Breast lesion sizing by B-mode imaging and sonoelastography in comparison to histopathological sizing--a prospective study.

PURPOSE: This prospective study evaluates whether sonoelastography can improve B-mode ultrasonographic sizing of breast tumors. Precise measuring is important for effective therapy planning for breast cancer patients. MATERIALS AND METHODS: The size of 100 surgically excised breast lesions (92 patients: 77 malignant, 23 benign) was compared to preoperative measurements. Lesions were imaged with both ultrasonographic techniques in identical planes. The largest sizes measured with each modality were compared to the largest histopathological measurements. The interobserver variability was also computed from measurements made by two examiners assessing identical planes. RESULTS: Both ultrasonographic measuring techniques underestimate lesion size. The sonoelastography measurements were within ± 5 mm of the histological size in 70.1 % of malignant lesions, and the B-mode measurements in 57.1 % of cases. Sonoelastography leads to more accurate measurements of 13.0 % of cases (statistically not significant). A total of 22 lesions were also imaged by a second examiner. Sonoelastography had 27.3 % less interobserver variability (examiners agreed in 36.4 % of sonoelastography and in 9.1 % of B-mode results). CONCLUSION: In this study there is no significant advantage of sonoelastography, although a tendency is apparent. The low interobserver variability also favors sonoelastography for preoperative diagnostics, since it may be less dependent on the observer than conventional B-mode imaging. The results of this prospective study require validation in a prospective multicenter study with larger case numbers.

Sonoelastography in the diagnosis of malignant and benign breast lesions: initial clinical experiences.

PURPOSE: This prospective study aimed to compare sonoelastography, B-mode ultrasonography, and mammography in terms of their ability to distinguish benign from malignant breast lesions. We also assessed how the diagnostic value of sonoelastography differs between palpable and clinically occult lesions. MATERIALS AND METHODS: Evaluation revealed a total of 97 lesions (66 benign; 31 malignant) without histological confirmation at the time of the initial examination. The sensitivity, specificity, positive (PPV) and negative predictive value (NPV) as well as efficiency were calculated. These parameters were separately assessed for palpable lesions and for non-palpable lesions. We subsequently compared these results. RESULTS: Sonography had a sensitivity of 97% and a specificity of 82% (PPV: 71 %, NPV: 98%, efficiency: 87%). For mammography, the respective figures were 84% and 89% (PPV: 79%, NPV: 92%, efficiency: 88%). Sonoelastography had a sensitivity of 71% and a specificity of 48% (PPV: 39%, NPV: 78%, efficiency: 56%). The combination of sonography and sonoelastography yielded a sensitivity of 100% and a specificity of 38% (PPV: 43%, NPV: 100%, efficiency: 58%). The sensitivity and specificity were not statistically different between the groups of palpable and non-palpable lesions. CONCLUSION: Sonoelastography is easily performed and not very time-consuming. Used by itself, the method is not more efficacious than alternative techniques. When used in conjunction with B-mode ultrasonography, the latter's sensitivity was increased, albeit at the expense of specificity.

Bioimpedance-based identification of malnutrition using fuzzy logic.

Protein-energy malnutrition reduces the quality of life, lengthens the time in hospital and dramatically increases mortality. Currently there is no simple and objective method available for assessing nutritional status and identifying malnutrition. The aim of this work is to develop a novel assistance system that supports the physician in the assessment of the nutritional status. Therefore, three subject groups were investigated: the first group consisted of 688 healthy subjects. Two additional groups consisted of 707 patients: 94 patients with primary diseases that are known to cause malnutrition, and 613 patients from a hospital admission screening. In all subjects bioimpedance spectroscopy measurements were performed, and the body composition was calculated. Additionally, in all patients the nutritional status was assessed by the subjective global assessment score. These data are used for the development and validation of the assistance system. The basic idea of the system is that nutritional status is reflected by body composition. Hence, features of the nutritional status, based on the body composition, are determined and compared with reference ranges, derived from healthy subjects' data. The differences are evaluated by a fuzzy logic system or a decision tree in order to identify malnourished patients. The novel assistance system allows the identification of malnourished patients, and it can be applied for screening and monitoring of the nutritional status of hospital patients.

Online analysis of intracranial pressure waves.

Brain pressure waves, particularly B-waves, as they were described by Lundberg contain valuable diagnostic information about the patient's status. So far, this information cannot be used on a daily routine basis because manual analysis of the recorded ICP data is very time consuming. Furthermore, the analysis is only retrospective, after incidents of interest occurred. To overcome these drawbacks, an automated B-wave detection system has been developed. Using a direct spectral estimation method (so called ARMA approach) the most significant spectral components and their amplitudes in the ICP signal are identified. The frequency with the biggest amplitude inside the B-wave band is a reliable indicator of B-wave activity and can be displayed on-line on a bedside computer.