A Nomogram for Predicting Malignancy in Small and Non-parallel BI-RADS 4A Breast Lesions: A Novel Approach for Risk Stratification and Clinical Decision Support

doi:10.21203/rs.3.rs-5290756/v1

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A Nomogram for Predicting Malignancy in Small and Non-parallel BI-RADS 4A Breast Lesions: A Novel Approach for Risk Stratification and Clinical Decision Support

https://doi.org/10.21203/rs.3.rs-5290756/v1

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Purpose

Breast Imaging Reporting and Data System (BI-RADS) 4A breast lesions are often confusing for surgeons due to high false-positive outcomes. This study was conducted to analyze the factors of small and non-parallel BI-RADS 4A breast lesions and developed a predictive model to stratify the malignancy risk.

Methods

For this retrospective study, 282 patients were recruited in the First Affiliated Hospital of Nanjing Medical University from January 2020 to December 2023. Logistic regression analysis was used to identify risk factors and develop a predictive model to differentiate between benign and malignant BI-RADS 4A breast lesions. The effectiveness of the model was assessed by the receiver operating characteristic (ROC) curve and the decision curve analysis (DCA).

Results

The proportion of malignant tumors was 20.6% (58/282) in this study. A diagnostic model compromised age, menopausal status, and margin was built and shown as a nomogram. The area under the ROC curve was 0.747 and 0.741 in the training and test cohort, respectively. DCA demonstrated that the model could achieve benefits for patients. Moreover, we stratified the breast lesions into low-, medium- and high-risk groups according to the malignancy risk calculated by the model. Only 10% (5/50) and 4.8% (1/21) were malignant in the low-risk group in the training cohort and test cohort.

Breast cancer

Ultrasound

BIRADS 4A

Predictive model

Nomogram

Breast cancer is a female malignant tumor with the highest morbidity, which seriously threatens women's health and quality of life. Ultrasound (US), mammography, and magnetic resonance imaging (MRI) are the most commonly used breast examination modalities. MRI is difficult to popularize in clinical practice due to its high cost. Mammography is a routine screening method for breast cancer. However, the dense breast tissue of Asian women results in a lower sensitivity of mammography than that of other ethnic populations^{1, 2}. Therefore, ultrasound has become the first choice for many Chinese clinicians for breast disease due to its low cost, real-time, non-invasive, and high sensitivity.

The American Society of Radiology released the fifth edition of the Breast Imaging Reporting and Data System (BI-RADS) Ultrasound lexicon in 2013, which subdivided BI-RADS 4 category lesions into 4A, 4B, and 4C. The subcategory of BI-RADS 4 lesions depends on the experience of the sonographers, and there is no objective guideline for the subcategory³. The positive predictive value of BI-RADS 4A lesions was 2% -10%, indicating a low risk of malignancy. On the other hand, 4A is the most common subcategory in BI-RADS 4, accounting for 55.6%⁴. A large number of biopsies and surgeries of BI-RADS 4A breast lesions show benign pathology finally^{5, 6}, which not only increases treatment costs but also causes unnecessary complications and anxiety for patients. Therefore, it is necessary to stratify the risk of BI-RADS 4A breast lesions, and clinicians can suggest regular follow-up for low-risk patients^{7, 8}.

The orientation between the long axis of the mass and the skin, including parallel and non-parallel, is an important feature for characterizing breast lesions in the BI-RADS ultrasound lexicon. The non-parallel orientation indicates that the lesion spreads through the tissue plane boundary, which has a high predictive value for malignant tumors and has been confirmed in previous studies on breast disease^9–13. Moreover, non-parallel orientation is an independent risk factor of poor prognosis for breast cancer patients, accompanied by poor disease-free survival (DFS)^{14, 15}. Therefore, sonographers often diagnose breast lesions as the BI-RADS 4 category due to their non-parallel orientation, leading to subsequent biopsies or surgeries. However, we found that most non-parallel nodules with small sizes are benign in pathology. Chen et al. identified that the diagnostic accuracy of ultrasound for lesions with small diameters is not as good as the larger ones, and the ability of non-parallel orientation to predict malignancy of small lesions is not significant¹⁶. These researches reported that the malignancy rate in BI-RADS 4A is correlated with the size of breast lesions. Meanwhile, some studies suggested that breast cancer detected by ultrasound is often diagnosed at an early stage, with an average size of 1cm, but this is at the cost of a higher false positive rate^{2, 17}.

