Mammography pain in women treated conservatively for breast cancer.

In this study, pain during mammography in women treated conservatively for breast cancer was examined. It studied pain intensity and its relation to a variety of demographic, medical, and pain coping variables as well as to objective measures of breast compression. Ninety-nine women, treated with lumpectomy (with or without radiation) and undergoing follow-up screening mammography, were asked about strategies they use to cope with everyday pain and then were asked to report pain experienced during the mammogram. Treated and untreated breasts were rated separately and compared with a sample of 125 control women with no history of breast cancer. Women reported significantly greater pain in the treated breast (41% greater than the untreated breast and 32% greater than the control group). There was no consistent relationship between mammography pain and pain coping. Average intensity of pain at last mammogram was the best predictor of pain in both breasts. Women treated conservatively for breast cancer experience significantly greater pain during mammography of their treated breast. Radiologists and technologists can identify women at risk for a painful mammogram by asking about the pain at last mammogram. By applying pain-reducing interventions, they might be able to make the mammography experience more tolerable for these women.

Sonography of solid breast lesions: observer variability of lesion description and assessment.

OBJECTIVE: The purpose of this study was to measure the level of inter- and intraobserver agreement and to evaluate the causes of variability in radiologists' descriptions and assessments of sonograms of solid breast masses. MATERIALS AND METHODS: Sixty sonograms of solid masses were evaluated independently by five radiologists. Observers used the lexicon of a recently published benchmark report on sonographic appearances of breast masses to determine mass shape, margin, echogenicity, echo texture, presence of echogenic pseudocapsule, and acoustic transmission. Final diagnostic assessments were determined by applying the rule-based model of the same benchmark report to the radiologists' descriptions. In addition, one observer interpreted each case twice to evaluate intraobserver variability. Inter- and intraobserver variability were measured using Cohen's kappa statistic. We also investigated causes of variability in radiologists' descriptions. RESULTS: Interobserver agreement ranged from lowest for determining the presence of an echogenic pseudocapsule (kappa = .09) to highest for determining mass shape (kappa = .8). Intraobserver agreement was lowest for mass echo texture (kappa = .24) and greatest for mass shape (kappa = .79). Variability in descriptions of lesions contributed to interobserver (kappa = .51) and some intraobserver (kappa = .66) inconsistency in assessing the likelihood of malignancy. CONCLUSION: Lack of uniformity among observers' use of descriptive terms for solid breast masses resulted in inconsistent diagnoses. The need for improved definitions and additional illustrative examples could be addressed by developing a standardized lexicon similar to that of the Breast Imaging Reporting and Data System.

The use of acoustic streaming in breast lesion diagnosis: a clinical study.

Results from a clinical study are presented, in which ultrasonically-induced acoustic streaming was successfully used to differentiate fluid-filled lesions (cysts) from solid lesions in the breast. In this study, high-intensity ultrasound pulses from a modified commercial scanner were used to induce acoustic streaming in cyst fluid, and this motion was detected using Doppler methods. Acoustic streaming was generated and detected in 14 of 15 simple cysts, and 4 of 14 sonographically indeterminate breast lesions. This lesion differentiation method appears to be particularly suited for diagnosis of small, possibly newer, cysts that appear indeterminate on conventional sonography due to their size. The results indicate that this method would be a useful adjunct to conventional sonography for the purpose of breast lesion classification.

Effect of patient history data on the prediction of breast cancer from mammographic findings with artificial neural networks.

