Mammography

About: Mammography is a research topic. Over the lifetime, 20643 publications have been published within this topic receiving 513679 citations.

Detecting breast cancer with non-contrast MR imaging: combining diffusion-weighted and STIR imaging.

Abstract: We combined diffusion-weighted (DWI) and short TI inversion recovery (STIR) imaging to evaluate the diagnostic capability of non-contrast magnetic resonance (MR) imaging to detect breast cancer. Seventy women patients underwent mammography and MR imaging with combined DWI (b factor: 1000) and STIR that revealed malignancy, and postoperative pathological examination confirmed breast cancer. Interpreted images were evaluated for sensitivity, false negative rate (FN), sensitivity by pT, and sensitivity by background density of the mammary gland. Of the 70 cases, 68 were diagnosed as cancer by DWI and STIR (sensitivity, 97% [68/70]; FN, 2.9% [2/70]). Sensitivities by pT were: pTis, 67% (4/6); pT1, 100% (33/33); and pT2-4, 100% (31/31). No significant differences were observed in sensitivity between pT1 and pT2-4 (P<0.001). Sensitivities by background density of mammary gland were: fatty/scattered fibroglandular tissue, 95% (20/21) and heterogeneous fibroglandular tissue/mostly fibroglandular tissue, 98% (48/49). No significant differences were observed (P<0.001). Two cases, an intraductal and an apocrine carcinoma, were incorrectly diagnosed by MR imaging. Precise diagnosis of breast cancer is possible with combined DWI and STIR, even in non-contrast MR imaging, regardless of the diameter or background density of mammary gland. It is hoped that non-contrast MR imaging that combines DWI and STIR will become an established clinical screening method.

Bias Associated With Self-Report of Prior Screening Mammography

Abstract: Background: Self-reported screening behaviors from national surveys often overestimate screening use, and the amount of overestimation may vary by demographic characteristics. We examine self-report bias in mammography screening rates overall, by age, and by race/ethnicity. Methods: We use mammography registry data (1999-2000) from the Breast Cancer Surveillance Consortium to estimate the validity of self-reported mammography screening collected by two national surveys. First, we compare mammography use from 1999 to 2000 for a geographically defined population (Vermont) with self-reported rates in the prior two years from the 2000 Vermont Behavioral Risk Factor Surveillance System. We then use a screening dissemination simulation model to assess estimates of mammography screening from the 2000 National Health Interview Survey. Results: Self-report estimates of mammography use in the prior 2 years from the Vermont Behavioral Risk Factor Surveillance System are 15 to 25 percentage points higher than actual screening rates across age groups. The differences in National Health Interview Survey screening estimates from models are similar for women 40 to 49 and 50 to 59 years and greater than for those 60 to 69, or 70 to 79 (27 and 26 percentage points versus 14, and 14, respectively). Overreporting is highest among African American women (24.4 percentage points) and lowest among Hispanic women (17.9) with non-Hispanic White women in between (19.3). Values of sensitivity and specificity consistent with our results are similar to previous validation studies of mammography. Conclusion: Overestimation of self-reported mammography usage from national surveys varies by age and race/ethnicity. A more nuanced approach that accounts for demographic differences is needed when adjusting for overestimation or assessing disparities between populations. (Cancer Epidemiol Biomarkers Prev 2009;18(6):1699–705)

Is cost a barrier to screening mammography for low-income women receiving Medicare benefits? A randomized trial.

Abstract: Background: In 1991, Medicare began covering screening mammograms subject to copayment and deductible. This study evaluated the effectiveness of Medicare in removing financial barriers to screening mammography among low-income older women. Methods: In an inner-city public hospital's General Medicine Clinic, 119 consecutive, eligible, and consenting Medicare-enrolled women without known risk factors for breast cancer other than age, and no mammogram in the previous 2 years, were entered into a randomized controlled trial with follow-up after 2 months. The mean age was 71 years; 77% were black, 92% had an annual income below $10 000, and 52% had had a previous mammogram. All patients were counseled concerning indications for screening mammograms and Medicare coverage, and all were referred to a low-cost mammography facility. Sixty-one subjects were randomly assigned a voucher for a free screening mammogram at the referral facility. Obtaining a mammogram within 60 days of study entry was the main outcome measure. Results: Of the women given vouchers, 27 (44%) obtained screening mammograms, compared with six (10%) of those without vouchers ( P Conclusion: In a low-income, inner-city population of older women, financial barriers to screening mammography persist despite Medicare coverage. (Arch Intern Med. 1994;154:1217-1224)

Mammographic detection of recurrent cancer in the irradiated breast.

Abstract: Recurrence of cancer in the irradiated breast is an uncommon but potentially curable problem. Posttreatment mammograms were studied in 45 patients who had biopsies of an irradiated breast for suspected local recurrence to evaluate the usefulness of mammography in detecting such recurrences. Of 23 biopsy-proven recurrences, eight (35%) were detected by mammography only, nine (39%) were detected by physical examination only, and six (26%) were detected by both. Mammographic findings in recurrent malignancy included microcalcifications in six, microcalcifications associated with a mass in four, soft-tissue masses in three, and inflammatory changes in one. The results show that mammographic follow-up is complementary to physical examination in the detection of local recurrence in women who have undergone radiation therapy for early breast cancer.

Convolutional neural networks for mammography mass lesion classification

Abstract: Feature extraction is a fundamental step when mammography image analysis is addressed using learning based approaches. Traditionally, problem dependent handcrafted features are used to represent the content of images. An alternative approach successfully applied in other domains is the use of neural networks to automatically discover good features. This work presents an evaluation of convolutional neural networks to learn features for mammography mass lesions before feeding them to a classification stage. Experimental results showed that this approach is a suitable strategy outperforming the state-of-the-art representation from 79.9% to 86% in terms of area under the ROC curve.