Differentiation of cancerous lesions in excised human breast specimens using multiband attenuation profiles from ultrasonic transmission tomography.
OBJECTIVE
This study examines the tissue differentiation capability of the recently developed high-resolution ultrasonic transmission tomography (HUTT) system in the context of differentiating between benign and malignant tissue types in mastectomy specimens.


METHODS
Eight mastectomy patients provided breast specimens with benign and malignant lesions. The specimens were scanned by the HUTT system with a pair of either 8- or 4-MHz transducers. Multiband HUTT images over the frequency range from 2 to 10 MHz provide characteristic profiles of frequency-dependent attenuation, termed "multiband profiles," at individual pixels. These features are classified through a novel algorithm of "segment-wise classification" that identifies the disjoint segments of various tissue types and subsequently classifies them into respective diagnostic categories using a measure of proximity to the respective multiband profile templates that have been previously obtained from reference data.


RESULTS
We preformed intraspecimen and interspecimen analyses of 108 slices from 8 mastectomy specimens for which "ground truth" was provided by pathology reports. The average performance indices for 2-way classification (malignant versus nonmalignant tissue) in these intraspecimen (interspecimen) specimen studies were found to be sensitivity of 81.9% (89.6%), specificity of 92.9% (92.1%), and accuracy of 89.2% (89.4%), whereas the indices for the 3-way classification were moderately lower.


CONCLUSIONS
The results have shown the potential of the HUTT technology for reliable differentiation of cancerous lesions from benign changes and normal tissue in mastectomy specimens using frequency-dependent ultrasound attenuation profiles.
Jeong
Jeong-Won
JW
Alfred E. Mann Institute for Biomedical Engineering, University of Southern California, 1042 W 36th Pl, DRB B21, Los Angeles, CA 90089, USA. jeongwon.jeong@gmail.com
Shin
Dae C
DC
Do
Syn-Ho
SH
Blanco
Cesar
C
Klipfel
Nancy E
NE
Holmes
Dennis R
DR
Hovanessian-Larsen
Linda J
LJ
Marmarelis
Vasilis Z
VZ
D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D066298:In Vitro Techniques; D008408:Mastectomy; D010363:Pattern Recognition, Automated; D019047:Phantoms, Imaging; D012680:Sensitivity and Specificity; D014463:Ultrasonography / Q000379:methods*