Computer-aided diagnosis of soft tissue tumors on high-resolution ultrasonography with geometrical and morphological features.
OBJECTIVE
The aim of this study was to develop a semi-automated computer-aided diagnosis (CAD) system based on high-resolution ultrasonography for classifying benign and malignant soft tissue tumors (STTs).


METHODS
One hundred seven patients with STTs (70 benign and 37 malignant) were enrolled, and regions of interest were manually delineated for analysis. Sixteen tumor shape features, including five geometric features and 11 morphologic features (six old and five new normalized radial length [NRL] features) were individually evaluated using Student's t test and the area under the receiver-operating characteristic curve (A(z)). Then linear discriminant analysis with stepwise feature selection was used to construct a semi-automated CAD system with old NRL features, new NRL features, and all features combined. Additionally, two experienced radiologists participated in malignancy grading of tumors. To investigate the associations among CAD results, pathologic results, and radiologists' rankings, Spearman's rank correlation coefficient was used in the statistical analysis.


RESULTS
The results showed that 11 features had P values < .05, and five of the proposed features were significant. The optimal CAD system achieved accuracy of 87.9%, sensitivity of 89.2%, specificity of 87.1%, and an A(z) value of 0.93. Correlation between pathologic results and radiologists' rankings was obtained (radiologist A: r=0.62, P < .01; radiologist B: r=0.61, P < .01). In addition, a higher correlation between pathologic results and CAD results (r=0.73, P < .01) was demonstrated.


CONCLUSIONS
This semi-automated CAD method based on tumor shape features can successfully distinguish between benign and malignant STTs. It can also provide a second opinion to ultrasound for the diagnosis of STTs.
Institute of Biomedical Engineering, National Yang Ming University, No. 155 Sec. 2 Linong Street, Beitou District, Taipei City 112, Taiwan.
Chih-Yen
CY
Chen
Hong-Jen
HJ
Chiou
Yi-Hong
YH
Chou
See-Ying
SY
Chiou
Hsin-Kai
HK
Wang
Szu-Yuan
SY
Chou
Huihua Kenny
HK
Chiang
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001185:Artificial Intelligence; D002648:Child; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012983:Soft Tissue Neoplasms / Q000000981:diagnostic imaging*; D014463:Ultrasonography; D055815:Young Adult