Incorporation of contrast-enhanced ultrasound in the differential diagnosis for breast lesions with inconsistent results on mammography and conventional ultrasound

Abstract

OBJECTIVE:

To identify the efficacy of contrast-enhanced ultrasound (CEUS) in re-evaluating masses with inconsistent Breast Imaging Reporting and Data System (BI-RADS) on mammography (MG) and conventional ultrasound (US).

MATERIALS AND METHODS:

A total of 637 breast lesions were evaluated with MG, US, and CEUS within 6 months and assessed as BI-RADS MG and US. CEUS was used as an additional screening to rerate BI-RADS US according to a five-point system. Lesions were divided into consistent or inconsistent group on the basis of BI-RADS MG and US assessment. The performance of MG, US, and CEUS in the overall and inconsistent group as well as the clinicopathological differences between consistent and inconsistent group were compared using Z test, Mann–Whitney U test, and t-test.

RESULTS:

The respective AUCs of MG and US were 0.742, 0.843 for overall group and 0.412, 0.789 for inconsistent group. The corresponding values of rerated CEUS BI-RADS were 0.958 and 0.950, which were significantly prior to those of MG and US (p < 0.001). Younger age, negative lymph node status, and dense breast were significantly associated with inconsistent group.

CONCLUSION:

Incorporation of CEUS to re-evaluate lesions can improve the diagnostic efficacy comparing to MG or US alone especially when disagreement occurred.

Keywords

Contrast enhanced ultrasound conventional ultrasound inconsistent BI-RADS MG and BI-RADS US assessment

1 Introduction

Breast cancer is the most commonly diagnosed cancer and the leading cause of cancer-related death among women in China [1]. Early detection and treatment are critical to reduce mortality and improve quality of life [2]. However, despite being the only screening tool proven to reduce breast cancer-related death [3, 4], mammography (MG) has rather a low sensitivity of 30–48% in dense breasts [4] and may hence result in a relatively high rate of false-negative [5] and misdiagnosed advanced breast cancer lesions.

Dense breast has been reported by several studies as a risk factor for breast cancer, with the risk varying by 4–6 folds from the lowest to highest category of density, respectively [6, 7]. Generally, the breast density of women in Asian countries are higher than those in western countries [8 –11], thus, conventional US is an attractive screening tool. However, conventional US remains controversial owing to its high false-positive rate. The Breast Imaging Reporting and Data System (BI-RADS) assessment in MG and conventional US are routinely taken into consideration, but discordant results may confuse clinicians and lead to incorrect treatment recommendations such as unnecessary biopsy or high interval cancer rate owing to conservative follow-up. Additionally, breast cancers detected by US are commonly smaller in size and earlier in stage than those detected by MG [12, 13]. Thus, an efficient method to deal with the inconsistent results is urgently needed. Contrast-enhanced US (CEUS) is a novel imaging technology, and its diagnostic efficiency for breast lesions had been confirmed in previous studies [14 –16]. CEUS is comparatively more valuable as it can not only show the morphology but also delineate the microvascular architecture of breast lesions. However, whether CEUS is an efficient supplemental screening tool for lesions with inconsistent MG and conventional US scores is still unclear.

Herein, this study aimed to investigate the role of CEUS in the diagnosis of breast lesions with inconsistent assessment on MG and conventional US.

2 Materials and methods

2.1 Patients

This study was a retrospective analysis of data collected between January 2015 and January 2019 from 663 women with 663 pathologically confirmed breast lesions. These women underwent MG, conventional US, and CEUS all within 6 months. The exclusion criteria were as follows: (i) incomplete or missing imaging information (n = 8); (ii) personal history of breast cancer or any previous treatment or interventional diagnosis (BI-RADS 6) (n = 13); (iii) pregnant or breastfeeding (n = 2); and (iv) presence of non-mammary gland changes such as fatty tissue disease (n = 3). Ultimately, 26 lesions were excluded from this study, and 637 breast lesions comprising 354 benign and 283 malignant lesions were analyzed. For women with more than one pathologically confirmed lesion, the largest or the most suspicious lesion was considered in this study. This retrospective study was approved by the institutional ethics committee of Shanghai General Hospital, and informed consent for CEUS was obtained from each patient.

