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Purpose: To evaluate the use of artificial intelligence (AI) to shorten digital breast tomosynthesis (DBT) reading time while maintaining or improving accuracy., Materials and Methods: A deep learning AI system was developed to identify suspicious soft-tissue and calcified lesions in DBT images. A reader study compared the performance of 24 radiologists (13 of whom were breast subspecialists) reading 260 DBT examinations (including 65 cancer cases) both with and without AI. Readings occurred in two sessions separated by at least 4 weeks. Area under the receiver operating characteristic curve (AUC), reading time, sensitivity, specificity, and recall rate were evaluated with statistical methods for multireader, multicase studies., Results: Radiologist performance for the detection of malignant lesions, measured by mean AUC, increased 0.057 with the use of AI (95% confidence interval [CI]: 0.028, 0.087; P < .01), from 0.795 without AI to 0.852 with AI. Reading time decreased 52.7% (95% CI: 41.8%, 61.5%; P < .01), from 64.1 seconds without to 30.4 seconds with AI. Sensitivity increased from 77.0% without AI to 85.0% with AI (8.0%; 95% CI: 2.6%, 13.4%; P < .01), specificity increased from 62.7% without to 69.6% with AI (6.9%; 95% CI: 3.0%, 10.8%; noninferiority P < .01), and recall rate for noncancers decreased from 38.0% without to 30.9% with AI (7.2%; 95% CI: 3.1%, 11.2%; noninferiority P < .01)., Conclusion: The concurrent use of an accurate DBT AI system was found to improve cancer detection efficacy in a reader study that demonstrated increases in AUC, sensitivity, and specificity and a reduction in recall rate and reading time.© RSNA, 2019See also the commentary by Hsu and Hoyt in this issue., (2019 by the Radiological Society of North America, Inc.)

Purpose: To assess the effect of using computer-aided detection (CAD) in second-read mode on readers' accuracy in interpreting computed tomographic (CT) colonographic images., Materials and Methods: The contributing institutions performed the examinations under approval of their local institutional review board, with waiver of informed consent, for this HIPAA-compliant study. A cohort of 100 colonoscopy-proved cases was used: In 52 patients with findings positive for polyps, 74 polyps of 6 mm or larger were observed in 65 colonic segments; in 48 patients with findings negative for polyps, no polyps were found. Nineteen blinded readers interpreted each case at two different times, with and without the assistance of a commercial CAD system. The effect of CAD was assessed in segment-level and patient-level receiver operating characteristic (ROC) curve analyses., Results: Thirteen (68%) of 19 readers demonstrated higher accuracy with CAD, as measured with the segment-level area under the ROC curve (AUC). The readers' average segment-level AUC with CAD (0.758) was significantly greater (P = .015) than the average AUC in the unassisted read (0.737). Readers' per-segment, per-patient, and per-polyp sensitivity for all polyps of 6 mm or larger was higher (P < .011, .007, .005, respectively) for readings with CAD compared with unassisted readings (0.517 versus 0.465, 0.521 versus 0.466, and 0.477 versus 0.422, respectively). Sensitivity for patients with at least one large polyp of 10 mm or larger was also higher (P < .047) with CAD than without (0.777 versus 0.743). Average reader sensitivity also improved with CAD by more than 0.08 for small adenomas. Use of CAD reduced specificity of readers by 0.025 (P = .05)., Conclusion: Use of CAD resulted in a significant improvement in overall reader performance. CAD improves reader sensitivity when measured per segment, per patient, and per polyp for small polyps and adenomas and also reduces specificity by a small amount., ((c) RSNA, 2010.)