RATIONALE AND OBJECTIVES: The authors evaluated the contribution of medical history data to the prediction of breast cancer with artificial neural network (ANN) models based on mammographic findings. MATERIALS AND METHODS: Three ANNs were developed: The first used 10 Breast Imaging Reporting and Data System (BI-RADS) variables; the second, the BI-RADS variables plus patient age; the third, the BI-RADS variables, patient age, and seven other history variables, for a total of 18 inputs. Performance of the ANNs and the original radiologist's impression were evaluated with five metrics: receiver operating characteristic area index (Az); specificity at given sensitivities of 100%, 98%, and 95%; and positive predictive value. RESULTS: All three ANNs consistently outperformed the radiologist's impression over all five performance metrics. The patient-age variable was particularly valuable. Adding the age variable to the basic ANN model, which used only the BI-RADS findings, significantly improved Az (P = .028). In fact, replacing all history data with just the age variable resulted in virtually no changes for Az or specificity at 98% sensitivity (P = .324 and P = .410, respectively). CONCLUSION: Patient age was an important variable for the prediction of breast cancer from mammographic findings with the ANNs. For this data set, all history data could be replaced with age alone.

Fat necrosis in the breast: sonographic features.

PURPOSE: To evaluate the ultrasonographic (US) features and evolution of fat necrosis in the breast. MATERIALS AND METHODS: The US features of 31 breast masses in 23 patients were reviewed. Fat necrosis was diagnosed on the basis of histologic (n = 20) and initial or follow-up (minimum follow-up, 15 months) mammographic (n = 11) findings. RESULTS: Sonograms demonstrated discrete masses in all but one patient. On the basis of the predominant US finding, masses were categorized as solid (n = 15), complex with mural nodules (n = 7), complex with echogenic bands (n = 4), anechoic with posterior acoustic enhancement (n = 2), anechoic with shadowing (n = 2), or no mass visible (n = 1). Distortion of the normal parenchymal architecture was seen in 21 masses. In four six masses, 1-26-month follow-up sonograms showed evolution of the US appearance. One solid mass remained solid appearing. Complex masses tended to evolve: Three became more solid appearing, and one became more cystic. No mass enlarged; two remained stable, and four decreased in size. CONCLUSION: A spectrum of US findings is associated with fat necrosis. If fat necrosis is suspected and mammographic findings are suspicious, knowledge of the US appearance and evolution of these patterns may enable imaging follow-up of these lesions rather than needless biopsy.

Postmenopausal estrogen use and invasive versus in situ breast cancer risk.

To examine the effect of cancer histopathology on the relationship between estrogen-replacement therapy (ERT) use and breast cancer risk, we performed a case-control study of 109 postmenopausal women 45 years or older with in situ or invasive breast cancer matched to 545 controls. When in situ and invasive tumors were combined, the overall odds ratio (OR) describing the association between ERT use and breast cancer risk was not statistically significantly elevated (adjusted OR = 1.48, 95% confidence interval [CI] = 0.89-2.47). When the analyses were confined to women with invasive disease, risk estimates were uniformly higher (adjusted OR = 1.85, 95% CI = 1.00-3.45). In contrast, the overall estimate for the relationship between ERT use and in situ breast cancer was close to 1 (adjusted OR = 1.08, 95% CI = 0.42-2.77). The positive association between ERT use and invasive breast cancer we observed, and the lack of association in women with in situ disease, may represent a distinct biological difference or may be related to the small sample size of our study.

Intracapsular implant rupture: MR findings of incomplete shell collapse.

The objective of this study was to determine the frequency and significance of the MR findings of incomplete shell collapse for detecting implant rupture in a series of surgically removed breast prostheses. MR images of 86 breast implants in 44 patients were studied retrospectively and correlated with surgical findings at explantation. MR findings included (a) complete shell collapse (linguine sign), 21 implants; (b) incomplete shell collapse (subcapsular line sign, teardrop sign, and keyhole sign), 33 implants; (c) radial folds, 31 implants; and (d) normal, 1 implant. The subcapsular line sign was seen in 26 implants, the teardrop sign was seen in 27 implants, and the keyhole sign was seen in 23 implants. At surgery, 48 implants were found to be ruptured and 38 were intact. The MR findings of ruptured implants showed signs of incomplete collapse in 52% (n = 25), linguine sign in 44% (n = 21), and radial folds in 4% (n = 2). The linguine sign perfectly predicted implant rupture, but sensitivity was low. Findings of incomplete shell collapse improved sensitivity and negative predictive values, and the subcapsular line sign produced a significant incremental increase in predictive ability. MRI signs of incomplete shell collapse were more common than the linguine sign in ruptured implants and are significant contributors to the high sensitivity and negative predictive values of MRI for evaluating implant integrity.

