DOI: https://doi.org/10.21203/rs.2.14015/v3
Background To investigate the performance of primary ultrasound (US) screening for breast cancer, and that of supplemental US screening for breast cancer after negative mammography (MAM).
Methods Electronic databases (PubMed, Scopus, Wed of Science, and Embase) were systematically searched to identify relevant studies published between January 2003 and May 2018. Only high-quality or fair-quality studies reporting any of the following performance values for supplemental or primary US screening were included: sensitivity, specificity, cancer detected rate (CDR), recall rate (RR), biopsy rate (BR), and proportions of invasive cancers (ProIC) or node-negative invasive cancers (ProNNIC) among screening-detected cancers.
Results Twenty-three studies were included, including 12 studies in which supplemental US screening was used after negative MAM and 11 joint screening studies in which both MAM and US were used as primary screening methods. Meta-analyses revealed that supplemental US screening could detect 96% [95% confidential intervals (CIs): 82% to 99%] of occult breast cancers missed by MAM and identify 94% (95% CIs: 88% to 97%) of healthy women, with a CDR of 2.9/1000 (95%CIs: 1.8/1000 to 3.9/1000), RR of 8.6% (95%CIs: 4.8% to 13.5%), BR of 3.9% (95%CIs: 2.5% to 5.5%), ProIC of 73.9% (95%CIs: 49.0% to 93.7%), and ProNNIC of 72.6% (95%CIs: 51.9% to 90.0%). Compared with primary MAM screening, primary US screening led to the recall of significantly more women with positive screening results [1.2% (95%CIs:0.4% to 1.9%), P =0.003] and detected significantly more invasive cancers [20.8% (95%CIs: 14.6% to 27.0%), P < 0.001]. However, there were no significant differences for other performance measures between the two screening methods, including sensitivity, specificity, CDR, BR, and ProNNIC.
Conclusions Current evidence suggests that supplemental US screening could detect occult breast cancers missed by MAM. Primary US screening has shown to be comparable to primary MAM screening in women with dense breasts in terms of sensitivity, specificity, cancer detection rate, and biopsy rate, but with higher recall rates and higher detection rates for invasive cancers.
Cancer is a global public health issue in the world. In 2016, an estimated 17.2 million cancer cases and 8.9 million cancer deaths occurred worldwide [1]. For women, both the most commonly occuring cancer and the leading cause of cancer deaths and disability-adjusted life-years (DALYs) was breast cancer (1.7 million incident cases, 535, 000 deaths, and 14.9 million DALYs)[1]. Over the years, the burden of cancer has shifted from more developed countries to less developed countries [2]. Moreover, the burden is expected to grow worldwide due to the aging of the population and the adoption of lifestyle behaviors such as smoking, poor diet, physical inactivity, and reproductive changes (including lower parity and later age at first birth), particularly in less developed countries[2]. Therefore, broad prevention measures, such as cancer screening, are urgently needed to control this increasing burden, especially in less developed countries.
Mammography (MAM) has been used to screen for breast cancer since the 1970s and is now widely available in developed countries. However, in less developed counties, such as China, MAM is not easily accessible due to several barriers, including insufficient MAM equipment, inadequate insurance coverage for MAM, and widely dispersed populations [3]. Moreover, MAM has a low sensitivity in women with dense breasts [4], who could suffer a higher risk of breast cancer than those without dense breasts [5]. Worrisome research from Denmark and Netherlands showed that nearly one in every three or half of screening-detected breast cancers represents overdiagnosis, respectively [6, 7].
Recent data indicates that supplemental ultrasonography (US) screening could detect occult breast cancers missed by MAM, and primary US screening seems perform comparably to primary MAM screening [8-11]. However, systematic reviews of the performances of supplemental or primary US screening have been published only in limited studies. Moreover, among broad screening studies in which both MAM and US were used as primary screening methods, researchers just focused on the performance differences between joint screening and MAM screening alone. Limited studies investigated the independent performances of primary US screening. Therefore, we conducted this systematic review and meta-analysis to provide a global profile of supplemental US screening after negative MAM screening or primary US screening for breast cancers.
This meta-analysis was reported in line with the preferred reporting items for a systematic review and meta-analysis of diagnostic test accuracy studies: The PRISMA-DTA Statement (Supplementary S1) [12].
Types of studies and participants
Randomized-controlled trials (RCTs), prospective or retrospective screening cohort studies focusing on the performance of primary US screening for breast cancer or performance of supplemental US screening for breast cancer after negative MAM were included. The screening performance included the following indicators: sensitivity, specificity, cancer detected rate (CDR), recall rate (RR), biopsy rate (BR), and proportions of invasive cancers (ProIC) or node-negative invasive cancers (ProNNIC) among screening-detected cancers. The types of US included were hand-held ultrasonography (HHUS) and automated whole breast ultrasonography (ABUS). Diagnostic studies of patients with histopathologically proven breast cancer or women with suspicious finding after initial screening were excluded. Screening studies for second cancers among women previously diagnosed with breast cancer were also excluded.
Searching strategies
A comprehensive search was conducted according to the Cochrane handbook guidelines. The American College of Radiology (ACR) developed the Breast Imaging Reporting and Data System (BI-RADS) classification for breast ultrasonography examinations starting in 2003 [13]. Electronic databases (PubMed, Scopus, Wed of Science, and Embase) were systematically searched to identify relevant studies published in English between January 2003 and May 2018. Five groups of key words were used in the searching strategies: (1) breast neoplasm, breast cancer, breast carcinoma; (2) ultrasound, ultrasonography; (3) screening; (4) supplemental, supplementary, adjunct, adjunctive, combined, joint, primary, single, alone; (5) sensitivity, specificity, detection rate, recall rate, biopsy rate. Reference lists from retrieved articles were also reviewed. Detailed searching strategies are referred to in the supplementary S2.
Selection of studies
Two authors independently screened the titles and abstracts of all selected articles to confirm their eligibility. All selected articles were analyzed by EndNote software that allows reviewers to manage articles and detect duplicate publications. When two or more articles from the same trial were selected, the article with the larger sample size, longer duration of follow-up, or the latest results was included. Any disagreement on the selection of articles was discussed and arbitrated by a third author. Details of the selection process are provided in the supplementary S3.
