CEBP Translational Cancer Medicine 2008: Cancer Clinical Trials and Personalized Medicine
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Cancer Epidemiology Biomarkers & Prevention Vol. 11, 1389-1393, November 2002
© 2002 American Association for Cancer Research

Accuracy of Mammographic Breast Density Analysis: Results of Formal Operator Training1

Sven Prevrhal, John A Shepherd2, Rebecca Smith-Bindman, Steven R. Cummings and Karla Kerlikowske

Departments of Radiology [S. P., J. A. S., R. S-B.], Medicine [S. R. C., K. K.], Epidemiology and Biostatistics [S. R. C., K. K.], and General Internal Medicine Section, Department of Veterans Affairs [K. K.], University of California, San Francisco, California 94143-1250


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Mammographic density is a major risk factor for breast cancer. Breast density is not routinely quantified for research studies because present methods are time intensive and manual, and require expert training. We investigated whether individuals with and without backgrounds in radiology or medicine can achieve sufficient accuracy when compared with an expert (gold standard) reader of mammographic breast density. Nine readers (three radiologists, two non-radiology physicians, and four nonphysicians) assessed breast density on 144 digitized films (60 contralateral films of breast cancer cases and 84 controls) on a computer workstation with custom software. Readings were compared with a radiologist with training in mammography and density reading that read the same films. A correlation of r = 0.9 or higher with the gold standard reading was met by three of three radiologists, one of two nonradiology physicians, and one of four nonphysicians. Intrareader reproducibility measured as the residual sum of mean errors averaged 10% mammographic density for all readers and 9% for readers with a correlation of 0.9 or higher with the gold standard. The odds ratios associated with breast cancer when films with mammographic breast density of 50% or greater are considered "dense" ranged from 3.1 to 3.9 or a 1.9–2.4-per-population-SD increase in percentage density. Although it is advantageous to have a radiological background when quantifying mammographic density, it is not a prerequisite. Applying strict validation criteria to qualify readers to quantify mammographic breast density for research studies will enhance the chance of accurately assessing breast density and discriminating women at high and low risk of breast cancer.


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
The mammographic appearance of the breast depends on its composition of radiolucent fatty tissue and more radiopaque epithelial or stromal tissue. Different radiological grading schemes like the density/pattern classification by Wolfe (1) , or relative dense area assessment, show a strong association with subsequent development of breast cancer. In fact, breast density is perhaps the strongest but least recognized risk factor for breast cancer. Many studies have shown that women whose breast X-rays are composed of at least 50% "dense" area have a three to five times greater risk of breast cancer than women with less than 25% dense area (1, 2, 3, 4) .

Breast density can be crudely graded using a subjective scale that takes into account the quantitative (amount of density) and qualitative nature of the density (diffuse or associated with ductal structures; Refs. 1 , 5 , 6 ). Qualitative methods have a limited number of density categories and can detect only very large changes in density. Because of their subjectivity, qualitative methods have substantial intra- and interobserver variation (7) . A more quantitative approach has been used to measure the area of dense breast as a proportion of the total projected breast area, or "mammographic density" (1 , 8 , 9) . Mammographic density is expressed as PD3 defined as PD = (radiographic dense area)/(total breast area) on a scale from 0 to 100% (10) .

Mammographic density is not routinely quantified for research studies because current methods are time intensive, manual, and require expert training. On the other hand, a quantitative measurement appears to be superior to qualitative categorical methods such as Wolfe (5) and the American College of Radiology Breast Imaging and Reporting Data System (BI-RADS; Ref. 11 ). A recent tamoxifen trial measured breast density as a surrogate end point for breast cancer risk and found that the most significant annual changes in breast density were observed with a quantitative measurement (12) .

Although, breast density has been shown to be a power indicator of cancer risk, there is no generalized method for training and validating individuals to perform the measure. We also ask the question: does it require a specialist in mammography to delineate the dense regions in the mammogram? If not, this may make the technique more clinically available by increasing the number of available trainees. In this study, we attempt to train people with a formal education in radiology, other fields of medicine, and those with a nonmedical background to quantify mammographic density using a predefined training program. A secondary goal of the study was to demonstrate the association of quantitative PD measurement with the risk of breast cancer.


