Breast carcinoma in sclerosing adenosis: a clinicopathological and immunophenotypical analysis on 206 lesions

Nonneoplastic and neoplastic sclerosing lesions of the breast

Sclerosing lesions of the breast encompass a spectrum of benign and malignant entities and often pose a diagnostic challenge. Awareness of key morphologic features and pitfalls in the assessment of morphology and immunophenotype is essential to avoid over- or underdiagnosis and ensure optimal clinical management. This review summarizes nonneoplastic sclerosing lesions such as radial scar/complex sclerosing lesion, sclerosing adenosis, sclerosing intraductal papilloma, sclerosing variants of ductal adenoma and nipple adenoma, and fibroadenoma with extensive sclerosis, including their clinical presentation, characteristic morphology, differential diagnostic considerations, appropriate immunohistochemical work-up, when needed, and the clinical significance. In addition, atypical or neoplastic entities (such as atypical ductal hyperplasia, ductal carcinoma in situ, low-grade adenosquamous carcinoma, and fibromatosis-like metaplastic carcinoma) that can involve these sclerosing lesions are also briefly discussed.

Value of multimodal imaging in the diagnosis of breast sclerosing adenosis associated with malignant lesions

AIM

To explore the diagnostic value of multimodal imaging techniques, including automatic breast volume scanner (ABVS), mammography (MG), and magnetic resonance (MRI) in breast sclerosing adenosis (SA) associated with malignant lesions.

METHODS

From January 2018 to October 2020, 76 patients (88 lesions) with pathologically confirmed as SA associated with malignant or benign lesions were retrospective analyzed. All patients completed ABVS examination, 58 patients (67 lesions) with MG and 50 patients (62 lesions) with MRI were also completed before biopsy or surgical excision, of which, six patients (eight lesions) diagnosed as Breast Imaging Reporting and Data System (BI-RADS) category 3 by all imaging examinations underwent surgical excision without biopsy, other 70 patients (80 lesions) with BI-RADS category 4 or above by any imaging examination completed biopsy, including 65 patients (75 lesions) were further surgical excised and the other five patients (five lesions) were just followed up. All lesions were retrospectively described and classified, and were divided into benign group and malignant group according to their pathological results. Image features of different examination methods between the two groups were compared and analyzed. A ROC curve was established using the sensitivity of BI-RADS categories to predict malignant lesions in different imaging techniques as the ordinate and 1-specificity as the abscissa.

RESULTS

88 lesions including 26 purely SA and 45 SA associated with benign lesions were classified as benign group, and the remaining 17 SA associated with malignant lesions were classified as malignant group. On ABVS, 40 mass lesions, their heterogeneous echo, not circumscribed margin and coronal convergence signs were statistically significant for malignant lesions (p < .05), but the remain 48 nonmass lesions lack specific sonographic features. On MG, 12 showed negative results, 55 showed with microcalcification, mass, structural distortion, and asymmetric density shadow, of which 11 lesions had the above two signs at the same time, but only microcalcification had statistical difference between the two groups. 35 mass enhanced lesions and 27 nonmass enhanced lesions on MRI, but there were no significant difference between their pathological results. Time signal intensity curves showed no differences, but ADC value <1.10 × 10^-3  mm² /s is more significant in malignant lesions (p < .05). The area under the ROC curve (AUC) of BI-RADS classification of ABVS, MG, and MRI in the diagnosis of malignant lesions were 0.611, 0.474, and 0.751, respectively, and the AUC of the combined diagnosis of the three was 0.761.

CONCLUSION

Mass lesions with heterogeneous echo, not circumscribed margin and coronal convergence sign on ABVS, microcalcification on MG and the ADC value <1.10 × 10^-3  mm² /s on MRI are significant signs for SA associated with malignant lesions. The combined diagnosis of the three methods was the highest, and the following were MRI, ABVS, and MG. Therefore, be cognizant of significant characteristics in SA associated with malignancy showed in different imaging examinations can improve the preoperative evaluation of SA and better provide basis for subsequent clinical decision-making.

A deep learning model for breast ductal carcinoma in situ classification in whole slide images

The pathological differential diagnosis between breast ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) is of pivotal importance for determining optimum cancer treatment(s) and clinical outcomes. Since conventional diagnosis by pathologists using microscopes is limited in terms of human resources, it is necessary to develop new techniques that can rapidly and accurately diagnose large numbers of histopathological specimens. Computational pathology tools which can assist pathologists in detecting and classifying DCIS and IDC from whole slide images (WSIs) would be of great benefit for routine pathological diagnosis. In this paper, we trained deep learning models capable of classifying biopsy and surgical histopathological WSIs into DCIS, IDC, and benign. We evaluated the models on two independent test sets (n= 1382, n= 548), achieving ROC areas under the curves (AUCs) up to 0.960 and 0.977 for DCIS and IDC, respectively.

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.

Conventional and contrast-enhanced ultrasound features in sclerosing adenosis and correlation with pathology

OBJECTIVES

To evaluate the efficacy of conventional ultrasound (US) and contrast-enhanced ultrasound (CEUS) in differential diagnosis of sclerosing adenosis (SA) from malignance and investigate the correlated features with pathology.

METHODS

We retrospectively enrolled 103 pathologically confirmed SA. All lesions were evaluated with conventional US while 31 lesions with CEUS. Lesions were divided into SA with or without benign lesions (Group 1, n = 81) and SA with malignancy (Group 2, n = 22). Performance of two methods were analyzed. The ultrasonographic characteristics were compared between two groups with Student's t-test for measurement and chi-squared or Fisher's exact test for count data.

RESULTS

There were 22 lesions complicated with malignancy, and the mean age of Group 2 was higher than Group 1 (55.27 vs. 41.57, p < 0.001). The sensitivity, specificity and accuracy of conventional US and CEUS were 95.45%, 46.91%, 57.28% and 100%, 62.5%, 70.97%. Angularity (p < 0.001), spicules (p = 0.023), calcification (p = 0.026) and enlarged scope (p = 0.012) or crab claw-like enhancement (p = 0.008) in CEUS were more frequent detected in SA with malignancy.

CONCLUSIONS

Though CEUS showed an improved accuracy, the performance of ultrasound in the diagnosis of SA was limited. Awareness and careful review of the histopathologically related imaging features can be helpful in the diagnosis of SA.