Abstract
Mammography is the keystone of breast cancer screening. Yet high sensitivity is achieved at the cost of low specificity - only one-third of patients recalled will have breast cancer. Computer-aided detection (CAD) is a potentially valuable tool for assisting the breast radiologist to improve positive prediction values. However, to date, CAD has not reliably altered screening outcomes and the large proportion of false positives remains a drawback. We describe a novel method to improve CAD performance called Cartesian Genetic Programming (CGP); a machine-based learning algorithm, akin to genetic evolution.
Highlights
Breast magnetic resonance imaging (MRI) is being increasingly used in breast cancer to look for extent of disease, in high-risk screening and in the dense breast
We found that there is no statistical difference in accuracy in US axillary staging between invasive lobular carcinoma (ILC) and invasive ductal cancer (IDC)
The screening mammograms of 4,109 women enrolled in Predicting Risk of Cancer at Screening (PROCAS) were visually assessed independently by two experienced film readers, who recorded their estimates of percentage density on visual analogue scales (VAS)
Summary
Breast MRI is being increasingly used in breast cancer to look for extent of disease, in high-risk screening and in the dense breast. Conclusion: Percutaneous SLN localisation using combined radioisotope and ultrasound guidance is feasible Use of this method to guide needle biopsy of the axilla could increase the preoperative diagnosis rate for axillary lymph node metastases in breast cancer patients. Methods: Seven mammographic film readers re-assessed the density of 100 normal full-field digital mammogram cases for which they had made density estimates at least 1 year previously as part of the Predicting Risk of Cancer at Screening (PROCAS) study. We aimed to analyse radiological and pathological trends between screen-detected and interval breast cancers, and determine our screening lesion miss rate. Methods: All patients on our Breast Cancer Database diagnosed between April 2002 and March 2012, aged between 35 and 39 years at diagnosis, were identified. Best Practice Diagnostic Guidelines for Patients Presenting with Breast Symptoms
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