Charlene A. Sennett

Axillary Lymph Nodes Suspicious for Breast Cancer Metastasis: Sampling with US-guided 14-Gauge Core-Needle Biopsy—Clinical Experience in 100 Patients

PURPOSE To study the clinical usefulness of ultrasonography (US)-guided core-needle biopsy (CNB) of axillary lymph nodes and the US-depicted abnormalities that may be used to predict nodal metastases. MATERIALS AND METHODS This retrospective study was HIPAA compliant and institutional review board approved; the requirement for informed patient consent was waived. US-guided 14-gauge CNB of abnormal axillary lymph nodes was performed in 100 of 144 patients with primary breast cancer who underwent US assessment of axillary lymph nodes. A biopsy needle with controllable action rather than a traditional throw-type needle was used. US findings were considered suspicious for metastasis if cortical thickening and/or nonhilar blood flow (NHBF) to the lymph node cortex was present. The absence of any discernible fatty hilum was also noted. RESULTS Nodal metastases were documented at CNB in 64 (64%) of the 100 patients. All 36 patients with negative biopsy results underwent subsequent sentinel lymph node biopsy (SLNB), which yielded negative findings in 32 (89%) patients and revealed metastasis in four (11%). All 44 patients who did not undergo CNB because of negative US results subsequently underwent SLNB, which revealed lymph node metastasis in 12 (27%) patients. Cortical thickening was found in 63 (79%) of the total of 80 metastatic nodes, but only a minority (n = 26 [32%]) of the nodes had an absent fatty hilum. NHBF to the cortex was detected in 52 (65%) metastatic nodes. Both absence of a fatty hilum (metastasis detected in 26 [93%] of 28 nodes) and cortical thickening combined with NHBF (metastasis detected in 52 [81%] of 64 nodes) had a high positive predictive value. No clinically important complications were encountered with the biopsy procedures. CONCLUSION Axillary lymph nodes with abnormal US findings can be sampled with high accuracy and without major complications by using a modified 14-gauge CNB technique.

A dual‐stage method for lesion segmentation on digital mammograms

Mass lesion segmentation on mammograms is a challenging task since mass lesions are usually embedded and hidden in varying densities of parenchymal tissue structures. In this article, we present a method for automatic delineation of lesion boundaries on digital mammograms. This method utilizes a geometric active contour model that minimizes an energy function based on the homogeneities inside and outside of the evolving contour. Prior to the application of the active contour model, a radial gradient index (RGI)-based segmentation method is applied to yield an initial contour closer to the lesion boundary location in a computationally efficient manner. Based on the initial segmentation, an automatic background estimation method is applied to identify the effective circumstance of the lesion, and a dynamic stopping criterion is implemented to terminate the contour evolution when it reaches the lesion boundary. By using a full-field digital mammography database with 739 images, we quantitatively compare the proposed algorithm with a conventional region-growing method and an RGI-based algorithm by use of the area overlap ratio between computer segmentation and manual segmentation by an expert radiologist. At an overlap threshold of 0.4, 85% of the images are correctly segmented with the proposed method, while only 69% and 73% of the images are correctly delineated by our previous developed region-growing and RGI methods, respectively. This resulting improvement in segmentation is statistically significant.

US-guided Core Needle Biopsy of Axillary Lymph Nodes in Patients with Breast Cancer: Why and How to Do It

Axillary lymph node status is an extremely important prognostic factor in the assessment of new breast cancer patients. Sentinel lymph node biopsy is now often performed instead of axillary dissection for lymph node staging but raises numerous issues of practicality. Sentinel lymph node biopsy can be avoided if lymph node metastasis is documented presurgically, making an alternative staging method desirable. Although not widely performed for axillary lymph node staging, ultrasonography (US)-guided core needle biopsy is a well-established procedure for the breast and other organs, with a higher success rate in terms of tissue diagnosis than fine-needle aspiration biopsy. Improvements in US have established it as a valuable method for evaluating lymph nodes. US findings in abnormal lymph nodes include cortical thickening and diminished or absent hilum. In addition, color Doppler US of abnormal axillary lymph nodes often shows hyperemic blood flow in the hilum and central cortex or abnormal (nonhilar cortical) blood flow. US-guided core needle biopsy of axillary lymph nodes in breast cancer patients can yield a high accuracy rate with no significant complications, given the use of a biopsy device with controllable needle action, a clear understanding of anatomy, and good skills for controlling the needle.