2.2 Mammography (MG)

The mammographic system used was Senographe DS (General Electric Medical Systems, Milwaukee, Wisconsin). The MG was performed according to national and international practice guidelines for each patient. All patients underwent a routine screening MG in the oblique mediolateral and craniocaudal views by a technician qualified as the Mammography Quality Standards Act [17, 18] and with more than 3 years of experience in MG screening.

2.2.1 Ultrasound equipment

APlio 500 (TOSHIBA, Japan) equipped with a 10 MHz linear transducer was used for ultrasonic breast screening. Sulphurhexafluoride microbubbles (SonoVue®, BRACCO, Italy) was used as the contrast agent.

2.2.2 Conventional and contrast-enhanced ultrasound examination

All US scanning were performed by sonographers with 3–20 years of experience in breast US in accordance with American Institute of Ultrasound Medicine (AIUM) guidelines [19] and 2–6 years of experience in breast CEUS. All patients were placed in a supine position with their breasts fully exposed during the examination. Upon lesion detection, its size (maximal diameter), location, and the 2D and color doppler characteristics were noted. Pictures were stored.

A plane of the lesion with the richest blood supply was chosen as the CEUS target section; alternatively, the plane with maximal diameter or most irregular shape was chosen if no plane with abundant blood supply was found. To accurately locate the lesion, especially in case of tiny ones, dual image mode was applied during the whole procedure. Minimal compression was maintained to refrain from compressing the vessels, particularly in case of superficial lesions. The mechanical index was set at 0.06, 4.8 mL of Sulphurhexafluoride microbubbles (SonoVue®, BRACCO, Italy) was injected via the antecubital vein as a bolus followed by 5–10 mL of saline injection. Continuous imaging was recorded immediately after injection and lasted for 180 s for further analysis.

Lesions were carefully checked to ensure that the same ones in MG were considered.

2.3 Image analysis

The MG and conventional US images were initially classified according to the BI-RADS lexicon by two skilled radiologists blinded to the pathological results and each other findings. One of the radiologists had 2–5 years and the other had 10–20 years of experience in MG diagnosis. Similarly, both radiologists of the conventional US group had 3–20 years of experience in breast US diagnosis. In case of disagreement, two readers jointly re-evaluated the lesion and arrived at a consensus. According to the BI-RADS lexicon, the lesions were scored as categories 0; 1; 2; 3; 4a, 4b, 4c; 5. The lesions with BI-RADS category 1, 2, or 3 were considered benign, and those with category 4a, 4b, 4c, or 5 were considered malignant. The mammographic density was divided as a (almost entirely fatty), b (scattered fibroglandular breast tissue), c (heterogeneous dense breast tissue), or d (extremely dense breast tissue) depending on the proportion of fibroglandular tissue. All CEUS images were read by two radiologists with 2–6 years’ experience in CEUS. The CEUS image was analyzed on the basis of conventional US, and the characteristics of enhancement intensity and time compared to surrounding normal breast tissue; internal homogeneity; perfusion defect; enhancement direction; margin; size; with regular (Fig. 1) or irregular shape and particular enhancement patterns (such as ring and crab claw-like (Fig. 2)) in CEUS mode [20, 21]. Then, the rerated CEUS BI-RADS was determined according to a five-score system in breast CEUS supposed by Luo [21].

