False-positive incidental lesions detected on contrast-enhanced breast MRI: Clinical and Imaging Features

doi:10.21203/rs.3.rs-1602423/v1

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Research Article

False-positive incidental lesions detected on contrast-enhanced breast MRI: Clinical and Imaging Features

https://doi.org/10.21203/rs.3.rs-1602423/v1

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Journal Publication

published 06 Feb, 2023

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Purpose

To identify features of the incidental MRI-detected enhancing lesions associated with false-positive outcomes, impacting patient care.

Methods

Institutional review board-approved retrospective imaging studies and patient's chart review of consecutive asymptomatic women with incidental MRI enhancing lesions detected between January-December 2018, who underwent MRI-guided biopsy. Lesions' frequency and imaging features were recorded, differentiating benign and high-risk lesions. The results were correlated with histopathology as the ground truth or uneventful follow-up of at least one year. Univariate analysis explored correlation between baseline variables and false-positive results.

Results

Two-hundred-nineteen women (median age, 49 years; range, 26–85 years) with 219 incidental MRI enhancing lesions that underwent MRI-guided vacuum-assisted biopsy during the study period formed the study cohort. Out of 219, 180 lesions (82.2%) yielded benign pathology results, 137 benign (76%) and 43 high-risk (24%) outcomes. Variables associated with true-positive results were age OR 1.05 (CI 95%, 1.02–1.08), p = 0.0015, irregular compared with oval/round mass shape lesion OR 11.2 (CI 95%, 1.6–78.4), p = 0.015, and clumped/clustered ring compared with homogeneous non-mass enhancement OR 3.17 (CI 95%, 1.38–7.28), p < 0.0066. T2-hyperintense mass-lesion correlated to the false-positive result OR 0.13 (95% CI, 0.02–0.76), p = 0.024.

Conclusion

Young patients, oval/round mass-lesion shape, T2-hyperintense mass-lesion, and homogeneous pattern of non-mass enhancement showed the strongest association with false-positive results of incidental MRI enhancing lesions. It may impact the patient's management, suggesting follow-up rather than interventional procedure when these demographic/imaging parameters are present, consequently decreasing patient's anxiety and health care costs.

Breast MRI

False positive

breast cancer

biopsy

The use of magnetic resonance imaging (MRI) in breast imaging has increased in recent years, representing an important tool in breast cancer diagnosis, especially in high-risk populations [1–5]. In addition, breast MRI has been discussed as a potential supplemental screening tool for women at average risk who have dense breast tissue [6–10]. MRI has yielded consistently higher sensitivity and increased cancer detection rates compared with digital mammography (DM) [1–10].

Nevertheless, breast MRI has frequently been reported to result in a large number of false-positive diagnoses. Kuhl, C.K. reported 55.2% false positive in MRI of 366 asymptomatic women with average risk of breast cancer [11]. In principle, all suspicious findings that prompt biopsy but yield nonmalignant tissues are equally considered false positives, which specifically in the breast, is caused by a quite heterogeneous group of tissue changes.

In fact, contrary to what is observed on mammography, the MRI-detected false-positive lesions are usually associated with proliferative results and more high-risk lesions diagnosed, such as atypical ductal hyperplasia and lobular neoplasia [11], for which most practice guidelines recommend intensified surveillance, preventive surgery, or even chemoprevention [12]. Thus, although their diagnosis may provide valuable information to guide further patient management, such false-positive diagnoses add to the overall cost of screening because they require additional workup by imaging or biopsy, may cause physical harm because of additional morbidity associated with biopsy procedures, and may cause emotional harm because they may generate breast cancer anxiety in the patient [13–16]. Accordingly, the reported high number of false-positive diagnoses has been a major reason for limiting the acceptance of breast MRI as a screening tool [17–19].

The review of the literature reveals that there are several articles on sensitivity, specificity and false positive rate of breast MRI, but few studies with conflicting results focused on evaluating lesion characteristics associated with false-positive results [20, 21]. For example, Batzer et al. [20] showed that non-mass-like lesions are more associated with false-positive results, while Myers et al. [21] did not show this association. Furthermore, the larger tumor diameter of the lesion despite the lesion type shows association with malignancy in the Myers study [21] but was only confirmed for mass lesions in the Batzer study [20]. As such, these discrepant results highlight the need for additional studies in this setting. Over time, machines and software have also developed, potentially affecting results.

