Assessment of Diagnostic Accuracy and Efficiency of Categories 4 and 5 of the Second Edition of the BI-RADS Ultrasound Lexicon in Diagnosing Breast Lesions

A clinical-radiomics nomogram based on multimodal ultrasound for predicting the malignancy risk in solid hypoechoic breast lesions

Background

In routine clinical examinations, solid hypoechoic breast lesions are frequently encountered, but accurately distinguishing them poses a challenge. This study proposed a clinical-radiomics nomogram based on multimodal ultrasound that enhances the diagnostic accuracy for solid hypoechoic breast lesions.

Method

This retrospective study analyzed ultrasound strain elastography (SE) and automated breast volume scanner images (ABVS) of 423 solid hypoechoic breast lesions from 423 female patients in our hospital between August 2019 and May 2022. They were assigned to the training (n=296) and validation (n=127) groups in a 7:3 ratio by generating random numbers. Radiomics features were extracted and screened from ABVS and SE images, followed by the calculation of the radiomics score (Radscore) based on these features. Subsequently, a nomogram was constructed through multivariate logistic regression to assess the malignancy risk in breast lesions by combining Radscore with Breast Imaging Reporting and Data System (BI-RADS) scores and clinical risk factors associated with breast malignant lesions. The diagnostic performance, calibration performance, and clinical usefulness of the nomogram were assessed by the area under the curve (AUC) of the receiver operating characteristic curve, the calibration curve, and the decision analysis curve, respectively.

Results

The diagnostic performance of the nomogram is significantly superior to that of both the clinical diagnostic model (BI-RADS model) and the multimodal radiomics model (SE+ABVS radiomics model) in training (AUC: 0.972 vs 0.930 vs 0.941) and validation group (AUC:0.964 vs 0.916 vs 0.933). In addition, the nomogram also exhibited a favorable goodness-of-fit and could lead to greater net benefits for patients.

Conclusion

The nomogram enables a more effective assessment of the malignancy risk of solid hypoechoic breast lesions; therefore, it can serve as a new and efficient diagnostic tool for clinical diagnosis.

Can Short-term Follow-up with Ultrasound be Offered as an Acceptable Alternative to Immediate Biopsy or Surgery for Patients with First Ultrasound Diagnosis of BI-RADS 4A Lesions?

OBJECTIVES

To evaluate the relevant factors associated with malignancy in Breast Imaging Reporting and Data System (BI-RADS) 4A and to determine whether it was possible to establish a safe follow-up guideline for lower-risk 4A lesions.

METHODS

In this retrospective study, patients categorized as BI-RADS 4A on ultrasound who underwent ultrasound-guided biopsy or/and surgery between June 2014 and April 2020 was analyzed. Classification-tree method and cox regression analysis were used to explore the possible correlation factors of malignancy.

RESULTS

Among 9965 patients enrolled, 1211 (mean age, 44.3 ± 13.5 years; range, 18-91 years) patients categorized as BI-RADS 4A were eligible. The result of cox regression analysis revealed the malignant rate was only associated with patient age (hazard ratio (HR) = 1.038, p < 0.001, 95% confidence interval (CI): 1.029-1.048) and the mediolateral diameter of the lesion (HR = 1.261, p < 0.001, 95% CI: 1.159-1.372). The malignant rate for patients (≤ 36 y) with BI-RADS 4A lesions (the mediolateral diameter ≤ 0.9 cm) was 0.0% (0/72). This subgroup included fibrocystic disease and adenosis in 39 patients (54.2%), fibroadenoma in 16 (22.2%), intraductal papilloma in 8 (11.1%), inflammatory lesions in 6 (8.3%), cyst in 2 (2.8%), and hamartoma in 1 (1.4%).

CONCLUSIONS

Patient age and lesion size are associated with the rate of malignancy in BI-RADS 4A. For patients with lower-risk BI-RADS 4A lesions (≤ 2% likelihood of malignancy), short-term follow-up with ultrasound may be offered as an acceptable alternative to immediate biopsy or surgery.

The value of MRI for downgrading of breast suspicious lesions detected on ultrasound

BACKGROUND

Most of suspicious lesions classified as breast imaging reporting and data system (BI-RADS) 4A and 4B categories on ultrasound (US) were benign, resulting in unnecessary biopsies. MRI has a high sensitivity to detect breast cancer and high negative predictive value (NPV) to exclude malignancy. The purpose of this study was to investigate the value of breast MRI for downgrading of suspicious lesions with BI-RADS 4A and 4B categories on US.