Predicting breast cancer invasion with artificial neural networks on the basis of mammographic features.

PURPOSE: To evaluate whether an artificial neural network (ANN) can predict breast cancer invasion on the basis of readily available medical findings (ie, mammographic findings classified according to the American College of Radiology Breast Imaging Reporting and Data System and patient age). MATERIALS AND METHODS: In 254 adult patients, 266 lesions that had been sampled at biopsy were randomly selected for the study. There were 96 malignant and 170 benign lesions. On the basis of nine mammographic findings and patient age, a three-layer backpropagation network was developed to predict whether the malignant lesions were in situ or invasive. RESULTS: The ANN predicted invasion among malignant lesions with an area under the receiver operating characteristic curve (Az) of .91 +/- .03. It correctly identified all 28 in situ cancers (specificity, 100%) and 48 of 68 invasive cancers (sensitivity, 71%). CONCLUSION: The ANN used mammographic features and patient age to accurately classify invasion among breast cancers, information that was previously available only by means of biopsy. This knowledge may assist in surgical planning and may help reduce the cost and morbidity of unnecessary biopsy.

Axillary lymph nodes: mammographic, pathologic, and clinical correlation.

OBJECTIVE: The purpose of this study was to determine the cause and frequency of axillary abnormalities seen mammographically and to evaluate the imaging characteristics of lymphadenopathy that are associated with malignancy. MATERIALS AND METHODS: Ninety-six axillary abnormalities seen mammographically in 94 patients were retrospectively reviewed and correlated with the clinical diagnoses and pathologic results found in the medical records. For each abnormality, the length, margins, and presence of microcalcifications were noted. Logistic regression was used to determine an association between these findings and status (benign or malignant). RESULTS: Seventy-six of 94 patients had lymphadenopathy. Eighteen of 94 patients had an abnormality other than lymphadenopathy. Because two of these 94 patients had more than one abnormality, a total of 96 abnormalities occurred, 20 of which were due to an abnormality other than lymphadenopathy. Regarding the 76 cases of lymphadenopathy, the most frequent diagnosis was nonspecific benign lymphadenopathy in 29% (n = 22) of cases, followed by metastatic breast cancer in 26% (n = 20) and chronic lymphocytic leukemia or well-differentiated lymphocytic lymphoma in 17% (n = 13). Other causes (n = 21) included collagen vascular disease, lymphomas other than well-differentiated lymphocytic lymphoma, metastatic disease from nonbreast primary site, metastatic disease from unknown primary site, sarcoidosis. HIV-related lymphadenopathy, and reactive lymphadenopathy associated with a breast abscess. An association between length of nonfatty lymph nodes and malignant status was statistically significant at the .001 level. When a length greater than 33 mm was used as a predictor of malignancy, the specificity and sensitivity were 97% and 31%, respectively. We found an association between malignancy and nonfatty lymph nodes with ill-defined or spiculated margins (p = .053). Regarding the 20 abnormalities other than lymphadenopathy, epidermal cysts (n = 7) were most prevalent. CONCLUSION: The most common axillary abnormality revealed on mammography was abnormal lymph nodes. Homogeneously dense (nonfatty) axillary lymph nodes were strongly associated with malignancy when the lymph nodes were longer than 33 mm, had ill-defined or spiculated margins, or contained intranodal microcalcifications. However, our study confirmed that in most cases benign and malignant lymph nodes cannot be distinguished from each other mammographically.

Pain during mammography: characteristics and relationship to demographic and medical variables.