Recently, several investigators have developed computer aided and machine learning methods for diagnosis and the quantitative characterization of breast lesions on clinical images. Radiomics, a computer-aided diagnosis provides computer-extracted characteristics of tumor. Although some studies showed that artificial intelligence and Radiomics systems improved radiologists’ performance in differentiating benign and malignant enhancing lesions detected at breast MRI, most AI methods are still in the technical development and research phase and many efforts should be taken for clinical translation. In addition, they are not widely available worldwide [22–24].

Therefore, this study aims to identify demographic characteristics and imaging findings, including morphological appearance and kinetic pattern of enhancement of the incidental MRI-detected enhancing lesions related to false-positive results. These findings will potentially help reduce the overall cost associated with MRI additional workup and patient harm.

Patient selection

Consecutive data of patients with MRI-detected enhancing lesions who have undergone MRI-guided percutaneous biopsy in 2018 from three tertiary hospitals: Sinai Health System, University Health Network, and Women's College Hospital, affiliated with the University of Toronto were retrospectively reviewed. The patient's informed consent was waived. Only those who have undergone surgical excision or were followed clinically or by imaging for at least one year from the date of the last imaging examination were included.

All patients had ultrasound or mammography before MRI, but the included lesions did not have mammography or ultrasound correlated lesions. The included lesions had BIRADS 4 or 5 in enhanced MRI, which make them candidate for MRI guided biopsy. Patient's demographics and MRI findings, imaging-guided biopsy results, and if applicable, the pathological outcome after excisional surgery were reviewed.

Imaging technique

MRI was performed using state-of-the-art equipment and standard-of-care technique following American College of Radiology (ACR) quality standards [25]. In all cases, MRI examinations were performed on a 1.5-T system (Signa Excite, GE Medical Systems) or Espree or Avanto (Siemens Healthcare) and 3.0-T system (Verio; Siemens Healthcare) with a standard, bilateral, dedicated breast coil (Sentinelle Vanguard; Sentinelle Medical, Inc.). The sequences included precontrast axial T1, T2-weighted images with fat suppression, and dynamic contrast-enhanced (DCE) T1-weighted imaging sequences. The DCE sequence consisted of a pre-contrast scan and four post-contrast scans. MRI examinations for premenopausal patients were scheduled in the second week of the menstrual cycle to minimize enhancement of benign breast parenchyma (25).

Imaging interpretation

All MRI data were reviewed by two breast fellowship-trained radiologists (AA,12 years of experience, VF, 20 years of experience) from the same department that were blinded to the surgical or core needle biopsy pathology results and the clinical outcomes. The readers described MRI findings following the Breast Imaging Reporting and Data System (BIRADS) lexicon [25, 26]. Discrepant results of lesion characteristics between radiologists were assessed by consensus. The imaging studies with final assessment category BI-RADS 4 and BI-RADS 5 were considered positive, and all other results were considered negative.

Demographic data were recorded, including age, personal history of breast cancer, risk factors including genetic mutation, and family history of breast cancer. In addition, the MRI indication (screening; staging; surveillance; problem-solving; others) and imaging findings were retrieved (parenchymal enhancement; T1 signal, T2 signal, mass or non-mass enhancement; shape and margin for masses; and enhancement pattern and distribution for non-mass enhancement, and dynamic pattern).

Interventional procedures and pathology

Percutaneous MRI-guided biopsies were performed by attending breast imaging experienced radiologists at the same hospital within 3 weeks from the enhanced MR, using a 9-gauge MRI-compatible vacuum-assisted device (ATEC, Suros Surgical systems), with 8 to 12 samples obtained.