The treatment of small and non-parallel BI-RADS 4A breast lesions is a clinical challenge, and there has no study reported before. Our primary objective is to distinguish whether those breast lesions are benign or malignant. Then we conduct a predictive model and stratify the malignancy risk by using clinical information and ultrasound features, minimizing unnecessary invasive procedures.

Study population

This study retrospectively enrolled a total of 282 patients with breast lesions who accepted diagnosis and treatment in the First Affiliated Hospital of Nanjing Medical University from January 2020 to December 2023. The inclusion criteria are as follows: (a) female patients over the age of 18; (b) a small (< 1.5cm), non-parallel breast lesion with clear ultrasound image, diagnosed as BI-RADS 4A; (c) available clinical information and precise pathological results. The exclusion criteria are as follows: (a) patients who undergo mass biopsy before ultrasound examination; (b) history of malignant tumors in the breast or other organs. (c) patients who have undergone surgery or biopsy on the ipsilateral breast. The flow diagram of screened and excluded patients of this study is shown in Fig. 1.

The present study was approved by the ethics committees of the First Affiliated Hospital with Nanjing Medical University (2024-SR-720) and was in compliance with the Helsinki Declaration. As this was a retrospective study, informed consent from the study participants was waived.

Data collection and examination

The instruments used for ultrasound examination are MyLab Twice (Esaote, Italy) and Acuson Sequoia (Siemens, Germany), both equipped with high-frequency broadband linear array probes with high-resolution ultrasound units. US examination includes 2D, color Doppler imaging, and elastography. The vascularity (absent, internal, peripheral) and the amount of blood signals (grade 0, 1, 2, 3) were evaluated based on Adler's index. The elasticity images were assessed by using the five-point scoring system on strain elastography¹⁸. Scores 1 and 2 were considered soft, scores 3 for lesions with an intermediate degree of stiffness, and scores 4 and 5 for hard. We selected the one with the highest possibility of malignancy for analysis when there are multiple masses. We collected and recorded the ultrasound features of all lesions, including longest diameter, glandular tissue component (GTC)¹⁹, echo pattern, shape, margin, posterior features, calcification, ductal dilation, vascularity, the amount of blood signals, elasticity, and ultrasound BI-RADS category. Two experienced ultrasound physicians (Q.L and J.D) in breast disease examination reviewed the data separately. They remained blinded to the patient’s clinical history, other imaging results, and pathological findings.

Additional data from the 282 patients were collected as follows: (1) demographic data (age and menopausal status); (2) histological information, including histological grading, molecular subtype, and lymph node involvement.

Statistical Analysis

The categorical variables were analyzed utilizing either the Chi-square test or Fisher's exact test, whereas continuous variables were evaluated with either the Student's t-test or the Mann-Whitney U test when appropriate. Furthermore, these statistical approaches were employed to compare clinical, pathological, and US examination characteristics between benign mass or malignant tumors. A multivariable logistic regression analysis, implemented through the stepwise backward regression, resulted in the development of a nomogram. A receiver operating characteristic (ROC) curve was performed to assess the model's effectiveness. Additionally, a calibration curve and the Hosmer-Lemeshow goodness of fit test were allowed for the evaluation of discrepancies between the model's predictions and actual outcomes. To evaluate the clinical utility of the diagnostic model, decision curve analysis (DCA) was undertaken, leveraging the dataset to quantify net benefits across varying threshold probabilities. Based on the risks of the predictive model, the subjects are grouped into quartiles, with the upper quartile considered as the high-risk group, the lower quartile as the low-risk group, and the remaining two quartiles as the medium-risk group. The prediction model's formula was derived, and statistical significance was determined at a P-value threshold of < 0.05 using two-tailed statistical analysis. All statistical analyses were performed utilizing R software, version 4.3.2.

Characteristics of the training and test cohorts

This study enrolled 282 female patients (median age, 47 years; range 20–81 years) with BI-RADS 4A breast lesions that featured as a non-parallel orientation on ultrasound. We randomly divided all the subjects at a ratio of 2:1 into a training cohort (N = 197) and a test cohort (N = 85). The malignant proportion was 20.6% (58/282) for all patients, while 20.3% (40/197) in the training cohort and 21.2% (18/85) in the test cohort. US features and clinicopathological findings of the training and test cohorts were summarized in Table 1. There were no differences in the baseline characteristics between the training and test datasets.

