Role of preoperative breast dual-energy contrast-enhanced digital mammography in ductal carcinoma in situ

Role of contrast-enhanced mammography in the preoperative detection of ductal carcinoma in situ of the breasts: a comparison with low-energy image and magnetic resonance imaging

OBJECTIVES

To compare contrast-enhanced mammography (CEM) with low-energy image (LEI) alone and with magnetic resonance imaging (MRI) in the preoperative diagnosis of ductal carcinoma in situ (DCIS).

METHODS

In this single-center retrospective study, we reviewed 98 pure DCIS lesions in 96 patients who underwent CEM and MRI within 2 weeks preoperatively. The diagnostic performances of each imaging modality, lesion morphology, and extent were evaluated.

RESULTS

The sensitivity of CEM to DCIS was similar to that of MRI (92.9% vs. 93.9%, p = 0.77) and was significantly higher than that of LEI alone (76.5%, p = 0.002). The sensitivity of CEM to calcified DCIS (92.4%) was not significantly different from LEI alone (92.4%) and from MRI (93.9%, p = 1.00). However, CEM contributed to the simultaneous comparison of calcifications with enhancements. CEM had considerably higher sensitivity compared with LEI alone (93.8% vs. 43.8%, p < 0.001) and performed similarly to MRI (93.8%, p = 1.00) for noncalcified DCIS. All DCIS lesions were enhanced in MRI, whereas 94.9% (93/98) were enhanced in CEM. Non-mass enhancement was the most common presentation (CEM 63.4% and MRI 66.3%). The difference between the lesion size on each imaging modality and the histopathological size was smallest in MRI, followed by CEM, and largest in LEI.

CONCLUSION

CEM was more sensitive than LEI alone and comparable to MRI in DCIS diagnosis. The enhanced morphology of DCIS in CEM was consistent with that in MRI. CEM was superior to LEI alone in size measurement of DCIS.

CLINICAL RELEVANCE STATEMENT

This study investigated the value of CEM in the diagnosis and evaluation of DCIS, aiming to offer a reference for the selection of examination methods for DCIS and contribute to the early diagnosis and precise treatment of DCIS.

KEY POINTS

• DCIS is an important indication for breast surgery. Early and accurate diagnosis is crucial for DCIS treatment and prognosis. • CEM overcomes the deficiency of mammography in noncalcified DCIS diagnosis, exhibiting similar sensitivity to MRI; and CEM contributes to the comparison of calcification and enhancement of calcified DCIS, thereby outperforming MRI. • CEM is superior to LEI alone and slightly inferior to MRI in the size evaluation of DCIS.

Evolving Treatment Paradigms for Low-Risk Ductal Carcinoma In Situ: Imaging Needs

Ductal carcinoma in situ (DCIS) is a nonobligate precursor to invasive cancer that classically presents as asymptomatic calcifications on screening mammography. The increase in DCIS diagnoses with organized screening programs has raised concerns about overdiagnosis, while a patientcentric push for more personalized care has increased awareness about DCIS overtreatment. The standard of care for most new DCIS diagnoses is surgical excision, but nonsurgical management via active monitoring is gaining attention, and multiple clinical trials are ongoing. Imaging, along with demographic and pathologic information, is a critical component of active monitoring efforts. Commonly used imaging modalities including mammography, ultrasound, and MRI, as well as newer modalities such as contrast-enhanced mammography and dedicated breast PET, can provide prognostic information to risk stratify patients for DCIS active monitoring eligibility. Furthermore, radiologists will be responsible for closely surveilling patients on active monitoring and identifying if invasive progression occurs. Active monitoring is a paradigm shift for DCIS care, but the success or failure will rely heavily on the interpretations and guidance of radiologists.

The imaging appearances of non-calcified ductal carcinoma in situ: A pictorial essay

Non-calcified ductal carcinoma in situ (NCDCIS) presents as a heterogeneous entity on various imaging modalities, most frequently presenting symptomatically as a palpable lump. The combination of multiple modalities and knowledge of its potential radiological appearances are important in minimising misdiagnosis. Compared to conventional 2D mammography, both sonography and digital breast tomosynthesis show higher diagnostic accuracy in the detection of NCDCIS. Newer modalities of contrast-enhanced digital mammography and MRI have limited data at present, but early results indicate greater sensitivity for the detection of lesions that may be occult on ultrasound or mammography. Here, we present an illustrative study highlighting the varied appearances of NCDCIS on several imaging modalities including a brief review of the literature.

Development and validation of a deep learning model for breast lesion segmentation and characterization in multiparametric MRI

Importance

The utilization of artificial intelligence for the differentiation of benign and malignant breast lesions in multiparametric MRI (mpMRI) assists radiologists to improve diagnostic performance.

