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MP 6.01.45 Computer-Aided Evaluation of Malignancy with Magnetic Resonance Imaging of the Breast

Medical Policy    


Original Policy Date

Last Review Status/Date
Reviewed with literature search/1:2015



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Our medical policies are designed for informational purposes only and are not an authorization, or an explanation of benefits, or a contract.  Receipt of benefits is subject to satisfaction of all terms and conditions of the coverage.  Medical technology is constantly changing, and we reserve the right to review and update our policies periodically.


The use of computer-aided evaluation (CAE) is proposed to assist radiologists’ interpretation of contrast-enhanced magnetic resonance imaging (MRI) of the breast. MRI of the breast is suggested as an alternative or adjunct to mammography or other screening and diagnostic tests because of its high sensitivity in detecting breast lesions. However, it has a high false positive rate because of the difficulty in distinguishing between benign and malignant lesions. MRI may be used to screen women at high genetic risk of breast cancer or to look for more extensive disease in women diagnosed with breast cancer who are eligible for breast-conserving surgery; it is also being studied to gauge the impact of cancer treatment (for a discussion of other potential indications, see Policy No. 6.01.29, MRI of the breast). The CAE systems reviewed in this policy are intended to improve the specificity of MRI in detecting or measuring malignant tissue, while maintaining the generally high sensitivity of MRI. An improved ability to identify MRI-detected lesions that are almost certainly benign could potentially reduce biopsy rates. There is anecdotal information that MRI also may reduce reoperation rates among patients undergoing breast-conserving surgery by more clearly identifying the tissue that should be removed. CAE also may reduce the time needed to interpret breast MRI images, which currently takes longer than reading mammograms.

CAE systems for MRI essentially provide easier ways of interpreting the patterns of contrast enhancement across a series of images, which in turn may help identify lesions and their likelihood of being malignant. Two key aspects of enhancement (also called kinetics) are examined: (1) Within the first minute or so, how quickly does the lesion enhance up to a certain threshold (eg, 50%, 100% of the initial value; rapid enhancement above 90% in 90 seconds suggests malignancy)? (2) What is the subsequent pattern of enhancement (ie, continues to increase, plateaus, or declines [called washout, which is associated with malignancy])?(1) In contrast to computer-aided detection (CAD) systems used with mammography, CAE for MRI is not primarily intended to identify lesions for consideration by a radiologist. Unlike the subtle appearance of lesions on mammography, most cancers enhance on MRI. The challenge is determining which lesions are benign and which are malignant. A large number of images are produced during MRI of the breast: images are taken at varying “depths” throughout each breast multiplied by the number of times the breast is imaged to capture different time points in the enhancement process; this can produce hundreds of images. Radiologists view the images to detect suspicious areas, and then pick a region of interest and look at the enhancement pattern. However, there may be variations across radiologists in the regions of interest selected and in the precise definition of the region of interest. CAE systems, in contrast, use color-coding and differences in hue to indicate the pattern of enhancement for each pixel in the breast image, thereby allowing radiologist to analyze enhancement patterns systematically. CAE systems for MRI of the breast were initially called CAD (computer-aided detection) systems, the same terminology used for mammography. However, the focus with MRI of the breast is on improving specificity (distinguishing malignant from benign) rather than increasing sensitivity (ie, detection), as in mammography. The authors of 2 studies refer to CADstream as a computer-aided evaluation (CAE) program,(2,3) and that terminology has been adopted in this policy.

Regulatory Status

Several CAE systems for use with MRI of the breast have 510(k) marketing clearance from the U.S. Food and Drug Administration (FDA). Some of these systems may have broader uses beyond breast MRI. There also may be some overlap in the functions performed by these devices and other image-processing systems.

