In breast cancer preoperative determination of the tumor size is important for planning breast-conserving operations. In 100 patients with breast cancer, the preoperative tumor size was measured using clinical, mammographic and sonographic examinations and correlated with the results of a subsequent histological examination. Using a high-resolution real-time system, 98 tumors were visible. It was possible to detect not only early tumors under 1 cm in diameter, but also intraductal tumor components. This contributed greatly to the accuracy of the diagnosis. The sonographic measurement of tumor size demonstrated a correlation coefficient of 0.91 and was thus superior to mammography (0.79) and palpation (0.77). Measurement of the total tumor spread, including 39 multicentric lesions, showed an overestimation of 5% for the mammographic measurements and an overestimation of 4% for the sonographic measurements. Tumor extension was underestimated in 33% of the mammograms but in only 3% using ultrasound examination. The results, along with those of other studies, highlight the role of sonography in the diagnosis of breast cancer.
Color Doppler and Duplex measurements were obtained in 83 (42 benign, 41 malignant) ovarian tumors in postmenopausal patients. An ATL UM9/HDI was used. The following flow criteria were analyzed: lowest resistance index (RI) and pulsatility index (PI), total number of arteries and number of central arteries and the maximum, mean and sum of systolic, end-diastolic and time-averaged maximum velocities of all intratumoral vessels. In 98% of malignant and in 85% of benign lesions, vessels were detected. All flow criteria showed highly significant differences between benign and malignant tumors (p < 0.0001). However, there was a considerable overlap between benign and malignant tumors (e.g. the median of the lowest RI was 0.62 (range 0.26-1.0) for benign and 0.40 (0.22-0.66) for malignant tumors; the median of the maximum systolic velocity was 17.5 cm/s (range 5.2-61.5 cm/s) for benign and 47.05 cm/s (14.6-105.0 cm/s) for malignant tumors). Differentiation of malignant tumors by the lowest RI and PI, number of arteries and maximum of systolic flow velocities gave a sensitivity of 77-85%, specificity of 77-83% and accuracy of 80-84%. Differentiation was superior by calculation of the maximum end-diastolic velocities and by the summation of the systolic, end-diastolic and time-averaged maximum flow velocities: sensitivity 90-9.5% specificity 83-86% and accuracy 87-91%. This study confirms that a single measurement is not sufficient for an accurate differentiation of ovarian lesions and, besides the measurement of minimum RI and PI, the measurements of flow velocities as Doppler criteria play an important role.
Color Doppler allows flow detection in small tumor vessels. Due to the vascularity associated with the growth of malignancies, this method can be used to differentiate between benign and malignant breast lesions. In order to study the typical flow characteristics, we investigated multiple Doppler flow parameters. A UM9 HDI (ATL) was used with a linear transducer L 10-5. The number of tumor vessels and the mean, maximum and total flow velocity were measured in 325 benign and 133 malignant lesions and showed highly significant differences (p < 0.0001). Flow profiles (resistance index and systolic/diastolic frequency ratio) showed a large overlap and did not allow accurate tumor differentiation when mean and minimum values were analyzed. Surprisingly, the maximum resistance index and systolic/diastolic frequency ratio were significantly higher in carcinomas than in benign lesions, but the overlap of the values was wider than the flow velocity measurements. Using the vessel number and the total tumor vascularity, 90% of all lesions could be differentiated. A difficulty that we encountered was that a few cancers have very low flow values and some of the proliferative benign lesions can have increased flow.
With four binary criteria, a useful diagnostic formula for tumor differentiation was obtained. However, estimates for sensitivity, specificity, and accuracy may be too optimistic because they were derived from the same data that were already used for model selection.
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