Table 1

Demographic and clinicopathological characteristics of patients
	Training cohort (N = 197)	Test cohort (N = 85)	P-value
Age
Mean (SD)	46.1 (11.2)	46.3 (10.7)	0.924
Menopausal status
Pre-menopause	132 (67.0%)	60 (70.6%)	0.65
Post-menopause	65 (33.0%)	25 (29.4%)
Maximal diameter
Mean (SD)	8.85 (2.64)	8.69 (2.67)	0.646
GTC
< 50%	130 (66.0%)	59 (69.4%)	0.672
≥ 50%	67 (34.0%)	26 (30.6%)
Echo pattern
Complex cystic and solid	8 (4.1%)	3 (3.5%)	0.685
Hypoechoic	180 (91.4%)	80 (94.1%)
Isoechoic	3 (1.5%)	0 (0%)
Heterogeneous	6 (3.0%)	2 (2.4%)
Shape
Regular	41 (20.8%)	18 (21.2%)	1
Irregular	156 (79.2%)	67 (78.8%)
Margin
Circumscribed	69 (35.0%)	30 (35.3%)	1
Not circumscribed	128 (65.0%)	55 (64.7%)
Posterior features
No posterior features	165 (83.8%)	70 (82.4%)	0.766
Enhancement	11 (5.6%)	5 (5.9%)
Shadowing	19 (9.6%)	10 (11.8%)
Combined pattern	2 (1.0%)	0 (0%)
Calcification
No	171 (86.8%)	78 (91.8%)	0.323
Yes	26 (13.2%)	7 (8.2%)
Ductal dilation
No	172 (87.3%)	73 (85.9%)	0.894
Yes	25 (12.7%)	12 (14.1%)
Vascularity
Absent	48 (24.4%)	18 (21.2%)	0.695
Vessels in rim	60 (30.5%)	24 (28.2%)
Internal vascularity	89 (45.2%)	43 (50.6%)
Blood flow grades
Absent	48 (24.4%)	18 (21.2%)	0.841
Grade 1	68 (34.5%)	34 (40.0%)
Grade 2	74 (37.6%)	30 (35.3%)
Grade 3	7 (3.6%)	3 (3.5%)
Elasticity assessment
Soft	26 (13.2%)	19 (22.4%)	0.0756
Intermediate	78 (39.6%)	30 (35.3%)
Hard	54 (27.4%)	16 (18.8%)
Missing	39 (19.8%)	20 (23.5%)
Pathology
Benign	157 (79.7%)	67 (78.8%)	0.586
Carcinoma in situ	12 (6.1%)	5 (5.9%)
In situ carcinoma with microinvasion	1 (0.5%)	2 (2.4%)
Invasive carcinoma	27 (13.7%)	11 (12.9%)

Risk factors associated with malignant lesions in the training cohort

In univariate analyses of these 197 patients from the training cohort (Table 2), those with malignant lesions were older than those with benign lesions (OR 1.06; 95% CI 1.03–1.10; P = 0.001). Moreover, among the malignant patients, the rate of post-menopause female was significantly higher than that of benign patients (OR 4.24; 95% CI 2.07–8.92; P < 0.001). In terms of US imaging, more malignant lesions were found to have no circumscribed margin (OR 3.80; 95% CI 1.61–10.52; P = 0.005) and posterior shadowing feature (OR 3.27; 95% CI 1.18–8.80; P = 0.019) than benign lesions. No significant differences were observed between the two cohorts regarding maximal diameter (P = 0.951), GTC (P = 0.821), echo pattern (P = 0.155), shape (P = 0.154), calcification (P = 0.104), duct dilatation (P = 0.277), vascularity (P > 0.05), elasticity assessment (P = 0.229). In multivariate analyses, menopausal status (OR 4.12; 95% CI 1.98–8.80; P < 0.001) and margin (OR 3.64; 95% CI 1.50–10.2; P = 0.008) were still independently associated with malignancy.

