Assessing the stand-alone sensitivity of computer-aided detection with cancer cases from the Digital Mammographic Imaging Screening Trial.

OBJECTIVE: The purpose of this study was to assess the sensitivities and false-detection rates of two computer-aided detection (CADe) systems when applied to digital or film-screen mammograms in detecting the known breast cancer cases from the Digital Mammographic Imaging Screening Trial (DMIST) breast cancer screening population. MATERIALS AND METHODS: Available film-screen and digital mammograms of 161 breast cancer cases from DMIST were analyzed by two CADe systems, iCAD Second-Look and R2 ImageChecker. Three experienced breast-imaging radiologists reviewed the CADe marks generated for each available cancer case, recording the number and locations of CADe marks and whether each CADe mark location corresponded with the known location of the cancer. RESULTS: For the 161 cancer cases included in this study, the sensitivities of the DMIST reader without CAD were 0.43 (69/161, 95% CI 0.35-0.51) for digital and 0.41 (66/161, 0.33-0.49) for film-screen mammography. The sensitivities of iCAD were 0.74 (119/161, 0.66-0.81) for digital and 0.69 (111/161, 0.61-0.76) for film-screen mammography, both significantly higher than the DMIST study sensitivities (p < 0.0001 for both). The average number of false CADe marks per case of iCAD was 2.57 (SD, 1.92) for digital and 3.06(1.72) for film-screen mammography. The sensitivity of R2 was 0.74 (119/161, 0.66-0.81) for digital, and 0.60 (97/161, 0.52-0.68) for film-screen mammography, both significantly higher than the DMIST study sensitivities (p < 0.0001 for both). The average number of false CADe marks per case of R2 was 2.07 (1.57) for digital and 1.52 (1.45) for film-screen mammography. CONCLUSION: Our results suggest the use of CADe in interpretation of digital and film-screen mammograms could lead to improvements in cancer detection.

Cancer cases from ACRIN digital mammographic imaging screening trial: radiologist analysis with use of a logistic regression model.

PURPOSE: To determine which factors contributed to the Digital Mammographic Imaging Screening Trial (DMIST) cancer detection results. MATERIALS AND METHODS: This project was HIPAA compliant and institutional review board approved. Seven radiologist readers reviewed the film hard-copy (screen-film) and digital mammograms in DMIST cancer cases and assessed the factors that contributed to lesion visibility on both types of images. Two multinomial logistic regression models were used to analyze the combined and condensed visibility ratings assigned by the readers to the paired digital and screen-film images. RESULTS: Readers most frequently attributed differences in DMIST cancer visibility to variations in image contrast--not differences in positioning or compression--between digital and screen-film mammography. The odds of a cancer being more visible on a digital mammogram--rather than being equally visible on digital and screen-film mammograms--were significantly greater for women with dense breasts than for women with nondense breasts, even with the data adjusted for patient age, lesion type, and mammography system (odds ratio, 2.28; P < .0001). The odds of a cancer being more visible at digital mammography--rather than being equally visible at digital and screen-film mammography--were significantly greater for lesions imaged with the General Electric digital mammography system than for lesions imaged with the Fischer (P = .0070) and Fuji (P = .0070) devices. CONCLUSION: The significantly better diagnostic accuracy of digital mammography, as compared with screen-film mammography, in women with dense breasts demonstrated in the DMIST was most likely attributable to differences in image contrast, which were most likely due to the inherent system performance improvements that are available with digital mammography. The authors conclude that the DMIST results were attributable primarily to differences in the display and acquisition characteristics of the mammography devices rather than to reader variability.

Insurance status and the severity of breast cancer at the time of diagnosis.

RATIONALE AND OBJECTIVES: To evaluate how the insurance status of women diagnosed with breast cancer correlates with size and stage at the time of diagnosis. METHODS AND MATERIALS: The age-adjusted incidence of early- and late-stage breast cancer as determined by the tumor node metastasis classification system of stages in situ, local, regional, or distant was calculated for insured and uninsured women from our institution's database between 2002 and 2004. Late-stage breast cancer was defined as present when patients had either regional or distant disease. Statistical analysis was conducted using generalized linear models and chi(2) tests. RESULTS: There were a total of 617 patients in our retrospective study. Of these, 564 (91.4%) had insurance and 53 (8.6%) were uninsured. Four hundred forty-seven (72.4%) patients were Caucasian and 170 (27.6%) patients were non-Caucasian. Of the 463 patients with early-stage breast cancer (0, I, or II), 433 (93.5%) had insurance and 30 (6.5%) were uninsured. Of the 154 patients with late-stage breast cancer (III or IV), 131 (85.1%) had insurance and 23 (14.9%) patients were uninsured. Analysis demonstrated that there was a significant effect in the insurance status on cancer stage (P = .006) and tumor size (P = .010). Compared to insured patients, uninsured patients had a 66% higher likelihood of presenting with a late-stage cancer and larger tumor. The analysis from the chi(2) test also supports the above with a significant association between patients' cancer stage and insurance status (P = .001) and also between tumor size and insurance status (P = .001). Patients' ages and geographic locations were not significant correlated with size and stage, but non-Caucasians had a significantly higher risk of larger tumors and more advanced stage than Caucasians (P < .005). CONCLUSIONS: Uninsured, non-Caucasian patients have a higher probability of presenting with a more advanced stage of breast cancer and larger tumor size than patients with insurance in a large university multidisciplinary breast cancer population.