A nomogram to predict for malignant diagnosis of BI-RADS Category 4 breast lesions

Prediction for Breast Cancer in BI-RADS Category 4 Lesion Categorized by Age and Breast Composition of Women in Songklanagarind Hospital

Background:

Older age and dense breast are the important risk factors for breast cancer. The ACR BI-RADS lexicon 5^th edition does not mention how patient age and breast density may affect the category assessment. The aim of this study was to investigate whether patient age and breast density influence the positive predictive value (PPV) of mammographic and ultrasonographic findings categorized as BI-RADS category 4 and subcategories 4a, 4b, and 4c among female patients.

Materials and Methods:

A retrospective study was conducted in Songklanagarind Hospital between January 1, 2016 and December 31, 2017 in female patients older than 18 years who had breast lesions categorized as BI-RADS category 4 and subcategories 4a, 4b, 4c. A total of 961 breast lesions consisted of 772 (80.33%) benign lesions and 189 (19.67%) malignant lesions. Categorization was done in each lesion based on age ranges of ≤35 years, >35 to 60 years, and >60 years and breast density according to mammographic breast composition. The PPV for each BI-RADS category was calculated based on the pathological diagnoses and were compared using the chi-square test.

Results:

The overall PPV in each subcategory was in the reference range. The PPV increased with increasing age: 4% vs. 22.63% vs. 36.67% for category 4 (p-value=0.01); 0% vs. 5.81% vs. 6.88% for subcategory 4a (p-value=0.002); 6.67% vs. 26.62% vs. 51.35% for subcategory 4b (p-value=0.001); and 33.33% vs. 76.92% vs. 81.82% for subcategory 4c (p-value=0.02). An association was not found between PPV and breast density.

Conclusion:

A significantly positive association was found between PPV and age in patients in BI-RADS subcategories 4a, 4b, and 4c. This study could not determine that mammographic breast composition according to the ACR BI-RADS 5th edition was associated with PPV due to improper sample distribution.

Association of the Differences in Average Glandular Dose with Breast Cancer Risk

Objectives. To compare the differences in normalized average glandular dose (NAGD) between the breasts of healthy subjects and those of cancer patients and to determine if the NAGD difference is associated with breast cancer risk and improves breast cancer classification. Materials and Methods. Craniocaudal view and mediolateral view full-field digital mammography (FFDM) images were obtained from 1682 healthy subjects whose breasts were categorized as Breast Imaging-Reporting and Data System (BI-RADS) I or II and from 811 biopsy-confirmed unilateral breast cancer patients whose breasts on the contralateral side were category I or II. Both populations were randomized into training and test sets. Multivariate logistic regression analysis was used to build the breast cancer risk assessment model, and the area under the receiver operating characteristic curve ( $A_{\text{z}}$ ) was used to evaluate the model. Twenty-two breast cancer patients who were originally categorized as BI-RADS I or II for both breasts, but were diagnosed with unilateral biopsy-confirmed breast cancer subsequently, were included to validate the model. Results. The NAGD differences in both FFDM images between tumor-bearing breasts and the healthy breasts of patients were significantly higher than those in healthy subjects ( $P<0.001$ ). The model with NAGD differences had a higher $A_{\text{z}}$ value than the model without NAGD differences. While there was no NAGD differences between originally healthy breasts of breast cancer patients, significant NAGD differences between now tumor-bearing breasts and the then previously healthy breasts were found in both FFDM images. Conclusions. NAGD differences between both breasts can be included in the breast cancer risk assessment model to evaluate breast cancer risk.

The use of the Gail model, body mass index and SNPs to predict breast cancer among women with abnormal (BI-RADS 4) mammograms

Introduction

Mammography screening results in a significant number of false-positives. The use of pretest breast cancer risk factors to guide follow-up of abnormal mammograms could improve the positive predictive value of screening. We evaluated the use of the Gail model, body mass index (BMI), and genetic markers to predict cancer diagnosis among women with abnormal mammograms. We also examined the extent to which pretest risk factors could reclassify women without cancer below the biopsy threshold.

Methods

We recruited a prospective cohort of women referred for biopsy with abnormal (BI-RADS 4) mammograms according to the American College of Radiology’s Breast Imaging-Reporting and Data System (BI-RADS). Breast cancer risk factors were assessed prior to biopsy. A validated panel of 12 single-nucleotide polymorphisms (SNPs) associated with breast cancer were measured. Logistic regression was used to assess the association of Gail risk factors, BMI and SNPs with cancer diagnosis (invasive or ductal carcinoma in situ). Model discrimination was assessed using the area under the receiver operating characteristic curve, and calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test. The distribution of predicted probabilities of a cancer diagnosis were compared for women with or without breast cancer.

Results

In the multivariate model, age (odds ratio (OR) = 1.05; 95% confidence interval (CI), 1.03 to 1.08; P < 0.001), SNP panel relative risk (OR = 2.30; 95% CI, 1.06 to 4.99, P = 0.035) and BMI (≥30 kg/m² versus <25 kg/m²; OR = 2.20; 95% CI, 1.05 to 4.58; P = 0.036) were significantly associated with breast cancer diagnosis. Older women were more likely than younger women to be diagnosed with breast cancer. The SNP panel relative risk remained strongly associated with breast cancer diagnosis after multivariable adjustment. Higher BMI was also strongly associated with increased odds of a breast cancer diagnosis. Obese women (OR = 2.20; 95% CI, 1.05 to 4.58; P = 0.036) had more than twice the odds of cancer diagnosis compared to women with a BMI <25 kg/m². The SNP panel appeared to have predictive ability among both white and black women.

