Predicting invasive breast cancer versus DCIS in different age groups.

BackgroundIncreasing focus on potentially unnecessary diagnosis and treatment of certain breast cancers prompted our investigation of whether clinical and mammographic features predictive of invasive breast cancer versus ductal carcinoma in situ (DCIS) differ by age.MethodsWe analyzed 1,475 malignant breast biopsies, 1,063 invasive and 412 DCIS, from 35,871 prospectively collected consecutive diagnostic mammograms interpreted at University of California, San Francisco between 1/6/1997 and 6/29/2007. We constructed three logistic regression models to predict the probability of invasive cancer versus DCIS for the following groups: women ≥ 65 (older group), women 50-64 (middle age group), and women < 50 (younger group). We identified significant predictors and measured the performance in all models using area under the receiver operating characteristic curve (AUC).ResultsThe models for older and the middle age groups performed significantly better than the model for younger group (AUC = 0.848 vs, 0.778; p = 0.049 and AUC = 0.851 vs, 0.778; p = 0.022, respectively). Palpability and principal mammographic finding were significant predictors in distinguishing invasive from DCIS in all age groups. Family history of breast cancer, mass shape and mass margins were significant positive predictors of invasive cancer in the older group whereas calcification distribution was a negative predictor of invasive cancer (i.e. predicted DCIS). In the middle age group--mass margins, and in the younger group--mass size were positive predictors of invasive cancer.ConclusionsClinical and mammographic finding features predict invasive breast cancer versus DCIS better in older women than younger women. Specific predictive variables differ based on age

Can high-frequency ultrasound predict metastatic lymph nodes in patients with invasive breast cancer?

Aim To determine whether high-frequency ultrasound can predict the presence of metastatic axillary lymph nodes, with a high specificity and positive predictive value, in patients with invasive breast cancer. The clinical aim is to identify patients with axillary disease requiring surgery who would not normally, on clinical grounds, have an axillary dissection, so potentially improving outcome and survival rates. Materials and methods The ipsilateral and contralateral axillae of 42 consecutive patients with invasive breast cancer were scanned prior to treatment using a B-mode frequency of 13 MHz and a Power Doppler frequency of 7 MHz. The presence or absence of an echogenic centre for each lymph node detected was recorded, and measurements were also taken to determine the L/S ratio and the widest and narrowest part of the cortex. Power Doppler was also used to determine vascularity. The contralateral axilla was used as a control for each patient. Results In this study of patients with invasive breast cancer, ipsilateral lymph nodes with a cortical bulge ≥3 mm and/or at least two lymph nodes with absent echogenic centres indicated the presence of metastatic axillary lymph nodes (10 patients). The sensitivity and specificity were 52.6% and 100%, respectively, positive and negative predictive values were 100% and 71.9%, respectively, the P value was 0.001 and the Kappa score was 0.55.\ud Conclusion This would indicate that high-frequency ultrasound can be used to accurately predict metastatic lymph nodes in a proportion of patients with invasive breast cancer, which may alter patient management

How are false negative cases perceived by mammographers? Which abnormalities are misinterpreted and which go undetected?

A radiographic ‘false negative’ or a case which has been ‘missed’ can be categorised in terms of errors of search (where gaze does not fall upon the abnormality); detection (a perceptual error where the abnormality may be physically ‘seen’ but remains undetected) and misinterpretation (a perceptual error whereby an abnormality, although detected, is not deemed worthy of further assessment). This study aims to investigate perceptual errors in mammographic film-reading and will focus on the later of the two error types, namely errors of misinterpretation and errors of non-detection. Previous research has shown, on a self-assessment scheme of recent and difficult breast-screening cases, that certain feature types are susceptible to errors of misinterpretation and others to errors of non-detection. This self assessment scheme, ‘PERFORMS’ (Personal Performance in Mammographic Screening), is undertaken by the majority (at present over 90%) of breast-screening mammographers in the UK Breast Screening Programme. The scheme is completed biannually and confidentially and participants receive immediate and detailed feedback on their performance. Feedback from the scheme includes information detailing their false negative decisions including case classifications (benign or malignant), feature type (masses, calcification, asymmetries, architectural distortions and others) and case perception error (percentage of misinterpretation and percentage of non-detection). Results from a recent round of PERFORMS (n=506), revealed that certain feature types had significantly higher percentages of error overall (including architectural distortion and asymmetries), and that these feature types also showed significant differences for error type. Implications for real-life screening practice were explored using real-life self-reported data on years of screening experience

