Mammography

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

Diagnostic Accuracy of 99mTc-Sestamibi Breast Imaging: Multicenter Trial Results

Abstract: Although mammography is well established as a first-line tool for breast cancer screening and detection, efforts to develop complementary procedures continue. Observation of 99mTc-sestamibi tumor uptake provided the impetus for its evaluation as an adjunctive technique. This trial9s objectives were to determine in a multicenter trial the diagnostic accuracy of 99mTc-sestamibi in women with suspected breast cancer and to investigate factors influencing diagnostic accuracy. Methods: Our multicenter trial enrolled 673 women (387 with nonpalpable abnormalities; 286 with palpable abnormalities) scheduled for excisional biopsy or mastectomy. Blinded and unblinded interpretations of scintigraphic images were compared with core laboratory established histopathologic diagnoses to define the diagnostic accuracy of 99mTc-sestamibi breast imaging. Results: Blinded readers9 diagnostic accuracy was 78%–81%. Inter-reader agreement was excellent, ranging from 95% to 100% (κ = 0.82–0.99). Overall institutional sensitivity and specificity for 99mTc-sestamibi breast imaging were 75.4% and 82.7%, respectively. In this population with a 40.1% disease prevalence, the positive predictive value was 74.5% and the negative predictive value was 83.4%. The negative predictive value was 94% in patients with a 40% or lower mammographic likelihood of breast cancer. Sensitivity was higher for palpable abnormalities; specificity was higher for nonpalpable abnormalities. Sensitivity was decreased for tumors 1 cm, and palpable abnormalities.

Computerized image analysis: estimation of breast density on mammograms

Abstract: An automated image analysis tool is being developed for estimation of mammographic breast density, which may be useful for risk estimation or for monitoring breast density change in a prevention or intervention program. A mammogram is digitized using a laser scanner and the resolution is reduced to a pixel size of 0.8 mm X 0.8 mm. Breast density analysis is performed in three stages. First, the breast region is segmented from the surrounding background by an automated breast boundary-tracking algorithm. Second, an adaptive dynamic range compression technique is applied to the breast image to reduce the range of the gray level distribution in the low frequency background and to enhance the differences in the characteristic features of the gray level histogram for breasts of different densities. Third, rule-based classification is used to classify the breast images into several classes according to the characteristic features of their gray level histogram. For each image, a gray level threshold is automatically determined to segment the dense tissue from the breast region. The area of segmented dense tissue as a percentage of the breast area is then estimated. In this preliminary study, we analyzed the interobserver variation of breast density estimation by two experienced radiologists using BI-RADS lexicon. The radiologists' visually estimated percent breast densities were compared with the computer's calculation. The results demonstrate the feasibility of estimating mammographic breast density using computer vision techniques and its potential to improve the accuracy and reproducibility in comparison with the subjective visual assessment by radiologists.

Triple approach in the diagnosis of dominant breast masses: Combined physical examination, mammography, and fine‐needle aspiration

Abstract: In an attempt to reduce the number of breast biopsies done for benign breast disease in patients with breast lumps, we evaluated prospectively the sensitivity and specificity of the combination of three diagnostic modalities: clinical examination, mammography, and fine-needle aspiration cytologic examination (FNA). A total of 234 patients with a breast mass had a physical examination, a mammogram, and FNA, and were listed as malignant/suspicious or benign. All patients underwent a subsequent biopsy: 110 were found to have breast cancer, and 124 had a benign lesion. The sensitivity and specificity of the individual tests were as follows: 89% and 73%, respectively, for mammographic examination; 93% and 97% for FNA cytologic examination; and 89% and 60% for physical examination. For the combined triad of tests, the sensitivity was 100% and specificity 57%. All patients who had breast cancer had positive findings for malignancy in one or more of the diagnostic tests, i.e., 100% sensitively. All patients who had negative findings for malignancy in all three diagnostic tests had benign lesions, i.e., a negative predictive value of 100%. We conclude that breast masses can be diagnosed with a high degree of accuracy by combined physical, mammographic, and fine-needle aspiration cytologic examination. Patients in whom physical examination, mammography, and FNA were negative for malignancy can be safely observed, obviating the need for an open biopsy.