Fig.1

A lesion with inconsistent mammography (MG) and conventional ultrasound (US) results in a 40-year-old woman. The routine craniocaudal (a) and mediolateral oblique (b) MGs show an extremely dense breast with focal asymmetry hence classified as BI-RADS 0. (c) conventional US shows a suspicious lesion with irregular shape, obscure margin, and uneven internal echo, and classified as BI-RADS 5. (d) on CEUS mode, the lesion exhibited iso and synchronous enhancement with a clear outline, regular shape and was given a score of 2 by CEUS. Thus, the lesion was downgraded as BI-RADS 3. (e) The final histological confirmation (200×) revealed a fibroadenoma.

Fig.2

A lesion with consistent MG and conventional US results in a 64-year-old woman. The routine craniocaudal (a) and mediolateral oblique (b) MGs show a heterogeneous dense breast with suspicious mass hence classified as BI-RADS 4a. (c) conventional US shows a suspicious lesion with irregular shape, obscure margin, and uneven internal echo, hence classified as BI-RADS 4a. (d) On CEUS mode, the lesion exhibited hyper- and earlier heterogeneous enhancement with a larger size, unclear margin, crab claw-like shape, perfusion defect, and was given a score of 5 by CEUS. Thus, the lesion was upgraded as BI-RADS 4c. (e) The final histological confirmation (200×) was DCIS.

2.4 Pathology

All patients underwent core biopsy or surgery 1–3 days after all three imaging examinations. The pathology results were considered as the reference standard.

2.5 Statistical analysis

SPSS 19.0 software (SPSS, Chicago, IL, USA) was used for statistical analysis, and p < 0.05 was considered to indicate statistical significance. The data were analyzed as follows: First, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of BI-RADS-US, BI-RADS-MG, and CEUS re-rated BI-RADS were calculated. Second, the 637 lesions were divided into two groups depending on whether the BI-RADS categories were consistent or not between conventional US and MG. Then, the diagnostic accordance rate of all three methods in the two groups were assessed and compared. Third, the ROC curves were constructed, and AUCs were calculated to compare the comprehensive diagnostic performance of each method. Z test was used to compare the difference in AUC for three methods. Last, clinicopathological features between the two groups were compared using the t-test, chi-squared test and Mann–Whitney U test.

3 Results

3.1 Study population and pathological results

The mean age of the patients was 47.67±13.27 years. The mean size of the masses was 20.34±10.54 mm. Among the 637 lesions, 426 were single distributed while 211 were multiple distributed, and 492 were ipsilateral whereas 145 were bilateral. The density of each breast was classified into one of four categories as defined by the BI-RADS system. Heterogeneously dense and extremely dense were defined as dense breasts [22]; 435 women were grade c, while 62 were grade d in their mammographic density assessment. Demographic details of patients and masses are provided in Table 1. Of the 637 lesions, 283 (44.43%) were malignant and 354 (55.57%) were benign. The results are summarized in Table 2.

Table 1
Basic characteristics of patients and breast masses

Characteristics

Age (y)

Mean 47.67±13.27

Range 18–94

Size (mm)

Mean 20.34±10.54

Range 2.5–80

Distribution

Single 426 (66.88%)

Multiple 211 (33.12%)

Location

Left 241 (37.83%)

Right 251 (39.40%)

Bilateral 145 (22.76%)

Mammographic density

A 15 (2.35%)

B 125 (19.62%)

C 435 (68.29%)

D 62 (9.73%)

Characteristics
Age (y)
Mean	47.67±13.27
Range	18–94
Size (mm)
Mean	20.34±10.54
Range	2.5–80
Distribution
Single	426 (66.88%)
Multiple	211 (33.12%)
Location
Left	241 (37.83%)
Right	251 (39.40%)
Bilateral	145 (22.76%)
Mammographic density
A	15 (2.35%)
B	125 (19.62%)
C	435 (68.29%)
D	62 (9.73%)

Numbers in parentheses are percentages.