  • The 3TP (3 Time Point) Software Option, manufactured by 3TP LLC (now called CAD Sciences, White Plains, NY), was cleared on June 23, 2003. iCAD acquired CAD Sciences in 2008 and is now marketing a system called SpectraLook™ with CADVue™, which was FDA-cleared on July 20, 2012. According to documents filed with the FDA, the 3TP Software Option is “intended to be used as a postprocessing software package designed to provide a reliable means for visualizing the presence and pattern of contrast-induced enhancement on MR datasets.” It provides a color-coded image that indicates the likelihood that each pixel shows malignant or benign tissue based on the changes in enhancement at 3 points in time, which are defined by the software program.
  • CADstream™, which is manufactured by Confirma Inc. (Kirkland, WA), was cleared on July 30, 2003; Merger Healthcare (Hartland, WI) subsequently acquired Confirma. CADstream is described as a

“Computer Aided Detection (CAD) system intended for use in analyzing magnetic resonance imaging (MRI) studies. CADstream automatically registers serial patient image acquisitions to minimize the impact of patient motion, segments and labels tissue types based on enhancement characteristics (parametric image maps), and performs other user-defined postprocessing functions (image subtractions, multiplanar reformats, maximum-intensity projections). When interpreted by a skilled physician, this device provides information that may be useful in screening and diagnosis…Patient management should not be based solely on the results of the CADstream analysis.”

It also provides automated determination of volumes of interest. In addition, CADstream can be used during MRI-guided biopsies.

  • Aegis™ (Sentinelle Medical Inc, Toronto, Ontario, Canada) received 510(k) marketing clearance from the FDA on February 9, 2007, as substantially equivalent to CADstream Version 4.0. However, in the 510(k) documents, the manufacturer states that the primary goal of Aegis is “to identify where and how deep a biopsy or localization needle should be inserted into an imaged breast.”
  • DynaCAD® (MRI Devices Corp, Waukesha, WI; now from Invivo Corp, Orlando, FL) was cleared July 21, 2004.
  • z3D Contrast Acuity Software (Clario Medical Imaging Inc., Seattle, WA) was cleared September 5, 2008 and is apparently used in conjunction with CAE for MRI systems.

FDA product code: LLZ 


The use of computer-aided evaluation (CAE) for interpretation of magnetic resonance imaging (MRI) of the breast is considered investigational.

Policy Guidelines 

There is an add-on CPT category III code for the use of computer-aided evaluation (CAE) with MRI of the breast:

0159T Computer-aided detection, including computer algorithm analysis of magnetic resonance imaging (MRI) data for lesion detection/characterization, pharmacokinetic analysis, with further physician review for interpretation, breast MRI.

This code would be used with the code for breast MRI –77058-77059.

Benefit Application
BlueCard /National Account Issues 

State or federal mandates (e.g., FEP) may dictate that all FDA-approved devices may not be considered investigational and, thus, these devices may be assessed only on the basis of their medical necessity.


This policy was created in 2006 based on a TEC Assessment4 and has been updated periodically using MEDLINE. The most recent literature review was conducted on December 22, 2014.

To demonstrate the impact of computer-aided evaluation (CAE) in the diagnosis of breast cancer, studies that compare the sensitivity and specificity of magnetic resonance imaging (MRI) with and without the use of CAE systems are needed. Such studies can demonstrate the incremental diagnostic accuracy of CAE compared with no CAE. Ideally, these studies should be prospective and should evaluate a population of patients similar to that presenting for breast cancer screening in a clinical setting.

To demonstrate clinical utility, prospective studies that evaluate whether incremental diagnostic accuracy leads to changes in management and improved outcomes are needed. Changes in management might include changes in the decision to perform biopsies and in subsequent management decisions based on biopsy results.

The literature review focuses on studies that compare the diagnostic accuracy of MRI with and without CAE. There are no prospective studies of this type identified in the literature. Retrospective studies generally do not include a population similar to that presenting in clinical care; rather, they use an enriched population that includes a greater proportion of patients with cancer than would be expected in consecutive patients presenting for screening. A representative sample of these studies is discussed next.

What Is the Incremental Improvement in Diagnostic Accuracy for Breast Cancer When CAE Is Added to Standard MRI?

