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The purpose of this research was to model the familial clustering of breast cancer and to provide an accurate risk estimate for individuals from the general population, based on their family history of breast and ovarian cancer. We constructed a genetic model as an extension of a model by Claus et
The presence of clustered microcalcifications is one of the earliest signs in breast cancer detection. Although there exist many studies broaching this problem, most of them are nonreproducible due to the use of proprietary image datasets. We use a known subset of the currently largest publicly
We have developed a multistage computer-aided diagnosis (CAD) scheme for the automated segmentation of suspicious microcalcification clusters in digital mammograms. The scheme consisted of three main processing steps. First, the breast region was segmented and its high-frequency content was enhanced
The frailty model, an extension of the proportional hazards model, is often used to model clustered survival data. However, some extension of the ordinary frailty model is required when there exist competing risks within a cluster. Under competing risks, the underlying processes affecting the events
Automated cell nucleus segmentation is the key to gain further insight into cell features and functionality which support computer-aided pathology in early diagnosis of diseases such as breast cancer and brain tumour. Despite considerable advances in automated segmentation, it still remains a
We propose a regularization based approach for case-adaptive classification in computer-aided diagnosis (CAD) of breast cancer. The goal is to improve the classification accuracy on a query case by making use of a set of similar cases retrieved from an existing library of known cases. In the
OBJECTIVE
The authors propose an image-retrieval based approach for case-adaptive classifier design in computer-aided diagnosis (CADx). The conventional approach in CADx is to first train a pattern-classifier based on a set of existing training samples and then apply this classifier to subsequent
This paper attempts to pinpoint different techniques for Pectoral Muscle (PM) segmentation, Microcalcification (MC) detection and classification in digital mammograms. The segmentation of PM and detection of MC and its classification are mostly based on image processing and data mining Humans are daily exposed to background radiation and various sources of oxidative stress. My research has focused in the last 12 years on the effects of ionizing radiation on DNA, which is considered as the key target of radiation in the cell. Ionizing radiation and endogenous cellular oxidative
Circulating tumor cells (CTCs) are shed from solid cancers in the form of single or clustered cells, and the latter display an extraordinary ability to initiate metastasis. Yet, the biological phenomena that trigger the shedding of CTC clusters from a primary cancerous lesion are poorly understood.
Breast cancer is the most common type of cancer among women in the western world. While mammography is regarded as the most effective tool for the detection and diagnosis of breast cancer, the interpretation of mammograms is a difficult and error-prone task. Hence, computer aids have been developed
Clustered microcalcifications (MC) in mammograms can be an important early sign of breast cancer in women. Their accurate detection is important in computer-aided detection (CADe). In this paper, we propose the use of a recently developed machine-learning technique--relevance vector machine
UNASSIGNED
Complex clusters of rearrangements in cancer genomes are a challenge to interpret. Some are clear amplifications of driver oncogenes but others are less well understood. Detailed analysis of rearrangements within these complex clusters could reveal new insights into selection, and
OBJECTIVE
In the past two decades, nonpalpable breast cancer incidence has greatly increased by routine screening mammography. Microcalcifications are the most frequent radiological feature.
METHODS
Between 1964 and 1994, 789 breast cancers revealed by clustered microcalcifications without palpable