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. (c) RSNA, 2007.
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. (c) RSNA, 2007.
Authors: I L Štepán-Buksakowska; J M Accurso; F E Diehn; J Huston; T J Kaufmann; P H Luetmer; C P Wood; X Yang; D J Blezek; R Carter; C Hagen; D Hořínek; A Hejčl; M Roček; B J Erickson Journal: AJNR Am J Neuroradiol Date: 2014-06-12 Impact factor: 3.825