Registry Review Webinar Breast - Module 05 - Malignant
Radiology. 2011 February; 258(2): 417–425.
The Mammographic Density of a Mass Is a Significant Predictor of Chest Cancer
Ryan Due west. Woods, MD, MPH,1 Gale Due south. Sisney, Md, Lonie R. Salkowski, Physician, Kazuhiko Shinki, PhD, Yunzhi Lin, MS, and Elizabeth S. Burnside, MD, MPH, MS 
- Supplementary Materials
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Supplemental Table
GUID: F4935A84-8C16-45C3-82B4-4FD90DF7990E
GUID: B1BA0AFA-1CEE-4270-B3D9-0DAB2AA62CC4
Abstract
Purpose:
To determine whether the mammographic density of noncalcified solid chest masses is associated with malignancy and to measure out the agreement between prospective and retrospective assessment.
Materials and Methods:
The institutional review lath approved this study and waived informed consent. Three hundred forty-viii consecutive breast masses in 328 women who underwent image-guided or surgical biopsy between Oct 2005 and December 2007 were included. All 348 biopsy-proved masses were randomized and assigned to a radiologist who was blinded to biopsy results for retrospective cess past using the Breast Imaging Reporting and Data Organisation (retrospectively assessed data set). Clinical radiologists prospectively assessed the density of 180 of these masses (prospectively assessed information ready). Pathologic result at biopsy was the reference standard. Benign masses were followed for at to the lowest degree i twelvemonth past linking each patient to a cancer registry. Univariate analyses were performed on the retrospectively assessed data set. The association of mass density and malignancy was examined by creating a logistic model for the prospectively assessed information set. Agreement between prospective and retrospective assessments was calculated by using the κ statistic.
Results:
In the retrospectively assessed data set, 70.2% of high-density masses were malignant, and 22.3% of the isodense or low-density masses were malignant (P < .0001). In the prospective logistic model, high density (odds ratio, 6.half-dozen), irregular shape (odds ratio, nine.9), spiculated margin (odds ratio, 20.3), and age (β = 0.09, P < .0001) were significantly associated with the probability of malignancy. The κ value for prospective-retrospective agreement of mass density was 0.53.
Conclusion:
High mass density is significantly associated with malignancy in both retrospectively and prospectively assessed data sets, with moderate prospective-retrospective agreement. Radiologists should consider mass density equally a valuable descriptor that can stratify chance.
© RSNA, 2010
Supplemental material: http://radiology.rsna.org/lookup/suppl/doi:ten.1148/radiol.10100328/-/DC1
Introduction
Despite the relatively long history of mammography and recent technical advances, the positive predictive value (PPV) of biopsy for cancer ranges between fifteen% and 35% and is dependent on both the prevalence of cancer in the population and the historic period of the patient (ane). Benign biopsies account for a large office of the cost of a breast screening program and may atomic number 82 to substantial patient anxiety (ii). One way researchers take sought to meliorate the functioning of mammography and increase the accuracy of the decision to biopsy is through improve estimation of breast cancer gamble on the basis of mammographic findings (3,4).
The Breast Imaging Reporting and Data System (BI-RADS) (5) was adult in part to improve the predictive adequacy of mammography, and research has shown that both the terminal cess categories (half-dozen–12) and the individual descriptive terms (eight,13–20) can be used to accurately and reliably estimate the take chances for malignancy. For example, the PPVs for carcinoma of masses with spiculated margins and those with irregular shape are 81% and 73%, respectively (8). Similarly, the chance of malignancy for confining lesions categorized as probably benign (ie, BI-RADS 3) is less than 2% (18).
The predictive usefulness of the mammographic attenuation of a mass (called the mass density in the BI-RADS lexicon) remains controversial. Some investigators suggest that high-density lesions are more than likely to be malignant on account of the greater density of cellular components and reactive fibrosis surrounding a cancerous tumor (1,21,22). Notwithstanding, this proposed association has not been proved in the literature and is based solely on skilful opinion. To our noesis, the only studies that examined mass density retrospectively evaluated small series of solid masses and concluded that mass density was difficult to consistently evaluate and contributed less to predicting malignancy than previously idea (23,24). Since that time, however, technical advances, including the utilize of digital detectors, accept improved the diagnostic capability of mammography. The conclusions of those studies may no longer exist valid. In addition, one of the most influential manufactures in establishing the predictive value of BI-RADS descriptors did non assess mass density but cited information technology every bit important time to come work (eight). Research has indicated that high mass density may indeed ameliorate the prediction of malignancy (25).