TEC Assessment (2006)

The 2006 TEC Assessment summarized 4 published articles and 4 abstracts that compared the accuracy of MRI with and without CAE.(4) (The reviewed studies focused on commercially available CAE systems, but some articles on other systems were included. In addition, studies had to report on cancer detection based on histologic results.) Three of the articles reported on development and validation of CAE systems aimed at distinguishing between malignant and benign lesions, and they used information on women with known lesions. The fourth article(5) provided information on one of the noncommercial systems used to evaluate women with cancer who were eligible for breast-conserving therapy. Additional findings (other lesions or larger lesions) were found in 48 of the 116 women (41%); approximately 80% of these women had further workup; and in 27 of these women, the findings were malignant. The area under the receiver operating characteristic (ROC) curve was 0.91±0.04 for the radiologist reading and 0.98±0.04 for the combined radiologist and computerized reading (p=0.03). However, the ability to generalize these results and the clinical impact of the findings is uncertain.

Four abstracts of studies were included in the TEC Assessment because of the small number of studies identified. However, the need to exercise caution in using results from abstracts must be kept in mind as these results are reviewed. Of the 4 abstracts, 2 used CADstream, 1 did not report the system used, and 1 was an excerpt from an article that summarized the results of 3 earlier abstracts on the 3TP system. It is unclear whether the current 3TP system has been modified substantially from the version used in these studies. Once again, these abstracts report on the results of CAE with MRI among women with known lesions.

Finally, DeMartini et al (2005) reported on the use of CAE with MRI in 15 patients to assess the impact of chemotherapy.(6) This small study found there were a substantial number of false negative results for residual malignancy using CAE—a different type of problem than found with most other uses of MRI, ie, too many false-positive results.

Conclusions of the TEC Assessment were that the literature on CAE with MRI of the breast was sparse overall and that few studies addressed specific situations in which CAE with MRI is used in a clinical setting. The few articles and abstracts calculated test characteristics on the basis of lesions and not the number of women or breasts. In a screening population, many women would not have any lesions. Including these women might alter the results. Given MRI’s lower sensitivity in detecting ductal carcinoma in situ (DCIS), the mix of DCIS versus masses would affect the calculations of sensitivity and specificity and might affect the impact of the CAE system.

Subsequent Studies

Since the TEC Assessment was completed in 2006, there have been a number of relevant studies published. All of these have been retrospective analyses that included populations of patients that are not representative of those seen in clinical care.

A 2011 systematic review(7) identified 10 CAE studies in women with benign and/or malignant breast lesions (BIRADS category ≥2), including 2 studies described next.(8,9) In meta-analyses of 3 studies (211 lesions, 55% malignant), 1 of which used 3.0-T MRI,(8) sensitivity of experienced radiologists’ blinded readings was 89% both with and without CAE, but specificity decreased from 86% (95% confidence interval [CI], 79 to 91) without CAE to 82% (95% CI, 76 to 87) with CAE, a statistically nonsignificant difference. The authors attributed the decrease to a greater reliance by radiologists on the contrast enhancement pattern provided by CAE in the absence of morphology data, which CAE does not provide. For residents with limited breast MRI experience, specificity was approximately 78% with or without CAE, but sensitivity increased from 72% (95% CI, 62 to 81) without CAE to 89% (95% CI, 80 to 94) with CAE, a statistically nonsignificant difference. Statistical heterogeneity was moderate to substantial (range, 56%-83%) for all results except for the specificity of residents’ readings both with and without CAE, which had low to moderate statistical heterogeneity (range, 24%-33%).