Objective: The purpose of this study was to evaluate computer-aided detection (CAD) performance with full-field digital mammography (FFDM)., Materials and Methods: CAD (Second Look, version 7.2) was used to evaluate 123 cases of breast cancer detected with FFDM (Senographe DS). Retrospectively, CAD sensitivity was assessed using breast density, mammographic presentation, histopathology results, and lesion size. To determine the case-based false-positive rate, patients with four standard views per case were included in the study group. Eighteen unilateral mammography examinations with nonstandard views were excluded, resulting in a sample of 105 bilateral cases., Results: CAD detected 115 (94%) of 123 cancer cases: six of six (100%) in fatty breasts, 63 of 66 (95%) in breasts containing scattered fibroglandular densities, 43 of 46 (93%) in heterogeneously dense breasts, and three of five (60%) in extremely dense breasts. CAD detected 93% (41/44) of cancers manifesting as calcifications, 92% (57/62) as masses, and 100% (17/17) as mixed masses and calcifications. CAD detected 94% of the invasive ductal carcinomas (n = 63), 100% of the invasive lobular carcinomas (n = 7), 91% of the other invasive carcinomas (n = 11), and 93% of the ductal carcinomas in situ (n = 42). CAD sensitivity for cancers 1-10 mm (n = 55) was 89%; 11-20 mm (n = 37), 97%; 21-30 mm (n = 16), 100%; and larger than 30 mm (n = 15), 93%. The CAD false-positive rate was 2.3 marks per four-image case., Conclusion: CAD with FFDM showed a high sensitivity in identifying cancers manifesting as calcifications and masses. Sensitivity was maintained in cancers with lower mammographic sensitivity, including invasive lobular carcinomas and small neoplasms (1-20 mm). CAD with FFDM should be effective in assisting radiologists with earlier detection of breast cancer. Future studies are needed to assess CAD accuracy in larger populations.

Background: The objective of this study was to evaluate the performance of a computer-aided detection (CAD) system for the detection of breast cancer, based on mammographic appearance and histopathology., Methods: From 1000 consecutive screening mammograms from women with biopsy-proven breast carcinoma, 273 mammograms were selected randomly for retrospective evaluation by CAD. The sensitivity of the CAD system for breast cancer was assessed from the proportion of masses and microcalcifications detected. The corresponding tumor histopathologies also were evaluated. Normal mammograms (n = 155 patients) were used to determine the false-positive rate of the system., Results: Of the 273 breast carcinomas, 149 appeared mammographically as masses, and 88 appeared as microcalcifications, including 36 carcinomas that presented as mixed lesions. The CAD system marked 125 of 149 masses correctly (84%), marked 86 of 88 microcalcifications correctly (98%), and marked 32 of 36 of mixed lesions correctly (89%.). The system showed a high sensitivity for the detection of ductal carcinoma in situ (95%; 73 of 77 lesions), invasive lobular carcinoma (95%; 18 of 19 lesions), invasive ductal carcinoma (85%; 125 of 147 lesions), and invasive mammary carcinoma (90%; 27 of 30 lesions). The highest CAD system sensitivity was for all invasive carcinomas that presented as microcalcifications (100%). On normal mammograms, there was an average of 1.3 false-positive CAD marks per image., Conclusions: The CAD system correctly marked a large majority of biopsy-proven breast cancers, with a greater sensitivity for lesions with microcalcifications and without significant impact of performance based on tumor histopathology. CAD was highly effective in detecting invasive lobular carcinoma (sensitivity, 95%) and ductal carcinoma in situ (sensitivity, 95%). CAD represents a useful tool for the detection of breast cancer.

Objective: The purpose of our study was to evaluate the performance of a computer-aided detection (CAD) system in the detection of breast cancer based on mammographic appearance and lesion size., Conclusion: The CAD system correctly marked most biopsy-proven breast cancers, with a greater sensitivity for microcalcification than for mass lesions but with no significant difference in performance based on cancer size. CAD was highly effective in detecting even the smallest lesions, with a sensitivity of 92% for lesions of 5 mm or less. CAD is a useful tool for the detection of breast cancer.