The purposes of our written report were to determine whether the mammographic density of noncalcified solid breast masses is associated with malignancy and to measure out the agreement between prospective and retrospective assessment.
Materials and Methods
The Institutional Review Lath of the Academy of Wisconsin School of Medicine and Public Health canonical the written report protocol and waived informed consent. The study fully complied with the Health Insurance Portability and Accountability Act. This study analyzes a consecutive serial of noncalcified breast masses that were assessed retrospectively and prospectively.
Subjects
The inclusion benchmark for our study was any breast abnormality for which a percutaneous core biopsy (with ultrasonographic [Us], stereotactic, or magnetic resonance [MR] imaging guidance) or a surgical biopsy was performed betwixt October 2005 and December 2007. In that location were 704 paradigm-guided percutaneous core or surgical biopsies of palpable or nonpalpable suspicious breast abnormalities that met this criterion.
Exclusion criteria were (a) biopsies performed for calcifications or a calcified mass (n = 309), (b) no diagnostic mammogram available at our establishment (n = 42), and (c) male patient sex (n = 5). This left 348 eligible biopsies to be included in our study. Each biopsy was targeted to a unmarried mass, which was defined as a space-occupying lesion seen in ii dissimilar projections with convex-out borders that was denser in the center than at the periphery. There were no fat-containing masses referred for biopsy. Each mass was adamant at The states to be solid. Four MR imaging–guided biopsies were performed to sample the most suspicious enhancing area of a mass already identified on mammography (Tabular array i).
Table ane
Patient, Biopsy, and Imaging Characteristics in the Retrospectively and Prospectively Assessed Data Sets
Reference Standard
A board-certified pathologist evaluated all masses at the time of biopsy to determine the pathologic diagnosis, which was the reference standard for our study. For all cases, surgical pathology results were used when available. Pathologic diagnoses were grouped into benign and malignant categories on the basis of the specific pathologic findings. Nosotros considered invasive cancer (ductal and lobular) and ductal carcinoma in situ to be malignant. High-risk benign lesions, such every bit atypia or radial scars, were considered to be benign. Rigorous radiologic-pathologic correlation was performed weekly, and excision of atypia or radial scars used surgical biopsy as the basis for determining the pathologic diagnosis. In improver, any mass with a biopsy that was deemed discordant, atypical, or insufficient was recommended for excision as described by the American College of Radiology guidelines. For benign results, each subject field was matched to a hospital-based cancer registry and followed for at least 12 months (mean, 24.7 months; range, 12–38 months) to establish benignity. A 12-month fourth dimension frame was used for follow-up since this has been established as an adequate amount of time to minimize false-negative results in a mammography inspect (v).
Imaging and Evaluation
For the purposes of our study, diagnostic mammography was defined equally mammography using views tailored to the finding, including spot compression views and a true lateral project, in concert with or following routine screening craniocaudal and mediolateral oblique views. At our institution, the recommended work-upward of masses includes diagnostic mammography views earlier United states of america evaluation in patients over the age of 30 years and United states of america prior to diagnostic mammography in patients 30 years and younger.
All mammographic studies were performed with dedicated mammography equipment at a big academic dedicated breast imaging middle (University of Wisconsin Chest Center, Madison, Wis). Analog mammographic examinations were performed by using Senographe DMR (GE Healthcare, Milwaukee, Wis) or M-4 (Lorad Breast Imaging, Danbury, Conn) units along with a screen-film technique (Min-R 2000; Kodak Health Imaging, Rochester, NY). Digital mammographic examinations were performed by using a Senographe 2000D unit of measurement (GE Healthcare). A technologist chose the type of equipment on the footing of room availability. All analog films in this written report were digitized by using DiagnosticPRO Reward (VIDAR Systems, Herndon, Va) with software optimized to scan images in Digital Imaging and Communications in Medicine mammography format with a 44-μm spot size pick, 12-bit imaging output, and an optical density of 0.05–iv.0. An automatic digitizer calibration, a closed-loop quality assurance system, automatically prompted calibration before every film was digitized and eliminated the demand for user intervention. This feature results in virtually no variation in image quality, ensures excellent grey-scale reproduction in every prototype, and exceeds the American College of Radiology Teleradiology Exercise Guidelines. Of the 334 diagnostic mammograms obtained, 116 (34.7%) were digitized analog examinations, and 218 (65.3%) were digital examinations.
