Official Proceedings - AIUM

American Institute of Ultrasound in Medicine Proceedings J Ultrasound Med 32(suppl):S1–S134, 20131538495 Evaluation of Cervical Cancer Detection With AcousticRadiation Force Impulse Ultrasound Imaging: PreliminaryResultsYijin Su,* Lianfang Du, Ying Wu, Juan Zhang, XuemeiZhang, Xiao Jia, Yingyu Cai, Yunhua Li, Jing Zhao, QianLiu School of Medicine, Shanghai Jiaotong University,Shanghai, ChinaObjectives—To evaluate the application of acoustic radiationforce impulse (ARFI) ultrasound imaging and its potential value for characterizingcervical cancer.Methods—ARFI of the uterine cervix was performed in 58 patientswith cervical cancer before operation. Elastographic imaging (EI),Virtual Touch tissue imaging (VTI), and Virtual Touch tissue quantification(VTQ; Siemens Medical Solutions, Mountain View, CA) wereused to qualitatively and quantitatively analyze the elasticity and hardnessof lesions.Results—Compared to the surrounding cervix tissue, the EI,VTI, and VTQ images between malignant lesions and surrounding normaltissues showed a significant difference (P < .001).Conclusions—ARFI of the uterine cervix may be an objectivemethod for assessment of softening of tissue. It has high sensitivity andspecificity in evaluating cervical cancer and therefore has good diagnosticvalue in clinical applications.Table 1. Comparison of VTQ Between Cervical Cancer and Normal TissueTissue Cases, n Mean, m/s SD, m/sCervical cancer 58 3.41 1.59Normal tissue 58 2.12 1.27Compared with normal tissue, VTQ of cervical cancer was higher (P < .001).1512000 Texture-Based Ovarian Tumor Characterization Using3-Dimensional UltrasoundU. Rajendra Acharya, 1 Stefano Guerriero, 2 Filippo Molinari, 3Luca Saba, 4 Jasjit Suri 5,6 *1 Electronics and Computer Engineering,Ngee Ann Polytechnic, Singapore; 2 Obstetrics andGynecology, University of Cagliari, Cagliari, Italy; 3 Electronicsand Telecommunications, Politecnico Torino, Torino, Italy;4Radiology, Azienda Ospedaliero Universitaria di Cagliari,Cagliari, Italy; 5 Global Biomedical Technologies, Roseville,California USA; 6 Biomedical Engineering, Idaho State University,Pocatello, Idaho USAObjectives—Among gynecologic malignancies, ovarian canceris the most frequent cause of death. Differential diagnosis is difficult,thus exposing patients to unneeded surgical treatment. We developed acomputer-aided diagnostic technique that uses ultrasound images of theovary to accurately classify benign and malignant ovarian tumors.Methods—Twenty women (age range, 29–74 years; mean ±SD, 49.5 ± 13.48 years), 11 premenopausal and 9 postmenopausal, wererecruited for this study. The histologic specimens revealed 10 malignantand 10 benign lesions. Prior to surgery, each patient was associated witha 3D volume of 100 images. Feature extraction was made by using localbinary pattern and laws texture energy. The data were used to train a classifierbased on a support vector machine (SVM) with 5 different kernels.The data set was randomly split into 10 equal folds, each fold containingthe same ratio of nonrepetitive samples from both classes (malignant andbenign). At each iteration, 9 folds were used to train the SVM, and 1 foldwas classified. We iterated the procedure 10 times to explore all the possiblecombinations. The averages of the performance metrics obtained inall the iterations are reported as the overall performance metrics.Results—The performance metrics obtained on training theSVM classifier of various kernel configurations using the 14 significantfeatures are reported in Table 1. All the kernels demonstrated excellentability in classifying the samples from both classes. The highest accuracyof 99.9% was registered by the radial basis function (RBF) kernel.Conclusions—The novelty of this study is the use of low-costultrasound images and a highly discriminating combination of simple texturefeatures fed to an SVM classifier to obtain the highest accuracy ofnearly 100% in ovarian tumor classification.Table 1. Classifier PerformanceSVM Accuracy, Sensitivity, Specificity, PPV,Kernel TP TN FP FN % % % %Linear 100 99 0 1 99.8 99.6 100 100Poly 1 100 100 0 0 99.8 99.6 100 100Poly 2 100 100 0 0 99.9 100 99.9 99.9Poly 3 100 100 0 0 99.8 99.9 99.8 99.8RBF 100 100 0 0 99.9 100 99.8 99.8FN indicates false-negative; FP, false-positive; PPV, positive predictive value;TN, true-negative; and TP, true-positive.1512001 Tumor Characterization From 3-Dimensional GynecologicUltrasound: A New Online Feature-Based ParadigmU. Rajendra Acharya, 1 Luca Saba, 2 Filippo Molinari, 3Stefano Guerriero, 4 Jasjit Suri 5,6 * 1Electronics and ComputerEngineering, Ngee Ann Polytechnic, Singapore; 2 Radiology,Azienda Ospedaliero Universitaria di Cagliari, Cagliari,Italy; 3 Electronics and Telecommunications, Politecnico Torino,Torino, Italy; 4 Obstetrics and Gynecology, University ofCagliari, Cagliari, Italy; 5 Global Biomedical Technologies,Roseville, California USA; 6 Biomedical Engineering, IdahoState University, Pocatello, Idaho USAObjectives—Among gynecologic malignancies, ovarian canceris the most frequent cause of death. Differential diagnosis is difficult, exposingpatients to unneeded surgical treatment. The objective of this workwas to develop a computer-aided diagnostic (CAD) technique that uses 3Dacquired ultrasound images of the ovary and image-mining algorithms tocharacterize and classify benign and malignant ovarian tumors.Methods—Twenty women (age range, 29–74 years; mean ±SD, 49.5 ± 13.5 years), 11 premenopausal and 9 postmenopausal, were recruitedfor this study. The histologic specimens revealed 10 had malignantand 10 had benign lesions. Prior to surgery, each patient wasassociated with a 3D volume of 100 images. We extracted features basedon the textural changes in the image and also features based on the higherorderspectra (HOS) information. The significant features were then selectedand used to train and evaluate the decision tree (DT) classifier.The data set was randomly split into 10 equal folds, each fold containingthe same ratio of nonrepetitive samples from both the classes (malignantand benign). At each iteration, 9 folds were used to train the DT, and 1fold was classified. We iterated the procedure 10 times to explore all thepossible combinations. The averages of the performance obtained in all theiterations are reported as the overall performance.Results—The simple DT classifier presented high accuracy of95.1%, sensitivity of 92.5%, and specificity of 97.7%. Full performanceis given in Table 1.Conclusions—A novel combination of 4 texture and HOSbased features that adequately quantify the nonlinear changes in both benignand malignant ovarian ultrasound images was used to develop classifiers.The CAD tool would be a more objective alternative to manualanalysis of ultrasound images, which might result in interobserver variations.The system can be installed as a stand-alone software application inthe physician’s office at no extra cost.S47