Importance of a Personal History of Breast Cancer as a Risk Factor for the Development of Subsequent Breast Cancer: Results From Screening Breast MRI

OBJECTIVE The purposes of this study were to assess the importance of a personal history of breast cancer as a risk factor for patients referred for screening breast MRI and to evaluate the importance of this risk factor compared with family history. MATERIALS AND METHODS A retrospective review of screening breast MRI performed from 2004 to 2012 included a total of 702 patients, 465 of whom had undergone annual MRI and 237 of whom had undergone MRI every 6 months as part of a research protocol. RESULTS Of the patients screened, 208 had a personal history of breast cancer, and 345 had a family history as the sole risk factor. An additional 97 patients had both risk factors. The absolute risk for detection of breast cancer at screening MRI among patients with a personal history of cancer was 2.8% (95% CI, 0.6-5.2%). The absolute risk for patients with a strong family history of cancer was 2.0% (95% CI, 0.5-3.5%). The relative risk for detection of breast cancer given a personal history was 1.42 (95% CI, 0.48-4.17) compared with family history. The relative risk when both risk factors were present compared with having only a family history was 3.04 (95% CI, 1.05-8.86). CONCLUSION A personal history of breast cancer is an important risk factor for the development of subsequent breast cancer. Given the results, consideration should be given to MRI screening of patients with a personal history of breast cancer.

Multimodality computer-aided breast cancer diagnosis with FFDM and DCE-MRI.

RATIONALE AND OBJECTIVES To investigate a multimodality computer-aided diagnosis (CAD) scheme that combines image information from full-field digital mammography (FFDM) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for computerized breast cancer classification. MATERIALS AND METHODS From a retrospective FFDM database with 432 lesions (255 malignant, 177 benign) and a retrospective DCE-MRI database including 476 lesions (347 malignant, 129 benign), we constructed a multimodality dataset of 213 lesions (168 malignant, 45 benign). Each lesion was present on both FFDM and DCE-MRI images and deemed to be a difficult case given the necessity of having both clinical imaging exams. Using a manually indicated lesion location (ie, a seed point on FFDM images or a region of interest on DCE-MRI images, the computer automatically segmented the mass lesions and extracted lesion features). A subset of features was selected using linear stepwise feature selection and merged by a Bayesian artificial neural network to yield an estimate of the probability of malignancy. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of the selected features in distinguishing between malignant and benign lesions. RESULTS With leave-one-lesion-out cross-validation on the multimodality dataset, the mammography-only features yielded an area under the ROC curve (AUC) of 0.74 +/- 0.04, and the DCE-MRI-only features yielded an AUC of 0.78 +/- 0.04. The combination of these two modalities, which included a spiculation feature from mammography and two kinetic features from DCE-MRI, yielded an AUC of 0.87 +/- 0.03. The improvement of combining multimodality information was statistically significant as compared to the use of single modality information alone. CONCLUSIONS A CAD scheme that combines features extracted from FFDM and DCE-MRI images may be advantageous to single-modality CAD in the task of differentiating between malignant and benign lesions.

Evaluation of Computer-aided Diagnosis on a Large Clinical Full-field Digital Mammographic Dataset

RATIONALE AND OBJECTIVES To convert and optimize our previously developed computerized analysis methods for use with images from full-field digital mammography (FFDM) for breast mass classification to aid in the diagnosis of breast cancer. MATERIALS AND METHODS An institutional review board approved protocol was obtained, with waiver of consent for retrospective use of mammograms and pathology data. Seven hundred thirty-nine FFDM images, which contained 287 biopsy-proven breast mass lesions, of which 148 lesions were malignant and 139 lesions were benign, were retrospectively collected. Lesion margins were delineated by an expert breast radiologist and were used as the truth for lesion-segmentation evaluation. Our computerized image analysis method consisted of several steps: 1) identified lesions were automatically extracted from the parenchymal background using computerized segmentation methods; 2) a set of image characteristics (mathematic descriptors) were automatically extracted from image data of the lesions and surrounding tissues; and 3) selected features were merged into an estimate of the probability of malignancy using a Bayesian artificial neural network classifier. Performance of the analyses was evaluated at various stages of the conversion using receiver-operating characteristic analysis. RESULTS An area under the curve value of 0.81 was obtained in the task of distinguishing between malignant and benign mass lesions in a round-robin by case evaluation on the entire FFDM dataset. We failed to show a statistically significant difference (P = .83) compared to results from our previous study in which the computerized classification was performed on digitized screen-film mammograms. CONCLUSIONS Our computerized analysis methods developed on digitized screen-film mammography can be converted for use with FFDM. Results show that the computerized analysis methods for the diagnosis of breast mass lesions on FFDM are promising, and can potentially be used to aid clinicians in the diagnostic interpretation of FFDM.

Automated Method for Improving System Performance of Computer-Aided Diagnosis in Breast Ultrasound

The purpose of this research was to demonstrate the feasibility of a computerized auto-assessment method in which a computer-aided diagnosis (CADx) system itself provides a level of confidence for its estimate for the probability of malignancy for each radiologist-identified lesion. The computer performance was assessed within a leave-one-case-out protocol using a database of sonographic images from 542 patients (19% cancer prevalence). We investigated the potential of computer-derived confidence levels both as 1) an output aid to radiologists and 2) as an automated method to improve the computer classification performance-in the task of differentiating between cancerous and benign lesions for the entire database. For the former, the CADx classification performance was assessed within ranges of confidence levels. For the latter, the computer-derived confidence levels were used in the determination of the computer-estimated probability of malignancy for each actual lesion based on probabilities obtained from different views. The use of this auto-assessment method resulted in the modest but statistically significant increase in the area under the receiver operating characteristic (ROC) curve (AUC value) of 0.01 with respect to the performance obtained using the ldquotraditionalrdquo CADx approach, increasing the AUC value from 0.89 to 0.90 (p -value 0.03). We believe that computer-provided confidence levels may be helpful to radiologists who are using CADx output in diagnostic image interpretation as well as for automated improvement of the CADx classification for cancer.

