Screening with mammography uses X-ray imaging to find breast cancer before a lump can be felt. The goal is to treat cancer earlier, when a cure is more likely. The review includes seven trials that involved 600,000 women in the age range 39 to 74 years who were randomly assigned to receive screening mammograms or not. The studies which provided the most reliable information showed that screening did not reduce breast cancer mortality. Studies that were potentially more biased (less carefully done) found that screening reduced breast cancer mortality. However, screening will result in some women getting a cancer diagnosis even though their cancer would not have led to death or sickness. Currently, it is not possible to tell which women these are, and they are therefore likely to have breasts or lumps removed and to receive radiotherapy unnecessarily. If we assume that screening reduces breast cancer mortality by 15% after 13 years of follow-up and that overdiagnosis and overtreatment is at 30%, it means that for every 2000 women invited for screening throughout 10 years, one will avoid dying of breast cancer and 10 healthy women, who would not have been diagnosed if there had not been screening, will be treated unnecessarily. Furthermore, more than 200 women will experience important psychological distress including anxiety and uncertainty for years because of false positive findings.
Women invited to screening should be fully informed of both the benefits and harms. To help ensure that the requirements for informed choice for women contemplating whether or not to attend a screening programme can be met, we have written an evidence-based leaflet for lay people that is available in several languages on www.cochrane.dk. Because of substantial advances in treatment and greater breast cancer awareness since the trials were carried out, it is likely that the absolute effect of screening today is smaller than in the trials. Recent observational studies show more overdiagnosis than in the trials and very little or no reduction in the incidence of advanced cancers with screening.

/pdf/screening-for-breast-cancer-with-mammography-36k7l5kzia.pdf

Screening for breast cancer with mammography.

Microwaves and millimeter waves have been used extensively to image dielectric bodies. The application of microwaves in biomedical imaging and diagnostics, however, remains a field with many uncharted territories. This article is an overview on medical imaging using microwave imaging for breast cancer and its challenges, hopes, and outlook.

Microwave Imaging for Breast Cancer

Clusters of fine, granular microcalcifications in mammograms may be an early sign of disease. Individual grains are difficult to detect and segment due to size and shape variability and because the background mammogram texture is typically inhomogeneous. The authors develop a 2-stage method based on wavelet transforms for detecting and segmenting calcifications. The first stage is based on an undecimated wavelet transform, which is simply the conventional filter bank implementation without downsampling, so that the low-low (LL), low-high (LH), high-low (HL), and high-high (HH) sub-bands remain at full size. Detection takes place in HH and the combination LH+HL. Four octaves are computed with 2 inter-octave voices for finer scale resolution. By appropriate selection of the wavelet basis the detection of microcalcifications in the relevant size range can be nearly optimized. In fact, the filters which transform the input image into HH and LH+HL are closely related to prewhitening matched filters for detecting Gaussian objects (idealized microcalcifications) in 2 common forms of Markov (background) noise. The second stage is designed to overcome the limitations of the simplistic Gaussian assumption and provides an accurate segmentation of calcification boundaries. Detected pixel sites in HH and LH+HL are dilated then weighted before computing the inverse wavelet transform. Individual microcalcifications are greatly enhanced in the output image, to the point where straightforward thresholding can be applied to segment them. FROG curves are computed from tests using a freely distributed database of digitized mammograms.

