health system and registry, the Committee recommends use of PPV2. Another limitation of PPV that influences its usefulness is that it is affected by the rate of cancer within the population examined. The PPV will be higher in populations with higher cancer rates. For example, an interpreting physician practicing among older populations of women versus younger will have a higher PPV, just because the risk of breast cancer is higher among older women. PPV1 will vary depending on the proportion of patients who are having an incident versus prevalent screen. Unfortunately, a high PPV does not necessarily correlate with better performance. For example, the interpreting physician who recommends biopsy for only larger, more classic lesions will have a higher PPV, but will miss the smaller, more subtle, and less characteristic lesions that may be more important to patient outcomes (Sickles, 1992). Therefore the Committee recommends measuring the cancer detection rate in addition to PPV2 in order to facilitate interpretation of the measure. A higher PPV2 should occur in a population with a higher cancer detection rate (see section below on Cancer Detection Rate). Negative Predictive Value (NPV) Negative predictive value (NPV) is the proportion of all women with a negative result who are actually free of the disease [TN/(FN+TN)]. Monitoring NPV is not a requirement of MQSA, and in practice, the NPV is rarely used because it involves tracking women with negative examinations (linkage to regional tumor registry is required). Cancer Detection Rate Cancer detection rate is the number of women found to have breast cancer per 1,000 women examined. This rate is meaningless unless screening mammograms are assessed separately from diagnostic evaluations. This measure is similar to sensitivity, but includes all examinations (not just cancer cases) in the denominator. The advantage is that interpreting physicians know the total number of examinations they have interpreted and can identify the cancers resulting from biopsies they recommended or performed.
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Improving Breast Imaging Quality Standards The disadvantage is that differences in the cancer detection rate may reflect not only differences in performance, but also differences in the rate and risk of cancer in the population served. A high cancer detection rate relative to other interpreting physicians may simply indicate that the interpreting physician is caring for an older population of women who are at higher risk for cancer, not that he or she is necessarily highly skilled at finding cancer. This difference can be mitigated by adjusting the cancer rate to a standard population age distribution if adequate numbers exist in each age group to allow rate estimates. For radiologists comparing their own measures over time, these kinds of adjustments are less important if the population characteristics are stable. Other factors that could influence the cancer detection rate include the proportion of women having their first (prevalent) screen and the proportion having a repeat (incident) screen, the interval since the prior screen, differing practices with respect to who is included in screenings, whether practices read examinations individually as they are completed or in batches at a later time (mode of interpretation), and how long a physician has been in practice (van Landeghem et al., 2002; Harvey et al., 2003; Smith-Bindman et al., 2003). Interpretive sensitivity and specificity are higher on first screens compared to incident screens, presumably due to slightly larger tumors being found at prevalent screens (Yankaskas et al., 2005). For incident screens, the longer the time since the prior mammogram, the better interpretative performance appears, again because tumors will be slightly larger (Yankaskas et al., 2005). Some practices offer only diagnostic mammography to high-risk women with a history of breast cancer, while others will offer screening. Excluding such women from the screening population will reduce the number of cancers at the time of screening and affect positive predictive values, but may also change a physician’s threshold for calling a positive test. Changes in the threshold for a positive test can affect performance, and this threshold seems to change with experience (Barlow et al., 2004). Abnormal Interpretation Rate The abnormal interpretation rate is a measure of the number of women whose mammogram interpretation leads to additional imaging or biopsy. For screening mammography, the term “recall rate” is often used. The recall rate is the proportion of all women undergoing screening mammography who are given a positive interpretation that requires additional examinations (Category 0 [minus the exams for which only comparison with outside films is requested], 4, or 5). Desirable goals for recall rates for highly skilled interpreting physicians were set at less than or equal to 10 percent in the early 1990s (Bassett et al., 1994). This measure is easy to calculate because it does not rely on establishing the cancer status of women. The disadvantage is that differences in this measure may not reflect differences in skill except when the rate is extraordinarily high or low. Again, this will depend on the proportion of prevalent to incident screens (Frankel et al., 1995), on the availability of previous films for comparison (Kan et al., 2000), and on the mode of interpretation (Sickles, 1992, 1995a; Ghate et al., 2005). Cancer Staging Cancer staging is performed after a breast cancer is diagnosed. Stage, along with other tumor prognostic indicators (e.g., tumor grade, hormone receptor status, and other factors), is used to determine the patient’s prognosis, and the combination of tumor