Eight radiologists (with 7–thirty years experience) interpreted the mammograms as part of clinical practice during the time frame from which we collected prospective clinical data. All eight radiologists practise inside the same group, and all meet the standards of the Mammography Quality Standards Act as qualified interpreting physicians. Three accept fellowship training in breast imaging (M.Southward.S., 50.R.Due south., E.South.B.). All mammographic images were evaluated on high-resolution picture archiving and advice organization workstations. Diagnostic mammograms with comparison studies, if available, were used for evaluation of all masses.
Retrospective Evaluation
All 348 consecutive biopsy-proved noncalcified masses were randomized and assigned to one of the three fellowship-trained interpreting radiologists (G.S.Southward., 50.R.South., E.S.B.) for retrospective cess of the mass density. These 348 masses made up our retrospectively assessed data ready. Each radiologist interpreted approximately 116 examinations and was blinded to biopsy results. Since some diagnostic mammography images showed more than than one finding, the radiologist was given the mammographic mass location, including chest laterality, clock face position, and depth. The radiologist evaluated the mass density as compared with the density of an equal portion of fibroglandular tissue and recorded one of the post-obit BI-RADS descriptors: low density, isodensity, or high density (Figs 1–iii). The radiologist retrospectively measured and recorded the greatest transverse width of the mass in millimeters if imaging size had not been recorded during prospective cess so that imaging mass size information were bachelor for all masses.
Mammographic image of a low-density mass (pointer).
Mammographic epitome of a loftier-density mass (arrow).
Mammographic image of an isodense mass (arrow).
Prospective Evaluation
Of the 348 sequent biopsy-proved masses in our data ready, 180 (51.7%) had been prospectively assessed for mass density at the time of initial interpretation. These 180 masses made upward our prospectively assessed information set. For these masses, i of the eight interpreting clinical radiologists prospectively assessed each mass by using the 4th edition of the BI-RADS lexicon (5) for mass descriptors and mass size at their discretion equally part of routine clinical practice. No formal training of the BI-RADS mass descriptors was provided to the interpreting radiologists. Thus, the criteria used by each interpreting radiologist were subjective and based on prior training and experience. Mass descriptors were directly entered into a structured reporting organisation (PenRad, Minnetonka, Minn) by the interpreting radiologist, and a last BI-RADS assessment category was assigned to all masses at the time of clinical interpretation. At that place were, therefore, 180 masses in which mass density was assessed both prospectively and retrospectively.
Statistical Analysis
We collected patient historic period and overall breast limerick from the original radiology study, and all mass descriptors, biopsy type, and epitome acquisition technique were obtained past querying the structured reporting system. All statistical analyses were performed past using R statistical software (version two.9.2, R Foundation for Statistical Computing, Vienna, Austria; available at http://www.R-project.org/) and the lme4 library (available at http://lme4.r-forge.r-project.org/). Nosotros combined depression density and isodense masses for all analyses because fewer than ten% of masses (32 of 348) were characterized as low density at retrospective assessment. Among the masses in which more than one descriptor was used to depict the margin (due north = 11), the well-nigh worrisome descriptor (in society of severity: spiculated, indistinct, obscured, microlobulated, circumscribed) was used in our analysis because radiologists employ the most worrisome to make clinical management decisions (viii). Only a single descriptor was used to describe mass shape and mass density for all masses. A P value of less than or equal to .05 was considered to betoken a significant departure for all analyses.
Descriptive statistics were calculated for subject age, overall breast limerick, biopsy type, image conquering technique, mass palpability, and pathologic issue. A χ2 exam for clustered data was performed on the to a higher place variables to evaluate whatsoever meaning differences between the retrospectively and prospectively assessed information sets. The frequency for which mass descriptors were reported prospectively amongst the 348 masses was calculated.
Retrospectively assessed data set.—We evaluated the association between the predictor variables (ie, mass density, mass size, overall breast composition, epitome acquisition technique, and discipline age) and the response variable (ie, malignancy) by using separate logistic linear mixed-furnishings models in the retrospectively assessed information ready. The logistic linear mixed-effects model immune united states of america to account for multiple masses on a single mammogram and within the same patient. We calculated P values and odds ratios with 95% confidence intervals for each variable. The logistic linear mixed-effects model used to determine the P value and odds ratio for mass density included overall breast composition; therefore, the results shown are controlled for overall breast composition. A χii test was used to evaluate the association between image acquisition technique and malignancy among loftier-density masses.