Preoperative MR imaging-guided needle localization of breast lesions.

Abstract: OBJECTIVE. MR imaging of the breast can depict cancer that is occult on mammography and at physical examination. Our study was undertaken to determine the ease of performance and the outcome of MR imaging—guided needle localization and surgical excision of breast lesions.MATERIALS AND METHODS. Retrospective review revealed 101 consecutive breast lesions that had preoperative MR imaging—guided needle localization with commercially available equipment, including a 1.5-T magnet with a breast surface coil, a dedicated biopsy compression device, and MR imaging—compatible hookwires. Imaging studies and medical records were reviewed.RESULTS. Histologic findings in these 101 lesions were carcinoma in 31 (30.7%), high-risk lesions (atypical ductal hyperplasia or lobular carcinoma in situ) in nine (8.9%), and benign lesions in 61 (60.4%). Fifteen (48.4%) of 31 carcinomas were ductal carcinoma in situ, and 16 (51.6%) were infiltrating carcinoma (size range, 0.1-2.0 cm; median, 1.2 cm). Carcinoma was found in 16 (45....

External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms

Abstract: Importance A computer algorithm that performs at or above the level of radiologists in mammography screening assessment could improve the effectiveness of breast cancer screening. Objective To perform an external evaluation of 3 commercially available artificial intelligence (AI) computer-aided detection algorithms as independent mammography readers and to assess the screening performance when combined with radiologists. Design, Setting, and Participants This retrospective case-control study was based on a double-reader population-based mammography screening cohort of women screened at an academic hospital in Stockholm, Sweden, from 2008 to 2015. The study included 8805 women aged 40 to 74 years who underwent mammography screening and who did not have implants or prior breast cancer. The study sample included 739 women who were diagnosed as having breast cancer (positive) and a random sample of 8066 healthy controls (negative for breast cancer). Main Outcomes and Measures Positive follow-up findings were determined by pathology-verified diagnosis at screening or within 12 months thereafter. Negative follow-up findings were determined by a 2-year cancer-free follow-up. Three AI computer-aided detection algorithms (AI-1, AI-2, and AI-3), sourced from different vendors, yielded a continuous score for the suspicion of cancer in each mammography examination. For a decision of normal or abnormal, the cut point was defined by the mean specificity of the first-reader radiologists (96.6%). Results The median age of study participants was 60 years (interquartile range, 50-66 years) for 739 women who received a diagnosis of breast cancer and 54 years (interquartile range, 47-63 years) for 8066 healthy controls. The cases positive for cancer comprised 618 (84%) screen detected and 121 (16%) clinically detected within 12 months of the screening examination. The area under the receiver operating curve for cancer detection was 0.956 (95% CI, 0.948-0.965) for AI-1, 0.922 (95% CI, 0.910-0.934) for AI-2, and 0.920 (95% CI, 0.909-0.931) for AI-3. At the specificity of the radiologists, the sensitivities were 81.9% for AI-1, 67.0% for AI-2, 67.4% for AI-3, 77.4% for first-reader radiologist, and 80.1% for second-reader radiologist. Combining AI-1 with first-reader radiologists achieved 88.6% sensitivity at 93.0% specificity (abnormal defined by either of the 2 making an abnormal assessment). No other examined combination of AI algorithms and radiologists surpassed this sensitivity level. Conclusions and Relevance To our knowledge, this study is the first independent evaluation of several AI computer-aided detection algorithms for screening mammography. The results of this study indicated that a commercially available AI computer-aided detection algorithm can assess screening mammograms with a sufficient diagnostic performance to be further evaluated as an independent reader in prospective clinical trials. Combining the first readers with the best algorithm identified more cases positive for cancer than combining the first readers with second readers.