Histopathology

The histopathologic result from the MRI-guided biopsy was considered the reference standard for lesion evaluation. Invasive cancer and ductal carcinoma in situ (DCIS) were considered malignant histopathological results. The histological results, including papillary lesions, complex sclerosing lesions (CCL), radial scar (RS), atypical ductal hyperplasia (ADH), atypical lobular hyperplasia (ALH), and lobular carcinoma in situ (LCIS) were categorized as high-risk lesions (HRL). Other pathologies are considered benign (fibrocystic changes, Pseudoangiomatous stromal hyperplasia, columnar cell changes or hyperplasia, focal fibroadenomatoid changes, stromal fibrosis). Patients with malignancy had surgical excision as per standard institutional practice. In addition, surgery was performed in some of biopsy-proven high-risk lesions, and benign pathology after multidisciplinary discussion, and based on patient’s and surgeon preferences.

All included women who did not undergo excisional surgery had a minimum of 12-month follow-up. The standard follow-up was a clinical exam, mammogram alone, or mammogram supplemented by MRI.

Statistical methods

Summary statistics such as means, medians, standard deviations, ranges, frequencies and proportions were reported to describe participant demographical and clinical characteristics. Wilcoxon rank sum test or Chi-square test was used to compare participant characteristics across pathology groups. Logistic regression model was used to evaluate the correlation between participant characteristics with true positive result for the main data set and subgroup analysis. Subgroup analysis was conducted for participants with mass and non-mass lesions, respectively.

Two-hundred-nineteen cases (median age, 49 years; range, 26–85 years) were included. The indication of MRI before biopsy were screening (n = 106) 48%; staging (n = 63) 29%; surveillance with history of previous malignant lumpectomy (n = 20) 9%; problem solving (n = 13) 6%; and others (n=17) 8%. Out of 219 MRI-guided biopsies, 137 (62.5%) lesions were benign, 39 (18%) malignant, and 43 (19.6%) high-risk.Demographic parameters and imaging findings of the MRI-biopsy lesions withbenign, high risk, and malignant outcomes are summarized in Table 1. The flowchart of the patients with MRI-guided biopsy in our center in 2018 is also presented [Figure 1].

The false-positive result in our study was 82.2%. Figures 2–4 show examples of false-positive cases.

Table 2 summarizes the univariate analysis's demographic and imaging features associated with malignant outcome. In addition, subgroup analysis for mass lesions and non-mass lesions were performed [Tables 3 and 4]

The univariate analysis showed that between a variety of clinical and pathological parameters, age was the only demographic characteristic that showed a significant influence on the differentiation of false-positive and true positive groups. Older women had more chance to have a true positive lesion, p=0.0015; OR 1.05 (CI 95%, 1.02 - 1.08).

Out of 219 cases, 41 (19%) of the cases were masses, while 178 (81%) were non-mass. The mass to non-mass lesions ratio was not significant between the true positive (malignant pathology result) and false-positive groups (benign or high-risk pathology result) with a p-value of 0.44.

Indication of MRI (p = 0.22), positive first-degree family history (p = 0.056), previous personal history of breast cancer (p = 0.068), and background parenchymal enhancement (p = 0.075) did not show significant influence on correct diagnosing of true positive lesions.

Tumor diameter had no influence on the correct diagnosis of both mass (p = 0.55) and non-mass lesions (p = 0.4). The mean diameter of false-positive masses was 7.34 mm (SD 1.70) and of true positive masses was 8.67 mm (SD 3.71). The mean diameter of false-positive non-masses was 16.28 mm (SD 15.94), and true positive non-masses was 19.13 mm (SD 16.31).

For masses, compared with oval/round shape, irregular shape significantly increased the ratio of true positive [p = 0.015, OR 11.20 (CI 95%, 1.6–78.4)]. In addition, hyperintense signal on the T2-weighted sequence (compared to the normal fibroglandular signal) was significantly related to the false-positive result [p = 0.024, OR 0.13 (95% CI, 0.02–0.76)]. Dynamic curve (p = 0.98), T1 signal (p = 0.25), and mass margin (p = 0.2) did not differentiate malignant from benign/high-risk lesions.