Conclusions

Breast cancer risk factors, including BMI and genetic markers, are predictive of cancer diagnosis among women with BI-RADS 4 mammograms. Using pretest risk factors to guide follow-up of abnormal mammograms could reduce the burden of false-positive mammograms.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-014-0509-4) contains supplementary material, which is available to authorized users.

Breast cancer risk prediction model: a nomogram based on common mammographic screening findings

OBJECTIVES

To develop a prediction model for breast cancer based on common mammographic findings on screening mammograms aiming to reduce reader variability in assigning BI-RADS.

METHODS

We retrospectively reviewed 352 positive screening mammograms of women participating in the Dutch screening programme (Nijmegen region, 2006-2008). The following mammographic findings were assessed by consensus reading of three expert radiologists: masses and mass density, calcifications, architectural distortion, focal asymmetry and mammographic density, and BI-RADS. Data on age, diagnostic workup and final diagnosis were collected from patient records. Multivariate logistic regression analyses were used to build a breast cancer prediction model, presented as a nomogram.

RESULTS

Breast cancer was diagnosed in 108 cases (31 %). The highest positive predictive value (PPV) was found for spiculated masses (96 %) and the lowest for well-defined masses (10 %). Characteristics included in the nomogram are age, mass, calcifications, architectural distortion and focal asymmetry.

CONCLUSION

With our nomogram we developed a tool assisting screening radiologists in determining the chance of malignancy based on mammographic findings. We propose cutoff values for assigning BI-RADS in the Dutch programme based on our nomogram, which will need to be validated in future research. These values can easily be adapted for use in other screening programmes.

KEY POINTS

• There is substantial reader variability in assigning BI-RADS in mammographic screening. • There are no strict guidelines linking mammographic findings to BI-RADS categories. • We developed a model (nomogram) predicting the presence of breast cancer. • Our nomogram is based on common findings on positive screening mammograms. • The nomogram aims to assist screening radiologists in assigning BI-RADS categories.

BI-RADS categorisation of 2,708 consecutive nonpalpable breast lesions in patients referred to a dedicated breast care unit

OBJECTIVES

To determine the malignancy rate of nonpalpable breast lesions, categorised according to the Breast Imaging Reporting and Data System (BI-RADS) classification in the setting of a Breast Care Unit.

METHODS

All nonpalpable breast lesions from consecutive patients referred to a dedicated Breast Care Unit were prospectively reviewed and classified into 5 BI-RADS assessment categories (0, 2, 3, 4, and 5).

RESULTS

A total of 2708 lesions were diagnosed by mammography (71.6%), ultrasound (8.7%), mammography and ultrasound (19.5%), or MRI (0.2%). The distribution of the lesions by BI-RADS category was: 152 in category 0 (5.6%), 56 in category 2 (2.1%), 742 in category 3 (27.4%), 1523 in category 4 (56.2%) and 235 in category 5 (8.7%). Histology revealed 570 malignant lesions (32.9%), 152 high-risk lesions (8.8%), and 1010 benign lesions (58.3%). Malignancy was detected in 17 (2.3%) category 3 lesions, 364 (23.9%) category 4 lesions and 185 (78.7%) category 5 lesions. Median follow-up was 36.9 months.

CONCLUSION

This pragmatic study reflects the assessment and management of breast impalpable abnormalities referred for care to a specialized Breast Unit. Multidisciplinary evaluation with BI-RADS classification accurately predicts malignancy, and reflects the quality of management. This assessment should be encouraged in community practice appraisal.

Evaluation of the effects of transaxillary breast augmentation on sentinel lymph node integrity

BACKGROUND

The transaxillary approach for breast augmentation has gained popularity because of the absence of scarring on the breast. However, the effects of this procedure on breast cancer detection and treatment (which rely heavily on the results of axillary status studies) remain under debate. Specifically, sentinel lymph node biopsy is not indicated for patients who have undergone axillary surgery, due to controversy about the interference of the axillary approach with evaluation of the axillary drainage.

OBJECTIVES

The authors evaluate changes in axillary lymphatic drainage in patients who underwent transaxillary breast augmentation.

METHODS

Twenty-seven patients who presented to Rio de Janeiro State University for breast augmentation were selected for this study. All patients underwent preoperative mammary lymphoscintigraphy, a subsequent transaxillary breast augmentation, and postoperative lymphoscintigraphy at 21 days and six months after the procedure. The postoperative imaging results examining the axillary lymphatic chain and the first axillary lymph node were analyzed and compared to the preoperative images.

RESULTS

One of the 27 patients (4.5%) demonstrated a lower rate of lymphatic drainage at 21 days postoperatively compared to preoperative values, but the flow rate had recovered by her six-month follow-up visit. All other patients showed no changes between the preoperative and postoperative images at either time point. The sentinel lymph node remained visible in all patients at all time points, and all breasts showed drainage primarily to the axillary lymphatic chain. Two patients experienced hematoma and one patient experienced late infection at four months postoperatively. The sentinel lymph node was still evident in both axillae.

CONCLUSIONS

The data showed preservation of lymphatic drainage and visible sentinel lymph nodes even after transaxillary breast augmentation. Therefore, the authors believe that this procedure does not alter the integrity of mammary drainage for properly selected patients. These data provide surgeons with a less invasive treatment option for patients with early breast cancer, even when they have undergone prior breast augmentation through a transaxillary approach.