Liu et al (2014) conducted a retrospective study to compare radiologists’ readings of 3.0-T MRI images ith readings by CAE (DynaCAD) in 78 consecutive patients with newly diagnosed breast lesions at a single institution in China.(10) Lesions less than 0.8 cm in long-axis diameter were excluded (sensitivity was not assessed). Diagnoses of 93 mass-like and non-mass-like (eg, DCIS, invasive lobular carcinoma, papilloma) were confirmed by needle core biopsy (n=13) or surgical histology (n=80). Of 51 mass-like lesions, 29 were malignant; of 42 non-mass-like lesions, 23 were malignant. Three experienced radiologists blinded to histologic diagnosis performed MRI readings, and 3 radiologists performed CAE readings; it is unclear whether these were the same radiologists. Overall diagnostic accuracy was 74% for radiologists and 87% for CAE. For mass-like lesions, accuracy was similar between radiologists and CAE. For non-mass-like lesions, accuracy was 67% for radiologists and 86% for CAE. Limitations of this study are those cited previously (retrospective design and calculation of test characteristics based on the number of lesions rather than on the number of women or the number of breasts). Further, results may be
applicable only to patients with lesions greater than 0.8 cm and possibly only to readings made by 3.0-T MRI.

Kim et al (2014) in South Korea conducted a retrospective study to evaluate the correlation between CAE-MRI and pathological review in 148 patients with breast cancer who received neoadjuvant chemotherapy.(11) Thirty-nine patients achieved pathological complete response (pCR, defined as no histopathological evidence of residual invasive cancer cells in the breast or axillary lymph nodes), and 109 patients did not achieve pCR. Among patients who achieved pCR, 49% of tumors showed a persistent pattern of enhancement, 27% showed a plateau pattern, and 23% showed washout. Among patients who did not achieve pCR, 42%, 33%, and 25% of tumors showed a persistent, plateau, or washout pattern, respectively. Only the plateau pattern was statistically (negatively) correlated with pCR (Kruskal–Wallis test, p=0.007). Agreement with pathological measurement of tumor diameter was greater for manual measurement using conventional MRI (intraclass correlation coefficient<0.637) compared with automated measurement by CAE-MRI (intraclass correlation coefficient<0.384).

Lehman et al (2013) reported on a multicenter, retrospective study of 9 experienced and 11 inexperienced radiologists who read a set of dynamic contrast-enhanced breast MRIs twice, once with and once without CADstream.(12) Of 70 MRIs in the set, 27 had benign outcome and 43 had malignant outcome. Among experienced readers, sensitivity increased from 84% without CAE to 91% with CAE, a statistically significant difference of 7 percentage points (95% CI, 4 to 11). Among inexperienced readers, sensitivity increased from 77% to 83%, a difference of 6 percentage points (95% CI, 1 to 10). Specificity (BIRADS category 3 considered negative) did not change with the addition of CAE for either group. Similarly, overall diagnostic accuracy did not change statistically for either group: For experienced readers, the area under the ROC curve (AUC) was 0.80 without CAE and 0.83 with CAE (although these values are reversed without subsequent correction in the narrative description of results). For inexperienced readers, the AUC was 0.77 without CAE and 0.79 with CAE. There was no significant difference in overall time to assessment with or without CAE.

Cho et al (2012) reported on a retrospective study that evaluated CAE in detection of contralateral breast cancer lesions in women with breast cancer.(13) There were 23 malignant and 29 benign lesions in 52 consecutive patients. Three experienced radiologists interpreted the images and provided their judgments about the probability of cancer. Sensitivity and specificity were determined with and without CAE for each radiologist. Mean sensitivity improved statistically significantly with CAE, from 76.8% to 92.8% (p value not reported), but specificity decreased by a statistically nonsignificant amount (40.2% to 35.6%; p not reported). There was an increase in the combined accuracy, as measured by the AUC from 0.603 to 0.667, but this difference was not statistically significant (p not reported).

Shimauchi et al (2011)(14) performed a retrospective analysis that was similar to previous studies. In this study, an initial set of 121 breast lesions (77 malignant, 44 benign) was used to train the CAD system. A sample of 30 malignant breast lesions and 30 benign lesions was used to test the incremental accuracy of computer-aided detection (CAD) when added to conventional MRI. Sensitivity for detection of malignancy was higher for CAD compared with conventional imaging (88% vs 83%, p=0.001), as was the mean by ROC analysis (0.84 vs 0.80, p=0.007). Mean specificity was not significantly greater in the CAD group compared with conventional imaging (53% vs 50%, p=0.20).