Reports of pain during mammography show that there is great variability in both the incidence of reported pain (0.2-62%) and the intensity of that pain. Much of that variability may be due to the measures used to rate mammography pain. This is the first study that has examined the incidence, quality and intensity of mammography pain using a variety of pain measures. A sample of 119 women undergoing screening mammography was studied using four pain scales, three well-validated measures frequently used in the pain research literature as well as a pain/discomfort measure frequently reported in the radiology literature. A large proportion (up to 91%) of women report having some degree of pain during mammography. The intensity of that pain was typically in the low to moderate range, but a small proportion of women (< 15%) reported intense pain. The incidence of reported pain was related to the pain measure used. Pain measures that provided a woman with many options for reporting pain were associated with a higher incidence of pain than a scale that provided only one or two options. Thus, some of the variability in reported incidence of pain during mammography can be explained by the pain scale used in the study. Demographic and medical variables could explain 18-20% of the variance in mammography pain. Two of the variables that were shown to consistently predict a painful mammographic experience were (1) average pain at the last mammogram and (2) breast density. This study demonstrated that the pain measure selected for use in a particular study may depend on the population being studied. A college education was found to be an important predictor of pain scores on the McGill Pain Questionnaire. Thus, this pain measure may be of limited usefulness in studying a population of women with little formal education.

Complex radial folds versus subtle signs of intracapsular rupture of breast implants: MR findings with surgical correlation.

Detection of intracapsular rupture of silicone breast prostheses using MR imaging is often performed by identifying the "linguine sign" [1]. The linguine sign is easily differentiated from simple radial folds that are seen in intact implants. However, more subtle signs of intracapsular rupture, including undulating subcapsular lines and the "teardrop sign," are less often recognized [2-5] and may prove difficult for the less experienced radiologist to differentiate from complex radial folds of intact implants. In this essay, we illustrate the MR imaging findings of complex radial folds in intact implants and compare them with findings of incomplete shell collapse in ruptured implants in a surgically confirmed series of explanted silicone breast prostheses.

Breast imaging reporting and data system standardized mammography lexicon: observer variability in lesion description.

OBJECTIVE: The American College of Radiology has recommended the Breast Imaging Reporting and Data System (BI-RADS) as a standardized scheme for describing mammographic lesions. The objective of this study was to measure inter- and intraobserver variabilities of radiologists' descriptions of mammographic lesions with the BI-RADS standardized lexicon. MATERIALS AND METHODS: Sixty mammographic studies with abnormal findings were independently evaluated by five radiologists. Readers described each lesion by selecting a single term from the BI-RADS lexicon for each of eight morphologic categories: calcification distribution, number, and description; mass margin, shape, and density; associated findings; and special cases. Additionally, each reader assessed the significance of each lesion on a five-point scale. One observer read each case twice. Inter- and intraobserver variabilities for each description and interpretation category of the BI-RADS lexicon were determined with Cohen's kappa statistic. Radiologists' specific use of calcification descriptors was evaluated in detail. RESULTS: Substantial agreement was observed between readers for choosing terms to describe masses and calcifications (kappa value range, 0.50 +/- 0.02-0.77 +/- 0.03). Intraobserver agreement for these categories was similar (kappa value range, 0.57 +/- 0.07-0.84 +/- 0.09). Considerable inter- and intraobserver variabilities were noted for the "associated findings" and "special cases" categories (kappa value range, -0.02 +/- 0.14-0.38 +/- 0.12), a result that in part reflected the small number of cases to which these categories were assigned. Moderate interobserver variability and little intraobserver variability in the interpretation of lesion significance were noted when an assessment classification similar to that of BI-RADS was used. Use of terms to describe calcifications did not always conform to BI-RADS-defined levels of suspicion. CONCLUSION: BI-RADS is moderately successful in providing a standardized language for physicians to describe lesion morphology. Efforts to reevaluate specific terms and the diagnostic significance assigned to calcification descriptors may prove useful in maintaining the promise of improved quality with the BI-RADS standardized mammography lexicon.