METHODS

Patients who underwent breast MRI for suspicious lesions classified as 4A and 4B categories were included in this retrospective study. Two radiologists were aware of the details of suspicious lesions detected on US and evaluated MR images. MRI BI-RADS categories were given by consensus on the basis on dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI). Pathological results and imaging follow-up at least 12 months were used as a reference standard. Sensitivity, specificity, positive predictive value (PPV), NPV and their 95% confidence interval (CI) were calculated for MRI findings.

RESULTS

One sixty seven patients with 186 lesions (US 4A category: 145, US 4B category: 41) consisted of the study cohort. The malignancy rate was 34.9% (65/186). On MRI, all malignancies showed true-positive results and 92.6% (112/121) benign lesions were correctly diagnosed. MRI increased PPV from 34.9% (65/186) to 87.8% (65/74) and reduced the false-positive biopsies by 92.6% (112/121). The sensitivity, specificity, PPV and NPV of MRI were 100% (95% CI: 94.5%-100%), 92.6% (95% CI: 86.3%-96.5%), 87.8% (95% CI: 78.2%-94.3%) and 100% (95% CI: 96.8%-100%), respectively. 2.2% (4/186) of suspicious lesions were additionally detected on MRI, 75% (3/4) of which were malignant.

CONCLUSION

MRI could downgrade suspicious lesions classified as BI-RADS 4A and 4B categories on US and avoided unnecessary benign biopsies without missing malignancy. Additional suspicious lesions detected on MRI needed further work-up.

The Added Value of a Computer-Aided Diagnosis System in Differential Diagnosis of Breast Lesions by Radiologists With Different Experience

OBJECTIVES

To evaluate the value of the computer-aided diagnosis system, S-Detect (based on deep learning algorithm), in distinguishing benign and malignant breast masses and reducing unnecessary biopsy based on the experience of radiologists.

METHODS

From February 2018 to March 2019, 266 breast masses in 192 women were included in our study. Ultrasound (US) examination, including S-Detect technique, was performed by the radiologist with about 10 years of clinical experience in breast US imaging. US images were analyzed by four other radiologists with different experience in breast imaging (radiologists 1, 2, 3, and 4 with 1, 4, 9, and 20 years, respectively) according to their clinical experience (with and without the results of S-Detect). Diagnostic capabilities and unnecessary biopsy of radiologists and radiologists combined with S-Detect were compared and analyzed.

RESULTS

After referring to the results of S-Detect, the changes made by less experienced radiologists were greater than experienced radiologists (benign or malignant, 44 vs 22 vs 14 vs 2; unnecessary biopsy, 34 vs 25 vs 10 vs 5). When combined with S-Detect, less experienced radiologists showed significant improvement in accuracy, specificity, positive predictive value, negative predictive value, and area under curve (P < .05), but not for experienced radiologists (P > .05). Similarly, the unnecessary biopsy rate of less experienced radiologists decreased significantly (44.4% vs 32.7%, P = .006; 36.8% vs 28.2%, P = .033), but not for experienced radiologists (P > .05).

CONCLUSIONS

Less experienced radiologists rely more on S-Detect software. And S-Detect can be an effective decision-making tool for breast US, especially for less experienced radiologists.

Development and External Validation of a Simple-To-Use Dynamic Nomogram for Predicting Breast Malignancy Based on Ultrasound Morphometric Features: A Retrospective Multicenter Study

Background

With advances in high-throughput computational mining techniques, various quantitative predictive models that are based on ultrasound have been developed. However, the lack of reproducibility and interpretability have hampered clinical use. In this study, we aimed at developing and validating an interpretable and simple-to-use US nomogram that is based on quantitative morphometric features for the prediction of breast malignancy.

Methods

Successive 917 patients with histologically confirmed breast lesions were included in this retrospective multicentric study and assigned to one training cohort and two external validation cohorts. Morphometric features were extracted from grayscale US images. After feature selection and validation of regression assumptions, a dynamic nomogram with a web-based calculator was developed. The performance of the nomogram was assessed with respect to calibration, discrimination, and clinical usefulness.

Results

Through feature selection, three morphometric features were identified as being the most optimal for predicting malignancy, and all regression assumptions of the prediction model were met. Combining all these predictors, the nomogram demonstrated a good discriminative performance in the training cohort and in the two external validation cohorts with AUCs of 0.885, 0.907, and 0.927, respectively. In addition, calibration and decision curves analyses showed good calibration and clinical usefulness.