Table 2

Pathology results

Pathological entities	No. of lesions
Benign	354 (55.57%)
Fibroadenoma	206 (58.19%)
Adenosis	96 (27.12%)
Papilloma	23 (6.50%)
Sclerosing adenosis	8 (2.26%)
Inflammation	5 (1.41%)
Benign phyllodes tumour	4 (1.13%)
Mammary duct ectasia	4 (1.13%)
Cyst	4 (1.13%)
Granulomatous mastitis	3 (0.85%)
Plasma cell mastitis	1 (0.28%)
Malignant	283 (44.43%)
Invasive ductal carcinoma	225 (79.51%)
Invasive lobular carcinoma	5 (1.77%)
Ductal carcinoma in situ	30 (10.60%)
Mucinous breast carcinoma	11 (3.89%)
Intraductal papillary carcinoma	5 (1.77%)
Apocrine carcinoma	2 (0.71%)
Paget’s disease	1 (0.35%)
Medullary breast carcinoma	1 (0.35%)
Malignant phyllodes tumour	1 (0.35%)
Spindle cell carcinoma	1 (0.35%)
Metastatic carcinoma	1 (0.35%)

Numbers in parentheses are percentages.

3.2 Distribution of MG, conventional US and CEUS re-rated results in the benign and malignant group

Distribution of MG, conventional US, and CEUS re-rated scores are presented in Table 3. Consistent and inconsistent results of MG and conventional US are shown in Table 4. Among the 354 benign masses, 75 were diagnosed as BI-RADS 3 or lower and 60 were diagnosed as BI-RADS 4a or higher by both MG and conventional US. In terms of inconsistent results, 137 were scored as BI-RADS 4a or greater by conventional US but as BI-RADS 3 or lower by MG; 11 were scored as BI-RADS 3 or lower by conventional US while BI-RADS 4a or greater by MG.

Table 3
Distribution of mammography and conventional US categories in benign and malignant lesions

Histology BI-RADS Category No. of lesions Total

BI-RADS-MG Category

0 1–3 4a 4b 4c 5

Benign 71 212 56 11 1 3 354

Malignant 42 49 71 30 26 65 283

BI-RADS-US Category

0 1–3 4a 4b 4c 5

Benign – 134 166 18 12 24 354

Malignant – 22 39 42 58 122 283

CEUS Re-rated BI-RADS-US Category

0 1–3 4a 4b 4c 5

Benign – 250 63 24 10 7 354

Malignant – 6 18 11 34 214 283

Histology	BI-RADS Category	No. of lesions	Total
	BI-RADS-MG Category
	0	1–3	4a	4b	4c	5
Benign	71	212	56	11	1	3	354
Malignant	42	49	71	30	26	65	283
	BI-RADS-US Category
	0	1–3	4a	4b	4c	5
Benign	–	134	166	18	12	24	354
Malignant	–	22	39	42	58	122	283
	CEUS Re-rated BI-RADS-US Category
	0	1–3	4a	4b	4c	5
Benign	–	250	63	24	10	7	354
Malignant	–	6	18	11	34	214	283

US, ultrasound; MG, mammography.

Table 4

Distribution of consistent and inconsistent results of mammography and conventional US in benign and malignant categories

	Benign	Malignant
MG inconsistent with US (n)	219	88
MG≥4a and US≤3	11	5
US≥4a and MG≤3	137	41
MG = 0 and us≤3	48	9
MG = 0 and us≥4a	23	33
MG consistent with US (n)	135	195
BI-RADS≤3	75	8
BI-RADS≥4a	60	187

Similarly, among the 285 malignant lesions, 187 were scored as BI-RADS 4a or higher and 8 lesions were scored as BI-RADS 3 or lower by both MG and conventional US. With respect to inconsistent results in malignant lesions, 41 were scored as BI-RADS 4a or higher using conventional US but as BI-RADS 3 or lower using MG; 5 were classified as BI-RADS 3 or lower using conventional US while as BI-RADS 4a or greater using MG.