Data extraction
Two authors independently extracted the following data from the qualifying studies: general information (name of first author, year of publication, and country or countries where the study was performed), design of study (sample size, mean age, percent of women with dense breasts among the whole population, type of US, screening mode), performance of US, and information for risk assessment of bias (detailed information referred to in the following section). Since there was not a consistent conclusion that dense breast can be regarded as an independent risk factor of breast cancer [5, 14], in order to avoid bringing 'high risk' labels to women with dense breasts, we collected information of dense breast as an attribute for average risk women. All data was entered into STATA 14.0 software for analysis. Any disagreements on data extracted were also discussed and arbitrated by the same third author.
Risk assessment of bias in included studies
Two investigators critically appraised all included studies independently according to the pre-specified criteria, which were adjusted from the USPSTF’s design-specific criteria and the STARD checklist for reporting diagnostic accuracy studies [15, 16]. The adjusted criteria included: (1) Included population came from a representative source population (Yes: general community women or well-defined high-risk women; No: women participants in an opportunistic screening and other undefined women) (2) Sample size was greater than or equal to 1000 (Yes/No) (3) Included studies clearly described the inclusion and exclusion criteria, and women who had a personal history of breast cancer were definitely excluded before screening (Yes/No) (4) In studies in which more than one screening method was used as the primary screening method, the readers of different screening methods were masked to each other (Yes/No) (5) All participants received US screening, or the proportion of missing data for either test was less than or equal to 5% (Yes/No) (6) US findings were interpreted according to BI-RADS criteria (Yes/No) (7) Women with positive results from index screening methods were ascertained with histopathology; and women with negative results were ascertained with a minimum 12-month clinical follow-up (reference standards) (Yes/No).
According to the above-mentioned criteria, high-quality studies were defined as those meeting at least six criteria for joint screening studies and five criteria for supplemental US screening studies. Fair-quality studies meet four or five criteria for joint screening studies and three or four criteria for supplemental US screening studies. Poor quality studies were defined as those meeting less than four criteria for joint screening studies and three criteria for supplemental US screening studies. Poor studies were excluded from the review.
Data synthesis and analysis
All data were extracted with pre-specified uniform tables and recalculated with uniform methods. The corresponding authors were contacted to obtain any missing information from their studies. For those studies in which the number of ‘examinations’ rather than the number of ‘women’ as the denominator to calculate the detection rate of breast cancer, each woman would be followed up several times, and every time she had an examination. Therefore, each woman would have several examinations in these stuides. In this study, if we changed the number of ‘women’ as the denominator to calculate the detection rate for these studies, the results would significantly be overestimated since the number of ‘women’ was significantly less than the number of ‘examinations’. Therefore, in order to follow the analysis protocol in the original studies and avoid potential overestimate in detection rate, we equate each examination with an independent woman.
The recall rate was calculated as the number of women recalled for further diagnosed examinations divided by the total number of women participated the screening. If the number of women recalled for any further diagnosed examinations was not available, the number of women with a positive result of index screening modality was used instead. The biopsy rate was calculated as the number of women recalled for pathological examination divided by the total number of women participated the screening.
The variation in different screening performances attributable to heterogeneity was measured as I2. If the P value for I2 was less than 0.1, significant heterogeneity was indicated among included trials and the random-effect model was used to combine screening performances [17]. Otherwise, the fixed-effect model was used if the P value for I2 was larger than 0.1. To search for sources of heterogeneity and obtain clinically meaningful estimates, subgroup analyses were conducted according to different studies characteristics, such as sample size > 1000 (Yes/No), all women with dense breasts (Yes/No), type of US (HHUS/ABUS), and quality assessment (Yes/No), whenever possible.
The package “midas” was used to combine sensitivity and specificity, to investigate whether there were potential publication biases among included studies, and to plot the summary receiver operating characteristic (SROC) curve with its 95% confidence and prediction contours [18]. The package “metaprop” was used to combine CDR, RR, BR, ProIC, and ProNNIC [19]. In addition, the package “metan” was used to compare the performances between MAM and US [20].
All meta-analyses were conducted with STATA software (version 14.0). All tests were two-sided, and P values of less than 0.05 for all meta-analyses indicated statistical significance.
Supplementary figure S3 shows a flowchart of the study selection procedure. The electronic searches yielded 1162 potentially relevant studies, of which 23 eligible studies were included in the final review [9-11, 21-40], including 12 studies in which supplemental US screening was used after negative MAM and 11 joint screening studies in which both MAM and US were used as primary screening methods.
Table 1 shows the baseline characteristics of the 23 studies. Twelve studies were conducted among women with dense breasts. Twenty studies screened women with HHUS. Twelve studies were conducted among general community women or well-defined high-risk women. Eleven studies definitely excluded women who had a personal history of breast cancer. Eight joint screening studies masked the results of primary MAM screening and primary US screening. Nineteen studies had low risk of incomplete data. Sixteen studies reported US results according to BI-RADS classification criteria. The reference standard in seventeen studies was pathologic examination combined with 12-month clinical follow-up. Finally, according to the pre-specified criteria, seven studies were of high quality, while the remaining 16 were of fair quality.
Screening accuracy for supplemental and primary US screening
Table 2 shows the original data of screening accuracy for supplemental and primary US screening among the included studies. Based on meta-analyses, supplemental US screening could detect 96% [95% confidential intervals (CIs): 82% to 99%; I2 = 66.3%, P < 0.01] of occult breast cancers missed by MAM and identify 94% (95% CIs: 88% to 97%; I2 = 99.8%, P < 0.01) of healthy women [Figure 1 (A), supplementary S4]. The area under the SROC (AUC) for supplemental US screening was 99% (95CIs: 97% to 99%) [Figure 1 (A)]. No publication bias was found among these studies (P = 0.465).