Artificial neural network: improving the quality of breast biopsy recommendations.

PURPOSE: To evaluate the performance and inter- and intraobserver variability of an artificial neural network (ANN) for predicting breast biopsy outcome. MATERIALS AND METHODS: Five radiologists described 60 mammographically detected lesions with the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) nomenclature. A previously programmed ANN used the BI-RADS descriptors and patient histories to predict biopsy results. ANN predictive performance was compared with the clinical decision to perform biopsy. Inter- and intraobserver variability of radiologists' interpretations and ANN predictions were evaluated with Cohen kappa analysis. RESULTS: The ANN maintained 100% sensitivity (23 of 23 cancers) while improving the positive predictive value of biopsy results from 38% (23 of 60 lesions) to between 58% (23 of 40 lesions) and 66% (23 of 35 lesions; P < .001). Interobserver variability for interpretation of the lesions was significantly reduced by the ANN (P < .001); there was no statistically significant effect on nearly perfect intraobserver reproducibility. CONCLUSION: Use of an ANN with radiologists' descriptions of abnormal findings may improve interpretation of mammographic abnormalities.

Computer-aided diagnosis of breast cancer: artificial neural network approach for optimized merging of mammographic features.

RATIONALE AND OBJECTIVES: An artificial neural network (ANN) approach was developed for the computer-aided diagnosis of mammography using an optimally minimized number of input features. METHODS: A backpropagation ANN merged nine input features (age plus eight radiographic findings extracted by radiologists) to predict biopsy outcome as its output. The features were ranked, and more important ones were selected to produce an optimal subset of features. RESULTS: Given all nine features, the ANN performed with a receiver operator characteristic area under the curve (Az) of .95 +/- .01. Given only the four most important features, the ANN performed with an Az of .96 +/- .01. Although not significantly better than the ANN with all nine features, the ANN with the four optimized features was significantly better than expert radiologists' Az of .90 +/- .02 (p = .01). This four-feature ANN had a 95% sensitivity and an 81% specificity. For cases with calcifications, the radiologists' performance dropped to an Az of .85 +/- .04, whereas a specialized three-feature ANN performed significantly better with an Az of .95 +/- .02 (p = .02). CONCLUSION: Given only four input features, the ANN predicted biopsy outcome significantly better than did expert radiologists, who also had access to other radiographic and nonradiographic data. The reduced number of features would substantially decrease data entry efforts and potentially improve the ANN's general applicability.

Breast cancer: prediction with artificial neural network based on BI-RADS standardized lexicon.

PURPOSE: To determine if an artificial neural network (ANN) to categorize benign and malignant breast lesions can be standardized for use by all radiologists. MATERIALS AND METHODS: An ANN was constructed based on the standardized lexicon of the Breast Imaging Recording and Data System (BI-RADS) of the American College of Radiology. Eighteen inputs to the network included 10 BI-RADS lesion descriptors and eight input values from the patient's medical history. The network was trained and tested on 206 cases (133 benign, 73 malignant cases). Receiver operating characteristic curves for the network and radiologists were compared. RESULTS: At a specified output threshold, the ANN would have improved the positive predictive value (PPV) of biopsy from 35% to 61% with a relative sensitivity of 100%. At a fixed sensitivity of 95%, the specificity of the ANN (62%) was significantly greater than the specificity of radiologists (30%) (P < .01). CONCLUSION: The BI-RADS lexicon provides a standardized language between mammographers and an ANN that can improve the PPV of breast biopsy.

Periprosthetic mycobacterial infection. CT and mammographic findings.

Organisms of the Mycobacterium fortuitum complex are an uncommon but important cause of periprosthetic infection following augmentation mammoplasty or other breast surgery. This etiological agent must be considered in the particular case of periprosthetic infection, because special handling of the fluid is crucial to enhance recovery of the organism. We describe the computed tomography (CT) and mammographic findings in such an abscess with respect to the clinical context and subsequent management. To our knowledge, CT findings associated with any periprosthetic breast infection have not been described.