In the evaluation of non-mass lesions, the only feature that significantly differentiated malignant and benign/high-risk lesions was enhancement pattern; compared to homogeneous, clumped and clustered ring of enhancement was significantly related to a true positive result, p < 0.0066, OR 3.17 (CI 95%, 1.38–7.28). Distribution (p = 0.44), T1 signal (p = 0.77), T2 signal (p = 0.17), and dynamic curve (p = 0.97), did not have significant difference in true positive and false positive groups.

The malignancy rate of 18% of MRI-detected lesions in our cohort is slightly lower than the BI-RADS lexicon benchmark of 20–50% [27] and aligns with the lower range of previously published studies, ranging from 18 to 60% [ 28–36], suggesting that there is no excessive number of biopsies in our institution. However, the high rate of false-positive results in our cohort, while similar to the previously published study focused on screening [37], warrants knowledge of which lesion characteristics are related to the false positive to minimize the cost of health care and the patient's anxiety. In this evaluation, young patients, oval/round mass-lesion shape, hyperintense mass-lesion on T2 weighted sequences, and homogeneous pattern of non-mass enhancement were the identified variables associated with false-positive results. They may be considered to forego biopsies in the future.

With respect to demographic characteristics, our results showed that lesions in older women had a greater chance of being true positive than false positive. However, the study by Myers et al. [21] did not show an association of age with malignancy (p = 0.43). This discrepancy can be related to the different populations included in the study. Although MRI indication did not show a statistically significant difference in the false positive rate in either study, our study was predominantly in high-risk populations, and Myers focused on breast cancer patients undergoing MRI for disease assessment.

Regarding the lesion features for mass-like lesions, contrary to previously published studies [20, 21], which showed that margin irregular [20] and spiculated [21] were parameters associated with malignancy, our study showed that irregular shape and not margin was the feature associated with malignancy. In fact, the difference in the results may be attributed to both, the small number of irregular shape lesions in our study limiting our statistical power, and the subjective analysis of these characteristics, which can cause possible overlapping between irregular margin and shape in the lesion classification. In addition, similar to Myers' study [21], the hyper signal on the T2-weighted sequence (compared to normal fibroglandular signal) was significantly related to false-positive results.

For non-mass lesions, to the best of our knowledge, this is the first time showing that any clumped and clustered ring of enhancement was significantly related to a true positive result, p < 0.0066.

Moreover, although a smaller number of high-risk lesions (24%, 43/180) were identified in our cohort compared to previously published study (44.8%, 81/202) [11], the rate of a high-risk lesion identified cannot be neglected considering its prognostic importance, which can reflect in different patient's management including intensified surveillance, preventive surgery, or even chemoprevention.

Avoiding biopsy of MRI-detected lesions with features related to the false-positive rate can potentially decrease healthcare cost and patient anxiety. Therefore, it is the main strength of our study.

However, there are several limitations. The main limitation lies in its retrospective design, which may be subject to selection bias. In this sense, the study design, excluding lesions for which biopsy was not recommended (findings that could be true negative or false negative), may have caused over or underestimation of certain characteristics of the lesion associated with benign or malignant results. Secondly although the MRI data were reviewed by two breast-trained radiologists and the discrepant results of lesion characteristics among radiologists were assessed by consensus, lesion classification based on subjective parameters defined by the BI- RADS can affect false-positive rates limiting the generalizability of results. In this sense, application of AI technology may surpass this subjectively assessment, providing a more reliable, objective way to differentiate benign and malignant lesions. Nevertheless, such applications are still in the technical development phase and are far from being incorporated into clinical practice [22–24, and 38 ].Third, MRI technology has evolved and proton MR spectroscopy and diffusion weighting can also affect false-positive results. However, these techniques require a longer acquisition time and have a substantial number of technical flaws, such as voxel shift, incomplete acquisition, or incorrect shimming, and therefore, their performance was not evaluated in our study [39, 40]. Last but not least, false positives may be affected depending on the indication of the MRI study, and therefore, our study focused primarily on high-risk patients may not be transferable to other settings.

