Mammographic and Ultrasonographic Findings of Different Breast Adenosis Lesions

Radiomics analysis combining gray-scale ultrasound and mammography for differentiating breast adenosis from invasive ductal carcinoma

Objectives

To explore the utility of gray-scale ultrasound (GSUS) and mammography (MG) for radiomic analysis in distinguishing between breast adenosis and invasive ductal carcinoma (IDC).

Methods

Data from 147 female patients with pathologically confirmed breast lesions (breast adenosis: 61 patients; IDC: 86 patients) between January 2018 and December 2022 were retrospectively collected. A training cohort of 113 patients (breast adenosis: 50 patients; IDC: 63 patients) diagnosed from January 2018 to December 2021 and a time-independent test cohort of 34 patients (breast adenosis: 11 patients; IDC: 23 patients) diagnosed from January 2022 to December 2022 were included. Radiomic features of lesions were extracted from MG and GSUS images. The least absolute shrinkage and selection operator (LASSO) regression was applied to select the most discriminant features, followed by logistic regression (LR) to construct clinical and radiomic models, as well as a combined model merging radiomic and clinical features. Model performance was assessed using receiver operating characteristic (ROC) analysis.

Results

In the training cohort, the area under the curve (AUC) for radiomic models based on MG features, GSUS features, and their combination were 0.974, 0.936, and 0.991, respectively. In the test cohort, the AUCs were 0.885, 0.876, and 0.949, respectively. The combined model, incorporating clinical and all radiomic features, and the MG plus GSUS radiomics model were found to exhibit significantly higher AUCs than the clinical model in both the training cohort and test cohort (p<0.05). No significant differences were observed between the combined model and the MG plus GSUS radiomics model in the training cohort and test cohort (p>0.05).

Conclusion

The effectiveness of radiomic features derived from GSUS and MG in distinguishing between breast adenosis and IDC is demonstrated. Superior discriminatory efficacy is shown by the combined model, integrating both modalities.

Efficacy and safety of percutaneous ultrasound-guided vacuum-assisted excision for the treatment of clinical benign breast lesions larger than 3 cm: a retrospective cohort study

Background

Breast ultrasound-guided vacuum-assisted excision (US-VAE) has become a scarless solution for the removal of benign breast lesions. This procedure is now favored by more and more female patients for its satisfactory cosmetic outcome and few postoperative complications. However, controversy have been raised regarding its efficacy and safety in treating larger benign breast lesions. This study aimed to evaluate whether US-VAE is sufficient for the treatment of clinical benign breast lesions larger than 3 cm and to investigate the lesion features that affect the complete excision rate and hematoma occurrence rate.

Methods

From January 2018 to July 2021, a total of 1,812 lesions in 1,367 patients underwent US-VAE at the Chinese People's Liberation Army General Hospital. A total of 89 benign breast lesions in 87 patients enrolled in this retrospective cohort study. The baseline clinical characteristics and ultrasonographic features of the lesions were recorded. Patients were followed up by US to record if there are any serious issues and the occurrence of hematoma and the recurrence of the lesions within 3 days and 6-12 months later, then at 1-year intervals. Lesions were classified to analyze the possible factors associated with complete excision rate and hematoma occurrence rate.

Results

The mean age was 35.9±9.5 years (range, 18-54 years), and the median maximum size of benign breast lesions was 3.5 cm (range, 3.1-5.0 cm). The complete excision rate was 91.0% (81/89). Histopathology (P=0.002) and vascularity (P=0.032) of lesions showed statistically significant differences in groups with or without recurrent lesions. A total of 17 cases (17/89, 19.1%) presented with hematoma after the procedure. The maximum lesion size in patients with hematoma was significantly larger than that in those without hematoma (P<0.001).

Conclusions

US-VAE is an effective and safe alternative method for the treatment of benign breast lesions larger than 3 cm, especially for fibroadenoma, adenosis, hamartoma. For benign phyllodes tumors and intraductal papillomas larger than 3 cm and lesions with hypervascularity, the possibility of recurrence after US-VAE should be noted. The size of lesions needs to be considered when evaluating the occurrence of hematoma after US-VAE.

Ultrasound-Based Nomogram for Distinguishing Malignant Tumors from Nodular Sclerosing Adenoses in Solid Breast Lesions

OBJECTIVES

Nodular sclerosing adenoses (NSAs) and malignant tumors (MTs) may coexist and are often classified into the same Breast Imaging Reporting and Data System (BI-RADS) category. We aimed to build and validate an ultrasound-based nomogram to distinguish MT from NSA for building a precise sequence of biopsies.

MATERIALS AND METHODS

The training cohort included 156 patients (156 masses) with NSA or MT at one study institution. We used best subset regression to determine the predictors for building a nomogram from ultrasonic characteristics and patients' age. Model performance and clinical utility were evaluated using Brier score, concordance (C)-index, calibration curve, and decision curve analysis. The independent validation cohort consisted of 162 patients (162 masses) from a separate institution.

RESULTS

Through best subset regression, we selected 6 predictors to develop nomogram: age, calcification, echogenic rim, vascularity distribution, tumor size, and thickness of breast parenchyma. Brier score and C-index of the nomogram in the training cohort were 0.068 and 0.967 (95% confidence interval [CI]: 0.941-0.993), respectively. In addition, calibration curve demonstrated good agreement between prediction and pathological result. In the validation cohort, the nomogram still obtained a favorable C-index score of 0.951 (95% CI: 0.919-0.983) and fine calibration. Decision curve analysis showed that the model was clinically useful.

CONCLUSIONS

If multiple NSA and MT masses are present in the same patient and are classified into the same BI-RADS category, our nomogram can be used as a supplement to the BI-RADS category for accurate biopsy of the mass most likely to be MT.