Ultrasound tissue characterization of breast biopsy specimens
| Autor: | Scott K. Holland, C. L. Mortensen, P. D. Edmonds, R. H. Lee, F. A. Marzoni, J. R. Hill, A. D. Valdes, P. Schattner, Joel F. Jensen |
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| Rok vydání: | 1991 |
| Předmět: |
Breast biopsy
Adult medicine.medical_specialty Pathology Adolescent Biopsy Transducers Breast Neoplasms Malignancy 01 natural sciences 030218 nuclear medicine & medical imaging Specimen Handling 03 medical and health sciences Breast Diseases 0302 clinical medicine 0103 physical sciences medicine Humans Radiology Nuclear Medicine and imaging Breast Fibrocystic Breast Disease 010301 acoustics Mathematics Aged Hyperplasia Radiological and Ultrasound Technology medicine.diagnostic_test business.industry Ultrasound Discriminant Analysis Middle Aged medicine.disease Linear discriminant analysis Fibrosis Regression Carcinoma Intraductal Noninfiltrating Test set Calibration Radiology False positive rate Ultrasonography Mammary business Adenofibroma Jackknife resampling |
| Zdroj: | Ultrasonic imaging. 13(2) |
| ISSN: | 0161-7346 |
| Popis: | Results of measurements of ultrasound speed and absorption coefficients in the range 3 to 8 MHz in breast tissues at 37 C are reported and analyzed in attempts to identify a set of ultrasound parameters capable of discriminating normal, benign, and malignant tissues. We analyzed 118 tissue regions, comprising 47 normal, 55 benign, and 16 malignant by straightline fitting of frequency dependence of attenuation. Data for ten additional regions, for a total of 128, became available and were added to the cohort when we subsequently fitted quadratic curves. Sound speed consistently emerged as the variable with greatest discriminating power, particularly for separating normal from benign and malignant tissue. Great difficulty was encountered in discriminating benign from malignant, even when the jackknife technique was used. More success was found with classification and regression trees (CART), although results were sensitive to assigned misclassification costs. Best results from straigh-line fits were obtained when discriminating malignant from combined normal/benign data after randomly assigning 75 percent of the data to the learning set and 25 percent to the test set. Then, 23 out of 25 normal/benign and 4 out of 4 malignant cases in the test set were correctly classified. With quadratic fitting, best results were obtained in the three-class case—the false positive rate for malignancy was reduced to zero in the learning ( 0 31 ) and test ( 0 10 ) sets. Nevertheless, the false negative rate increased to 13 out of 31 (42 percent) in the learning set, while attaining zero ( 0 4 ) in the test set. |
| Databáze: | OpenAIRE |
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