Consistency in recognizing microinvasion in breast carcinomas is improved by immunohistochemistry for myoepithelial markers

Improving the diagnosis of ductal carcinoma in situ with microinvasion without immunohistochemistry: An innovative method with H&E-stained and multiphoton microscopy images

Ductal carcinoma in situ with microinvasion (DCISM) is a challenging subtype of breast cancer with controversial invasiveness and prognosis. Accurate diagnosis of DCISM from ductal carcinoma in situ (DCIS) is crucial for optimal treatment and improved clinical outcomes. However, there are often some suspicious small cancer nests in DCIS, and it is difficult to diagnose the presence of intact myoepithelium by conventional hematoxylin and eosin (H&E) stained images. Although a variety of biomarkers are available for immunohistochemical (IHC) staining of myoepithelial cells, no single biomarker is consistently sensitive to all tumor lesions. Here, we introduced a new diagnostic method that provides rapid and accurate diagnosis of DCISM using multiphoton microscopy (MPM). Suspicious foci in H&E-stained images were labeled as regions of interest (ROIs), and the nuclei within these ROIs were segmented using a deep learning model. MPM was used to capture images of the ROIs in H&E-stained sections. The intensity of two-photon excitation fluorescence (TPEF) in the myoepithelium was significantly different from that in tumor parenchyma and tumor stroma. Through the use of MPM, the myoepithelium and basement membrane can be easily observed via TPEF and second-harmonic generation (SHG), respectively. By fusing the nuclei in H&E-stained images with MPM images, DCISM can be differentiated from suspicious small cancer clusters in DCIS. The proposed method demonstrated good consistency with the cytokeratin 5/6 (CK5/6) myoepithelial staining method (kappa coefficient = 0.818).

Updates in the Use of Immunohistochemical Stains in Breast and Gynecologic Pathology

CONTEXT.—

The use of immunohistochemical stains in breast and gynecologic pathology has become increasingly complex, with various diagnostic, prognostic, and predictive applications.

OBJECTIVE.—

To provide an update and review of immunohistochemical stains used in the practice of breast and gynecologic pathology. Established and new entities are reviewed, with descriptions of histomorphology and immunohistochemical staining patterns and discussion of interpretive pitfalls.

DATA SOURCES.—

Data were obtained from review of the English-language literature and firsthand experience of the authors in breast and gynecologic pathology.

CONCLUSIONS.—

Many entities in breast and gynecologic pathology benefit from evaluation with various immunohistochemical stains. These studies not only aid in the diagnosis and staging of tumors but also can provide prognostic and predictive information. Updated guidelines for recommended ancillary studies such as mismatch repair, p53, and human epidermal growth factor receptor 2 (HER2) studies in endometrium, as well as estrogen and progesterone receptors and HER2 in breast, are discussed. Finally, the use and interpretation of established and novel immunohistochemical stains are discussed in various breast and gynecologic malignancies.

Changes in the Diagnoses of Breast Core Needle Biopsies on Second Review at a Tertiary Care Center: Implications for Surgical Management

Core needle biopsy (CNB) of breast lesions is routine for diagnosis and treatment planning. Despite refinement of diagnostic criteria, the diagnosis of breast lesions on CNB can be challenging. At many centers, including ours, confirmation of diagnoses rendered in other laboratories is required before treatment planning. We identified CNBs first diagnosed elsewhere that were reviewed in our department over the course of 1 year because the patients sought care at our center and in which a change in diagnosis had been recorded. The outside and in-house CNB diagnoses were then classified based on Breast WHO Fifth Edition diagnostic categories. The impact of the change in diagnosis was estimated based on the subsequent surgical management. Findings in follow-up surgical excisions (EXCs) were used for validation. In 2018, 4950 outside cases with CNB were reviewed at our center. A total of 403 CNBs diagnoses were discrepant. Of these, 147 had a change in the WHO diagnostic category: 80 (54%) CNBs had a more severe diagnosis and 44 (30%) a less severe diagnosis. In 23 (16%) CNBs, the change of diagnostic category had no impact on management. Intraductal proliferations (n=54), microinvasive carcinoma (n=18), and papillary lesions (n=35) were the most disputed diagnoses. The in-house CNB diagnosis was confirmed in most cases with available excisions. Following CNB reclassification, 22/147 (15%) lesions were not excised. A change affecting the surgical management at our center occurred in 2.5% of all CNBs. Our results support routine review of outside breast CNB as a clinically significant practice before definitive treatment.