3.3 Diagnostic performance of MG, conventional US, and conventional US combined with CEUS

Table 5 shows the details of the diagnostic performance of all three methods. The sensitivity and accuracy of MG and conventional US were 67.84%, 74.57% and 92.22%, 62.01%, respectively. The corresponding values for rerated CEUS BI-RADS were 97.88% and 81.79%; the AUC of CEUS was prior to MG and conventional US not only in the overall group (0.958 vs. 0.742, p < 0.001 and 0.958 vs. 0.843, p < 0.001) but also in the group that showed inconsistent BI-RADS scores (0.950 vs. 0.412, p < 0.001 and 0.950 vs. 0.789, p < 0.001), indicating that CEUS is valuable in distinguishing benign and malignant lesions both in the overall and inconsistent groups.

Table 5
Performance of mammography, conventional US, and conventional US combined with CEUS

Sensitivity Specificity PPV NPV Accuracy Diagnosis accordance rate

AUC¹ AUC² AUC³ Consistent Inconsistent

MG 67.84 79.94 73.00 75.67 74.57 0.742 0.733 0.412 262/330 142/307

US 92.22 37.85 54.26 85.90 62.01 0.843 0.818 0.789 133/307

CEUS 97.88 70.62 72.7 97.66 81.79 0.958 0.962 0.950 251/330 239/307

p ^α – – – – – <0.001 <0.001 <0.001 – –

P ^β – – – – – <0.001 <0.001 <0.001 – –

	Sensitivity	Specificity	PPV	NPV	Accuracy	Diagnosis accordance rate
MG	67.84	79.94	73.00	75.67	74.57	0.742	0.733	0.412	262/330	142/307
US	92.22	37.85	54.26	85.90	62.01	0.843	0.818	0.789		133/307
CEUS	97.88	70.62	72.7	97.66	81.79	0.958	0.962	0.950	251/330	239/307
p ^α	–	–	–	–	–	<0.001	<0.001	<0.001	–	–
P ^β	–	–	–	–	–	<0.001	<0.001	<0.001	–	–

PPV, Positive Predictive Value; NPV, negative predictive value; AUC, area under the ROC curve. AUC¹, AUC of three methods in overall samples; AUC², AUC of three methods in lesions with BI-RADS US 4a or higher; AUC³, AUC of three methods in score-inconsistent group. p^α comparison of AUC between CEUS re-rated BI-RADS and MG; P^β comparison of AUC between CEUS re-rated BI-RADS and conventional US.

3.4 Comparison of the clinicopathological features between MG and conventional US consistent and inconsistent groups

As seen in Table 4, there were 330 consistent and 307 inconsistent scores made by MG and conventional US. The clinicopathological features between the two groups were compared, and the results revealed that younger women (p < 0.001), dense breast (p < 0.001), and breast cancer with negative lymph node (p = 0.040) were more commonly observed in the inconsistent group. Grade II and III breast cancer appeared more frequently in the consistent group but without significant differences between the two groups (p = 0.485) (Table 6).

Table 6
Comparison of the clinicopathological features between mammography and conventional US in the consistent and inconsistent groups

Consistent Inconsistent P

Grade^* 0.485

I 18 11

II 87 29

III 74 39

Lymph node status^# 0.040

Negative 122 66

Positive 73 22

Breast density <0.001

A 14 1

B 92 33

C 198 237

D 26 36

Age 51.28 43.78 <0.001

	Consistent	Inconsistent	P
Grade^*			0.485
I	18	11
II	87	29
III	74	39
Lymph node status^#			0.040
Negative	122	66
Positive	73	22
Breast density			<0.001
A	14	1
B	92	33
C	198	237
D	26	36
Age	51.28	43.78	<0.001

^*Grades only include invasive ductal carcinoma and DCIS.