Prospectively assessed information set up.—To evaluate the relative contribution of chest mass density to the probability of malignancy, we created a logistic linear mixed-furnishings model with beneficial or malignant issue as the dependent variable. We used a backward stepwise variable option method and chose the best-plumbing equipment model on the basis of the lowest Akaike Information Benchmark. This type of model was used to address clinically important predictors and remove those from the model that did non improve prediction accuracy. Overall breast composition was forced into the model regardless of its Akaike Data Criterion when assessing the association betwixt mass density and malignancy to account for the association between overall breast composition, mass density, and malignancy.
For the model, we transformed the mass size variable to the log scale to obtain constant variance and collapsed multilevel chiselled variables (ie, overall breast composition, mass shape, and mass margin) into dichotomous variables to address issues of multicollinearity. Overall breast limerick was collapsed to low fibroglandular density (ie, almost entirely fat and scattered fibroglandular densities) and high fibroglandular density (ie, heterogeneously dense and extremely dense). For shape, masses designated equally irregular were compared with those with all other shape descriptors. For margins, masses described equally spiculated were compared with those with all other margin descriptors.
The logistic model was constructed from the prospectively assessed data set just and estimated the relative contribution of chest mass density to the probability of malignancy while controlling for the independent variables of subject age, mass size, mass margin, mass shape, overall chest composition, prototype acquisition technique, and interpreting radiologist. Nosotros created this model to evaluate the relative contribution of breast mass density in a consecutive prospectively assessed data set with all clinically important mass descriptors, every bit this data set most accurately reflects routine clinical practice.
Accurateness and prospective-retrospective agreement.—We calculated the sensitivity, specificity, PPV, and negative predictive value of mass density with high density considered to be positive result and biopsy upshot (beneficial or cancerous) as the reference standard in the retrospectively assessed data ready. We measured prospective-retrospective agreement past comparing prospectively and retrospectively assessed mass density by using the κ statistic and the categories established past Landis and Koch (26) for the 180 masses with both prospective and retrospective mass density assessment.
Results
Population Characteristics
There were 348 full biopsied masses in our study. These masses were visualized on 334 diagnostic mammograms in 328 patients. There were 309 patients with solitary masses, 18 patients with two masses, and one patient with iii masses.
Descriptive statistics for field of study age, overall breast limerick, biopsy type, and image acquisition technique for the prospectively (n = 180) and retrospectively (n = 348) assessed data sets are summarized in Table 1. There were no significant differences between the prospective and retrospective data sets. Amid the 348 total masses, diagnostic mammography was performed for a palpable aberration for 88 (25.three%) masses.
Amid the 348 masses in the retrospectively assessed data set, at that place were 230 (66.1%) benign masses and 118 (33.9%) malignant masses. Among the 90 invasive ductal carcinomas, 77 were of the subtype non otherwise specified. The subtypes for the remaining 13 invasive ductal carcinomas were as follows: 5 tubular, four mucinous, three intracystic papillary, and i medullary (Table E1 [online]).
In the 348 masses studied, mass density was prospectively described in 52% (Table 2). Mass shape was assessed in 88%; and mass margin, in 81%.
Tabular array 2
Frequency of Breast Mass Descriptors in the Prospective Data Set
Retrospectively Assessed Data Prepare
Amidst the 348 retrospectively assessed masses, there were zip that contained fat, 264 (75.nine%) that were isodense or low density, and 84 (24.1%) that were high density. Among the categorical variables (Tabular array 3), there were meaning differences in mass density (P < .0001) and overall chest composition (P < .0001) between benign and cancerous masses. At that place were no significant differences in the proportion of cancerous cases between analog and digital acquisition techniques amongst the entire data set of masses, or among the 84 high-density masses alone (P > .99). Field of study age (P < .0001) and mass size (P = .005) were too meaning predictors of malignancy (Table 4).
Tabular array 3
Univariate Analysis of Categorical Variables in the Retrospective Data Set
Table four
Univariate Assay of Continuous Variables in the Retrospective Data Set
Prospectively Assessed Information Set
Among the 180 prospectively assessed masses, in that location were zero that independent fat, 99 (55.0%) that were isodense or low density, and 81 (45.0%) that were high density. In the logistic regression model (Table 5), the independent descriptors loftier mass density (odds ratio, half dozen.6), irregular mass shape (odds ratio, 9.9), spiculated mass margin (odds ratio, 20.3), and patient historic period (β = 0.09, P < .0001) significantly predicted the probability of malignancy. Mass size and prototype acquisition technique did not significantly ameliorate prediction accurateness and were removed from the model.