Among 11 joint screening studies, primary MAM screening could detect 64% (95%CIs: 53% to 74%; I2 = 93.5%, P < 0.01) of breast cancers and identify 97% (95% CIs: 94% to 99%; I2 = 99.9%, P < 0.01) of healthy women [Figure 1 (B), supplementary S5], respectively. Primary US screening could detect 55% (95%CIs: 37% to 71%; I2 = 95.5%, P < 0.01) of breast cancers and identify 98% (95CIs: 94% to 99%; I2 = 100%, P < 0.01) of healthy women [Figure 1 (C), supplementary S6]. The AUCs for primary MAM screening and primary US screening were 88% (95CIs: 85% to 91%) (Figure 1B) and 90% (95CIs: 87% to 93%) [Figure 1 (C)], respectively. No publication bias was found for both primary MAM screening (P = 0.209) and primary US screening (P = 0.466). No significant differences were found for either sensitivity [-10.9% (95%CIs: -29.0% to 7.2%), P = 0.239; I2 = 91.8%, P < 0.001] or specificity [-0.2% (95%CIs: -0.9% to 0.4%), P =0.510; I2 =96.7%, P < 0.001] between primary US screening and primary MAM screening (Figure 2).
Screening efficacy for supplemental and primary US screening
Table 3 shows the original data for screening accuracy for supplemental and primary US screening reported by the included studies. Meta-analyses showed that the summary CDR for supplemental US screening was 2.9/1000 (95%CIs: 1.7/1000 to 4.5/1000; I2 = 85.2%, P < 0.001), with a RR of 8.6% (95%CIs: 4.8% to 13.5%; I2 = 99.7%, P < 0.001) and a BR of 3.9% (95%CIs: 2.5% to 5.5%; I2 = 98.4%, P < 0.001) (Figure 3).
Among 11 joint screening studies, the summary CDRs for primary MAM screening and primary US screening were 4.5/1000 (95%CIs: 3.1/1000 to 6.0/1000; I2 = 89.6%, P < 0.001) and 3.7/1000 (95%CIs: 2.4/1000 to 5.2/1000; I2 = 91.0%, P < 0.001), with summary RRs of 4.1% (95%CIs: 2.0% to 7.0%; I2 = 99.8%, P < 0.001) and 5.3% (95%CIs: 2.5% to 9.2%; I2 = 99.8%, P < 0.001), and summary BRs of 1.4% (95%CIs: 0.4% to 2.9%; I2 = 99.0%, P < 0.001) and 1.9% (95%CIs: 0.8% to 3.4%; I2 = 98.7%, P < 0.001) (Figure 4). Compared to primary MAM screening, primary US screening recalled significantly more women with positive screening results [1.2% (95%CIs: 0.4% to 1.9%), P =0.003] (Figure 2). No significant differences were found for either CDR [-0.6/1000 (95%CIs:-1.7/1000 to 0.6/1000, P =0.334; I2 = 73.8%, P < 0.001] or BR [0.6% (95%CIs: -0.1% to 1.2%), P = 0.094; I2 = 92.2%, P < 0.001] for primary US screening compared to primary MAM screening (Figure 2).
Cancer characteristics for supplemental and primary US screening
Table 4 shows the original data for cancer characteristics for supplemental and primary US screening reported by the included studies. The studies from Corsetti [13], Youk [32], and Brancato [34] among the supplemetal US screening studies, as well as Shen [21], and Huang [28] among joint screening studies did not report detailed information of invasive cancers or node-negative invasive cancers among screening detected cancers, therefore, they are missed in table 4. After meta-analyses, 73.9% (95%CIs: 49.0% to 93.7%; I2 = 66.4%, P = 0.007) of cancers detected by supplemental US screening were invasive cancers, while 72.6% (95%CIs: 51.9% to 90.0%; I2 = 0.0%, P = 0.499) of cancers were node-negative invasive cancers (Figure 3).
Among 11 joint screening studies, 62.1% (95%CIs: 55.9% to 68.1%; I2 = 15.9%, P = 0.301) and 88.5% (95%CIs: 76.8% to 97.6%; I2 = 73.2%, P < 0.001) of cancers detected by primary MAM screening and by primary US were invasive cancers, while 53.0% (95%CIs: 21.5% to 83.4%; I2 = 93.1%, P < 0.001) and 64.1% (95%CIs: 34.7% to 89.5%; I2 = 90.6 %, P < 0.001) of cancers were node-negative invasive cancers (Figure 4). Compared to primary MAM screening, primary US screening detected significantly more invasive cancers [20.8%, 95% CIs (14.6% to 27.2%), P < 0.001; I2 = 0, P = 0.046] but a similar number of node-negative invasive cancers [6.2%, 95% CIs (-1.0% to 13.4%), P = 0.094; I2 = 0, P =0.810] (Figure 2).
Subgroup analyses
Subgroup analyses showed very similar results to those of primary analyses (Supplementary S7 and S8). In addition to results comparable to those observed in the primary analyses, lower sensitivity, higher specificity, higher cancer detection rate, and higher biopsy rate were found for supplemental US screening among women with dense breasts compared to those without dense breasts (Supplementary S7). Moreover, the differences for sensitivities, specificities, and cancer detection rates between primary MAM screening and primary US screening were larger among women with dense breasts compared to those without dense breasts (Supplementary S8).
The U.S. Preventive Services Task Force (USPSTF) had initially reviewed the performances and clinical outcomes of supplemental US screening in women with dense breasts or negative mammography [15]. However, only two studies were included. The authors concluded that the effects of supplemental US screening on breast cancer outcomes remain unclear due to sparse good evidence [15]. In addition, Gartlehnerhad systematically reviewed the evidence investigating the joint effectiveness of screening with MAM and US compared to MAM screening alone [41]. However, this review did not investigate the performance of primary US screening. Our study is the first systematic review and meta-analysis to investigate the performance of primary US screening for breast cancer, and this is also an important up-to-date systematic review and meta-analysis investigating the performance of supplemental US screening.
The role of supplemental US screening was first addressed in ACRIN 6666 by Berg in 2008 [4]. Berg concluded that adding US screening to MAM screening would yield an additional 1.1 to 7.2 cancers per 1000 high-risk women [4]. Our analyses also found a similar additional 1.8 to 3.9 cancers per 1000 examinations. Moreover, after re-analysis of ACRIN 6666, Berg concluded that ultrasound could be used as the primary screening test for breast cancer[11]. However, up to now, there have been no consistent conclusions concerning whether US screening should be recommended as a screening test for women in the screening guidelines for breast cancer. For example, the National Comprehensive Cancer Network, the European Society of Breast Imaging (EUSOBI), the Japanese Breast Cancer Society, and the Chinese Anti-Cancer Association (CACA) supported that supplemental US screening should be recommended for women with dense breasts after negative mammogram [42-45], while no clear recommendations of US screening were suggested by the USPSTF, the American Cancer Society, the American College of Physicians, and the Canadian Task Force on Preventive Health Care [46-49].