Breast lesions with myoepithelial phenotype

Myoepithelial cells (MECs) constitute a continuous layer of cells surrounding the breast glands, localised between the epithelial cells (ECs) and the basal membrane. MECs play important roles in normal mammary gland as they produce basal membrane and stimulate secretion. During neoplastic transformation, MECs act as a barrier preventing stromal invasion. MECs themselves can undergo a great variety of changes, ranging from hyperplastic to metaplastic, to neoplastic, and giving rise to a wide spectrum of morphological pictures sometimes difficult to interpret on routine diagnoses. Several benign and malignant breast tumours can present features of MECs differentiation. As these latter tumours are quite infrequent, the purpose of the present study is to offer a review of the morphological spectrum of MECs lesions, with correlations to prognosis.

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.

Non-calcified ductal carcinoma in situ of the breast: comparison of diagnostic accuracy of digital breast tomosynthesis, digital mammography, and ultrasonography

BACKGROUND

To retrospectively compare the diagnostic accuracy of digital breast tomosynthesis (DBT), digital mammography (DM), and ultrasonography (US) in non-calcified ductal carcinoma in situ (DCIS, include DCIS with micro-invasion).

PATIENTS AND METHODS

Ninety-eight patients with non-calcified DCIS (include DCIS with micro-invasion) were enrolled in our study. Breast carcinoma in situ was confirmed by surgical pathologic evaluation. Our Institutional Review Board granted approval and the participating women provided written informed consent. The imaging findings were evaluated according to the Breast Imaging Reporting and Data System (BI-RADS) of the American College of Radiology (ACR) by comparing the differences in the detection rate and diagnostic accuracy among the three techniques in all cases, in dense breasts, and in non-dense breasts.

RESULTS

The detection rates of DBT, DM, and US for non-calcified DCIS in all cases were 83.7, 68.4, and 94.9%, respectively, and in patients with dense breasts were 81.2, 63.8, and 95.0%. The detection rate of US was higher than DBT, which, in turn, was higher than DM both in all cases and in dense breasts. Pairwise comparisons among the three techniques showed that the differences were statistically significant (P = 0.000 and P = 0.000, respectively). The experts identified a case as abnormal for all criteria (BI-RADS score of 4B-5) in 68.4% of ratings using DBT, 43.9% of ratings using DM, and 66.3% of ratings using US; for dense breasts, the positive identification rates were 62.5% of ratings using DBT, 41.2% of ratings using DM, and 61.2% of ratings using US. The diagnostic accuracy of DBT and US was significantly higher than that of DM in all cases (P = 0.001 and P = 0.006, respectively) and in dense breasts (P = 0.007 and P = 0.011, respectively). The diagnostic accuracy of DBT was slightly higher than US in all cases and in dense breasts, but the difference was not statistically significant (P = 0.761 and P = 0.871, respectively). By DBT, most non-calcified cases of DCIS presented as a mass lesion (54.9%) with an irregular shape (46.7%), indistinct margin (53.3%), and isodense composition (71.1%). Using US, 72 of 93 patients (77.4%) were shown to have a mass. Most mass lesions had an irregular shape (83.3%), indistinct margin (55.5%), and parallel the skin (82.8%).

CONCLUSION

DBT and US gave better detection rates and diagnostic accuracy for non-calcified DCIS compared with DM in all cases and in dense breasts. The detection rate of DBT was lower than that of US in all cases and in dense breasts. The diagnostic accuracy of DBT was slightly higher than that of US in all cases and in dense breasts, but the difference was not statistically significant. Imaging findings for non-calcified DCIS were relatively non-specific.