A 2010 retrospective study evaluated CADstream, using images from a 3.0-T MRI system.(8) Of 426 women imaged consecutively, the final analysis comprised 36 women (42 lesions) with indeterminate mammographic and/or ultrasound results or high-risk screening. Blinded manual reading, by 2 experienced breast radiologists, was followed 6 months later by blinded reading of CAE results, by the same 2 breast radiologists and by 2 residents. For experienced radiologists, sensitivity and specificity of manual readings were 84.6% and 68.8%, respectively (BIRADS category 3 considered negative); sensitivity and specificity of CAE readings were 90.4% (p>0.05 vs manual readings) and 81.3% (p<0.05 vs manual readings), respectively. There were no statistically significant differences across CAE readers (residents compared with experienced breast radiologists, or each reader compared individually with the others). Although these results are interesting, this study has several limitations, including its retrospective nature, highly selective sample with a large proportion of cancer cases, and small number of readers. It also is unclear whether the results apply only to a 3.0-T MRI system. Further research is needed with larger, more robust studies to assess these findings.

Arazi-Kleinman et al (2009) performed a retrospective study that evaluated the sensitivity and specificity of the MRI CAD software, CAD-Gaea (Sentinelle Medical, Toronto, Canada; commercially released under the name Aegis software).(9) Patients were women at high risk of breast cancer (BRCA1- or BRCA2- mutation-positive, or calculated lifetime risk of being a mutation carrier ≥25%). From an initial sample of 1548 MRI studies on a 1.5-T system, the study sample comprised 56 lesions in 53 women. Thirty-nine percent of the lesions were malignant, and of those, 59% were DCIS. Relevance of this study for the present policy is limited because the study included only BIRADS 3 to 5 cases that were biopsied, and used a unilateral diagnostic fat-suppressed MRI protocol. Readings were performed by 2 experienced breast radiologists looking at different aspects of the CAE results, eg, different thresholds for initial enhancement and initial enhanced versus delayed pattern or both. The primary finding was that for invasive cancers, sensitivity was 100% using CAE (both initial and delayed enhancement patterns), but when DCIS was included, sensitivity dropped to 73%. Overall specificity (ie, including DCIS) was 56%. Prospective radiologist interpretation (as part of clinical care) was more sensitive than CAE-based interpretation (p=0.05) but less specific (p=0.01); performance values for prospective readings were not reported. The authors concluded that “[t]he breast MRI CAD system used could not improve the radiologists’ accuracy for distinguishing all malignant from benign lesions, due to the poor sensitivity for DCIS detection.”

Two articles were apparently based on one of the retrospective studies presented in an abstract form from the 2006 TEC Assessment. The first article, published in 2006, reported on 33 consecutive lesions biopsied under MRI guidance at a single institution.(2) The second article, published in 2007, reported on 155 consecutive lesions that appeared to subsume the 33 lesions included in the 2006 study; the later
article is therefore summarized here.(3) The lesions were not palpable or visible on mammography or sonography and were assessed with and without CAE.(2) All of these lesions were rated Breast Imaging Reporting and Data Systems (BIRADS) 4 or 5, ie, suspicious or highly suggestive of malignancy. Of these lesions, 64% were in recently diagnosed breast cancer patients, 14% were in high-risk patients being screened, and 14% were for problem-solving. Three different MRI protocols were used. CADstream was then retrospectively applied for this study. As expected, increasing the level of enhancement required (to 100%) lowered the number of false-positive results. At the 50% enhancement level, no statistically significant difference was found in positive predictive value between initial reading and subsequent application of CAE. At 100% enhancement, however, positive predictive value was significantly higher with CAE than without (30.4% vs 26.6%, respectively, p=0.02). Because radiologists who read each set of images with and without CAE were not necessarily the same, it is possible that some of this difference might be due to variation across readers rather than to the addition of CAE. There was no significant difference in subsequent enhancement patterns (ie, washout, persistent, or plateau) between benign and malignant lesions; and many lesions included diverse enhancement patterns.