Tabular array 5
Logistic Regression Analysis of Prospectively Assessed Mass Descriptors
Accuracy and Prospective-Retrospective Agreement
In the retrospectively assessed data set, the sensitivity of loftier breast mass density was fifty.0% (59 of 118), and the specificity was 89.1% (205 of 230). The PPV was seventy.ii% (59 of 84), and the negative predictive value was 77.seven% (205 of 264). The κ value for agreement between prospectively and retrospectively assessed mass density was 0.53 (95% conviction interval: 0.4, 0.65), reflecting moderate agreement, as defined by Landis and Koch (26).
Give-and-take
Our results show that high mass density is significantly associated with malignancy amongst masses assessed retrospectively and prospectively. Past assessing masses retrospectively and prospectively, nosotros can confidently conclude that mass density is a pregnant predictor of malignancy.
In our data prepare of retrospectively assessed masses, we have shown the predictive adequacy of this descriptor in a series of consecutive biopsy-proved cases, unbiased by those masses in which mass density was not prospectively reported. We found that mass density, mass size, overall breast composition, and patient historic period were all independent pregnant predictors of malignancy. We did run across some surprising results in this assay: Subjects with low-fibroglandular-density breasts had a greater likelihood of malignancy as compared with subjects with high-fibroglandular-density breasts. This result, which is in contrast to prove in the literature, is probably owing to greater sensitivity in detecting cancerous masses in breasts that have less fibroglandular density. This result may also be explained by the increased age of subjects with cancerous masses in our study, who likely had breast that were less dense than those in patients with beneficial masses.
Such an analysis of retrospectively assessed masses, all the same, does not accurately reflect the assessment of mass density in the context of the pressures of clinical exercise. Our prospectively assessed data set is complimentary to the retrospective information set since information technology reflects bodily routine clinical practise. Amidst our data set up of prospectively assessed masses, our logistic regression model showed that high mass density is significantly associated with malignancy, even after controlling for potential confounders, including mass shape, mass margin, overall breast composition, and subject field age.
In addition, we observed that while high breast mass density was merely moderately sensitive for detection of cancer by itself, the specificity of this measure was reasonably loftier. We determined that agreement was moderate betwixt retrospective and prospective assessment of breast mass density. Interestingly, we also found that high mass density was more ofttimes observed in the prospectively assessed data prepare (45.0% vs 24.ane%, χ2 = 23.1, P < .0001) equally compared with the retrospectively assessed information set. This may reflect an inclination toward using this descriptor more frequently in the midst of the pressures of clinical practice; however, further data would be helpful to validate this hypothesis.
In the past, the 2 studies (23,24) that analyzed mass density as a predictor of malignancy concluded that, although the majority of high-density masses are malignant, the presence of depression-density cancers and other indicators of malignancy make mass density a less reliable descriptor. Our report is different from these previous studies in several important means. First, our study of retrospectively and prospectively assessed masses provides a complete analysis of consecutive biopsy-proved cases, represents clinical practice, and allows for prospective-retrospective agreement analysis. Second, we used a logistic regression model to analyze our results, which considered a large range of potentially misreckoning factors of the human relationship between mass density and malignancy. Our logistic regression model showed the relative importance of mass density equally an associate of malignancy even when nosotros controled for other variables, such as margin, shape, overall breast composition, and subject historic period. The magnitude of the estimated odds ratio for each parameter suggests that mass margin and mass shape are the virtually of import indicators, followed by mass density. Finally, over the 20 years that have elapsed since these previous studies, many technical developments take improved mammographic prototype quality. Although our study did not find any significant event of digital versus analog mammography, overall improvements in image quality may have increased the accurateness of mass density as an indicator of malignancy in general.
The ability of mass density to exist used to help stratify the risk of malignancy has important clinical implications. Identifying additional important descriptors could help radiologists ameliorate the PPV of biopsy. In our study, the PPV of high mass density was just over 70%. Although a PPV in this range does non suggest that mass density has sufficient accuracy to avoid a biopsy, when used in combination with mass margin and shape, risk stratification may exist authentic enough to improve the PPV of biopsy. A possible line of future research includes determining the imaging characteristics of a mass that make it safe to forego biopsy.