Objectives

To develop an automated deep learning model for breast lesion segmentation and characterization and to evaluate the characterization performance of AI models and radiologists.

Materials and methods

For lesion segmentation, 2,823 patients were used for the training, validation, and testing of the VNet-based segmentation models, and the average Dice similarity coefficient (DSC) between the manual segmentation by radiologists and the mask generated by VNet was calculated. For lesion characterization, 3,303 female patients with 3,607 pathologically confirmed lesions (2,213 malignant and 1,394 benign lesions) were used for the three ResNet-based characterization models (two single-input and one multi-input models). Histopathology was used as the diagnostic criterion standard to assess the characterization performance of the AI models and the BI-RADS categorized by the radiologists, in terms of sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC). An additional 123 patients with 136 lesions (81 malignant and 55 benign lesions) from another institution were available for external testing.

Results

Of the 5,811 patients included in the study, the mean age was 46.14 (range 11–89) years. In the segmentation task, a DSC of 0.860 was obtained between the VNet-generated mask and manual segmentation by radiologists. In the characterization task, the AUCs of the multi-input and the other two single-input models were 0.927, 0.821, and 0.795, respectively. Compared to the single-input DWI or DCE model, the multi-input DCE and DWI model obtained a significant increase in sensitivity, specificity, and accuracy (0.831 vs. 0.772/0.776, 0.874 vs. 0.630/0.709, 0.846 vs. 0.721/0.752). Furthermore, the specificity of the multi-input model was higher than that of the radiologists, whether using BI-RADS category 3 or 4 as a cutoff point (0.874 vs. 0.404/0.841), and the accuracy was intermediate between the two assessment methods (0.846 vs. 0.773/0.882). For the external testing, the performance of the three models remained robust with AUCs of 0.812, 0.831, and 0.885, respectively.

Conclusions

Combining DCE with DWI was superior to applying a single sequence for breast lesion characterization. The deep learning computer-aided diagnosis (CADx) model we developed significantly improved specificity and achieved comparable accuracy to the radiologists with promise for clinical application to provide preliminary diagnoses.

Ductal Carcinoma in Situ: State-of-the-Art Review

Ductal carcinoma in situ (DCIS) is a nonobligate precursor of invasive cancer, and its detection, diagnosis, and management are controversial. DCIS incidence grew with the expansion of screening mammography programs in the 1980s and 1990s, and DCIS is viewed as a major driver of overdiagnosis and overtreatment. For pathologists, the diagnosis and classification of DCIS is challenging due to undersampling and interobserver variability. Understanding the progression from normal breast tissue to DCIS and, ultimately, to invasive cancer is limited by a paucity of natural history data with multiple proposed evolutionary models of DCIS initiation and progression. Although radiologists are familiar with the classic presentation of DCIS as asymptomatic calcifications at mammography, the expanded pool of modalities, advanced imaging techniques, and image analytics have identified multiple potential biomarkers of histopathologic characteristics and prognosis. Finally, there is growing interest in the nonsurgical management of DCIS, including active surveillance, to reduce overtreatment and provide patients with more personalized management options. However, current biomarkers are not adept at enabling identification of occult invasive disease at biopsy or accurately predicting the risk of progression to invasive disease. Several active surveillance trials are ongoing and are expected to better identify women with low-risk DCIS who may avoid surgery.

Microcalcifications Drive Breast Cancer Occurrence and Development by Macrophage-Mediated Epithelial to Mesenchymal Transition

BACKGROUND

This study aims to investigate: (a) the putative association between the presence of microcalcifications and the expression of both epithelial-to-mesenchymal transition and bone biomarkers, (b) the role of microcalcifications in the breast osteoblast-like cells (BOLCs) formation, and (c) the association between microcalcification composition and breast cancer progression.

METHODS

We collected 174 biopsies on which we performed immunohistochemical and ultrastructural analysis. In vitro experiments were performed to demonstrate the relationship among microcalcification, BOLCs development, and breast cancer occurrence. Ex vivo investigations demonstrated the significant increase of breast osteoblast-like cells in breast lesions with microcalcifications with respect to those without microcalcifications.

RESULTS

In vitro data displayed that in the presence of calcium oxalate and activated monocytes, breast cancer cells undergo epithelial to mesenchymal transition. Also, in this condition, cells acquired an osteoblast phenotype, thus producing hydroxyapatite. To further confirm in vitro data, we studied 15 benign lesions with microcalcification from patients that developed a malignant condition in the same breast quadrant. Immunohistochemical analysis showed macrophages' polarization in benign lesions with calcium oxalate.

CONCLUSIONS

Altogether, our data shed new light about the role of microcalcifications in breast cancer occurrence and progression.