Neural network analysis of breast cancer from mammographic evaluation

Mammographic differentiation of benign lesions from malignancies is a difficult task. We developed an artificial neural network [ANN] as a diagnostic aid in mammography using radiographic features as input. A three-layered ANN was used to differentiate malignant from benign findings in a group of patients with proven breast lesions on the basis of morphological data extracted from conventional mammograms. Our database included 122 patient records on 14 qualitative variables. The database was randomly divided into training and validation samples including 82 and 40 patient records, respectively, to construct the ANN and validate its performance. Sensitivity, specificity, accuracy and receiver operating characteristic curve [ROC] analysis for this method and the radiologist were compared. Our results showed that the neural network model was able to correctly classify 30 out of 40 cases presented in the validation sample. Comparing the output with that of the radiologist, showed a reasonable diagnostic accuracy [75%], a moderate specificity [64%] and a relatively high sensitivity [89%]. A diagnostic aid was developed that accurately differentiates malignant from benign pattern using radiological features extracted from mammograms

Biomechanical behavior of the common femoral artery: Healthy and atherosclerotic artery

The elastic modulus of elastic arteries has been extensively studied, while studies of muscular arteries are sparse. In this study, the elastic modulus of right common femoral artery [RCFA] were estimated with the kinetic pressure changes and compared in healthy and atherosclerotic groups. The relative diameter and kinetic pressure changes of the RCFA were measured and estimated using echo-tracking sonography and Doppler spectrum analysis in 41 men [16 healthy and 25 atherosclerosis diseases]. Then the kinetic elastic modulus in the RCFA was estimated in two groups. The results show that, the arterial strain was significantly high in healthy group relative to atherosclerotic group. The estimated values of kinetic elastic modulus of RCFA in atherosclerotic artery are significantly high compared with healthy group [P-value < 0.05]. It is concluded that in RCFA with large content of smooth muscles, mechanical properties [Kinetic elastic modulus] are affected by progression of atherosclerosis

Improving the performance of neural network in differentiation of breast tumors using wavelet transformation on dynamic MRI

A computer aided diagnosis system was established using the wavelet transform and neural network to differentiate malignant from benign in a group of patients with histo-pathologically proved breast lesions based on the data derived independently from time-intensity profile. The performance of the artificial neural network [ANN] was evaluated using a database with 105 patients' records each of which consisted of 8 quantitative parameters mostly derived from time-intensity profile using wavelet transform. These findings were encoded as features for a three-layered neural network to predict the outcome of biopsy. The network was trained and tested using the jackknife method and its performance was then compared to that of the radiologists in terms of sensitivity, specificity and accuracy using receiver operating characteristic curve [ROC] analysis. The network was able to classify correctly the 84 original cases and yielded a comparable diagnostic accuracy [80%], compared to that of the radiologist [85%] by performing a constructive association between extracted quantitative data and corresponding pathological results [r=0.63, p<0.001]. An ANN supported by wavelet transform can be trained to differentiate malignant from benign breast tumors with a reasonable degree of accuracy

[Biomechanical behavior of the common femoral artery: Healthy and atherosclerotic artery]

Background: The elastic modulus of elastic arteries has been extensively studied, while studies of muscular arteries are sparse. In this study, the elastic modulus of right common femoral artery [RCFA] were estimated with the kinetic pressure changes and compared in healthy and atherosclerosis groups

Materials and Methods: The relative diameter and kinetic pressure changes of the RCFA were measured and estimated using echo-tracking sonography and Doppler spectrum analysis in 41 men [16 healthy and 25 atherosclerosis diseases]. Then the kinetic elastic modulus in the RCFA was estimated in two groups

Results: The results show that, the arterial strain was significantly high in healthy group relative to atherosclerosis group. The estimated values of kinetic elastic modulus of RCFA in atherosclerotic artery are significantly high compared with healthy group [P-value < 0.05]

Conclusions: It is concluded that in RCFA with large content of smooth muscles, mechanical properties [Kinetic elastic modulus] are affected by progression of atherosclerosis

Comparison of logistic regression and neural network models in predicting the outcome of biopsy in breast cancer from MRI findings

We designed an algorithmic model based on the logistic regression analysis and a non-algorithmic model based on the Artificial Neural Network [ANN]. Materials and methods: The ability of these models was compared together in clinical application to differentiate malignant from benign breast tumors in a study group of 161 patients' records. Each patient's record consisted of 6 subjective features extracted from MRI appearance. These findings were encoded as features for an ANN as well as a logistic regression model [LRM] to predict biopsy outcome. After both models had been trained perfectly on samples [n=100], the validation samples [n=61] were presented to the trained network as well as the established LRMs. Finally, the diagnostic performance of models were compared to that of the radiologist in terms of sensitivity, specificity and accuracy, using receiver operating characteristic curve [ROC] analysis. The average output of the ANN yielded a perfect sensitivity [98%] and high accuracy [90%] similar to that one of an expert radiologist [96% and 92%] while specificity was smaller than that [67% verses 80%]. The output of the LRM using significant features showed improvement in specificity from 60% for the LRM using all features to 93% for the reduced logistic regression model, keeping the accuracy around 90%. Results show that ANN and LRM prove the relationship between extracted morphological features and biopsy results. Using statistically significant variables reduced LRM outperformed of ANN with remarkable specificity while keeping high sensitivity is achieved

application of discriminant analysis in differentiation of fibroadenoma and ductal carcinoma of breast tissue using ultrasound velocity measurement

Ultrasound propagation velocity was measured experimentally in normal, fibroadenoma and ductal carcinoma breast tissues, in order to distinguish normal breast tissue from tumors. Materials and methods: In quantitative measurements of ultrasound velocity, 403 breast tissue images were selected, comprising 130 normal breast tissue, 130 fibroadenoma, and 143 ductal carcinoma tumors. The cases were implanted in breast tissue mimicking materials and ultrasonic images [A-mode] at 35°C were processed and evaluated. It was observed that ultrasound propagation velocity is an important factor for distinguishing in vitro specimens of fibroadenoma and ductal carcinoma from normal tissue [P-value<0.005]. Evaluation of ultrasound velocities showed that from normal breast tissue, fibroadenoma and ductal carcinoma, ultrasound velocity increases respectively. The discriminant functions of types of lesions, based on ultrasound velocity, have been formulated by discriminant analysis. The results indicate that probability of discrimination, sensitivity and specificity for tumors and normal breast tissues are 72, 60 and 100 percents at 35°C. With measuring ultrasound velocities, we can distinguish normal breast tissue of from ductal carcinoma and fibroadenoma masses [with the probability of 100%]. It is proposed that probably by measuring attenuation coefficient and ultrasound velocity on time, fibroadenoma and ductal carcinoma tumors can be differentiated well