Research to improve the ability of CAE systems to provide incremental information and increase diagnostic accuracy continues. Studies use various metrics (eg, identification of the “most suspect” enhancement pattern of a lesion or the distribution of different enhancement patterns within a lesion(15-17); textural analysis of lesions(18,19) ; comparison with the contralateral breast20 ; determination of the type of
breast cancer and presence of metastases(21)). These studies did not necessarily use commercially available CAE systems. Further research is needed to evaluate the utility of all of these approaches.

What Is the Clinical Utility of CAE When Added to Standard MRI of the Breast?

There is no direct evidence that evaluates the impact of CAE on health outcomes. There are no prospective studies that examine whether management decisions are changed due to results of CAE. There are also no relevant modeling studies that estimate the impact of CAE on outcomes.

Decisions for biopsies may be changed as a result of CAE; in particular, biopsies may be performed in areas of abnormality identified by CAE that were not seen on standard MRI. This may in turn improve the detection rate for malignancies. It is also possible that the number of false-positive biopsies is increased when CAE is used. Because incremental changes in sensitivity and specificity with CAE are unknown, it is not possible to estimate the number of additional malignancies that would be detected by CAE, nor is it possible to determine the number of additional false-positive biopsies that would be performed. As a result, the clinical utility of CAE when added to standard MRI of the breast has yet to be determined.

Summary of Evidence

Available evidence comprises primarily retrospective studies that compare the accuracy of computeraided magnetic resonance imaging (MRI) of breast malignancy versus conventional imaging. Populations in these studies are not representative of patients seen in clinical care; rather they include samples of women who are highly selected and usually have far more cases of cancer than would be encountered in a screening population. As a result, true sensitivity and specificity of computer-aided MRI, and  incremental improvement in accuracy over conventional imaging, cannot be determined with certainty. Larger, well-designed, prospective studies are needed that include relevant clinical populations in order to determine whether computer-aided evaluation (CAE) results in a clinically significant improvement in diagnostic accuracy. Because of deficiencies in the available literature, the use of CAE of malignancy with MRI is considered investigational.

Practice Guidelines and Position Statements

Current breast cancer guidelines from the National Comprehensive Cancer Network (NCCN) do not address the use of CAE for contrast-enhanced MRI.(22-25)

In 2014, the American College of Radiology amended its 2011 practice parameter for the use of MRIguided breast interventional procedures.(26) Computer-aided evaluation of malignancy with breast MRI is not addressed.

The European Society of Breast Cancer Specialists issued consensus recommendations for MRI of the breast in 2010.27 This document stated, “We recommend the use of standardized interpretation systems such as the above mentioned BI-RADS lexicon, or equivalent. There is some evidence that software for breast MR computer-aided diagnosis (CAD) may be of benefit but insufficient to recommend the routine use of such systems.”

U.S. Preventive Services Task Force Recommendations
No U.S. Preventive Services Task Force recommendations for computer-aided evaluation of malignancy with breast MRI have been identified.

Medicare National Coverage
There is no national coverage determination (NCD). In the absence of an NCD, coverage decisions are left to the discretion of local Medicare carriers.