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
Subjects.
For 161 women ages 40 and older (64 with invasive breast cancer or ductal carcinoma in situ and 97 without breast cancer) who underwent screening mammography between April, 1985, and December, 1995, as part of the University of California-San Francisco (UCSF) Mobile Mammography Screening Program, we obtained one craniocaudal view of the right or left breast. For the 64 women that had been diagnosed with cancer, we selected the contralateral craniocaudal image without breast cancer taken at the time of diagnosis or a contralateral craniocaudal image from a screening examination that preceded the diagnosis of breast cancer. Women with bilateral breast cancer were not included in the study. Women without invasive breast cancer or ductal carcinoma in situ were randomly selected from the UCSF Mobile Mammography Screening Program database according to calendar year of the screening examination and age of the breast cancer cases. Institutional review board approval and patient informed consent were obtained.

Measurements.
All of the films were initially assessed by a radiologist with training in mammography and density reading (gold standard, R.S-B.). Films were viewed directly using a standard radiology light box. A wax pencil was used to outline the breast area and breast densities. Films, with the wax pencil marks, were digitized on a Lumisys LumiScan 200 radiographic film digitizer (Kodak, Inc.) at a resolution of 200 x 200 µm2 and the PD was determined by measuring the total area of the breast and number of pixels outlined in the dense regions using dedicated computer software. The software is based on the commercially available medical image-processing package MEDx (Version 3.31, Sensor Systems, Sterling, VA). Extensions to this package were written in the open-source scripting language Tcl/Tk (Version 8.3, www.tcltk.com).

Using the gold standard PD measurement, films were stratified into deciles of PD. For every density decile, 10 noncancer and 10 cancer films, when available, were selected to be included in a validation data set, resulting in 60 cancer and 84 noncancer images with PD ranging from 0 to 100%.

The wax pencil marks were erased from the validation set and films were redigitized without marks and patient identifiers. The digitized film files were transferred to CD-ROM for review by study readers.

The reading station program randomized the order of all films and consecutively displayed them with a default brightness/contrast setting on a high-resolution radiographic monitor. The reader was prompted to manually trace the breast contour using a polygonal drawing tool (clicking with the computer mouse inserts a polygon vertex at the cursor). After confirming the breast contour outline was correct, the reader proceeded to outline the dense areas of the breast using a "pencil" tool (the mouse cursor acts like the tip of a pencil). The number of dense regions was not limited and included zero for breasts that appeared to have no dense regions at all. PD was calculated as the ratio of the sum of all dense regions (overlaps are not counted twice) to the entire breast area. PD, and all drawn contours, were then stored in a database linked to the reader’s study identification number, film identification number, and reading session number. Fig. 1Citation shows a screenshot of the main program window during an analysis session.



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  		Fig. 1. Screenshot of mammographic breast density analysis program (outlines enhanced for this article).

 
Standardized Training and Study Readers.
We enrolled three radiologists with limited background in mammography (group RAD), two physicians with no background in radiology (group MD), and four nonphysicians (group NMD) with various backgrounds (one medical physicist, one research assistant with no experience in radiology, and two research assistants whose radiological experience was limited to bone densitometry.)

All of the readers were trained (by R.S-B.) in an hour-long training session in front of a light box (see Fig. 2Citation ). Mammography examinations ranging from very-low to very-high PD, and uniform to very-structured appearance were presented and discussed. After that, the readers were trained on the PD reading workstation (see Fig. 1Citation ). The readers could take as long as they wanted to read each film.



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  		Fig. 2. Training session with the gold standard reader (R. S-B.).

 
We selected a linear Pearson’s correlation of r < 0.9 or lower between the gold standard and study readers as the criterion for additional training. If the study reader did not achieve an r >= 0.9 after the first reading of the 144 selected films, the 10 cases with the biggest absolute difference from the gold standard were identified. The study readers compared their breast and dense tissue outlines with the gold standard outlines for those 10 cases and tried to identify any patterns that could account for the deviations from the gold standard to improve future readings. Scatter and Bland-Altman plots of study reader PD versus gold standard PD were also provided, and regression results were discussed with the study reader. After additional training, the study reader read the same 144 films a second time, again blinded to film identity. If a study reader’s correlation with the gold standard was not >=0.9, the study reader was not considered a certified reader for future studies.

Statistical Analysis.
Statistical analysis was programmed in SAS 8e (The SAS Institute, Cary, NC). Intrareader reproducibility was performed with ANOVA. Interreader agreement was calculated using linear regression and was expressed as Pearson product-moment correlations r. It is conceivable that reader reproducibility is dependent on breast density: "Extreme" cases might be easier to recognize and analyze than films in the midrange of PD values. Therefore, we categorized the average reading results (averaged over the two read passes) into four quarters (PD <25%; 25% <= PD < 50%; 50% <= PD < 75%; and PD >= 75%), and we calculated reproducibility separately for each of these four PD categories. For the same reasons as for intrareader reproducibility, we repeated the interreader regression analysis between readers and gold standard by PD category. Overall agreement between readers with similar background was tested with two-factor ANOVA, calculated separately for the reader groups RAD, MD, and NMD.