Seromas in residual fibrous capsules after explantation: mammographic and sonographic appearances.

PURPOSE: To evaluate the mammographic and sonographic findings associated with seromas that develop in residual fibrous capsules after explantation of breast prostheses. MATERIALS AND METHODS: Preoperative and postoperative mammograms were reviewed in 86 patients (mean age, 51 years; age range, 24-71 years) who had undergone surgical explantation of breast prostheses. Six seromas were found in four patients 46-68 years of age. Imaging findings were correlated with surgical and laboratory results for three seromas. A presumptive diagnosis was made of the other three lesions. RESULTS: Mammograms demonstrated all seromas as large, elliptic, water-opacity masses, some with well-circumscribed and some with irregular borders. Sonograms showed thin, compressible masses, two of which were flat and anechoic and one of which was hypoechoic. Three patients' images were initially misinterpreted, leading to excision of two seromas and aspiration of one. Seromas were not identified in patients whose implants were removed by means of complete capsulectomy. CONCLUSION: Radiologists must be aware of the imaging findings associated with seromas and of a patient's surgical history to avoid biopsy of benign lesions.

Papillary carcinoma of the breast: imaging findings.

Papillary carcinoma is a rare malignant tumor of the breast for which the survival rate is better than for most breast carcinomas. Histologically, invasive and in situ forms occur; the in situ form can extend throughout a ductal system (intraductal) or can be confined within a cystic structure (intracystic). Invasive papillary carcinoma can spread from either of the in situ forms but spreads more commonly from the intracystic type. Many reports in the literature have failed to differentiate invasive from in situ papillary carcinomas; similarly, the different mammographic patterns of the two in situ forms of these lesions have not been delineated clearly. Our review of 16 new cases of papillary carcinoma showed a frequent correlation between the histologic types and the mammographic appearance. The intraductal in situ form usually was characterized by clustered microcalcifications. The intracystic in situ type was associated with well-circumscribed masses on mammograms; these masses often were complex on sonograms. The purpose of this essay is to illustrate the mammographic and sonographic features of the histologic varieties of papillary carcinoma. Color Doppler sonograms and MR images of intracystic and invasive tumors also are included.

A novel ultrasonic technique for differentiating cysts from solid lesions: preliminary results in the breast.

The feasibility of a new ultrasonic technique to distinguish cysts from solid lesions is explored. High intensity pulses are used to induce acoustic streaming in cyst fluid, and this motion is detected using Doppler techniques. Acoustic streaming cannot be generated in solid lesions, therefore, its detection would indicate a cyst. In six of seven breast cysts motion was clearly generated and detected in vivo. Ultrasonic pulses with intensities up to 4.4 W cm-2 (I(spta) in water) were focused on the cysts for 10 s. Lesion diameters ranged from 0.6 to 2.5 cm; induced flow velocities were less than 4.0 cm s-1.

Prediction of breast cancer malignancy using an artificial neural network.

BACKGROUND: An artificial neural network (ANN) was developed to predict breast cancer from mammographic findings. This network was evaluated in a retrospective study. METHODS: For a set of patients who were scheduled for biopsy, radiologists interpreted the mammograms and provided data on eight mammographic findings as part of the standard mammographic workup. These findings were encoded as features for an ANN. Results of biopsies were taken as truth in the diagnosis of malignancy. The ANN was trained and evaluated using a jackknife sampling on a set of 260 patient records. Performance of the network was evaluated in terms of sensitivity and specificity over a range of decision thresholds and was expressed as a receiver operating characteristic curve. RESULTS: The ANN performed more accurately than the radiologists (P < 0.08) with a relative sensitivity of 1.0 and specificity of 0.59. CONCLUSIONS: An ANN can be trained to predict malignancy from mammographic findings with a high degree of accuracy.