  1. Cheng L, Li X. Breast magnetic resonance imaging: kinetic curve assessment. Gland Surgery. 2013;2(1):50-53. PMID
  2. Lehman CD, Peacock S, DeMartini WB, et al. A new automated software system to evaluate breast MR examinations: improved specificity without decreased sensitivity. AJR Am J Roentgenol. Jul 2006;187(1):51-56. PMID 16794155
  3. Williams TC, DeMartini WB, Partridge SC, et al. Breast MR imaging: computer-aided evaluation program for discriminating benign from malignant lesions. Radiology. Jul 2007;244(1):94-103. PMID 17507720
  4. Blue Cross and Blue Shield Association Technology Evaluation Center (TEC). Computer-Aided Detection of Malignancy with Magnetic Resonance Imaging of the Breast. TEC Assessments 2006; Volume 21, Tab 4. PMID
  5. Deurloo EE, Peterse JL, Rutgers EJ, et al. Additional breast lesions in patients eligible for breastconserving therapy by MRI: impact on preoperative management and potential benefit of computerised analysis. Eur J Cancer. Jul 2005;41(10):1393-1401. PMID 15913987
  6. Demartini WB, Lehman CD, Peacock S, et al. Computer-aided detection applied to breast MRI: assessment of CAD-generated enhancement and tumor sizes in breast cancers before and after neoadjuvant chemotherapy. Acad Radiol. Jul 2005;12(7):806-814. PMID 16039534
  7. Dorrius M, Weide MJ-v, Ooijen PA, et al. Computer-aided detection in breast MRI: a systematic review and meta-analysis. Eur Radiol. 2011/08/01 2011;21(8):1600-1608. PMID
  8. Meeuwis C, van de Ven SM, Stapper G, et al. Computer-aided detection (CAD) for breast MRI: evaluation of efficacy at 3.0 T. Eur Radiol. Mar 2010;20(3):522-528. PMID 19727750
  9. Arazi-Kleinman T, Causer PA, Jong RA, et al. Can breast MRI computer-aided detection (CAD) improve radiologist accuracy for lesions detected at MRI screening and recommended for biopsy in a high-risk population? Clin Radiol. Dec 2009;64(12):1166-1174. PMID 19913125
  10. Liu YH, Xu L, Liu LH, et al. 3.0T MR-CAD: Clinical Value in Diagnosis of Breast Tumor Compared with Conventional MRI. J Cancer. 2014;5(7):585-589. PMID 25057309
  11. Kim H, Kim HH, Park JS, et al. Prediction of pathological complete response of breast cancer patients undergoing neoadjuvant chemotherapy: usefulness of breast MRI computer-aided detection. Br J Radiol. Nov 2014;87(1043):20140142. PMID 25162970
  12. Lehman CD, Blume JD, DeMartini WB, et al. Accuracy and Interpretation Time of Computer- Aided Detection Among Novice and Experienced Breast MRI Readers. Am J Roentgenol. 2013/06/01 2013;200(6):W683-W689. PMID
  13. Cho N, Kim SM, Park JS, et al. Contralateral lesions detected by preoperative MRI in patients with recently diagnosed breast cancer: application of MR CAD in differentiation of benign and malignant lesions. Eur J Radiol. Jul 2012;81(7):1520-1526. PMID 21498016
  14. Shimauchi A, Giger ML, Bhooshan N, et al. Evaluation of clinical breast MR imaging performed with prototype computer-aided diagnosis breast MR imaging workstation: reader study. Radiology. Mar 2011;258(3):696-704. PMID 21212365
  15. Baltzer PA, Renz DM, Kullnig PE, et al. Application of computer-aided diagnosis (CAD) in MRmammography (MRM): do we really need whole lesion time curve distribution analysis? Acad Radiol. Apr 2009;16(4):435-442. PMID 19268855
  16. Yuan Y, Giger ML, Li H, et al. Multimodality computer-aided breast cancer diagnosis with FFDM and DCE-MRI. Acad Radiol. Sep 2010;17(9):1158-1167. PMID 20692620
  17. Ko ES, Choi HY, Lee BH, et al. Central washout sign in computer-aided evaluation of breast MRI: preliminary results. Acta Radiol. Apr 1 2011;52(3):256-263. PMID 21498360
  18. Holli K, Laaperi AL, Harrison L, et al. Characterization of breast cancer types by texture analysis of magnetic resonance images. Acad Radiol. Feb 2010;17(2):135-141. PMID 19945302
  19. Huang Y-H, Chang Y-C, Huang C-S, et al. Computer-aided diagnosis of mass-like lesion in breast MRI: Differential analysis of the 3-D morphology between benign and malignant tumors. Comput Methods Programs Biomed. 2013;112(3):508-517. PMID
  20. Yang Q, Li L, Zhang J, et al. Computer-Aided Diagnosis of Breast DCE-MRI Images Using Bilateral Asymmetry of Contrast Enhancement Between Two Breasts. J Digit Imaging. Sep 17 2013. PMID 24043592
  21. Bhooshan N, Giger ML, Jansen SA, et al. Cancerous breast lesions on dynamic contrastenhanced MR images: computerized characterization for image-based prognostic markers. Radiology. Mar 2010;254(3):680-690. PMID 20123903
  22. National Comprehensive Cancer Network (NCCN). Clinical practice guidelines in oncology: breast cancer, version 3.2014. Accessed December 22, 2014.
  23. National Comprehensive Cancer Network (NCCN). Clinical practice guidelines in oncology: genetic/familial high-risk assessment: breast and ovarian, version 2.2014. Accessed
    December 22, 2014.
  24. National Comprehensive Cancer Network (NCCN). Clinical practice guidelines in oncology: breast cancer risk reduction, version 1.2014. Accessed December 22, 2014.
  25. National Comprehensive Cancer Network (NCCN). Clinical practice guidelines in oncology: breast cancer screening and diagnosis, version 1.2014 (discussion update in progress). Accessed December 22, 2014.
  26. American College of Radiology. ACR Practice Guideline for the Performance of Magnetic Resonance Imaging-Guided Breast Interventional Procedures, amended 2014 (resolution 39). Accessed December 22, 2014.
  27. Sardanelli F, Boetes C, Borisch B, et al. Magnetic resonance imaging of the breast: recommendations from the EUSOMA working group. Eur J Cancer. May 2010;46(8):1296-1316. PMID 20304629  