Several reasons would lead to these inconsistent recommendations among current guidelines. As argued by USPSTF, sparse good evidence would be the major reason. However, as shown in our study, several high-quality studies and fair-quality studies had been conducted since 2003. Although EUSOBI supported supplemental US screening after MAM, it also addressed the concern that breast US was inappropriately suggested to be a primary screening method since primary US screening had not been shown to reduce mortality of breast cancer in the general female population. Moreover, US would lead to more biopsies and recalls than MAM [45]. In this systematic review, we did observe higher recall rates for US compared to MAM. We also observed higher biopsy rates for US compared to MAM; however, the difference was nonsignificant. This nonsignificant difference in biopsy rates between US and MAM may be due to small sample sizes, but it may also reflect no actual difference. In addition, there are several limitations of breast ultrasound that would make it inappropriate for a screening test. These include: US cannot take an image of the whole breast at once as MAM does; US cannot show microcalcifications, which would be the most common feature of tissue around a tumor; the skill level of the US operators makes a great difference in the screening results. However, as shown in our study, these concerns seemed not to cause significant differences in the sensitivity and specificity, or even in cancer detection rates and cancer characteristics (such as the proportion of node-negative invasive cancers) between primary US screening and primary MAM screening. Moreover, lower price, larger coverage, absence of radiation effects, and lower overdiagnosis rates for US compared to MAM make US more easily accepted in China and other countries [3, 50, 51].Therefore, Chinese Anti-Cancer Association and other societies supported supplemental US screening in their guidelines.
Lastly, the following results are significant. First, we observed significantly higher RR and ProIC for primary US screening compared with primary MAM screening. Higher recall rates would be an important barrier to promote US screening. More studies are needed to investigate the factors associated with higher recall rates of US screening so as to reduce unnecessary recalls. Second, as shown in supplementary S7, subgroup analyses did not find obvious differences in sensitivity, specificity or cancer detection rate for supplemental US screening after negative MAM screening between women with and without dense breasts. These results suggested that influence of dense breasts on the performance of US after negative MAM would be influenced by other factors. Moreover, as shown in supplementary S8, subgroup analyses also did not find significantly higher sensitivity for primary MAM compared to primary US among women with dense breasts. Small sample size could be an important factor, since only 3/11 exclusively recruited women with dense breasts (a proportion of 100% dense breasts) and only >40% of participating women had dense breasts in another 5/11 studies.
Limitations
First, due to lack of evidence for reduced mortality from breast cancer, we cannot conclude that US screening would lead to a long-term benefit. More studies with sophistacted design and long-time follow-up are needed to investigate the long-term benefits and potential risks (including false positivity, "unnecessary" recalls, and overdiagnosis) of primary US screening. Second, in addition to breast density, no studies investigated whether other risk factors (such as obesity) influenced the differences in screening performance between US and MAM. Therefore, we cannot conclude whether these different performances between US and MAM derived from confounding effects or from the actual differences between US and MAM. Third, as shown in table 3 and table 4, not all included studies reported all screening performances indexes (such as biopsy rate, proportions of invasive cancers, and proportions of node-negative invasive cancers). Therefore, meta-analysis results from studies reporting screening performances indexes would lead to biased results and complete reporting screening performances for US and MAM screening studies are needed to improve the current results. Fourth, combination data from repeated (longitudinal) US screening for a woman with data from an initial screening would also lead to biased results.
Current evidence suggests that supplemental US screening could detect occult breast cancers missed by MAM. Primary US screening has shown to be comparable to primary MAM screening in women with dense breasts in terms of sensitivity, specificity, cancer detection rate, and biopsy rate, but with higher recall rates and higher detection rates for invasive cancers. More studies are needed to investigate the long-term benefits and potential risks (including false positivity, "unnecessary" recalls, and overdiagnosis) of primary US screening. Moreover, we hope that US screening for breast cancer should deserve more attention in the future, not only because US is comparable to MAM in women with dense breasts in terms of sensitivity, specificity, cancer detection rate, and biopsy rate, but also because ultrasound is not radiated and is easier to access in low-resources areas, such as Chinese rural areas.
ABUS: automated whole breast ultrasonography; BR: biopsy rate; CDR: cancer detected rate; HHUS: hand-held ultrasonography; MAM: Mammography; ProIC: proportions of invasive cancers; ProNNIC: node-negative invasive cancers; RR: recall rate; US: ultrasonography; USPSTF: the U.S. Preventive Services Task Force; EUSOBI: European Society of Breast Imaging; CACA: Chinese Anti-Cancer Association.
Ethics approval and consent to participate
Not applicable
Consent for publication
Not applicable
Availability of data and materials
The datasets analysed during the current study available from the corresponding author on reasonable request.
Competing interests
The authors declare that they have no competing interests.
Funding
This work was supported by the Natural Science Foundation of Tianjin [Grant number 18JCQNJC80300, Funder: Yubei Huang]; Chinese National Key Research and Development Project [Grant number 2018YFC1315600, Funder: Fengju Song]; National Natural Science Foundation of China [Grant numbers 81502476, Funder: Yubei Huang]; and the Beijing Young Talent Program [Grant number 2016000021469G189, Funder: Lei Yang].
Authors' contributions
LY drafted and revised the manuscript. SW analyzed the data. LZ and CS colleted the data. FS reviewed the manuscript and provided useful revision suggestions. YH conceived and designed the study. All authors contributed interpreted findings, reviewed and approved the final version to be submitted.
Acknowledgements
Not applicable
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37 Honjo S, Ando J, Tsukioka T, et al. Relative and combined performance of mammography and ultrasonography for breast cancer screening in the general population: a pilot study in Tochigi Prefecture, Japan. Jpn J Clin Oncol 2007; 37: 715-720.