4 Discussion

In this study, the AUC of CEUS was greater than that of MG and conventional US, especially in the inconsistent BI-RADS scores group (p < 0.001). The sensitivity and accuracy of MG and conventional US were calculated, the corresponding values for CEUS were prior to that of MG and conventional US, indicating that using CEUS to re-evaluate the breast lesions not only improves accuracy but also retains comparatively high sensitivity. Combining CEUS with conventional US has been shown to increase the efficiency of US in differentiating benign and malignant breast lesions by several reported researches [23, 24], which were consistent with our results. However, it is still not that clear whether adding CEUS is worthwhile and efficient for breast lesions that show inconsistent MG and conventional US scores.

Besides, the diagnostic accordance rate of MG and conventional US was 262/330 in the consistent group, which exhibited a relatively good diagnostic performance. However, the diagnostic accordance of MG (142/307) and conventional US (133/307) in the inconsistent group was not that promising. The corresponding values for CEUS re-rated BI-RADS were 251/330 in the consistent and 239/307 in the inconsistent group, which exhibited rather stable diagnostic performance in both groups. Furthermore, younger women, dense breast (p < 0.001), and breast cancer with negative lymph node (p = 0.040) were more commonly observed in the inconsistent group in this study. Although grade II and III breast cancers appeared more frequently in the consistent group, this difference was not significant. The pathology grade was unrepresentative, as pathologists in our hospital only graded invasive ductal carcinoma and DCIS, which may explain the insignificant differences in pathology grade. These data indicated that adding CEUS to re-evaluate the inconsistent lesions is efficient in making differential diagnosis and detecting cancer at an early stage.

Finally, CEUS re-rated BI-RADS downgraded 107 lesions with BI-RADS US 4a or higher and upgraded 25 lesions with BI-RADS US 3 or lower in the inconsistent group. Among the 107 downgraded lesions, only one was malignant, and 59 women were under 40 years old. The AUC of the three methods for lesions with BI-RADS US 4a or higher was further compared, and the AUC of CEUS was greater than both those of MG and conventional US (0.962 vs. 0.733, p < 0.001 and 0.962 vs. 0.818, p < 0.001, respectively). These results highlight that CEUS is valuable in reducing the false positive rates of conventional US especially in younger women to avoid unnecessary biopsies as well as decreasing the misdiagnosis rate of MG and conventional US when inconsistent results occurred.

However, we found it was difficult to accurately diagnose inflammation; three cases of inflammatory lesions and two of granulomatous mastitis were over-diagnosed as malignancies by CEUS in this study. The extension of inflammation into the adjacent perilobular and interlobular and necrosis in the central lesion might be the reasons for overdiagnosed CEUS scores [25, 26]. Moreover, six malignant lesions were misdiagnosed by CEUS as benign, including IDC (n = 3), DCIS (n = 1), intraductal papillary carcinoma (n = 1), and mucinous breast carcinoma (n = 1). Angiogenesis is essential for tumour cell growth and infiltration and is the pathophysiological basis of CEUS in the breast lesions identification. However, all six missed malignant lesions were masses with a maximum diameter <15 mm; the growth of these small malignant tumors could rely on normal surrounding capillary network without forming substantial angiogenesis [20 , 28]. The lack of malformed neovascularity may lead to misdiagnosis. These findings require further research.

In general, the sensitivity, specificity, and accuracy of MG and conventional US were calculated in this study which was in accordance with previous research that MG has comparatively low sensitivity for dense breasts [5, 29], while conventional US is characterized as having both a high sensitivity and high false positive rate [3, 30]. Whereas the sensitivity of 67.84% of MG in this study was kind of lower than that reported in another study, which ranged from 70% to 90% [4]. However, the sensitivity of MG was reportedly as low as 30–48% in dense breasts, and women in China have a higher breast density than those in western countries [8 –10]. Moreover, dense breasts including heterogeneously dense and extremely dense breasts account for 78% of the overall sample in this study, and 63.42% (404/637) women were below the average menopausal age [31, 32]. Consequently, these may explain the comparatively low sensitivity of MG in this study.

