KEYWORDS: Breast cancer, Data mining, Knowledge management, Data modeling, Medical imaging, Medicine, Cancer, Data processing, Data acquisition, Mining, Interfaces, Databases, Image classification, Picture Archiving and Communication System
The paper presents a Case-Based Reasoning Tool for Breast Cancer Knowledge Management to improve breast cancer
screening. To develop this tool, we combine both concepts and techniques of Case-Based Reasoning (CBR) and Data
Mining (DM). Physicians and radiologists ground their diagnosis on their expertise (past experience) based on clinical
cases. Case-Based Reasoning is the process of solving new problems based on the solutions of similar past problems and
structured as cases. CBR is suitable for medical use. On the other hand, existing traditional hospital information systems
(HIS), Radiological Information Systems (RIS) and Picture Archiving Information Systems (PACS) don’t allow
managing efficiently medical information because of its complexity and heterogeneity. Data Mining is the process of
mining information from a data set and transform it into an understandable structure for further use. Combining CBR to
Data Mining techniques will facilitate diagnosis and decision-making of medical experts.
The paper provides a description of methodologies and techniques required for a Training System Development in the field of Senology (TSDS), based n the exploitation of senologic images (primarily mammograms but also echographic images or MRI) and their related clinical files. The aim of such a system is to help breast cancer screening in education. This system will help assist junior radiologists in routine critical use. Development of such a TSDS requires understanding of users’ needs (expertise and pedagogy), model design and system implementation. Specifications have been derived from the experience of the senologists from the Department of Radiology of the Necker Hospital (Paris, France), Department where the training system will be implemented. To be compliant with commercial systems for digital and CAD mammograms, terminological systems used by the TSDS to describe and index data must be based on DICOM and BI-RADS dictionaries. A detailed discussion of the choice of such a method and technique is provided and their respective contribution is described.
KEYWORDS: Databases, Mammography, Medical imaging, Data modeling, Radiology, Connectors, Breast cancer, Analytical research, Data archive systems, Data storage
In this paper, we present our approach in order to implement a Medical Image Database (MIDB) for archiving mammograms and their related information in the Department of Radiology of the Necker Hospital (Paris). The aim of such a database is to help breast cancer screening in clinics, research and education. As implementation of such a MIDB requires the understanding of users' needs, we have analyzed requirements by using the Crews-l'Ecritoire (Cooperative REquirements With Scenarios) approach developed in our laboratory. This approach is based on the 'Requirement Engineering' concept. It helps understanding users' needs using a semi-automatic analysis of textual scenarios, i.e. scenarios written in natural language. This approach mixes concepts of goals and of scenarios into the notion of 'Requirement Chunk'. Authored scenarios and goal discovery are guided by rules, which lead to a structured network of scenarios. Our analysis results in 58 Requirements Chunks gathering 72 authored scenarios and 300 goals which represent MIDB services requested by radiologists in the course of their daily practice.
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