Quantitative study of ductal breast cancer--patient targeted prognosis: an exploration of case base reasoning

A hybrid diagnosis model for determining the types of the liver disease

The symptoms of liver diseases are not apparent in the initial stage, and the condition is usually quite serious when the symptoms are obvious enough. Most studies on liver disease diagnosis focus mainly on identifying the presence of liver disease in a patient. Not many diagnosis models have been developed to move beyond the detection of liver disease. The study accordingly aims to construct an intelligent liver diagnosis model which integrates artificial neural networks, analytic hierarchy process, and case-based reasoning methods to examine if patients suffer from liver disease and to determine the types of the liver disease.

An intelligent model for liver disease diagnosis

OBJECTIVES

Liver disease, the most common disease in Taiwan, is not easily discovered in its initial stage; early diagnosis of this leading cause of mortality is therefore highly important. The design of an effective diagnosis model is therefore an important issue in liver disease treatment. This study accordingly employs classification and regression tree (CART) and case-based reasoning (CBR) techniques to structure an intelligent diagnosis model aiming to provide a comprehensive analytic framework to raise the accuracy of liver disease diagnosis.

METHODS

Based on the advice and assistance of doctors and medical specialists of liver conditions, 510 outpatient visitors using ICD-9 (International Classification of Diseases, 9th Revision) codes at a medical center in Taiwan from 2005 to 2006 were selected as the cases in the data set for liver disease diagnosis. Data on 340 patients was utilized for the development of the model and on 170 patients utilized to perform comparative analysis of the models. This paper accordingly suggests an intelligent model for the diagnosis of liver diseases which integrates CART and CBR. The major steps in applying the model include: (1) adopting CART to diagnose whether a patient suffers from liver disease; (2) for patients diagnosed with liver disease in the first step, employing CBR to diagnose the types of liver diseases.

RESULTS

In the first phase, CART is used to extract rules from health examination data to show whether the patient suffers from liver disease. The results indicate that the CART rate of accuracy is 92.94%. In the second phase, CBR is developed to diagnose the type of liver disease, and the new case triggers the CBR system to retrieve the most similar case from the case base in order to support the treatment of liver disease. The new case is supported by a similarity ratio, and the CBR diagnostic accuracy rate is 90.00%. Actual implementation shows that the intelligent diagnosis model is capable of integrating CART and CBR techniques to examine liver diseases with considerable accuracy. The model can be used as a supporting system in making decisions regarding liver disease diagnosis and treatment. The rules extracted from CART are helpful to physicians in diagnosing liver diseases. CBR can retrieve the most similar case from the case base in order to solve a new liver disease problem and can be of great assistance to physicians in identifying the type of liver disease, reducing diagnostic errors and improving the quality and effectiveness of medical treatment.

Benign, preinvasive and invasive ductal breast lesions. A comparative study with quantitative techniques: morphometry, image- and flow cytometry

The histological distinction between ductal hyperplasia of the breast, atypical ductal hyperplasia and ductal carcinoma in situ is difficult and subjective. To gain a better understanding of these lesions, we performed a comparative study comprising 20 cases of ductal hyperplasia without atypia, 20 cases of ductal hyperplasia with atypia, and 30 cases of ductal carcinoma in situ (well-, moderately- and poorly-differentiated), using quantitative techniques: image cytometry analysis, morphometry and DNA analysis, and DNA flow cytometry. Our results confirm that the mean nuclear area and volume progressively decreased from ductal carcinoma in situ to ductal hyperplasia without atypia. The difference was significant (p < 0.05) when comparing hyperplasia without atypia with hyperplasia with atypia and hyperplasia with atypia with poorly differentiated ductal carcinoma in situ. Atypical ductal hyperplasia values were comparable to those of well-differentiated ductal carcinoma in situ. DNA image cytometry proved significant (p < 0.05) when comparing hyperplasia without atypia with hyperplasia with atypia and hyperplasia with atypia with moderately- and poorly-differentiated ductal carcinoma in situ. DNA flow cytometry revealed significant differences only in the distribution of the DNA ploidy patterns (p < 0.05) when comparing hyperplasia without atypia with moderately- and poorly-differentiated DCIS. A comparison of the results obtained by image and flow cytometry showed that in 90% of the cases the IC and FC values were coincident, whereas in the remaining cases the DNA index was aneuploid by IC and diploid by FC.