Based on our result, young patients, oval/round mass-lesion shape, and homogeneous pattern of non-mass enhancement showed the strongest association with false-positive results of incidental enhancing lesions depicted by MRI. For participants with mass breast lesion, T2-hyperintense mass-lesion showed significant association with false-positive result. It may impact the patient's management with a suggestion of follow-up rather than interventional procedure when these demographic and imaging parameters are present, consequently decreasing the patient's anxiety and health care costs until upcoming new technologies including AI can be incorporated into the clinical practice to allow us reliably differentiate benign and malignant lesions, minimizing unnecessary biopsies, improving work flow and decreasing health care costs.

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Table 1. Demographic parameters and imaging findings of the MRI-biopsy lesions with benign, high risk and malignant

Covariate	Full Sample (n=219)	Benign (n=137)	High-risk lesion (n=43)	Malignant (n=39)	p-value
Age (years)					0.0015
Mean (sd)	49.7 (12.2)	47.6 (11.9)	51.1 (12.1)	55.5 (11.5)
Median (Min, Max)	49 (26,85)	46 (26,79)	52 (26,78)	53 (36,85)
Genetic mutation					0.42
Yes	13 (6)	7 (5)	2 (5)	4 (10)
No	206 (94)	130 (95)	41 (95)	35 (90)
Family History of BC					0.043
Yes	98 (45)	70 (51)	16 (37)	12 (31)
No	121 (55)	67 (49)	27 (63)	27 (69)
Personal History of BC					0.082
Y	100 (46)	55 (40)	22 (51)	23 (59)
N	119 (54)	82 (60)	21 (49)	16 (41)
Personal History of BC: Remote or Newly Diagnosed BC					0.88
Newly Diagnosed	62 (61)	34 (60)	13 (59)	15 (65)
Remote	40 (39)	23 (40)	9 (41)	8 (35)
Missing	117	80	21	16
Newly diagnosed BC: ipsilateral or contralateral					0.09
Ipsilateral	42 (41)	22 (39)	8 (36)	12 (52)
Contralateral	58 (57)	35 (61)	14 (64)	9 (39)
Bilateral	2 (2)	0 (0)	0 (0)	2 (9)
Missing	117	80	21	16
Indication					0.04
Screening	105 (48)	74 (54)	17 (40)	14 (36)
Staging	63 (29)	33 (24)	15 (35)	15 (38)
Surveillance	20 (9)	12 (9)	4 (9)	4 (10)
Problem-Solving	13 (6)	11 (8)	2 (5)	0 (0)
Other	17 (8)	6 (4)	5 (12)	6 (15)
Missing	1	1	0	0
MRI BPE					0.03
Minimal-mild	176 (81)	110 (81)	31 (72)	35 (90)
Moderate	37 (17)	25 (18)	10 (23)	2 (5)
Marked	5 (2)	1 (1)	2 (5)	2 (5)
Missing	1	1	0	0
Breast Lesion: Mass or NME					0.19
Mass	41 (19)	28 (20)	4 (9)	9 (23)
NME	178 (81)	109 (80)	39 (91)	30 (77)
Mass Lesion Shape					0.03
Oval/round	33 (85)	24 (92)	4 (100)	5 (56)
Irregular	6 (15)	2 (8)	0 (0)	4 (44)
Missing	180	111	39	30
Distribution of NME					0.003
focal/focus	105 (58)	71 (64)	18 (46)	16 (53)
ductal/linear	44 (24)	19 (17)	18 (46)	7 (23)
segmental	13 (7)	9 (8)	2 (5)	2 (7)
regional	16 (9)	12 (11)	0 (0)	4 (13)
multiple regional	1 (1)	0 (0)	0 (0)	1 (3)
Diffuse	1 (1)	0 (0)	1 (3)	0 (0)
Missing	39	26	4	9
NME pattern					0.02
homogeneous	135 (75)	89 (80)	29 (74)	17 (57)
heterogeneous	4 (2)	4 (4)	0 (0)	0 (0)
clumped	40 (22)	18 (16)	9 (23)	13 (43)
clustered ring	1 (1)	0 (0)	1 (3)	0 (0)
Missing	39	26	4	9
T2 signal					0.28
hyper	61 (28)	40 (29)	13 (30)	8 (21)
No	119 (54)	70 (51)	21 (49)	28 (72)
intermediate	38 (17)	26 (19)	9 (21)	3 (8)
low	1 (0)	1 (1)	0 (0)	0 (0)
T1 signal					0.50
No	165 (75)	100 (73)	36 (84)	29 (74)
Low & Iso	50 (23)	34 (25)	6 (14)	10 (26)
Hyper	4 (2)	3 (2)	1 (2)	0 (0)
Size (mm)					0.27
Mean (sd)	15.1 (14.9)	14.1 (14.4)	16.5 (16.4)	16.7 (15)
Median (Min,Max)	9 (3,100)	8 (4,71)	11 (4,100)	10 (3,60)
Dynamic curve					0.44
persistent	82 (37)	54 (39)	15 (35)	13 (33)
plateau	34 (16)	22 (16)	7 (16)	5 (13)
wash out	69 (32)	44 (32)	10 (23)	15 (38)
mixed	34 (16)	17 (12)	11 (26)	6 (15)