38 Wilczek B, Wilczek HE, Rasouliyan L, Leifland K. Adding 3D automated breast ultrasound to mammography screening in women with heterogeneously and extremely dense breasts: Report from a hospital-based, high-volume, single-center breast cancer screening program. Eur J Radiol 2016; 85: 1554-1563.
39 Brem RF, Tabar L, Duffy SW, et al. Assessing improvement in detection of breast cancer with three-dimensional automated breast US in women with dense breast tissue: the SomoInsight Study. Radiology 2015; 274: 663-673.
40 Kelly KM, Dean J, Comulada WS, Lee SJ. Breast cancer detection using automated whole breast ultrasound and mammography in radiographically dense breasts. Eur Radiol 2010; 20: 734-742.
41 Gartlehner G, Thaler K, Chapman A, et al. Mammography in combination with breast ultrasonography versus mammography for breast cancer screening in women at average risk. Cochrane Database Syst Rev 2013; 4: D9632.
42 Tozaki M, Kuroki Y, Kikuchi M, et al. The Japanese Breast Cancer Society clinical practice guidelines for screening and imaging diagnosis of breast cancer, 2015 edition. Breast Cancer 2016; 23: 357-366.
43 National Comprehensive Cancer Network. NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines):Breast Cancer Screening and Diagnosis. V1 ed.2016.
44 The Committee of Breast Cancer from the Chinese Anti-Cancer Association. Guidelines of Diagnosis and Treatment for Breast Cancer by the Chinese Anti-Cancer Association (2017 Edition). Journal of Chinese Oncology 2017; 27: 695-760.
45 Evans A, Trimboli RM, Athanasiou A, et al. Breast ultrasound: recommendations for information to women and referring physicians by the European Society of Breast Imaging. Insights Imaging 2018; 9: 449-461.
46 Siu AL. Screening for Breast Cancer: U.S. Preventive Services Task Force Recommendation Statement. Ann Intern Med 2016; 164: 279-296.
47 Wilt TJ, Harris RP, Qaseem A. Screening for cancer: advice for high-value care from the american college of physicians. Ann Intern Med 2015; 162: 718-725.
48 Oeffinger KC, Fontham ET, Etzioni R, et al. Breast Cancer Screening for Women at Average Risk: 2015 Guideline Update From the American Cancer Society. JAMA 2015; 314: 1599-1614.
49 Tonelli M, Connor GS, Joffres M, et al. Recommendations on screening for breast cancer in average-risk women aged 40-74 years. CMAJ 2011; 183: 1991-2001.
50 Dong H, Huang Y, Song F, et al. Improved Performance of Adjunctive Ultrasonography After Mammography Screening for Breast Cancer Among Chinese Females. Clin Breast Cancer 2017; 18: e353-e361.
51 Huang Y, Dai H, Song F, et al. Preliminary effectiveness of breast cancer screening among 1.22 million Chinese females and different cancer patterns between urban and rural women. Sci Rep 2016; 6: 39459.
Table 1. Characteristics of included studies
Author, year |
Country |
Age, years |
|
PerDB, % |
Type of US |
Sample size |
Screening mode |
Exclusion of BC |
Blinding |
Complete data |
BIRADS criteria |
FU, months |
Quality assessment |
Cohort type |
Supplemental US screening studies |
||||||||||||||
Tagliafico, 2016[21] |
Italy[21] |
51 |
|
100 |
HHUS |
3231 |
Community |
Yes |
- |
Yes |
No |
<12 |
Fair |
Prospective |
Kim, 2016[22] |
South Korea |
NR |
|
100 |
HHUS |
3171 |
Opportunistic |
Yes |
- |
Yes |
No |
12 |
Fair |
Retrospective |
Weigert, 2015[26] |
United States |
NR |
|
100 |
HHUS |
10282 |
Opportunistic |
NR |
- |
Yes |
Yes |
6 |
Fair |
Retrospective |
Hwang, 2015[25] |
South Korea |
50 |
|
78 |
HHUS |
1727 |
Opportunistic |
No |
- |
No |
Yes |
12 |
Fair |
Retrospective |
Moon, 2015[24] |
South Korea |
53 |
|
64 |
HHUS |
2005 |
Opportunistic |
NR |
- |
Yes |
Yes |
24 |
Fair |
Retrospective |
Parris, 2013[28] |
United States |
52 |
|
100 |
HHUS |
5519 |
Opportunistic |
No |
- |
Yes |
Yes |
NR |
Fair |
Retrospective |
Girardi, 2013[27] |
Italy |
51 |
|
45 |
HHUS |
22131 |
Opportunistic |
No |
- |
Yes |
Yes |
NR |
Fair |
Retrospective |
Leong, 2012[32] |
Singapore |
45 |
|
100 |
HHUS |
106 |
Community |
No |
- |
Yes |
No |
12-24 |
Fair |
Prospective |
Hooley, 2012[31] |
United States |
52 |
|
100 |
HHUS |
648 |
Opportunistic |
No |
- |
Yes |
Yes |
>15 |
Fair |
Retrospective |
Corsetti, 2011[33] |
Italy |
NR |
|
100 |
HHUS |
3356 |
Opportunistic |
Yes |
- |
Yes |
No |
12 |
Fair |
Retrospective |
Youk, 2011[34] |
South Korea |
48 |
|
100 |
HHUS |
446 |
Opportunistic |
No |
- |
Yes |
Yes |
24 |
Fair |
Retrospective |
Brancato, 2007[36] |
Italy |
52 |
|
100 |
HHUS |
5227 |
Opportunistic |
NR |
- |
Yes |
Yes |
NR |
Fair |
Prospective |
Joint screening studies |
||||||||||||||
Dong, 2017[9] |
China |
52 |
|
44 |
HHUS |
31918 |
Community |
Yes |
Yes |
Yes |
No |
12 |
High |
Prospective |
Ohuchi, 2016[10] |
Japan |
44 |
