Mammography screening using independent double reading with consensus: is there a potential benefit for computer-aided detection?

BACKGROUND: Double reading improves the cancer detection rate in mammography screening. Single reading with computer-aided detection (CAD) has been considered to be an alternative to double reading. Little is known about the potential benefit of CAD in breast cancer screening with double reading. PURPOSE: To compare prospective independent double reading of screen-film (SFM) and full-field digital (FFDM) mammography in population-based screening with retrospective standalone CAD performance on the baseline mammograms of the screen-detected cancers and subsequent cancers diagnosed during the follow-up period. MATERIAL AND METHODS: The study had ethics committee approval. A 5-point rating scale for probability of cancer was used for 23,923 (SFM = 16,983; FFDM = 6940) screening mammograms. Of 208 evaluable cancers, 104 were screen-detected and 104 were subsequent (44 interval and 60 next screening round) cancers. Baseline mammograms of subsequent cancers were retrospectively classified in consensus without information about cancer location, histology, or CAD prompting as normal, non-specific minimal signs, significant minimal signs, and false-negatives. The baseline mammograms of the screen-detected cancers and subsequent cancers were evaluated by CAD. Significant minimal signs and false-negatives were considered 'actionable' and potentially diagnosable if correctly prompted by CAD. RESULTS: CAD correctly marked 94% (98/104) of the baseline mammograms of the screen-detected cancers (SFM = 95% [61/64]; FFDM = 93% [37/40]), including 96% (23/24) of those with discordant interpretations. Considering only those baseline examinations of subsequent cancers prospectively interpreted as normal and retrospectively categorized as 'actionable', CAD input at baseline screening had the potential to increase the cancer detection rate from 0.43% to 0.51% (P = 0.13); and to increase cancer detection by 16% ([104 + 17]/104) and decrease interval cancers by 20% (from 44 to 35). CONCLUSION: CAD may have the potential to increase cancer detection by up to 16%, and to reduce the number of interval cancers by up to 20% in SFM and FFDM screening programs using independent double reading with consensus review. The influence of true- and false-positive CAD marks on decision-making can, however, only be evaluated in a prospective clinical study.

Computer-aided detection of solid lung nodules on follow-up MDCT screening: evaluation of detection, tracking, and reading time.

OBJECTIVE: The purpose of this article is to assess detection, tracking, and reading time of solid lung nodules > or = 4 mm on pairs of MDCT chest screening examinations using a computer-aided detection (CAD) system. MATERIALS AND METHODS: Of 54 pairs of low-dose MDCT chest examinations (1.25-mm collimation), two chest radiologists in consensus established that 25 examinations contained 52 nodules > or = 4 mm. All paired examinations were interpreted on the CAD workstation--first without and then with CAD input--for the detection and tracking of lung nodules. A subset of 33 examination pairs was later read on the clinical workstation used in daily practice, and the results were compared for reading time with those on the CAD workstation. RESULTS: After CAD input, the sensitivity for nodule detection increased statistically significantly for both readers (9.6% and 23%; p < or = 0.025). One cancer initially missed by one radiologist was correctly identified with CAD input. The overall reading time on the CAD workstation and clinical workstation was comparable for both radiologists. On average, readers spent 4-5 minutes per case to read the paired examinations on the CAD workstation and 6-8 seconds per CAD mark. The CAD system successfully matched 91.3% of nodules detected in both examinations. The overall rate of available CAD growth assessment was 54.9% of all nodule pairs. CONCLUSION: In the context of temporal comparison of MDCT screening examinations, the sensitivity of radiologists for detecting lung nodules > or = 4 mm increased significantly (p < or = 0.025) with CAD input without compromising reading time.

Effect of computer-aided detection on independent double reading of paired screen-film and full-field digital screening mammograms.

OBJECTIVE: The purpose of this study was to evaluate the performance and potential contribution of computer-aided detection (CAD) to independent double reading of paired screen-film and full-field digital screening mammograms. MATERIALS AND METHODS: The cases of 3,683 women who underwent both screen-film mammography and full-field digital mammography (FFDM) with independent double reading for each technique were followed for 2 years to include cancers detected in the interval between screening rounds and cancers detected at the next screening round. Fifty-five biopsy-proven cancers were diagnosed. The baseline screening mammograms of the 55 cancers were defined as having positive findings if at least one of two independent readers scored it 2 or higher on a 5-point rating scale. The baseline mammograms of interval (n = 10) or secondround (n = 16) cancers were retrospectively classified as overlooked (n = 2), minimal sign actionable (n = 8), minimal sign nonactionable (n = 5), and normal (n = 11). The baseline mammograms of these cases of cancer were evaluated with a CAD system, and the CAD results were compared (McNemar's test for paired proportions) with the findings at prospective independent double reading of mammograms obtained with each technique. RESULTS: For FFDM, CAD sensitivity was 95% (37/39) compared with 64% (25/39) for double reading (p = 0.006), and for screen-film mammography, CAD sensitivity was 85% (33/39) compared with 77% (30/39) for prospective double reading (p = 0.57) of radiographically visible lesions in baseline mammograms. CAD correctly marked five (13%) of 39 cancers on screen-film mammography and 14 (36%) of 39 cancers on FFDM not detected at prospective independent double reading. CONCLUSION: CAD showed the potential to increase the cancer detection rate for FFDM and for screen-film mammography in breast cancer screening performed with independent double reading.

