3-D color Doppler image quantification of breast masses

Carson, Paul L.; Fowlkes, J. Brian; Roubidoux, Marilyn A.; Moskalik, Aaron; Govil, Anurag; Normolle, Daniel P.; LeCarpentier, Gerald L.; Nattakom, Shiney; Helvie, Mark A.; Rubin, Jonathan M.

doi:10.1016/s0301-5629(98)00055-6

Cited by 36 publications

(24 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The 3D US data were acquired with an experimental system that was previously developed and tested at our institution (16,17). The 3D system consisted of a commercially available Logiq 700 (GE Medical Systems, Milwaukee, Wis) US scanner with an M12 linear-array transducer, a mechanical transducer-guiding system, and a computer workstation.…”

Section: D Us Imagingmentioning

confidence: 99%

Malignant and Benign Breast Masses on 3D US Volumetric Images: Effect of Computer-aided Diagnosis on Radiologist Accuracy

Sahiner¹,

Chan²,

Roubidoux³

et al. 2007

Radiology

Self Cite

125

View full text Add to dashboard Cite

Purpose-To retrospectively investigate the effect of using a custom-designed computer classifier on radiologists' sensitivity and specificity for discriminating malignant masses from benign masses on three-dimensional (3D) volumetric ultrasonographic (US) images, with histologic analysis serving as the reference standard.Materials and Methods-Informed consent and institutional review board approval were obtained. Our data set contained 3D US volumetric images obtained in 101 women (average age, 51 years; age range, 25-86 years) with 101 biopsy-proved breast masses (45 benign, 56 malignant). A computer algorithm was designed to automatically delineate mass boundaries and extract features on the basis of segmented mass shapes and margins. A computer classifier was used to merge features into a malignancy score. Five experienced radiologists participated as readers. Each radiologist read cases first without computer-aided diagnosis (CAD) and immediately thereafter with CAD. Observers' malignancy rating data were analyzed with the receiver operating characteristic (ROC) curve.Results-Without CAD, the five radiologists had an average area under the ROC curve (A z ) of 0.83 (range, 0.81-0.87). With CAD, the average A z increased significantly (P = .006) to 0.90 (range, 0.86-0.93). When a 2% likelihood of malignancy was used as the threshold for biopsy recommendation, the average sensitivity of radiologists increased from 96% to 98% with CAD, while the average specificity for this data set decreased from 22% to 19%. If a biopsy recommendation threshold could be chosen such that sensitivity would be maintained at 96%, specificity would increase to 45% with CAD.Conclusion-Use of a computer algorithm may improve radiologists' accuracy in distinguishing malignant from benign breast masses on 3D US volumetric images. © RSNA, 2007 Address correspondence to B.S. (berki@umich.edu).. Author contributions: Guarantors of integrity of entire study, B.S., H.P.C.; study concepts/study design or data acquisition or data analysis/interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; manuscript final version approval, all authors; literature research, B.S., H.P.C., M.A.R., L.M.H.; clinical studies, M.A.R., C.P.; experimental studies, B.S., M.A.R., M.A.H., C.P., J.B., A.V.N., C.B.; statistical analysis, B.S., H.P.C., L.M.H.; and manuscript editing, B.S., H.P.C., M.A.R., M.A.H.Supplemental material: http://radiology.rsnajnls.org/cgi/content/full/2423051464/DC1 Authors stated no financial relationship to disclose. NIH Public Access Author ManuscriptRadiology. Author manuscript; available in PMC 2010 January 2. NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author ManuscriptIn current clinical practice, the rate of positive biopsy results for breast cancer is about 15%-30% (1-3). To reduce patient anxiety and morbidity and to decrease health care costs, it is desirable to reduce the number of benign biopsy results without missing malignant lesions.Resul...

show abstract

Section: D Us Imagingmentioning

confidence: 99%

Malignant and Benign Breast Masses on 3D US Volumetric Images: Effect of Computer-aided Diagnosis on Radiologist Accuracy