|
NR |
HHUS |
36752 |
Community |
Yes |
Yes |
Yes |
Yes |
12 |
High |
Prospective |
Berg, 2016[11] |
United States |
55 |
|
100 |
HHUS |
2662 |
High-risk |
Yes |
Yes |
Yes |
Yes |
>12 |
High |
Prospective |
Shen, 2015[23] |
China |
46 |
|
NR |
HHUS |
4135 |
High-risk |
Yes |
Yes |
No |
Yes |
12 |
High |
Prospective |
Brem, 2015[39] |
United States |
53 |
|
100 |
ABUS |
15318 |
Community |
Yes |
No |
Yes |
Yes |
12 |
High |
Prospective |
Huang, 2012[30] |
China |
46 |
|
48 |
HHUS |
3028 |
Opportunistic |
Yes |
Yes |
Yes |
Yes |
12 |
High |
Prospective |
Kelly, 2010[40] |
United States |
53 |
|
68 |
ABUS |
4419 |
High-risk |
No |
Yes |
Yes |
Yes |
12 |
High |
Prospective |
Wilczek, 2016[38] |
Sweden |
50 |
|
100 |
ABUS |
1668 |
Community |
Yes |
No |
Yes |
No |
24 |
Fair |
Prospective |
Venturini, 2013[29] |
Italy |
46 |
|
55 |
HHUS |
1666 |
Community |
Yes |
No |
No |
Yes |
6 |
Fair |
Prospective |
Weinstein, 2009[35] |
United States |
49 |
|
60 |
HHUS |
609 |
High-risk |
No |
Yes |
No |
Yes |
12 |
Fair |
Prospective |
Honjo, 2007[37] |
Japan |
NR |
|
NR |
HHUS |
3453 |
Community |
NR |
Yes |
Yes |
No |
≥18 |
Fair |
Prospective |
PerDB, percent of women with dense breasts accounted for the whole population; US, ultrasonography; BC, breast cancer; BIRADS, Breast Imaging-Reporting and Data System; FU, follow-up; HHUS/ABUS, hand-held / automated breast ultrasonography.
Table 2. Screening accuracy for supplemental and primary US screening
Author, year |
Method |
Case |
|
Non-case |
Sensitivity (95% CI) |
Specificity (95% CI) |
||
+ |
- |
+ |
- |
|||||
Supplemental US screening studies |
||||||||
Tagliafico, 2016[21] |
Supplemental US |
23 |
1 |
|
65 |
3142 |
0.96(0.77-1.00) |
0.98(0.97-0.98) |
Kim, 2016[22] |
Supplemental US |
9 |
0 |
|
822 |
2340 |
1.00(0.63-1.00) |
0.74(0.72-0.76) |
Weigert, 2015[26] |
Supplemental US |
24 |
15 |
|
411 |
9832 |
0.62(0.45-0.76) |
0.96(0.96-0.96) |
Hwang, 2015[25] |
Supplemental US |
8 |
1 |
|
92 |
1626 |
0.89(0.51-0.99) |
0.95(0.93-0.96) |
Moon, 2015[24] |
Supplemental US |
4 |
0 |
|
619 |
1382 |
1.00(0.40-1.00) |
0.69(0.67-0.71) |
Parris, 2013[28] |
Supplemental US |
10 |
0 |
|
175 |
5334 |
1.00(0.66-1.00) |
0.97(0.96-0.97) |
Girardi, 2013[27] |
Supplemental US |
41 |
0 |
|
381 |
21709 |
1.00(0.89-1.00) |
0.98(0.98-0.98) |
Leong, 2012[32] |
Supplemental US |
2 |
0 |
|
12 |
92 |
1.00(0.20-1.00) |
0.88(0.80-0.94) |
Hooley, 2012[31] |
Supplemental US |
3 |
0 |
|
150 |
495 |
1.00(0.31-1.00) |
0.77(0.73-0.80) |
Corsetti, 2011[33] |
Supplemental US |
32 |
8 |
|
363 |
6821 |
0.80(0.64-0.90) |
0.95(0.94-0.95) |
Youk, 2011[34] |
Supplemental US |
10 |
1 |
|
41 |
394 |
0.91(0.57-1.00) |
0.91(0.87-0.93) |
Brancato, 2007[36] |
Supplemental US |
2 |
0 |
|
21 |
5204 |
1.00(0.20-1.00) |
1.00(0.99-1.00) |
Joint screening studies |
||||||||
Dong, 2017[9] |
Primary MAM |
84 |
15 |
|
604 |
31215 |
0.85(0.76-0.91) |
0.98(0.98-0.98) |
|
Primary US |
61 |
38 |
|
389 |
31430 |
0.62(0.51-0.71) |
0.99(0.99-0.99) |
Ohuchi, 2016[10] |
Primary MAM |
117 |
85 |
|
2300 |
33547 |
0.58(0.51-0.65) |
0.94(0.93-0.94) |
|
Primary US |
143 |
59 |
|
2289 |
33558 |
0.71(0.64-0.77) |
0.94(0.93-0.94) |
Berg, 2016[11] |
Primary MAM |
59 |
52 |
|
700 |
6662 |
0.53(0.43-0.63) |
0.90(0.90-0.91) |
|
Primary US |
58 |
53 |
|
1012 |
6350 |
0.52(0.43-0.62) |
0.86(0.85-0.87) |
Shen, 2015[23] |
Primary MAM |
8 |
6 |
|
3 |
6913 |
0.57(0.30-0.81) |
1.00(1.00-1.00) |
|
Primary US |
14 |
0 |
|
6 |
6910 |
1.00(0.73-1.00) |
1.00(1.00-1.00) |
Brem, 2015[39] |
Primary MAM |
82 |
30 |
|
2219 |
12987 |
0.73(0.64-0.81) |
0.85(0.85-0.86) |
|
Primary US |
30 |
82 |
|
2721 |
12485 |
0.27(0.19-0.36) |
0.82(0.81-0.83) |
Huang, 2012[30] |
Primary MAM |
28 |
5 |
|
48 |
2947 |
0.85(0.67-0.94) |
0.98(0.98-0.99) |
|
Primary US |
24 |
9 |
|
19 |
2976 |
0.73(0.54-0.86) |
0.99(0.99-1.00) |
Kelly, 2010[40] |
Primary MAM |
23 |
34 |
|
36 |
4326 |
0.40(0.28-0.54) |
0.99(0.99-0.99) |
|
Primary US |
38 |
19 |
|
61 |
4301 |
0.67(0.53-0.78) |
0.99(0.98-0.99) |
Wilczek, 2016[38] |
Primary MAM |
7 |
4 |
|
16 |
1641 |
0.64(0.32-0.88) |
0.99(0.98-0.99) |
|
Primary US |
4 |
7 |
|
27 |
1630 |
0.36(0.12-0.68) |
0.98(0.98-0.99) |
Venturini, 2013[29] |
Primary MAM |
12 |
2 |
|
99 |
1553 |
0.86(0.56-0.97) |
0.94(0.93-0.95) |
|
Primary US |
2 |
12 |
|
8 |
813 |
0.14(0.03-0.44) |
0.99(0.98-1.00) |
Weinstein, 2009[35] |
Primary MAM |
6 |
12 |
|
25 |
566 |
0.33(0.14-0.59) |
0.96(0.94-0.97) |
|
Primary US |
3 |
15 |
|
66 |
483 |
0.17(0.04-0.42) |
0.88(0.85-0.91) |
Honjo, 2007[37] |
Primary MAM |
7 |
6 |
|
272 |
3258 |
0.54(0.26-0.80) |
0.92(0.91-0.93) |
|
Primary US |
6 |
7 |
|
159 |
3371 |
0.46(0.20-0.74) |
0.95(0.95-0.96) |
CI, confidential interval; MAM, mammography; US, ultrasonography.