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Improving Breast Imaging Quality Standards markers and stage influences treatment. Cancer staging takes into account information regarding the tumor histological type and size, as well as regional lymph node status and distant metastases. Staging information, which is generally derived from pathology reports in varying forms, is useful for the mammography audit because women with advanced, metastatic tumors are more likely to die from the disease. However, tumor staging information is not always easily available to the imaging facility, and thus, may be more of a burden to acquire. Tumor Size The size of the breast cancer at the time of diagnosis is relevant only for invasive cancers. All patients with only DCIS are Stage 0, despite the extent of the DCIS. An interpreting physician who routinely detects smaller invasive tumors is likely to be more skilled at identifying small abnormalities in a mammogram. The proportion of invasive tumors less than 1.5 or 1.0 cm could be used as one measure. Using tumor size as a performance measure has several limitations; measurement of a tumor is an inexact science and may vary depending on what is recorded in a patient record or tumor registry (e.g., clinical size based on palpation, size based on imaging, size based on pathology), and who is doing the measuring. SEER (Surveillance, Epidemiology and End Results) registries use a hierarchy to choose which measurement to include. Heterogeneity will occur because not all measurements are available. Furthermore, the proportion of small tumors will be affected by the population of tumors seen by a given interpreting physician; for example, a physician reading more prevalent screens will have a greater proportion of large tumors because there are more large tumors in the population. The screening interval is also important when tumor size is used as a performance measure. A shift toward smaller tumor size has been noted in screened populations such as those in the Swedish randomized trials of mammography (Tabar et al., 1992). A similar shift is expected in other screened populations. In one study of a National Screening Program, invasive breast cancer tumor size at the time of discovery decreased from 2.1–2.4 cm to 1.1–1.4 cm between 1983 and 1997, within which time period the national screening program had been implemented (Scheiden et al., 2001). Axillary Lymph Node Status The presence or absence of cancer cells in the axillary lymph nodes is one of the most important predictors of patient outcome. The prognosis worsens with each positive node (containing cancer cells) compared to women with histologically negative lymph nodes. Node positivity, however, is not necessarily a useful surrogate measure of an interpreting physician’s interpretive performance because inherently aggressive tumors may metastasize to the axillary lymph nodes early, when the tumor is still small, or even before the tumor becomes visible on a mammogram.
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Improving Breast Imaging Quality Standards Area Under the Receiver Operating Curve2 (AUC) Interpreting physicians face a difficult challenge. While trying to find cancer they must also try to limit the number of false-positive interpretations. If the distribution of interpretations among women with cancer and women without breast cancer were graphed together on one x/y axis, it would look like Figure 2–1. Focusing on sensitivity simply indicates how an interpreting physician operates when cancer is present. Focusing on specificity simply indicates how an interpreting physician operates when cancer is not present. What is really needed to assess performance is the ability of the interpreting physician to simultaneously discriminate between women with and without cancer. This is FIGURE 2–1 Ideal (A) and actual common (B) distribution of mammography interpretation (BI-RADS Assessment Categories 1–5). 2 For a more detailed description of ROC curves, see Appendix C in Saving Women’s Lives (IOM, 2005).
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Improving Breast Imaging Quality Standards reflected in the overlap between the two distributions of interpretations in Figure 2–1, and is measured by the area (AUC) under the receiver operating curve (ROC) (Figure 2–2). ROC analysis was developed as a methodology to quantify the ability to correctly distinguish signals of interest from the background noise in the system. The ROC curves map the effects of varying decision thresholds and demonstrate the relationship between the true-positive rate (sensitivity) and the false-positive rate (specificity). If a reader’s interpretation is no better than a flip of the coin, the distribution of BI-RADS assessments in Figure 2–1 will overlap completely and the AUC in Figure 2–2 will be 0.5. If an interpreting physician has complete discrimination, the distribution of BI-RADS assessments will be completely separated for women with and without cancer, as in Figure 2–1a, and the AUC will be 1.0. An interpreting physician’s AUC therefore usually falls between 0.5 and 1.0. Estimating the AUC is possible if the status of all examined women is known and the appropriate computer software is employed. It has the advantage of reflecting the discriminatory ability of the interpreting physician and incorporates both sensitivity and specificity into a single measure, accounting for the trade-offs between the two measures. FIGURE 2–2 ROC analysis. If a reader is guessing between two choices (cancer versus no cancer), the fraction of true positives will tend to equal the fraction of false negatives. Thus, the resulting ROC curve would be at a 45-degree angle and the area under the curve, 0.5, represents the 50 percent accuracy of the test. In contrast, the ROC curve for a reader with 100 percent accuracy will follow the y-axis at a false-positive fraction of zero (no false positives) and travel along the top of the plot area at a true-positive fraction of one (all true positives). The area under the curve, 1.0, represents the 100 percent accuracy of the test. The hypothetical result for a reader with an area under the curve of 0.85 is shown for comparison.