Sahiner¹,

Chan²,

Roubidoux³

et al. 2007

Radiology

Self Cite

125

View full text Add to dashboard Cite

show abstract

“…After the tumor region was segmented, the useful features in previous study [8,10,11,34] were applied and incorporated into the tumor region information to extract the vascular properties, tumor-vessel features. Hence, the vessel features related to the tumors were extracted to analyze the tumor, such as blood vessel inside the tumor region, and blood vessel outside the tumor region, and in our study they are useful criteria for differentiating the tumors as malignant or benign.…”

Section: Discussionmentioning

confidence: 99%

Diagnosis of Solid Breast Tumors Using Vessel Analysis in Three-Dimensional Power Doppler Ultrasound Images

et al. 2013

View full text Add to dashboard Cite

This study aims to evaluate whether the distribution of vessels inside and adjacent to tumor region at threedimensional (3-D) power Doppler ultrasonography (US) can be used for the differentiation of benign and malignant breast tumors. 3-D power Doppler US images of 113 solid breast masses (60 benign and 53 malignant) were used in this study. Blood vessels within and adjacent to tumor were estimated individually in 3-D power Doppler US images for differential diagnosis. Six features including volume of vessels, vascularity index, volume of tumor, vascularity index in tumor, vascularity index in normal tissue, and vascularity index in surrounding region of tumor within 2 cm were evaluated. Neural network was then used to classify tumors by using these vascular features. The receiver operating characteristic (ROC) curve analysis and Student's t test were used to estimate the performance. All the six proposed vascular features are statistically significant (p<0.001) for classifying the breast tumors as benign or malignant. The A Z (area under ROC curve) values for the classification result were 0.9138. Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the diagnosis performance based on all six proposed features were 82.30 (93/113), 86.79 (46/53), 78.33 (47/60), 77.97 (46/59), and 87.04 % (47/54), respectively. The p value of A Z values between the proposed method and conventional vascularity index method using z test was 0.04.

show abstract

“…Carson et al already developed and clinically tested a model in which they incorporated dynamic parameters quantitatively, calculated from the 3-D Color Doppler ultrasound analysis of breast masses. 12 …”

Section: Discussionmentioning

confidence: 99%

Correlation of transrectal ultrasound, computer analysis of transrectal ultrasound and histopathology of radical prostatectomy specimen

Beerlage

Aarnink

Ruijter

et al. 2001

Prostate Cancer Prostatic Dis

View full text Add to dashboard Cite

A system for computerised analysis of ultrasonographic prostate images (AUDEX Automated Urologic Diagnostic EXpert system) for the detection of prostate carcinoma was developed. The ultimate goal is to develop a system that is reliable and non-observer dependent. Results of an earlier study with a small group were encouraging and this study describes the results of the computerised analysis in a larger group. Sixty-two patients who were scheduled to undergo a radical prostatectomy were prospectively analysed. The radical prostatectomy specimens were step-sectioned in the transverse plane, corresponding to the ultrasound pictures. Malignant regions identi®ed by each study were quanti®ed and compared by computer calculation. No correlation was observed between ultrasound analysis and pathology result. For the AUDEX analysis an overall sensitivity of 85% and a speci®city of 18% with only a diagnostic accuracy of 57% was noticed when presence or absence of malignancy was evaluated by octant (total 496). When applying a cut-off value of 0.5 ml the numbers were 71%, 33% and 55%, respectively. Correlation was signi®cantly better for the ventral octants. In this study the earlier results of our AUDEX system could not be con®rmed. Although sensitivity was good, speci®city and especially diagnostic accuracy were lower than expected. We have to conclude that the current settings are inappropriate for routine clinical use. Prostate Cancer and Prostatic Diseases (2001) 4, 56±62.

show abstract

3-D color Doppler image quantification of breast masses

Cited by 36 publications

References 10 publications

Malignant and Benign Breast Masses on 3D US Volumetric Images: Effect of Computer-aided Diagnosis on Radiologist Accuracy

Malignant and Benign Breast Masses on 3D US Volumetric Images: Effect of Computer-aided Diagnosis on Radiologist Accuracy

Diagnosis of Solid Breast Tumors Using Vessel Analysis in Three-Dimensional Power Doppler Ultrasound Images

Correlation of transrectal ultrasound, computer analysis of transrectal ultrasound and histopathology of radical prostatectomy specimen

Contact Info

Product

Resources

About