Despite these of import clinical implications, we found that the consistent measurement of mass density is challenging and that prospective-retrospective agreement in mass density measurement is moderate, which is similar to previous research (three,7,24,27). There are several possible reasons for the inconsistency of evaluation. First, optical density cannot exist directly measured with conventional two-view imaging owing to superimposed structures. Digital breast tomosynthesis or chest computed tomography would allow direct measurement of the attenuation, but these are non in routine clinical use at this time. 2nd, mass density evaluation can be difficult when the breast is predominantly fatty and at that place is little fibroglandular tissue to compare with or the only available surrounding fibroglandular tissue tin can only be seen through the mass. Third, big masses force the radiologist to brand a comparing on the basis of unequal volumes of breast tissue and mass. Finally, the bulk of masses are isodense, meaning that low- and high-density masses are seen relatively infrequently.
The prospective-retrospective agreement in our report was similar to that for mass shape and margin reported previously in the literature (7), suggesting that the challenges in consistently evaluating mass density do not eliminate the value of mass density as an indicator of malignancy. In the future, image processing of digital studies to more accurately quantify density could meliorate radiologists' evaluation of mass density. The evaluation of mass density may besides be improved with more consequent use of the mass density descriptor. Among our data set of 348 masses, mass density was prospectively used just over l% of the fourth dimension, whereas mass margin and shape descriptors were prospectively used approximately 85% of the time. Perhaps radiologists do non routinely assess mass density because they practice not understand its value on the basis of prior literature. With more than consistent use of the descriptor, interobserver variability may improve.
There were limitations to our study. Beginning, the number of masses in our prospectively assessed data set was relatively pocket-sized considering interpreting radiologists often did not report the mass density descriptor. Information technology would exist interesting to test the predictive capability of mass density in a larger population of masses that are more consistently described. 2d, our study evaluated only masses identified on diagnostic mammography, which may limit the generalizability of our results to the larger screening population, in which the prevalence of malignancy is much lower. Although our logistic model was relatively robust, the model suffered from issues of multicollinearity, making estimates of the relative contribution of each level of a mass descriptor incommunicable. Past using a larger amount of data, we may exist able to decide how the mass density descriptor should exist used in the context of mass margin and shape. Third, we followed benign masses for 1 year, which may limit our ability to discover false-negative results. Quaternary, we assessed the variability between prospective and retrospective assessments of mass density. Since our three readers who performed retrospective assessments participated in the initial interpretation of studies, this variability measurement is a combination of inter- and intraobserver variability. When creating these data sets, we did non code for initial interpreting radiologist, and since identifying information was removed from the records, we were unable to obtain these data later. Finally, although our digitized images passed all applicable quality control tests and were of such quality that they would laissez passer the facility's accreditation body'south phantom and clinical paradigm review process, there may be small mass attenuation differences attributable to the analog versus digital image processing techniques. Although not substantial in our analysis, pocket-sized differences may take influenced the results.
In conclusion, our written report shows that, in dissimilarity to previous inquiry, chest mass density is significantly associated with malignancy, even afterward controlling for other predictive variables. Future research should focus on means to improve the objective measurement of mass density. We believe that, when evaluating breast masses for biopsy, radiologists should consider the density of a mass equally a valuable descriptor that can help stratify risk.
Disclosures of Potential Conflicts of Interest: R.W.W. Financial activities related to the present commodity: institution received funding from National Institutes of Health grants K07-{"type":"entrez-nucleotide","attrs":{"text":"CA114181","term_id":"34967488","term_text":"CA114181"}}CA114181 and R01-CA127379. Fiscal activities not related to the present article: none to disclose. Other relationships: none to disclose. G.S.S. No potential conflicts of involvement to disembalm. L.R.S. No potential conflicts of interest to disembalm. G.S. No potential conflicts of involvement to disclose. Y.L. No potential conflicts of interest to disclose. E.South.B. Financial activities related to the present article: institution received funding from National Institutes of Health. Fiscal activities not related to the present article: none to disclose. Other relationships: none to disclose.
Supplementary Fabric
Received February 10, 2010; revision requested March 11; revision received July 29; accustomed August xiii; final version accepted September xx.
1 Current address: Section of Radiology, Johns Hopkins Hospital, Baltimore, Physician.
Funding: This research was supported past the National Institutes of Wellness (grants K07-CA114181; and R01-CA127379).
Abbreviations:
- BI-RADS
- Chest Imaging Reporting and Data System
- PPV
- positive predictive value
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Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3029888/
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