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Improving Breast Imaging Quality Standards The Committee also considered a number of other approaches that could potentially improve interpretive performance, such as double reading, use of CAD, increased continuing experience (interpretive volume) requirements, and CME programs that focus on interpretation and self-assessment. While there is some evidence to suggest that these approaches could also improve the quality of mammography interpretation, the data available to date are insufficient to justify changes to MQSA legislation or regulations. However, the Committee recommends that additional studies be rapidly undertaken to develop a stronger evidence base for the effects of CME, reader volume, double reading, and CAD on interpretive performance. REFERENCES Adcock KA. 2004. Initiative to improve mammogram interpretation. The Permanente Journal 8(2):12–18. American College of Radiology. 2003. ACR BI-RADS®—Mammography. In: ACR Breast Imaging Reporting and Data System, Breast Imaging Atlas. 4th ed. Reston, VA: American College of Radiology. Andersson I, Aspegren K, Janzon L, Landberg T, Lindholm K, Linell F, Ljungberg O, Ranstam J, Sigfusson B. 1988. Mammographic screening and mortality from breast cancer: The Malmo Mammographic Screening Trial. British Medical Journal 297(6654):943–948. Anttinen I, Pamilo M, Soiva M, Roiha M. 1993. Double reading of mammography screening films—one radiologist or two? Clinical Radiology 48(6):414–421. Applied Vision Research Institute. 2004. PERFORMS: SA2003 Report to the National Coordinating Committee for QA Radiologists. Derby, England: University of Derby. August DA, Carpenter LC, Harness JK, Delosh T, Cody RL, Adler DD, Oberman H, Wilkins E, Schottenfeld D, McNeely SG. 1993. Benefits of a multidisciplinary approach to breast care. Journal of Surgical Oncology 53(3):161–167. Baker JA, Rosen EL, Lo JY, Gimenez EI, Walsh R, Soo MS. 2003. Computer-aided detection (CAD) in screening mammography: Sensitivity of commercial CAD systems for detecting architectural distortion. American Journal of Roentgenology 181(4):1083–1088. Ballard-Barbash R, Taplin SH, Yankaskas BC, Ernster VL, Rosenberg RD, Carney PA, Barlow WE, Geller BM, Kerlikowske K, Edwards BK, Lynch CF, Urban N, Chrvala CA, Key CR, Poplack SP, Worden JK, Kessler LG. 1997. Breast Cancer Surveillance Consortium: A national mammography screening and outcomes database. American Journal of Roentgenology 169(4):1001–1008. Barlow WE, Chi C, Carney PA, Taplin SH, D’Orsi C, Cutter G, Hendrick RE, Elmore JG. 2004. Accuracy of screening mammography interpretation by characteristics of radiologists. Journal of the National Cancer Institute 96(24):1840–1850. Barlow WE, Lehman CD, Zheng Y, Ballard-Barbash R, Yankaskas BC, Cutter GR, Carney PA, Geller BM, Rosenberg R, Kerlikowske K, Weaver DL, Taplin SH. 2002. Performance of diagnostic mammography for women with signs or symptoms of breast cancer. Journal of the National Cancer Institute 94(15):1151–1159. Barton MB, Morley DS, Moore S, Allen JD, Kleinman KP, Emmons KM, Fletcher SW. 2004. Decreasing women’s anxieties after abnormal mammograms: A controlled trial. Journal of the National Cancer Institute 96(7):529–538.
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