BC = breast cancer; BPE = background parenchymal enhancement; NME = non-mass enhancement

Table 2 – Univariate analysis - Demographic and imaging features associated with malignant outcome

Covariate	OR(95%CI)	p-value	Global p-value
Age	1.05 (1.02,1.08)		0.0015
gene mutation			0.22
Yes	Reference
No	0.46 (0.13,1.58)
Family HX			0.056
Yes	Reference
No	2.06 (0.98,4.32)
Personal HX			0.068
Y	Reference
N	0.52 (0.26,1.05)
same or contralateral			0.3
Ipsilateral	Reference
Contralateral	0.46 (0.17,1.22)	0.12
Bilateral	0.00 (0,Inf)	0.99
Simultaneous or Metachronous			0.62
Synchronous	Reference
Metachronous	0.78 (0.30,2.06)
Indication			0.22
Screening	Reference
Staging	2.03 (0.91,4.56)	0.086
Surveillance	1.62 (0.47,5.57)	0.44
Problem-Solving	0.00 (0,Inf)	0.99
Other	3.55 (1.13,11.12)	0.03
MRI BPE			0.075
Minimal-mild	Reference
Moderate	0.23 (0.05,1.00)	0.051
Marked	2.69 (0.43,16.69)	0.29
breast Lesion: Mass/NME			0.44
Mass	Reference
NME	0.72 (0.31,1.66)
Size	1.01 (0.99,1.03)		0.44
Mass border/margin			0.2
Regular	Reference
Irregular	4.00 (0.48,33.58)
Mass shape			0.015
Oval/round	Reference
Irregular	11.20 (1.60,78.40)
NME pattern			0.022
homogeneous	Reference
heterogeneous	0.00 (0,Inf)	0.99
clumped/clustered ring	3.22 (1.40,7.40)	0.0058
NME Distribution			0.42
focal/focus	Reference
ductal/linear/segmental	1.04 (0.43,2.54)	0.93
regional/multiple regional/Diffuse	2.14 (0.67,6.83)	0.2
T1 signal			0.93
No	Reference
Low & Iso	1.17 (0.53,2.61)	0.7
Hyper	0.00 (0,Inf)	0.99
T2 signal			0.053
No	Reference
low/intermediate	0.27 (0.08,0.95)	0.041
hyper	0.49 (0.21,1.15)	0.1
dynamic curve			0.76
persistent	Reference
plateau	0.92 (0.30,2.80)	0.88
wash out	1.47 (0.65,3.36)	0.36
mixed	1.14 (0.39,3.29)	0.81

Note: Odds ratio (OR) is a measure of association between a variable and an binary outcome (false positive vs true positive). OR>1 indicates increased occurrence of true positive while OR <1 indicates decreased occurrence of true positive. P value<0.05 indicates the estimated OR is significantly different from 1.

Table 3 – Univariate analysis - Subgroup analysis for Mass lesions

3.1 Summary table of tumor size between false positive and true positive groups for Mass lesions

Covariate	Full Sample (n=41)	FP (n=32)	TP (n=9)	p-value
Size				0.55
Mean (sd)	7.63 (2.30)	7.34 (1.70)	8.67 (3.71)
Median (Q1,Q3)	7 (6,9)	7 (6,9)	7 (6,12)
Range (min, max)	(4,15)	(5,11)	(4,15)

Note: P=0.55>0.05, indicating that size is not significantly associated with correct diagnose for mass region.