Odds ratios to determine the association between breast density and breast cancer status were calculated in two ways. First, a fixed threshold of PD = 50% was chosen to discriminate between cancer and noncancer cases, and the odds ratio was calculated from the resulting contingency table. Second, to avoid bias introduced by the arbitrary threshold, we executed unconditional logistic regression analysis with PD as factor and cancer status as outcome. To arrive at meaningful SDs, we calibrated reading results to the gold standard as:

with intercept and slope being regression of the particular reader to gold standard for the logistic regression. This is a technique commonly used when comparing performance between readers or devices.


   Results
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
Three of three radiologists, one of two nonradiology physicians, and one of four nonphysicians had correlations >=0.9 compared with the gold standard. Table 1Citation shows the summary statistics, correlation with the gold standard, and odds ratios for all readers. The mean PD for the gold standard was PDmean = 38% for all of the subjects with a SD of 19%. In general, as the correlation with the gold standard reading increased, the odds ratio measuring breast densities association with cancer increased, but that was not true for all readers. Particularly, reader NMD2 had a very high odds ratio despite low correlation during her second reading. Reading time averaged between 2 and 3 min per film. Readers with correlations with the gold standard of greater than 0.9 tended to have slightly longer reading times (average, 2.1 min) than readers who failed (average, 1.5 min; t test, nonsignificant). RMSE values ranged around 10% PD for all readers (Table 1)Citation .


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  		Table 1 Reader validation results

The correlation coefficient (r) is with respect to the expert reader. RMSE and intercept are shown in units of PD. Regression is given as: reader value = gold value * slope + intercept. Odds ratio (OR) to discriminate cancer status is based on a simple 50% PD threshold with 95% lower (LCL) and upper (UCL) confidence limits. All of the intercepts and slopes were significantly (P < 0.05) different from 0 and 1, respectively.

 
We determined reproducibility for four readers who read all of the films twice and had correlation values >=0.9 on the first read, and who, therefore, did not receive additional training between the two readings. Readers RAD2 and RAD3 were not available to us for a second reading. Reproducibility ranged from RMSE = 6% PD to 11% PD and showed this range for all three of the reader groups. By comparison, the gold standard reader achieved RMSE = 6% PD (r = 0.95) on a subset of 100 duplicate readings. When categorized by PD quarters, we found that the reproducibility range was similar for each quarter. We found RSME = 8–13% PD for the first quarter (PDgold, <25%), RSME = 8–12% PD for the second, and RSME = 5–13% PD for the third quarter. We did not have enough data points for meaningful regression analysis of the fourth (PDgold, >75%) quarter.

Table 2Citation shows generalized ANOVA results for the comparison of overall reader-group performance. The RAD group exhibited highest intraclass correlation, followed by the MD group and the NMDs. The same was true when only validated readers were considered, but because only one NMD was validated, intraclass correlation could not be calculated for this group.


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  		Table 2 Intraclass Correlations, r2, by reader groupa

 
Table 3Citation lists odds ratios resulting from the unconditional logistic regression for a difference of PD = 1 SD, calculated for each study reader (ORSD). The ORSD values were significant and ranged from 1.7 to 2.1, being higher than that of the gold standard reader (ORSD = 1.9).


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  		Table 3 Logistic regression resultsa

Odds ratios (ORs) and their 95% confidence limits [lower (LCL) and upper (UCL)] of present cancer status for a change in PD of 1 SD for all validated readers (SD calibrated to gold standard) and the gold standard. SD is reader specific.

 
Interreader correlations for all of the study readers with a high correlation (r > 0.9) with the gold standard ranged from r = 0.82 to 0.94, showing that their readings correlated well with each other.