Conclusions

By incorporating US morphometric features, we constructed an interpretable and easy-to-use dynamic nomogram for quantifying the probability of breast malignancy. The developed nomogram has good generalization abilities, which may fit into clinical practice and serve as a potential tool to guide personalized treatment. Our findings show that quantitative morphometric features from different ultrasound machines and systems can be used as imaging surrogate biomarkers for the development of robust and reproducible quantitative ultrasound dynamic models in breast cancer research.

The Utility of the Fifth Edition of the BI-RADS Ultrasound Lexicon in Category 4 Breast Lesions: A Prospective Multicenter Study in China

RATIONALE AND OBJECTIVES

The objective of this study was to evaluate the utility of the fifth edition of the Breast Imaging-Reporting and Data System (BI-RADS) in clinical breast radiology by using prospective multicenter real-time analyses of ultrasound (US) images.

MATERIALS AND METHODS

We prospectively studied 2049 female patients (age range, 19-86 years; mean age 46.88 years) with BI-RADS category 4 breast masses in 32 tertiary hospitals. All the patients underwent B-mode, color Doppler US, and US elastography examination. US features of the mass and associated features were described and categorized according to the fifth edition of the BI-RADS US lexicon. The pathological results were used as the reference standard. The positive predictive values (PPVs) of subcategories 4a-4c were calculated.

RESULTS

A total of 2094 masses were obtained, including 1124 benign masses (54.9%) and 925 malignant masses (45.1%). For BI-RADS US features of mass shape, orientation, margin, posterior features, calcifications, architectural distortion, edema, skin changes, vascularity, and elasticity assessment were significantly different for benign and malignant masses (p< 0.05). Typical signs of malignancy were irregular shape (PPV, 57.2%), spiculated margin (PPV, 83.7%), nonparallel orientation (PPV, 63.9%), and combined pattern of posterior features (PPV, 60.6%). For the changed or newly added US features, the PPVs for intraductal calcifications were 80%, 56.4% for internal vascularity, and 80% for a hard pattern on elastography. The associated features such as architectural distortion (PPV, 89.3%), edema (PPV, 69.2%), and skin changes (PPV, 76.2%) displayed high predictive value for malignancy. The rate of malignant was 7.4% (72/975) in category 4a, 61.4% (283/461) in category 4b, and 93.0% (570/613) in category 4c. The PPV for category 4b was higher than the likelihood ranges specified in BI-RADS and the PPVs for categories 4a and 4c were within the acceptable performance ranges specified in the fifth edition of BI-RADS in our study.

CONCLUSION

Not only the US features of the breast mass, but also associated features, including vascularity and elasticity assessment, have become an indispensable part of the fifth edition of BI-RADS US lexicon to distinguish benign and malignant breast lesions. The subdivision of category 4 lesions into categories 4a, 4b, and 4c for US findings is helpful for further assessment of the likelihood of malignancy of breast lesions.

Does the radiologist need to rescan the breast lesion to validate the final BI-RADS US assessment made on the static images in the diagnostic setting?

Purpose: To assess whether radiologist needs to rescan the breast lesion to validate the final American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS) ultrasonography (US) assessment made on the static images in the diagnostic setting.

Patients and methods: Image data on 1,070 patients with 1,070 category 3–5 breast lesions with a pathological diagnosis scanned between January and June 2016 were included. Both real-time and static image assessments were acquired for each lesion. The diagnostic performance was evaluated by receiver operating characteristic (ROC) curves. The positive predictive values (PPVs) of each category in the two groups were calculated according to the ACR BI-RADS manual and compared. Kappas were determined for agreement on two assessment approaches.

Results: The sensitivity, specificity, PPV, and negative predictive value for real-time US were 98.9%, 58.2%, 44.8% and 99.4%, and for static images were 98.9%, 57.1%, 44.1% and 99.3%, respectively. The performance of the two groups was not significantly different (areas under ROCs: 0.786 vs 0.780, P=0.566) if the final assessment was only dichotomized as negative (category 3) and positive (categories 4 and 5). All PPVs of each category for each assessment were within the reference range provided by the ACR in 2013 except subcategory 4B (reference range: >10% and ≤50%) of static image evaluation, which was also significantly higher than that of real-time assessment (54.8% vs 40.7%, P=0.037). The overall agreement of the two approaches was moderate (κ=0.43–0.56 according to different detailed assessment).