Wavelet transforms for detecting microcalcifications in mammograms

Objective To examine the accuracy of artificial intelligence (AI) for the detection of breast cancer in mammography screening practice. Design Systematic review of test accuracy studies. Data sources Medline, Embase, Web of Science, and Cochrane Database of Systematic Reviews from 1 January 2010 to 17 May 2021. Eligibility criteria Studies reporting test accuracy of AI algorithms, alone or in combination with radiologists, to detect cancer in women’s digital mammograms in screening practice, or in test sets. Reference standard was biopsy with histology or follow-up (for screen negative women). Outcomes included test accuracy and cancer type detected. Study selection and synthesis Two reviewers independently assessed articles for inclusion and assessed the methodological quality of included studies using the QUality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. A single reviewer extracted data, which were checked by a second reviewer. Narrative data synthesis was performed. Results Twelve studies totalling 131 822 screened women were included. No prospective studies measuring test accuracy of AI in screening practice were found. Studies were of poor methodological quality. Three retrospective studies compared AI systems with the clinical decisions of the original radiologist, including 79 910 women, of whom 1878 had screen detected cancer or interval cancer within 12 months of screening. Thirty four (94%) of 36 AI systems evaluated in these studies were less accurate than a single radiologist, and all were less accurate than consensus of two or more radiologists. Five smaller studies (1086 women, 520 cancers) at high risk of bias and low generalisability to the clinical context reported that all five evaluated AI systems (as standalone to replace radiologist or as a reader aid) were more accurate than a single radiologist reading a test set in the laboratory. In three studies, AI used for triage screened out 53%, 45%, and 50% of women at low risk but also 10%, 4%, and 0% of cancers detected by radiologists. Conclusions Current evidence for AI does not yet allow judgement of its accuracy in breast cancer screening programmes, and it is unclear where on the clinical pathway AI might be of most benefit. AI systems are not sufficiently specific to replace radiologist double reading in screening programmes. Promising results in smaller studies are not replicated in larger studies. Prospective studies are required to measure the effect of AI in clinical practice. Such studies will require clear stopping rules to ensure that AI does not reduce programme specificity. Study registration Protocol registered as PROSPERO CRD42020213590.

https://www.bmj.com/content/bmj/374/bmj.n1872.full.pdf

Use of artificial intelligence for image analysis in breast cancer screening programmes: systematic review of test accuracy

A review of breast imaging has already appeared in 1982 in this journal. Consequently, the present article concentrates on a discussion of only those developments of a more recent nature. Although the emphasis is placed on the physical aspects of the different imaging methods concerned, the essential factors relating to the clinical background and the associated radiation risk are also outlined. The completeness of detail depends on the present clinical importance of the method under discussion. X-ray mammography, which is still the most important breast imaging technique and has proved to be an effective method for breast cancer screening, is therefore treated in greater detail. Since the early 1980s, ultrasound B-mode scanning has evolved to an indispensable adjunct to x-ray mammography. For Doppler sonography, diaphanography, contrast-enhanced MRI, CT and DSA, the visualization of a tumour depends essentially on the enhanced vascularity of the lesion. Whether this will prove to be a reliable indicator for malignancy remains to be shown in controlled clinical studies. Common to all imaging systems is the increasing use of digital methods for signal processing, which also offers the possibility of computer-aided diagnosis by texture analysis and pattern recognition.

http://iopscience.iop.org/article/10.1088/0031-9155/41/3/001/pdf

Recent developments in breast imaging.

Sensitivity and specificity of first screen mammography in a randomized screening trial at five centers are reported. A total of 23,101 women underwent mammography; in 139, breast cancer was detected at first screening; in 20, less than 12 months after first screening; and in 47, at second screening. All 206 cancer cases were histologically confirmed, and 174 were defined as being detectable at first screening. Average length of follow-up for all women was 3.2 years. Interpretations of first screen mammograms by the center radiologists were matched to known outcomes. Simultaneous blind review was performed by a single reference radiologist with mammograms from all 206 cancer cases and those of a random sample of 739 women not known to have breast cancer at 15 months or more after initial screening. Overall, the five screening centers achieved a sensitivity of 69% (range, 60%-78%), a specificity of 94% (range, 93%-96%), a positive predictive value of 8.6% (range, 3%-16%), and a negative predictive value of 99.7% (range, 99.6%-99.9%).

Sensitivity and specificity of first screen mammography in the Canadian National Breast Screening Study: a preliminary report from five centers.

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Sensitivity and specificity of first screen mammography in the Canadian National Breast Screening Study: a preliminary report from five centers.