3.2 Summary table of mass characteristics between false positive and true positive groups for Mass lesions

Covariate	OR(95%CI)	p-value	Global p-value
Size	1.27 (0.92,1.74)		0.14
Mass border/margin			0.2
Regular	Reference
Irregular	4.00 (0.48,33.58)
Mass shape			0.015
Oval/round	Reference
Irregular	11.20 (1.60,78.40)
T1 signal			0.25
No	Reference
Low & Iso	0.37 (0.07,1.99)
T2 signal			0.04
No	Reference
low/intermediate	0.09 (0.0077,1.03)	0.053
hyper	0.13 (0.02,0.76)	0.024
dynamic curve			0.98
persistent	Reference
plateau	0.00 (0,Inf)	0.99
wash out	1.47 (0.25,8.70)	0.67
mixed	0.00 (0,Inf)	1

3.3 Subgroup analysis for Mass shape (n=33)

Covariate	OR(95%CI)	p-value	Global p-value
Size	0.73 (0.38,1.41)		0.35
T1 signal			0.74
No	Reference
Low & Iso	0.67 (0.06,7.60)
T2 signal			0.18
No	Reference
low/intermediate	0.11 (0.0064,1.92)	0.13
hyper	0.12 (0.01,1.36)	0.087
dynamic curve			0.85
persistent	Reference
plateau	0.00 (0,Inf)	1
wash out	2.00 (0.19,20.97)	0.56

3.4 Subgroup analysis for Mass margin (n=35)

Covariate	OR(95%CI)	p-value	Global p-value
Size	1.03 (0.66,1.62)		0.89
T1 signal			0.38
No	Reference
Low & Iso	0.42 (0.06,2.93)
T2 signal			0.096
No	Reference
low/intermediate	0.07 (0.0048,1.14)	0.062
hyper	0.12 (0.01,1.03)	0.053
dynamic curve			0.97
persistent	Reference
plateau	0.00 (0,Inf)	0.99
wash out	1.25 (0.20,7.92)	0.81

Table 4. Subgroup analysis for Non-mass lesions

4.1 Summary table of tumor size between false positive and true positive groups for Non-Mass lesions

Covariate	Full Sample (n=178)	FP (n=148)	TP (n=30)	p-value
Size				0.4
Mean (sd)	16.76 (15.99)	16.28 (15.94)	19.13 (16.31)
Median (Q1,Q3)	10 (6,22)	10.00 (6.00,21.25)	12.50 (7.00,29.50)
Range (min, max)	(3,100)	(4,100)	(3,60)

Note: P=0.4>0.05, indicating that size is not significantly associated with correct diagnose for non-mass region.

4.2 Summary table of mass characteristics between false positive and true positive groups for Non-Mass lesions

Covariate	OR(95%CI)	p-value	Global p-value
Size	1.01 (0.99,1.03)		0.37
NME pattern			0.025
homogeneous	Reference
heterogeneous	0.00 (0,Inf)	0.99
clumped/clustered ring	3.17 (1.38,7.28)	0.0066
NME Distribution			0.44
focal/focus	Reference
ductal/linear/segmental	1.02 (0.42,2.48)	0.97
regional/multiple regional/Diffuse	2.09 (0.65,6.68)	0.21
T1 signal			0.77
No	Reference
Low & Iso	1.55 (0.47,5.13)	0.47
Hyper	0.00 (0,Inf)	0.99
T2 signal			0.17
No	Reference
low/intermediate	0.28 (0.06,1.27)	0.098
hyper	0.56 (0.20,1.58)	0.27
dynamic curve			0.97
persistent	Reference
plateau	1.11 (0.35,3.52)	0.86
wash out	1.27 (0.47,3.45)	0.64
mixed	1.23 (0.41,3.68)	0.71

NME – non-mass enhancement

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published 06 Feb, 2023

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Editorial decision: Major Revisions Needed
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False-positive incidental lesions detected on contrast-enhanced breast MRI: Clinical and Imaging Features

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