   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
A principle finding of this study is that nonradiologists were able to be validated to read mammographic density. We also found that readers who had PD readings that were highly correlated with a gold standard could accurately discriminate between noncancer and cancer cases with odds ratios of 1.9 to 2.4 per population SD increase in PD. These results are similar to those reported by Boyd et al. (2) , who used a six-category scale for semiquantitative assessment and found per-category risk increments of 1.4. Other groups have compared the extreme ends of the PD range. In a case-control study of 160 paired images, Wolfe et al. (1) compared the extreme ends of the PD range and found odds ratios of 4.3 (95% confidence interval, 1.8–10.4). In one of the definitive studies of breast density as an independent risk factor, with about 2000 controls and as many cases, Byrne et al. (3) found a steady increase of odds ratios associated with PD category when the readers categorized films into either the 0%-PD category or into one of successive 25%-PD categories. Specifically, comparing the odds of breast cancer for PD = 0% versus the odds at PD > 75% yielded a significant odds ratio of 4.5.

In other published studies, inter- and intrareader variability values range from 0.86 to 0.96, similar to the results reported here (13 , 14) . Jong et al. (13) found an overall correlation of 0.89 between two readers and noted that the type of dense tissue distribution (homogeneous, nodular, linear) had a strong influence. We did not see such an effect in this study. Although we did not evaluate reproducibility by type of dense tissue, we retrospectively categorized the films by their density. We did not observe differences in reproducibility between categories. This might be of particular importance for study populations with a skewed or preselected mammographic density distribution.

A limitation of this study is that not all of the readers were available for a second reading. Therefore, we could not present a complete picture of intra- and interreader variability. Although those readers could have exhibited a performance drop during the second read, three of four readers who did finish a second reading maintained or even improved their performance. In addition, as noted above, the intraclass correlations are similar to those reported by others. Our reproducibility analysis did not consider redigitization. Although we do not expect a large effect from digitization because routine digitizer quality assurance showed that the device is stable and linearly maps film absorbance to pixel gray-scale value, this step needs to be verified. Lastly, because we attempted to provide a full range of density values for all of the decades, this most probably inflated the PD variance and, thus, improved the correlation coefficient. However, it is our view that using this approach (stratified PD values in every decade) will allow others to reproduce our results.

Reader Quality Control.
Reader RAD1 in our study had PD readings highly correlated with the gold standard and high odds ratios predicting cancer risk on the first reading. When this reader was retested, he had a markedly lower correlation with the gold standard and slightly different slope and intercept. This suggests that PD readers for research studies may need to be monitored for consistent reading quality. Quantifying reproducibility is highly important because it influences the least significant difference that the breast density measure can detect with confidence. Continuous monitoring may be achieved by including films from the validation set with the study data so that, if necessary, a reader can be retrained, or removed if skills wane over time.

Of note, several readers achieved slightly, albeit nonsignificantly, higher odds ratios than the gold standard reader. This merely reflects that the gold standard itself is subjective but also suggests that the correlation criterion might need to be supplemented with a more objective one such as an odds ratio threshold. Automation would obviously alleviate the subjectivity problem. Efforts in this direction have recently been undertaken by a number of groups (15, 16, 17, 18) but are also based on maximization of the correlation to a human gold standard.

Conclusions.
The goal of this study was to establish whether or not a background in mammography or radiology is necessary to quantify mammographic breast density and to validate readers for future mammographic density studies based on the degree of correlation to a gold standard reader. We found that, although it seems beneficial to have a radiological background, it is not a prerequisite. Of nine study readers, five (all three radiologists, one of two physicians of other disciplines having some radiological background, and one of four nonphysicians) had readings that sufficiently correlated with the gold standard that they could be considered breast density readers for future research studies. All of the readers with breast density readings highly correlated with the gold standard reading achieved odds to predict breast cancer of similar magnitude (on the order of three with PD dichotomized as less than 50% or 50% or greater), which is comparable with values in the literature. Strict validation criteria must be applied to qualify readers for mammographic breast density quantification. For research studies, this will enhance the chance of accurately assessing breast density and discriminating women at high and low risk of breast cancer.


   Acknowledgments
 
We thank Drs. Yen Chen, Xiaoguang Cheng, Bo Fan, Gottfried Schaffler, and Jing Li, Cullen Meade; Kathy Cross; and Victor Torres for participating as readers, and producing 2728 reading results. We are also grateful to Dr. Ying Lu for valuable statistical advice.


   Footnotes
 
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1 Supported in part by a research grant from Synarc, Inc. Parts of this paper were presented as an InfoRad exhibit at the Radiological Society of North America Annual Meeting 2000, Abstract 9320IMA-i, Title "A Mammographic Density Reading Service for Clinical Drug Trials." Back

2 To whom requests for reprints should be addressed, at University of California-San Francisco, Department of Radiology, 533 Parnassus Avenue, Suite U368J, San Francisco, CA 94143-1250. Phone: (415) 502-6732; Fax: (415) 502-2663; E-mail: john.shepherd{at}oarg.ucsf.edu Back

3 The abbreviations used are: PD, percentage (breast) density; RMSE, root mean square error; RAD (group), radiologists (with limited background in mammography); MD (group), physicians (with no background in radiology); NMD (group), nonphysicians. Back

Received 11/12/01; revised 6/ 3/02; accepted 6/19/02.