Objective: Our aim was to determine whether breast density affects the performance of a computer-aided detection (CAD) system for the detection of breast cancer., Materials and Methods: Nine hundred six sequential mammographically detected breast cancers and 147 normal screening mammograms from 18 facilities were classified by mammographic density. BI-RADS 1 and 2 density cases were classified as nondense breasts; BI-RADS 3 and 4 density cases were classified as dense breasts. Cancers were classified as either masses or microcalcifications. All mammograms from the cancer and normal cases were evaluated by the CAD system. The sensitivity and false-positive rates from CAD in dense and nondense breasts were evaluated and compared., Results: Overall, 809 (89%) of 906 cancer cases were detected by CAD; 455/505 (90%) cancers in nondense breasts and 354/401 (88%) cancers in dense breasts were detected. CAD sensitivity was not affected by breast density (p=0.38). Across both breast density categories, 280/296 (95%) microcalcification cases and 529/610 (87%) mass cases were detected. One hundred fourteen (93%) of the 122 microcalcifications in nondense breasts and 166 (95%) of 174 microcalcifications in dense breasts were detected, showing that CAD sensitivity to microcalcifications is not dependent on breast density (p=0.46). Three hundred forty-one (89%) of 383 masses in nondense breasts, and 188 (83%) of 227 masses in dense breasts were detected-that is, CAD sensitivity to masses is affected by breast density (p=0.03). There were more false-positive marks on dense versus nondense mammograms (p=0.04)., Conclusion: Breast density does not impact overall CAD detection of breast cancer. There is no statistically significant difference in breast cancer detection in dense and nondense breasts. However, the detection of breast cancer manifesting as masses is impacted by breast density. The false-positive rate is lower in nondense versus dense breasts. CAD may be particularly advantageous in patients with dense breasts, in which mammography is most challenging.

Computer-aided detection (CAD) system sensitivity estimates without a radiologist in the loop are straightforward to measure but are extremely data dependent. The only relevant performance metric is improvement in CAD-assisted radiologist sensitivity. Unfortunately, this is difficult to accurately assess. Without a large study measuring the improvement in CAD-assisted radiologist sensitivity over the same cases, it is not possible to make valid comparisons between systems. As multiple CAD systems become commercially available, comparison issues need to be explored and resolved. Data from clinical trials of 2 systems are examined. Statistical hypothesis tests are applied to these data. Additionally, sensitivities of 2 systems are compared from an experiment testing over the same 120 cases. Even with large databases, there is not sufficient evidence to conclude performance differences exist between the 2 systems. It is prohibitively expensive to show conclusive sensitivity differences between commercially available mammographic CAD systems.

A new model-based vision (MBV) algorithm is developed to find regions of interest (ROI's) corresponding to masses in digitized mammograms and to classify the masses as malignant/benign. The MBV algorithm is comprised of five modules to structurally identify suspicious ROI's, eliminate false positives, and classify the remaining as malignant or benign. The focus of attention module uses a difference of Gaussians (DoG) filter to highlight suspicious regions in the mammogram. The index module uses tests to reduce the number of nonmalignant regions from 8.39 to 2.36 per full breast image. Size, shape, contrast, and Laws texture features are used to develop the prediction module's mass models. Derivative-based feature saliency techniques are used to determine the best features for classification. Nine features are chosen to define the malignant/benign models. The feature extraction module obtains these features from all suspicious ROI's. The matching module classifies the regions using a multilayer perceptron neural network architecture to obtain an overall classification accuracy of 100% for the segmented malignant masses with a false-positive rate of 1.8 per full breast image. This system has a sensitivity of 92% for locating malignant ROI's. The database contains 272 images (12 b, 100 microm) with 36 malignant and 53 benign mass images. The results demonstrate that the MBV approach provides a structured order of integrating complex stages into a system for radiologists.

A general purpose two-dimensional (2-D) image processing software system was used to produce high quality three-dimensional (3-D) surface reconstructions from serial sections such as CT scan slices. Depth-encoded 3-D surface images, gradient-shaded 3-D surface images, and weighted sums of these two images were computed. Images that simulate transmission radiographs ("volumetric" views) were created from the same slice data. Hidden surfaces were displayed by reconstructing in 3-D only subvolumes of the original data set. The 2-D image processing functions used were limited to: planar subimage selection and merge, arithmetic and boolean operations, piecewise linear gray scale transform, convolution (1-D), and format conversion (byte-integer-float). Using these methods any user with a general purpose 2-D image processing system can analyze and view multi-slice data as 3-D volume and surface projections.