5 Limitation

First, this was a single-center study, these dates only reflected only a small proportion of the women presenting to our hospital and may not be applicable to the entire population. Second, intra-observer and inter-observer variability or image acquisition reproducibility were not assessed. Third, the plane with abundant blood supply or maximal diameter or irregular shape was chosen and remained unchanged in the whole CEUS procedure. It is likely that some important information may have been lost owing to the selection of a single plane. Fourth, quantitative analysis of CEUS was not applied. Last, the diagnostic efficacy of CEUS is not good in inflammatory disease. Therefore, further studies are required to validate our results.

6 Conclusion

Adding CEUS to masses with inconsistent results between MG and conventional US is valuable in making a differential diagnosis. Moreover, negative lymph node, dense breasts and younger women were more commonly encountered in the inconsistent group. Thus, CEUS is a worthwhile defective solution to re-evaluate the inconsistent masses to reduce the misdiagnoses of more curable breast cancer as well as unnecessary biopsies by improving the diagnostic efficacy of breast lesions in younger women.

Footnotes

Acknowledgments

This work was supported in part by Shanghai Hospital Development Center (Grant: SHDC12016233), the Science and Technology Commission of Shanghai Municipality (Grant: 124119a3201), and the National Natural Science Foundation of China (Grant: 81671699).

References

Bray

, Ferlay

, Soerjomataram

, Siegel

, Torre

, Jemal

. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Ca Cancer J Clin. 2018;68(6):394–424.

Harbeck

, Gnant

. Breast cancer. Lancet. 2017;389(10074):1134–50.

Lee

, Yi

, Jang

, Chang

, Cho

, Moon

. Supplemental screening breast US in women with negative mammographic findings: Effect of routine axillary scanning. Radiology. 2018;286(3):830–7.

Hooley

, Greenberg

, Stackhouse

, Geisel

, Butler

, Philpotts

. Screening US in patients with mammographically dense breasts: Initial experience with Connecticut Public Act 09-41. Radiology. 2012;265(1):59–69.

Sprague

, Stout

, Schechter

, van Ravesteyn

, Cevik

, Alagoz

, et al. Benefits, harms, and cost-effectiveness of supplemental ultrasonography screening for women with dense breasts. Ann Intern Med. 2015;162(3):157–66.

McCormack

, dos Santos Silva

. Breast density and parenchymal patterns as markers of breast cancer risk: A meta-analysis. Cancer Epidemiol Biomarkers Prev. 2006;15(6):1159–69.

, J

. Role of breast density measurement in screening for breast cancer. Climacteric. 2018;21(3):214–20.

, Zhang

, Fan

, Pang

, Zhang

, Wang

, et al. A nation-wide multicenter 10-year (1999-2008) retrospective clinical epidemiological study of female breast cancer in China. BMC Cancer. 2011;11:364.

Park

, Cho

, Lee

, Song

, Suh

, Choi

, et al. Does breast density measured through population-based screening independently increase breast cancer risk in Asian females? Clin Epidemiol. 2018;10:61–70.

10.

Shen

, Zhou

, Xu

, Zhang

, Duan

, Huang

, et al. A multi-centre randomised trial comparing ultrasound vs mammography for screening breast cancer in high-risk Chinese women. Br J Cancer. 2015;112(6):998–1004.

11.

Stomper

, D’Souza

, DiNitto

, Arredondo

. Analysis of parenchymal density on mammograms in 1353 women 25-79 years old. AJR Am J Roentegnol. 1996;167(5):1261–5.

12.

Nothacker

, Duda

, Hahn

, Warm

, Degenhardt

, Madjar

, et al. Early detection of breast cancer: Benefits and risks of supplemental breast ultrasound in asymptomatic women with mammographically dense breast tissue. A systematic review. BMC Cancer. 2009;9(1):335.

13.

Kuhl

, Strobel

, Bieling

, Leutner

, Schild

, Schrading

. Supplemental breast MR imaging screening of women with average risk of breast cancer. Radiology. 2017;283(2):361–70.

14.