Conclusion: Both static image and real-time assessment had similar diagnostic performance if only the treatment recommendations were considered, that is, follow-up or biopsy. However, as for subcategory 4B lesions without obviously benign or malignant US features, real-time scanning by the interpreter is recommended to obtain a more accurate BI-RADS assessment after assessing static images.

Subcategorization of Ultrasonographic BI-RADS Category 4: Assessment of Diagnostic Accuracy in Diagnosing Breast Lesions and Influence of Clinical Factors on Positive Predictive Value

To evaluate the diagnostic accuracy of subcategories 4a-4c of the second edition of the Breast Imaging Reporting and Data System (BI-RADS) ultrasonography (US) lexicon, and to investigate whether clinical factors influence the positive predictive values (PPVs). Overall, 1240 breast lesions in 1227 women diagnosed on ultrasound as category 4 and with pathology were included. The PPV with 95% confidence interval (CI) was 13.6% (95% CI: 11%, 16%) in BI-RADS 4a, 50.0% (95% CI: 44%, 56%) in BI-RADS 4b and 86.0% (95% CI: 82%, 90%) in BI-RADS 4c. Patients' age significantly affected PPVs for subcategories 4a-4c, whereas radiologists' experience and application time had little influence on PPVs for subcategories 4a-4c. In conclusion, the diagnostic accuracies of subcategories 4b and 4c were superior to subcategory 4a. Patients' age significantly affected PPVs for subcategories 4a-4c. Utilizing the subcategories of category 4 was a feasible method regardless of radiologists' experience and application time.

Does patient age affect the PPV3 of ACR BI-RADS Ultrasound categories 4 and 5 in the diagnostic setting?

OBJECTIVES

To calculate the positive predictive value of biopsies performed (PPV₃) of the Ultrasound section of the American College of Radiology Breast Imaging Reporting and Data System (ACR BI-RADS US) atlas categories 4 and 5 in different age groups and to determine whether patient age influences the PPV₃ of each category in the diagnosis of breast lesions.

METHODS

We identified 2,433 ACR BI-RADS US categories 4 and 5 lesions with a known pathological diagnosis in 2,433 women. The patients were classified into three age groups (<35, 35-50, and >50 years). The age-related PPV₃ of each category in the three age groups were calculated based on the pathological diagnoses and compared using the chi-squared test.

RESULTS

The overall PPV₃ of each category was within the reference range provided by the ACR in 2013. PPV₃ gradually increased with increasing age in patients with category 4 lesions. PPV₃ in the oldest group with subcategories 4A and 4B lesions were close to or exceeded the reference values.

CONCLUSIONS

PPV₃ and age were significantly associated in patients with category 4 lesions according to the newest edition of ACR BI-RADS US in the diagnostic setting. Closer attention should be given to older patients when assigning a final assessment category.

KEY POINTS

• In patients with category 4 lesions , the likelihood of malignancy is associated with age. • In patients with category 5 lesions, the association is not definite. • Closer attention should be given to older patients in applying the ACR BI-RADS US.

Percutaneous Needle Biopsies of the Breast in Women Younger than 35 Years: Minimally or Excessively Invasive?

Percutaneous needle biopsy (PNB) of the breast is commonly used for diagnosis of breast pathology, but has been less studied in young women. We sought to determine the effectiveness and necessity of PNB in patients younger than 35 years of age. The charts of sequential patients <35 years who underwent PNB between February 2013 and May 2016 were reviewed; 181 PNB were performed in 127 patients. Median age was 30 years (13–34). Indications for PNB were Breast Imaging Reporting and Data System (BIRADS) ≥4 in 137 (75.7%) cases, with mass on imaging in 139 (76.8%). Carcinoma was diagnosed in 12 (6.6%), PNB in eight unique patients (6.3%). Other PNB pathology included atypia in four (2.2%) patients; papillary lesion, five (2.8%); benign lymph node, 10 (5.5%); fibroepithelial lesion, 15 (8.3%); benign breast tissue, 63 (34.8%); and fibroadenoma, 72 (39.8%). Women with atypia or malignancy were older than those with benign findings (30.9 vs 28.0 years, P = 0.002). No other patient or imaging factors were significantly associated with pathologic diagnosis on PNB. Routine PNB for all BIRADS 4 findings may be over-used in young women as most results are benign and subsequent surgical findings are concordant. Improved diagnostic accuracy of breast imaging is warranted to reduce unnecessary procedures.