Utility of Preoperative Ultrasound for Predicting pN2 or Higher Stage Axillary Lymph Node Involvement in Patients With Newly Diagnosed Breast Cancer

OBJECTIVE The objective of our study was to report the positive predictive value (PPV) of ultrasound of the axilla to predict pN2 or higher disease in breast cancer patients. MATERIALS AND METHODS A retrospective study of 559 patients with newly diagnosed invasive breast cancer from 2005 through 2009 was performed. All patients underwent ipsilateral axillary ultrasound for staging purposes. Ultrasound findings were considered suspicious for metastasis if cortical thickening or nonhilar blood flow to the cortex was present. Suspicious lymph nodes were classified on the basis of their features as high, intermediate, or low suspicion. The standard of truth was confirmed pathologically. RESULTS Either pN2 or pN3 disease was found in 50 of 181 (28%) patients with positive findings on an ultrasound study and 10 of 378 (3%) patients with a negative ultrasound study (p < 0.01). When two or more lymph nodes of high suspicion or a total of three or more lymph nodes of any combination of high suspicion and intermediate suspicion were detected, patients were likely to have pN2 or pN3 disease (PPV, 82%). Either pN2 or pN3 disease was found in two of 122 (2%) patients whose primary cancers were up to 10 mm and 58 of 437 (13%) patients whose primary cancers were larger than 10 mm (p < 0.001). Ultrasound of the patient with tumors larger than 10 mm showing at least two highly suspicious nodes had a PPV of 87% for predicting pN2 or higher disease. CONCLUSION Ultrasound was useful for predicting pN2 or higher axillary disease in breast cancer patients. Preoperative ultrasound assessment for staging of axillary lymph nodes might help avoid underestimation at sentinel lymph node biopsy.

Breast US Computer-aided Diagnosis System: Robustness across Urban Populations in South Korea and the United States

PURPOSE To evaluate the robustness of a breast ultrasonographic (US) computer-aided diagnosis (CAD) system in terms of its performance across different patient populations. MATERIALS AND METHODS Three US databases were analyzed for this study: one South Korean and two United States databases. All three databases were utilized in an institutional review board-approved and HIPAA-compliant manner. Round-robin analysis and independent testing were performed to evaluate the performance of a computerized breast cancer classification scheme across the databases. Receiver operating characteristic (ROC) analysis was used to evaluate performance differences. RESULTS The round-robin analyses of each database demonstrated similar results, with areas under the ROC curve ranging from 0.88 (95% confidence interval [CI]: 0.820, 0.918) to 0.91 (95% CI: 0.86, 0.95). The independent testing of each database, however, indicated that although the performances were similar, the range in areas under the ROC curve (from 0.79 [95% CI: 0.730, 0.842] to 0.87 [95% CI: 0.794, 0.923]) was wider than that with the round-robin tests. However, the only instances in which statistically significant differences in performance were demonstrated occurred when the Korean database was used in a testing capacity in independent testing. CONCLUSION The few observed statistically significant differences in performance indicated that while the US features used by the system were useful across the databases, their relative importance differed. In practice, this means that a CAD system may need to be adjusted when applied to a different population.

Performance of breast ultrasound computer-aided diagnosis: dependence on image selection.

RATIONALE AND OBJECTIVES The automated classification of sonographic breast lesions is generally accomplished by extracting and quantifying various features from the lesions. The selection of images to be analyzed, however, is usually left to the radiologist. Here we present an analysis of the effect that image selection can have on the performance of a breast ultrasound computer-aided diagnosis system. MATERIALS AND METHODS A database of 344 different sonographic lesions was analyzed for this study (219 cysts/benign processes, 125 malignant lesions). The database was collected in an institutional review board-approved, Health Insurance Portability and Accountability Act-compliant manner. Three different image selection protocols were used in the automated classification of each lesion: all images, first image only, and randomly selected images. After image selection, two different protocols were used to classify the lesions: (a) the average feature values were input to the classifier or (b) the classifier outputs were averaged together. Both protocols generated an estimated probability of malignancy. Round-robin analysis was performed using a Bayesian neural network-based classifier. Receiver-operating characteristic analysis was used to evaluate the performance of each protocol. Significance testing of the performance differences was performed via 95% confidence intervals and noninferiority tests. RESULTS The differences in the area under the receiver-operating characteristic curves were never more than 0.02 for the primary protocols. Noninferiority was demonstrated between these protocols with respect to standard input techniques (all images selected and feature averaging). CONCLUSION We have proved that our automated lesion classification scheme is robust and can perform well when subjected to variations in user input.