Computer-aided detection, including computer algorithm analysis of MRI image data for lesion detection/characterization, pharmacokinetic analysis, with further physician review for interpretation, breast MRI  

ICD-9 Procedure 



ICD-9 Diagnosis 

174.0 – 174.9 

Malignant neoplasm of female breast 


175.0 – 175.9 

Malignant neoplasm of male breast 



Secondary malignant neoplasm of breast 



Carcinoma in situ of breast 



Lump or mass of breast 



Personal history of breast cancer 



Family history of breast cancer 




ICD-10-CM (effective 10/1/15)   Investigational for all diagnoses
  C50.01-C50.929 Malignant neoplasm of breast code range
  V79.81 Secondary malignant neoplasm of breast
  D05.01-D05.99 Carcinoma in situ of breast
  N63 Unspecified lump in breast
  Z80.3 Family history of malignant neoplasm of breast
  Z85.3 Personal history of malignant neoplasm of breast
ICD-10-PCS (effective 10/1/15)   No code specific to CAD
  BH30Y0Z, BH30YZZ, BH30ZZZ, BH31Y0Z, BH31YZZ, BH31ZZZ, BH32Y0Z, BH32YZZ, BH32ZZZ Magnetic resonance imaging, breast(s), code list

Type of Service 


Place of Service 



CAD, Magnetic Resonance Imaging (MRI) Breast
Computer-Aided Detection with Magnetic Resonance Imaging (MRI), Breast
Magnetic Resonance Imaging (MRI), Breast, with Computer-Aided Detection

Policy History





Add to Radiology section

New policy

04/09/08 Replace policy   Policy updated. Title revised- “detection” changed to “evaluation”. The acronym “CAD” changed to “CAE” throughout policy. Reference includes more information, references 4-5 added. Policy statement unchanged.  
04/24/09 Replace policy  Policy updated with search with literature search in March 2009; policy statement unchanged 
06/10/10 Replace policy Policy updated with literature search; references 6-10 added. Policy statement unchanged
1/12/12 Replace policy Policy updated with literature search; references 8,10,11,14 added. Policy statement unchanged
1/10/13 Replace policy Policy updated with literature review, reference 9 added. Policy statement unchanged.
1/09/14 Replace policy Policy updated with literature search on December 10, 2013; references 1, 9, 12, 17, 18, and 20-24 added. Regulatory Status and Rationale sections reorganized and references renumbered. Policy statement unchanged.
1/15/15 Repalce policy Policy updated with literature review through December 22, 2014; references 10-11 added. Policy statement unchanged.

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