Xiao

, Ou

, Yang

, Wu

, Luo

. Breast contrast-enhanced ultrasound: Is a scoring system feasible? A preliminary study in China. PloS One. 2014;9(8):e105517.

15.

Xiao

, Dong

, Jiang

, Guan

, Wu

, Luo

. Incorporating contrast-enhanced ultrasound into the BI-RADS scoring system improves accuracy in breast tumor diagnosis: A preliminary study in China. Ultrasound Med Biol. 2016;42(11):2630–8.

16.

, Wu

, Chen

, Gu

. Application of contrast-enhanced ultrasound in the diagnosis of small breast lesions. Clin Hemorheol Microcirc. 2018;70(3):291–300.

17.

Hoffman

, Rheinstein

, Houn

. The mammography quality standards act of 1992. Am Fam Physician. 1994;49(8):1965–70.

18.

Paquelet

. Medicare mammography and the mammography quality standards act of 1992. Radiology. 1994;190(3):47a–9a.

19.

AIUM practice guideline for the performance of a breast ultrasound examination. J Ultrasound Med. 2009;28(1):105–9.

20.

Liu

, Jiang

, Dai

, Zhu

, Wang

, Zhang

, et al. Differentiation of benign and malignant sub-1-cm breast lesions using contrast-enhanced sonography. J Ultrasound Med. 2015;34(1):117–23.

21.

Xiao

, Jiang

, Wu

, Guan

, Qin

, Luo

. Diagnosis of sub-centimetre breast lesions: Combining BI-RADS-US with strain elastography and contrast-enhanced ultrasound-a preliminary study in China. Eur Radiol. 2017;27(6):2443–50.

22.

Wilczek

, Wilczek

, Rasouliyan

, Leifland

. 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(9):1554–63.

23.

Quan

, Hong

, Zhang

, Mei

, You

, Huang

. The clinical role of contrast enhanced ultrasound in differential diagnosis of BI-RADS 4 breast disease. Clin Hemorheol Microcirc. 2019;72(3):293–303.

24.

Kapetas

, Clauser

, Woitek

, Wengert

, Lazar

, Pinker

, et al. Quantitative multiparametric breast ultrasound: Application of contrast-enhanced ultrasound and elastography leads to an improved differentiation of benign and malignant lesions. Invest Radiol. 2019;54(5):257–64.

25.

Lin

, Liu

, Guo

, Wei

, Zhu

. Granulomatous mastitis: Presentation, treatment, and outcome. Am Surg. 2014;80(3):E82–3.

26.

Liu

, Jiang

, Liu

, Zhu

, Sun

, Chang

. Contrast-enhanced breast ultrasonography: Imaging features with histopathologic correlation. J Ultrasound Med. 2009;28(7):911–20.

27.

Weidner

, Semple

, Welch

, Folkman

. Tumor angiogenesis and metastasis–correlation in invasive breast carcinoma. N Engl J Med. 1991;324(1):1–8.

28.

Zhao

, Liu

, Wei

, Xu

, Wu

, Xu

, et al. Contrast-enhanced ultrasonography features of breast malignancies with different sizes: Correlation with prognostic factors. Biomed Res Int. 2015;2015:613831.

29.

Wanders

, Holland

, Veldhuis

, Mann

, Pijnappel

, Peeters

, et al. Volumetric breast density affects performance of digital screening mammography. Breast Cancer Res Treat. 2017;162(1):95–103.

30.

Berg

. Current status of supplemental screening in dense breasts. J Clin Oncol. 2016;34(16):1840–3.

31.

Laven

JSE

, Visser

, Uitterlinden

, Vermeij

, Hoeijmakers

JHJ

. Menopause: Genome stability as new paradigm. Maturitas. 2016;92:15–23.

32.

Gray

, Schiff

, Fitzpatrick

, Kimura

, Aviv

, Starr

. Leukocyte telomere length and age at menopause. Epidemiology. 2014;25(1):139–46.