Table 2

Logistics analysis of patients in training cohort (N = 197)
	Univariate analysis		Multivariate analysis
	OR [95%CI]	P	OR [95%CI]	P
Age	1.06 [1.03, 1.10]	0.001
Menopausal status
Pre-menopause	Ref
Post-menopause	4.24 [2.07, 8.92]	< 0.001	4.12 [1.98, 8.80]	< 0.001
Maximal diameter(mm)	1.01 [0.88, 1.15]	0.899
≤ 10	Ref
> 10	0.97 [0.42, 2.12]	0.951
GTC
< 50%	Ref
≥ 50%	0.92 [0.43, 1.90]	0.821
Echo pattern
Hypoechoic	Ref
Not hypoechoic	0.23 [0.01, 1.16]	0.155
Shape
Regular	Ref
Irregular	2.08 [0.82, 6.41]	0.154
Margin
Circumscribed	Ref
Not circumscribed	3.80 [1.61, 10.52]	0.005	3.64 [1.50, 10.32]	0.008
Posterior features
No posterior features	Ref
Enhancement	1.00 [0.15, 4.13]	1
Shadowing	3.27 [1.18, 8.80]	0.019
Combined pattern	NA	NA
Calcification
No	Ref
Yes	0.29 [0.05, 1.04]	0.104
Ductal dilation
No	Ref
Yes	0.50 [0.11, 1.54]	0.277
Vascularity
Absent	Ref
Vessels in rim	0.52 [0.18, 1.40]	0.198
Internal vascularity	1.04 [0.46, 2.45]	0.929
Blood flow grades
Absent	Ref
Grade 1	1.21 [0.52, 2.93]	0.664
Grade 2	0.59 [0.23, 1.50]	0.262
Grade 3	NA	NA
Elasticity assessment
Soft	Ref
Intermediate	1.00 [0.31, 3.86]	1
Hard	2.12 [0.67, 8.13]	0.229
NA: not available
Ref: reference

Development and validation of a nomogram

Menopausal status and margin were incorporated into a logistics model. Although age did not emerge as an independent factor predictive of malignancy in the multivariate analysis, it was nonetheless incorporated into the model, as numerous prior studies have suggested a higher incidence of malignancy in breast masses among older patients. Finally, a diagnostic model that compromised three variables was established and presented as a nomogram (Fig. 2). In accordance with the ROC curves, the AUC was 0.747 in the training cohort and 0.741 in the test cohort (Fig. 3). The calibration curve presented a good agreement between the predicted and observed probabilities (Fig. 4A). The Hosmer‒Lemeshow test was not significant (P > 0.05), suggesting that the model did not deviate and fit well. The DCA showed that the use of the diagnostic model to predict the risk of malignant breast lesions yielded more benefits (Fig. 4B).

Application of the nomogram to stratify the malignancy risk

According to the nomogram derived from the diagnostic model, we calculated the prediction probability of risk degree for all the patients in the training cohort. The upper quarter value of 0.23 and lower quarter value of 0.08 were set as the cut-off points. Patients were regarded as “high risk” with a risk degree more than or equal to 0.23, and as “low risk” with a risk degree less than or equal to 0.08. Others were regarded as “medium risk”. As shown in Fig. 5, in the low-risk group, 90% (45/50) were benign lesions, while only 10% (5/50) were malignant. 53.1% (26/49) were benign lesions in the high-risk group, and 46.9% (23/49) were malignant. In the test cohort, with the same cut-off as the training cohort, there were 41.2% (7/17) of patients with malignant pathology in the high-risk group, and 58.8% (10/17) patients with benign pathology. Comparatively, only 4.8% (1/21) of patients in the low-risk group had malignant pathology, while 95.2% (20/21) had benign pathology.

In this study, we retrospectively analyzed characteristics of small BI-RADS 4A breast lesions, which featured non-parallel on US. The results showed that menopausal status and margin were independently associated with malignancy. A predictive model was then built and presented as a nomogram. The AUC of ROC was 0.747 in the training cohort and 0.741 in the test cohort. Finally, we stratified the breast lesions into a low-risk group, medium-risk group, and high-risk group according to the malignancy risk calculated by nomogram. 10% of patients had malignant pathology in the low-risk group in the training cohort, and 4.8% of patients had malignant pathology in the low-risk group in the test cohort.

In BI-RADS lexicon, non-parallel orientation was defined as the longest axis of the breast lesion is not parallel to the skin. Non-parallel orientation was frequently associated with malignant masses and had a high predictive value for malignancy. These had been demonstrated in numerous studies. Moreover, some studies have investigated that non-parallel orientation was related to advanced cancer and a poor prognosis^{15, 20}. Significantly, in most previous studies, non-parallel orientation was defined as the anterior-posterior dimension greater than the horizontal dimension. However, round masses and those obliqued to the skin were also considered non-parallel orientation according to the BI-RADS lexicon, which resulted in the difficulties in practical applications and more false-positive cases. Wu et al. quantified the orientation angle in their study and found a significant difference between malignant and benign breast lesions²¹. Accurate and objective measurement of the orientation angle led to better prediction of malignancy compared to the traditional "taller than wide" orientation method. Similarly, Zhu et al. calculated ALS (angle between the long axis of the lesion and skin) with artificial intelligence and found increased ALS in malignant masses compared with the benign ones²². Nevertheless, there were no significant differences in height-to-width ratio between benign and malignant masses. Our study was the first to investigate the risk factor in non-parallel breast lesions. We retrospectively analyzed the images and the orientation was defined based on subjective judgment but not precise measurement. Some biases were inevitable, although our data were examined by two experienced sonographers. We hypothesized that objective orientation evaluation will improve the prediction of the diagnostic model in the future research.