   References
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

  1. Wolfe J. N., Saftlas A. F., Salane M. Mammographic parenchymal patterns and quantitative evaluation of mammographic densities: a case-control study. Am. J. Roentgenol., 148: 1087-1092, 1987.[Abstract/Free Full Text]
  2. Boyd N. F., Byng J. W., Jong R. A., et al Quantitative classification of mammographic densities and breast cancer risk: results from the Canadian National Breast Screening study. J. Natl. Cancer Inst. (Bethesda), 87: 670-675, 1995.[Abstract/Free Full Text]
  3. Byrne C., Schairer C., Wolfe J. N., et al Mammographic features and breast cancer risk: effects with time, age, and menopause status. J. Natl. Cancer Inst. (Bethesda), 87: 1622-1629, 1995.[Abstract/Free Full Text]
  4. Saftlas A. F., Wolfe J. N., Hoover R. N., et al Mammographic parenchymal patterns as indicators of breast cancer risk. Am. J. Epidemiol., 129: 518-526, 1989.[Abstract/Free Full Text]
  5. Wolfe J. N. Risk for breast cancer development determined by mammographic parenchymal pattern. Cancer (Phila.), 37: 2486-2492, 1976.[Medline]
  6. Saftlas A. F., Szklo M. Mammographic parenchymal patterns and breast cancer risk. Epidemiol. Rev., 9: 146-174, 1987.[Free Full Text]
  7. Kerlikowske K., Grady D., Barclay J., et al Variability and accuracy in mammographic interpretation using the American College of Radiology Breast Imaging Reporting and Data System. J. Natl. Cancer Inst. (Bethesda), 90: 1801-1809, 1998.[Abstract/Free Full Text]
  8. Brisson J., Merletti F., Sadowsky N. L., Twaddle J. A., Morrison A. S., Cole P. Mammographic features of the breast and breast cancer risk. Am. J. Epidemiol., 115: 428-437, 1982.[Abstract/Free Full Text]
  9. Boyd N. F., O’Sullivan B., Campbell J. E., et al Mammographic signs as risk factors for breast cancer. Br. J. Cancer, 45: 185-193, 1982.[Medline]
  10. Byng J. W., Boyd N. F., Fishell E., John R. A., Yaffe M. J. The quantitative analysis of mammographic densities. Phys. Med. Biol., 39: 1629-1638, 1994.[Medline]
  11. Kopans D. B., D’Orsi C. J., Adler D. D., et al . American College of Radiology Breast Imaging and Reporting Data System (BI-RADS), Ed. 3 American College of Radiology Reston, VA 1998.
  12. Chow C. K., Venzon D., Jones E. C., Premkumar A., O’Shaughnessy J., Zujewski J. Effect of tamoxifen on mammographic density. Cancer Epidemiol. Biomark. Prev., 9: 917-921, 2000.[Abstract/Free Full Text]
  13. Jong R., Fishell E., Little L., Lockwood G., Boyd N. F. Mammographic signs of potential relevance to breast cancer risk: The agreement of radiologists’ classification. Eur. J. Cancer Prev, 5: 281-286, 1996.[Medline]
  14. Ursin G., Astrahan M. A., Salane M., et al The detection of changes in mammographic densities. Cancer Epidemiol. Biomark. Prev., 7: 43-47, 1998.[Abstract]
  15. Byng J. W., Yaffe M. J., Lockwood G. A., Little L. E., Tritchler D. L., Boyd N. F. Automated analysis of mammographic densities and breast carcinoma risk. Cancer (Phila.), 80: 66-74, 1997.[Medline]
  16. Boone J. M., Lindfors K. K., Beatty C. S., Seibert J. A. A breast density index for digital mammograms based on radiologists’ ranking. J. Digit. Imaging, 11: 101-115, 1998.[Medline]
  17. Heine J. J., Velthuizen R. P. A statistical methodology for mammographic density detection. Med. Phys., 27: 2644-2651, 2000.[Medline]
  18. Lou S. L., Fan Y. Automatic evaluation of breast density for full field mammography . SPIE Medical Imaging 2000, SPIE San Diego, CA 2000.



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