COMPUTER AIDED SYSTEM FOR BREAST CANCER DIAGNOSIS USING CURVELET TRANSFORM

Breast cancer is a leading cause of death among women worldwide. Early detection is the key for improving breast cancer prognosis. Digital mammography remains one of the most suitable tools for early detection of breast cancer. Hence, there are strong needs for the development of computer aided diagnosis (CAD) systems which have the capability to help radiologists in decision making. The main goal is to increase the diagnostic accuracy rate. In this thesis we developed a computer aided system for the diagnosis and detection of breast cancer using curvelet transform. Curvelet is a multiscale transform which possess directionality and anisotropy, and it breaks some inherent limitations of wavelet in representing edges in images. We started this study by developing a diagnosis system. Five feature extraction methods were developed with curvelet and wavelet coefficients to differentiate between different breast cancer classes. The results with curvelet and wavelet were compared. The experimental results show a high performance of the proposed methods and classification accuracy rate achieved 97.30%. The thesis then provides an automatic system for breast cancer detection. An automatic thresholding algorithm was used to separate the area composed of the breast and the pectoral muscle from the background of the image. Subsequently, a region growing algorithm was used to locate the pectoral muscle and suppress it from the breast. Then, the work concentrates on the segmentation of region of interest (ROI). Two methods are suggested to accomplish the segmentation stage: an adaptive thresholding method and a pattern matching method. Once the ROI has been identified, an automatic cropping is performed to extract it from the original mammogram. Subsequently, the suggested feature extraction methods were applied to the segmented ROIs. Finally, the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classifiers were used to determine whether the region is abnormal or normal. At this level, the study focuses on two abnormality types (mammographic masses and architectural distortion). Experimental results show that the introduced methods have very high detection accuracies. The effectiveness of the proposed methods has been tested with Mammographic Image Analysis Society (MIAS) dataset. Throughout the thesis all proposed methods and algorithms have been applied with both curvelet and wavelet for comparison and statistical tests were also performed. The overall results show that curvelet transform performs better than wavelet and the difference is statistically significant

Recall Rates in Screening Mammography: Variability in Performance and Decisions

Having a high recall rate may increase the probability of cancer being detected earlier, however it also has been related to increased false positive decisions, causing significant psychological and economical costs for both screened women and the mammography screening service. Therefore, the purpose of this thesis is to explore the impact of various recall rates on breast radiologists’ performance in a laboratory setting. Methods This study was designed to encompass two aspects 1) the effect of setting varying recall rates on the performance of breast radiologists in screening mammography 2) types of mammographic appearances of breast cancer are more likely to be missed at different recall rates. Five Australian breast radiologists were recruited to read one single test set of 200 mammographic cases (180 normal and 20 abnormal cases) over three different recall rate conditions: free recall, 15% and 10%. These radiologists were tasked with marking the location of suspicious lesions and providing a confidence. Results A significant decrease in radiologists’ performance was observed when reading at lower recall rates, with lower sensitivity (P=0.002), case location sensitivity (P=0.002) and ROC AUC (P=0.003). Reading at a lower recall rate had a significant increase in specificity (P=0.002). The second study of this thesis showed that breast radiologists demonstrated lower sensitivity and receiver ROC AUC for non-specific density (NSD) (P=0.04 and P=0.03 respectively) and mixed features (P=0.01 and P=0.04 respectively) when reading at 15% and 10% recall rates. No significant change was observed on cancer characterized with stellate masses (P=0.18 and P=0.54 respectively) and architectural distortion (P=1.00 and P=0.37 respectively). Conclusion Reducing the number of recalled cases to 10% significantly reduced breast radiologists’ performance. Stellate masses were likely to be recalled (90.0%) while NSDs were likely to be missed (45.6%) at reduced recall rates

Mammography

In this volume, the topics are constructed from a variety of contents: the bases of mammography systems, optimization of screening mammography with reference to evidence-based research, new technologies of image acquisition and its surrounding systems, and case reports with reference to up-to-date multimodality images of breast cancer. Mammography has been lagged in the transition to digital imaging systems because of the necessity of high resolution for diagnosis. However, in the past ten years, technical improvement has resolved the difficulties and boosted new diagnostic systems. We hope that the reader will learn the essentials of mammography and will be forward-looking for the new technologies. We want to express our sincere gratitude and appreciation?to all the co-authors who have contributed their work to this volume