Small breast lesion remains a diagnostic challenge for conventional ultrasound. The feature of the lesion may not be accurate, especially those less than 1 cm, and it is difficult to analyze according to the Breast Imaging Reporting and Data System²³. Studies have proven that the accuracy of breast US in differentiating between benign and malignant masses was lower in small groups. Therefore, many researchers combined traditional ultrasound with elastography or contrast-enhanced ultrasound to improve the diagnostic ability of small breast masses^24–26. In our study, elasticity assessment was not significantly different between the benign and malignant lesions. Chen et al. found that margin was the only significant factor in evaluating breast masses ≤ 1cm¹⁶. Wu et al. found that spiculated margin and anti-parallel orientation at US were associated with poor prognosis in patients with small breast cancer¹⁵. This is generally consistent with our results, that margin was the only statistically significant sonographic feature in multivariate analyses. Ultrasound is more accurate in evaluating the margin of lesions than mammography, especially in populations with dense breasts ²⁷. Consequently, the findings of our study had important implications for clinical practices.

The clinical management of patients with BI-RADS 4A breast lesions has always been a thorny issue. Invasive biopsy can diagnose cancers at an early stage, but it may also increase patients' anxiety and unnecessary costs. Many studies have integrated multiple methods to establish a predicted model for downgrading 4A breast lesions, such as radiomics and artificial intelligence, and they have achieved satisfactory AUC^{3, 7, 28}. Notably, our studies focused on the specific lesions, which featured small size and non-parallel orientation on ultrasound. The nomogram incorporated three variables: age, menopausal status, and margin. The diagnostic efficiency of the model was acceptable, with an AUC of the ROC curve of 0.75. More importantly, the diagnostic model in this study was convenient and widely available in clinical practice. Age was an independent factor related to malignancy, and this has been confirmed in previous studies²⁹. Similarly, age was also an important factor in our study, and as an objective variable, the accuracy of results could be increased. Finally, we further conducted risk stratification. Based on the malignancy ratio in each group, we have different recommendations for patients with different risk degrees. Biopsies are strongly recommended for patients who are in high-risk groups. Whereas intensive follow-up observation can be suggested for patients in low-risk groups. For medium-risk patients, combining other imaging examinations such as MR and contrast-enhanced ultrasound may benefit them more.

There are some limitations in this study. First, this was a retrospective study and only pathologically diagnosed patients were enrolled, but a large number of BI-RADS 4A patients were follow-up observed. This resulted in the selection bias, high malignant ratio and the relatively small size of our sample. In the future research, we will recruit patients with stable imaging examinations in at least one-year follow-up, and conduct some prospective research. Second, our research is a single-center study. The diagnosis of BI-RADS 4A lesions on US is dependent on the institution and sonographers training. Therefore, more data from other institutions should be included to validate this model and increase both its generalizability and clinical applicability.

we analyzed the characteristics of BI-RADS 4A breast lesions, which featured small and non-parallel on US. A diagnostic model was built and shown as a nomogram. We further stratify the breast lesions into different risk groups. Biopsies are recommended for patients who are in a high-risk group, while intensive follow-up observation may be suggested for patients in the low-risk group.

Conflict of interest

The authors declare that they have no conflict of interest.

Author Contribution

Qin Li and Xiaowei Sun collected the data. Qin Li performed the statistical analyses. Wenbin Zhou, Hong Pan, and Kai Zhang designed and supervised the study. Xiaowei Sun prepared the manuscript. All the authors have read and approved the final manuscript.

Acknowledgments

This work was supported by the Natural Science Foundation of Jiangsu Province (BK20230017 W.Z.).

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No competing interests reported.

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A Nomogram for Predicting Malignancy in Small and Non-parallel BI-RADS 4A Breast Lesions: A Novel Approach for Risk Stratification and Clinical Decision Support

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Abstract

Purpose

Methods

Results

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Introduction

Methods

Study population

Data collection and examination

Statistical Analysis

Results

Characteristics of the training and test cohorts

Risk factors associated with malignant lesions in the training cohort

Development and validation of a nomogram

Application of the nomogram to stratify the malignancy risk

Discussion

Conclusion

Declarations

Conflict of interest

Author Contribution

Acknowledgments

References

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