A new near-term breast cancer risk prediction scheme based on the quantitative analysis of ipsilateral view mammograms.

PURPOSE: To help improve efficacy of screening mammography and eventually establish an optimal personalized screening paradigm, this study aimed to develop and test a new near-term breast cancer risk prediction scheme based on the quantitative analysis of ipsilateral view of the negative screening mammograms. METHODS: The dataset includes digital mammograms acquired from 392 women with two sequential full-field digital mammography examinations. All the first ("prior") sets of mammograms were interpreted as negative during the original reading. In the sequential ("current") screening, 202 were proved positive and 190 remained negative/benign. For each pair of the "prior" ipsilateral mammograms, we adaptively fused the image features computed from two views. Using four different types of image features, we built four elastic net support vector machine (EnSVM) based classifiers. Then, the initial prediction scores form the 4 EnSVMs were combined to build a final artificial neural network (ANN) classifier that produces the final risk prediction score. The performance of the new scheme was evaluated by using a 10-fold cross-validation method and an assessment index of the area under the receiver operating characteristic curve (AUC). RESULTS: A total number of 466 features were initially extracted from each pair of ipsilateral mammograms. Among them, 51 were selected to build the EnSVM based prediction scheme. The AUC = 0.737 ±â€¯0.052 was yielded using the new scheme. Applying an optimal operating threshold, the prediction sensitivity was 60.4% (122 of 202) and the specificity was 79.0% (150 of 190). CONCLUSION: The study results showed moderately high positive association between computed risk scores using the "prior" negative mammograms and the actual outcome of the image-detectable breast cancers in the next subsequent screening examinations. The study also demonstrated that quantitative analysis of the ipsilateral views of the mammograms enabled to provide useful information in predicting near-term breast cancer risk.

Using multiscale texture and density features for near-term breast cancer risk analysis.

PURPOSE: To help improve efficacy of screening mammography by eventually establishing a new optimal personalized screening paradigm, the authors investigated the potential of using the quantitative multiscale texture and density feature analysis of digital mammograms to predict near-term breast cancer risk. METHODS: The authors' dataset includes digital mammograms acquired from 340 women. Among them, 141 were positive and 199 were negative/benign cases. The negative digital mammograms acquired from the "prior" screening examinations were used in the study. Based on the intensity value distributions, five subregions at different scales were extracted from each mammogram. Five groups of features, including density and texture features, were developed and calculated on every one of the subregions. Sequential forward floating selection was used to search for the effective combinations. Using the selected features, a support vector machine (SVM) was optimized using a tenfold validation method to predict the risk of each woman having image-detectable cancer in the next sequential mammography screening. The area under the receiver operating characteristic curve (AUC) was used as the performance assessment index. RESULTS: From a total number of 765 features computed from multiscale subregions, an optimal feature set of 12 features was selected. Applying this feature set, a SVM classifier yielded performance of AUC = 0.729 ± 0.021. The positive predictive value was 0.657 (92 of 140) and the negative predictive value was 0.755 (151 of 200). CONCLUSIONS: The study results demonstrated a moderately high positive association between risk prediction scores generated by the quantitative multiscale mammographic image feature analysis and the actual risk of a woman having an image-detectable breast cancer in the next subsequent examinations.

Prediction of near-term risk of developing breast cancer using computerized features from bilateral mammograms.

Asymmetry of bilateral mammographic tissue density and patterns is a potentially strong indicator of having or developing breast abnormalities or early cancers. The purpose of this study is to design and test the global asymmetry features from bilateral mammograms to predict the near-term risk of women developing detectable high risk breast lesions or cancer in the next sequential screening mammography examination. The image dataset includes mammograms acquired from 90 women who underwent routine screening examinations, all interpreted as negative and not recalled by the radiologists during the original screening procedures. A computerized breast cancer risk analysis scheme using four image processing modules, including image preprocessing, suspicious region segmentation, image feature extraction, and classification was designed to detect and compute image feature asymmetry between the left and right breasts imaged on the mammograms. The highest computed area under curve (AUC) is 0.754±0.024 when applying the new computerized aided diagnosis (CAD) scheme to our testing dataset. The positive predictive value and the negative predictive value were 0.58 and 0.80, respectively.

Seroma formation following breast cancer surgery.

A seroma is the most frequent complication of breast cancer surgery, the etiology of which remains obscure. We reviewed our data to determine the factors related to the incidence of seroma formation in our patients. A retrospective analysis of the records of 359 consecutive patients (334 Hispanic; 93%) who underwent primary surgical therapy from January 1, 1996 to December 31, 2000, with either modified radical mastectomy (MRM) or wide local excision (WLE) and axillary lymph node dissection (ALND) was performed. In all cases, removal of the breast was performed using electrocoagulation, and sharp dissection was used in the axilla. One-eighth inch closed suction round drains were used. Early arm motion was encouraged. The seroma rate was compared to the age of the patient, the presence and number of positive axillary lymph nodes, the total number of axillary lymph nodes removed, tumor size, weight of the patient, the use of neoadjuvant chemotherapy, and the type of surgery performed. The overall seroma rate was 15.8%. Seromas occurred in 19.9% of patients undergoing MRM and in 9.2% of patients undergoing breast-conserving surgery (p=0.01). The seroma rate was not influenced by any other tested variables. All seromas were easily managed with aspiration and pressure; this technical maneuver allowed seroma resolution in all patients except one following one to six aspirations. A seroma did not delay initiation of chemotherapy. No patient developed a capsule requiring excision. In our experience, a seroma is a "necessary evil;" it will occur unpredictably in a predictable number of patients.