Table 3. Screening efficacy for supplemental and primary US screening
Author, year |
Method |
Cancer detected rate, 1/1000 |
|
Recall rate, % |
|
Biopsy rate, % |
|||
Number |
95%CI |
Number |
95%CI |
Number |
95%CI |
||||
Supplemental US screening studies |
|||||||||
Tagliafico, 2016[21] |
Supplemental US |
23/3231 women |
7.1(4.6-10.8) |
|
88/3231 |
2.7(2.2-3.4) |
|
46/3231 |
1.4(1.1-1.9) |
Kim, 2016[22] |
Supplemental US |
9/3171 women |
2.8(1.4-5.6) |
|
831/3171 |
26.2(24.7-27.8) |
|
131/3171 |
4.1(3.5-4.9) |
Weigert, 2015[26] |
Supplemental US |
24/10282 women |
2.3(1.5-3.5) |
|
435/10282 |
4.2(3.9-4.6) |
|
|
|
Hwang, 2015[25] |
Supplemental US |
8/1727 women |
4.6(2.2-9.5) |
|
100/1727 |
5.8(4.8-7.0) |
|
37/1727 |
2.1(1.5-3.0) |
Moon, 2015[24] |
Supplemental US |
4/2005 women |
2.0(0.6-5.5) |
|
623/2005 |
31.1(29.1-33.2) |
|
|
|
Parris, 2013[28] |
Supplemental US |
10/5519 women |
1.8(0.9-3.4) |
|
185/5519 |
3.4(2.9-3.9) |
|
185/5519 |
3.4(2.9-3.9) |
Girardi, 2013[27] |
Supplemental US |
41/22131 women |
1.9(1.3-2.5) |
|
422/22131 |
1.9(1.7-2.1) |
|
422/22131 |
1.9(1.7-2.1) |
Leong, 2012[32] |
Supplemental US |
2/106 women |
18.9(3.3-73.2) |
|
14/106 |
13.2(7.7-21.5) |
|
14/106 |
13.2(7.7-21.5) |
Hooley, 2012[31] |
Supplemental US |
3/648 women |
4.6(1.2-14.7) |
|
153/648 |
23.6(20.4-27.1) |
|
46/648 |
7.1(5.3-9.4) |
Corsetti, 2011[33] |
Supplemental US |
32/7224 examinations |
4.4(3.1-6.3) |
|
395/7224 |
5.5(5.0-6.0) |
|
395/7224 |
5.5(5.0-6.0) |
Youk, 2011[34] |
Supplemental US |
10/446 examinations |
22.4(11.4-42.2) |
|
51/446 |
11.4(8.7-14.8) |
|
49/446 |
11.0(8.3-14.4) |
Brancato, 2007[36] |
Supplemental US |
2/5227 women |
0.4(0.1-1.5) |
|
23/5227 |
0.4(0.3-0.7) |
|
23/5227 |
0.4(0.3-0.7) |
Joint screening studies |
|||||||||
Dong, 2017[9] |
Primary MAM |
84/31918 women |
2.6(2.1-3.3) |
|
688/31918 |
2.2(2.0-2.3) |
|
|
|
|
Primary US |
61/31918 women |
1.9(1.5-2.5) |
|
450/31918 |
1.4(1.3-1.5) |
|
|
|
Ohuchi, 2016[10] |
Primary MAM |
117/36049 women |
3.2(2.7-3.9) |
|
2417/36049 |
6.7(6.4-7.0) |
|
|
|
|
Primary US |
143/36049 women |
4.0(3.4-4.7) |
|
2432/36049 |
6.7(6.5-7.0) |
|
|
|
Berg, 2016[11] |
Primary MAM |
59/7473 examinations |
7.9(6.1-10.2) |
|
453/7473 |
6.1(5.5-6.6) |
|
97/7473 |
1.3(1.1-1.6) |
|
Primary US |
58/7473 examinations |
7.8(6.0-10.1) |
|
515/7473 |
6.9(6.3-7.5) |
|
266/7473 |
3.6(3.2-4.0) |
Shen, 2015[23] |
Primary MAM |
8/6930 examinations |
1.2(0.5-2.4) |
|
11/6930 |
0.2(0.1-0.3) |
|
7/6930 |
0.1(0.0-0.2) |
|
Primary US |
14/6930 examinations |
2.0(1.2-3.5) |
|
20/6930 |
0.3(0.2-0.5) |
|
17/6930 |
0.2(0.1-0.4) |
Brem, 201537 |
Primary MAM |
82/15318 women |
5.4(4.3-6.7) |
|
2301/15318 |
15.0(14.5-15.6) |
|
586/15318 |
3.8(3.5-4.1) |
|
Primary US |
30/15318 women |
2.0(1.3-2.8) |
|
2751/15318 |
18.0(17.4-18.6) |
|
552/15318 |
3.6(3.3-3.9) |
Huang, 2012[30] |
Primary MAM |
28/3028 women |
9.2(6.3-13.5) |
|
105/3028 |
3.5(2.9-4.2) |
|
|
|
|
Primary US |
24/3028 women |
7.9(5.2-12.0) |
|
318/3028 |
10.5(9.4-11.7) |
|
|
|
Kelly, 2010[40] |
Primary MAM |
23/4419 women |
5.2(3.4-7.9) |
|
59/4419 |
1.3(1.0-1.7) |
|
59/4419 |
1.3(1.0-1.7) |
|
Primary US |
38/4419 women |