Computer-aided detection of solid lung nodules in lossy compressed multidetector computed tomography chest exams.

RATIONALE AND OBJECTIVES: To assess the effect of three-dimensional (3D) lossy image compression of multidetector computed tomography chest scans on computer-aided detection (CAD) of solid lung nodules greater than 4 mm in size. MATERIALS AND METHODS: A total of 120 cases, acquired with 1.25-mm collimation, were collected from 5 different sites, of which 66/120 were low-dose cases. Two chest radiologists established that 37 cases had no actionable lung nodules; the remaining 83 cases contained 169 nodules (range 3.8-35.0 mm, mean 5.8 mm +/- 3.0 [SD]). All cases were compressed using the 3D Set Partitioning in Hierarchical Trees algorithm to 24:1, 48:1, and 96:1 levels. A study of the effect of compression on computer-aided detection (CAD) sensitivity was performed at operating points of 2.5 false marks (FM), 5 FM, and 10 FM per case using McNemar's test. Logistic regression models were used to evaluate the impact on CAD sensitivity by compression level on nodule and image characteristics. RESULTS: Compared with no compression, there was no significant degradation in CAD sensitivity found at any of the studied compression levels and operating points. However, between compression levels, there was marginal association with sensitivity. Specifically, 24:1 level was significantly better than 96:1 at all operating points, and occasionally better than no compression at 10 FM/case. Based on multivariate analysis, nodule location was found to be a significant predictor (P = .01) with a lower sensitivity associated with juxtapleural nodules. Nodule size, dose, reconstruction filter, and contrast medium were not significant predictors. CONCLUSION: CAD detection performance of solid lung nodules did not suffer until 48:1 compression.

Computer aided detection (CAD): an overview.

Computer aided detection (CAD) is a technology designed to decrease observational oversights--and thus the false negative rates--of physicians interpreting medical images. Prospective clinical studies have demonstrated an increase in breast cancer detection with CAD assistance. This overview briefly describes the metrics that have been used to define CAD system performance.

Computer-aided detection in diagnostic mammography: detection of clinically unsuspected cancers.

OBJECTIVE: We had two objectives: to determine the percentage of women presenting with clinical findings whose diagnostic mammogram led to detection of a breast cancer at a site distant from the original clinical complaint and to assess the performance of computer-aided detection (CAD) on diagnostic mammography. MATERIALS AND METHODS: Three institutions contributed consecutive cases in which a mammogram was obtained to evaluate a clinical finding, after which a histologic diagnosis of breast cancer was made. Clinical data and the mammograms were reviewed to determine the nature of the clinical findings and to document the location and characteristics of 212 biopsy-proven cancers in 197 patients who met the study criteria. Standard four-view breast mammograms were then analyzed by a CAD system. RESULTS: The most common clinical finding was a palpable mass (90%, 177/197), with nipple discharge (5%, 9/197), focal tenderness or pain (2%, 5/197), and miscellaneous complaints (3%, 6/197) also noted. Two separate cancers were found in 7.6% (15/197) of the cases. In another 7.6% (15/197) of the cases, the single diagnosed cancer was not at the location of the specific clinical finding. The CAD system correctly marked 87% (26/30) of those cancers that were clinically unsuspected (i.e., not at the location of the clinical finding). CONCLUSION: Breast cancers occurred at locations other than the site of the presenting clinical finding in 15% (30/197) of patients undergoing diagnostic mammography in whom a cancer was detected. CAD identified 87% of these incidentally detected cancers and may therefore be useful as a detection aid to the radiologist when interpreting diagnostic mammograms.

Invasive lobular carcinoma of the breast: mammographic characteristics and computer-aided detection.

PURPOSE: To characterize the mammographic appearance of invasive lobular carcinoma in a large series of screening-detected consecutive breast cancers and to evaluate the ability of a computer-aided detection system to mark these carcinomas. MATERIALS AND METHODS: Investigators used the Breast Imaging Reporting and Data System lexicon to characterize lesions as part of a retrospective review of 90 screening mammographic examinations that led to biopsy-proved diagnosis of 94 invasive lobular carcinoma lesions. The 40 available prior mammographic examinations (obtained 9-24 months earlier) were also reviewed to characterize any visible findings. The results of a computer-aided detection analysis were compared with the images, and the sensitivity of the algorithm was calculated for correct detection of the lesions. RESULTS: Fifty-six (60%) of 94 lesions manifested as masses, of which 40 (71%) were described as irregular and spiculated; 20 (21%) of 94, as architectural distortions; and the remainder, 18 (20%), as either asymmetric densities or calcifications. On the screening mammograms showing biopsy-proved cancers, the sensitivity of the computer-aided detection system was 86 (91%) of 94 lesions. Thirty-one of the 40 prior mammograms showed retrospectively visible findings, and 24 (77%) of 31 were marked by the computer-aided detection system. CONCLUSION: Spiculated masses and architectural distortions are the predominant appearances of invasive lobular carcinoma, and a computer-aided detection system correctly marked a high percentage of invasive lobular carcinoma lesions.

Computer-aided detection in oncologic imaging: screening mammography as a case study.

Accuracy of the imaging report is dependent on the observational and interpretive skills of the radiologist, which varies between observers. Over the past several decades, research programs have focused on the use of computer algorithms to address both the perception and the interpretation aspects of diagnostic imaging. Computer-based technology that analyzes images in order to detect features of disease is called computer-aided detection (CAD). This paper reviews the current status of CAD as used with screening mammography.