8.6(6.2-11.9) |
|
99/4419 |
2.2(1.8-2.7) |
|
99/4419 |
2.2(1.8-2.7) |
Wilczek, 2016[38] |
Primary MAM |
7/1668 women |
4.2(1.8-9.0) |
|
23/1668 |
1.4(0.9-2.1) |
|
11/1668 |
0.7(0.3-1.2) |
|
Primary US |
4/1668 women |
2.4(0.8-6.6) |
|
31/1668 |
1.9(1.3-2.7) |
|
12/1668 |
0.7(0.4-1.3) |
Venturini, 2013[29] |
Primary MAM |
12/1666 women |
7.2(3.9-12.9) |
|
76/1666 |
4.6(3.6-5.7) |
|
14/1666 |
0.8(0.5-1.4) |
|
Primary US |
2/835 women |
2.4(0.4-9.6) |
|
87/835 |
10.4(8.5-12.7) |
|
10/835 |
1.2(0.6-2.3) |
Weinstein, 2009[35] |
Primary MAM |
6/609 women |
9.9(4.0-22.4) |
|
31/609 |
5.1(3.5-7.2) |
|
21/609 |
3.4(2.2-5.3) |
|
Primary US |
3/567 women |
5.3(1.4-16.7) |
|
39/567 |
6.9(5.0-9.4) |
|
20/567 |
3.5(2.2-5.5) |
Honjo, 2007[37] |
Primary MAM |
7/3543 women |
2.0(0.9-4.3) |
|
279/3543 |
7.9(7.0-8.8) |
|
|
|
|
Primary US |
6/3543 women |
1.7(0.7-3.9) |
|
165/3543 |
4.7(4.0-5.4) |
|
|
|
CI, confidential interval; MAM, mammography; US, ultrasonography.
Table 4. Cancer characteristics for supplemental and primary US screening for breast cancer
Author, year |
Method |
Proportions of invasive cancers, % |
|
Proportions of node-negative invasive cancers, % |
||
Number |
95%CI |
Number |
95%CI |
|||
Supplemental US screening studies |
||||||
Tagliafico, 2016[21] |
Supplemental US |
22/23 |
95.7(78.1-99.9) |
|
13/21 |
61.9(38.4-81.9) |
Kim, 2016[22] |
Supplemental US |
7/9 |
77.8(40.0-97.2) |
|
|
|
Weigert, 2015[26] |
Supplemental US |
10/22 |
45.5(24.4-67.8) |
|
|
|
Hwang, 2015[25] |
Supplemental US |
7/8 |
87.5(47.4-99.7) |
|
6/7 |
85.7(42.1-99.6) |
Moon, 2015[24] |
Supplemental US |
2/4 |
50.0(6.8-93.2) |
|
1/2 |
50.0(1.3-98.7) |
Leong, 2012[32] |
Supplemental US |
1/2 |
50.0(1.3-98.7) |
|
|
|
Hooley, 2012[31] |
Supplemental US |
2/3 |
66.7(9.4-99.2) |
|
2/2 |
100(15.8-100) |
Joint screening studies |
||||||
Dong, 2017[9] |
Primary MAM |
30/63 |
47.6(34.9-60.5) |
|
16/30 |
53.3(34.3-71.7) |
|
Primary US |
25/46 |
54.3(39.0-69.1) |
|
13/25 |
52.0(31.3-72.2) |
Ohuchi, 2016[10] |
Primary MAM |
73/117 |
62.4(53.0-71.2) |
|
54/73 |
74.0(62.4-83.6) |
|
Primary US |
111/143 |
77.6(69.9-84.2) |
|
89/111 |
80.2(71.5-87.1) |
Berg, 2016[11] |
Primary MAM |
41/59 |
69.5(56.1-80.8) |
|
18/41 |
43.9(28.5-60.3) |
|
Primary US |
53/58 |
91.4(81.0-97.1) |
|
34/53 |
64.2(49.8-76.7) |
Brem, 2015[39] |
Primary MAM |
51/82 |
62.2(50.8-72.7) |
|
2/48 |
4.2(0.5-14.3) |
|
Primary US |
28/30 |
93.3(77.9-99.2) |
|
2/27 |
7.4(0.9-24.3) |
Kelly, 2010[40] |
Primary MAM |
17/23 |
73.9(51.6-89.8) |
|
|
|
|
Primary US |
35/38 |
92.1(78.6-98.3) |
|
|
|
Wilczek, 2016[38] |
Primary MAM |
5/7 |
71.4(29.0-96.3) |
|
|
|
|
Primary US |
4/4 |
100(39.8-100) |
|
|
|
Venturini, 2013[29] |
Primary MAM |
8/12 |
66.7(34.9-90.1) |
|
1/5 |
20.0(0.5-71.6) |
|
Primary US |
2/2 |
100(15.8-100) |
|
1/2 |
50.0(1.3-98.7) |
Weinstein, 2009[35] |
Primary MAM |
3/6 |
50.0(11.8-88.2) |
|
3/3 |
100(29.2-100) |
|
Primary US |
3/3 |
100(29.2-100) |
|
3/3 |
100(29.2-100) |
Honjo, 2007[37] |
Primary MAM |
3/7 |
42.9(9.9-81.6) |
|
3/3 |
100(29.2-100) |
|
Primary US |
5/6 |
83.3(35.9-99.6) |
|
4/4 |
100(39.8-100) |
CI, confidential interval; MAM, mammography; US, ultrasonography.