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Objectives: To evaluate the added value of contrast-enhanced ultrasound (CEUS) on top of breast conventional imaging for predicting the upgrading of ductal carcinoma in situ (DCIS) to invasive cancer after surgery., Methods: This retrospective study enrolled 140 biopsy-proven DCIS lesions in 138 patients and divided them into two groups based on postoperative histopathology: non-upgrade and upgrade groups. Conventional ultrasound (US), mammography (MMG), CEUS and clinicopathological (CL) features were reviewed and compared between the two groups. The predictive performance of different models (with and without CEUS features) for histologic upgrade were compared to calculate the added value of CEUS., Results: Fifty-nine (42.1 %) lesions were histologically upgraded to invasive cancer after surgery. By logistic regression analyses, we found that high-grade DCIS at biopsy (P=0.004), ultrasonographic lesion size > 20 mm (P=0.007), mass-like lesion on US (P=0.030), the presence of suspicious calcification on MMG (P=0.014), the presence of perfusion defect (P=0.005) and the area under TIC>1021.34 ml (P<0.001) on CEUS were six independent factors predicting concomitant invasive components after surgery. The CL+US+MMG model made with the four predictors in the clinicopathologic, US and MMG categories yielded an area under the receiver operating curve (AUROC) value of 0.759 (95 % CI: 0.680-0.828) in predicting histological upgrade. The combination model built by adding the two CEUS predictors to the CL+US+MMG model showed higher predictive efficacy than the CL+US+MMG model (P=0.018), as the AUROC value was improved to 0.861 (95 % CI: 0.793-0.914)., Conclusions: The addition of contrast-enhanced ultrasound to breast conventional imaging could improve the preoperative prediction of an upgrade to invasive cancer from CNB -proven DCIS lesions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Rationale and Objectives: The prognosis of ductal carcinoma in situ with microinvasion (DCISM) is more similar to that of small invasive ductal carcinoma (IDC) than to pure ductal carcinoma in situ (DCIS). It is particularly important to accurately distinguish between DCISM and DCIS. The present study aims to compare the clinical and imaging characteristics of contrast-enhanced mammography (CEM) and magnetic resonance imaging (MRI) between DCISM and pure DCIS, and to identify predictive factors of microinvasive carcinoma, which may contribute to a comprehensive understanding of DCISM in clinical diagnosis and support surveillance strategies, such as surgery, radiation, and other treatment decisions., Materials and Methods: Forty-seven female patients diagnosed with DCIS were included in the study from May 2019 to August 2023. Patients were further divided into two groups based on pathological diagnosis: DCIS and DCISM. Clinical and imaging characteristics of these two groups were analyzed statistically. The independent clinical risk factors were selected using multivariate logistic regression and used to establish the logistic model [Logit(P)]. The diagnostic performance of independent predictors was assessed and compared using receiver operating characteristic (ROC) analysis and DeLong's test., Results: In CEM, the maximum cross-sectional area (CSAmax), the percentage signal difference between the enhancing lesion and background in the craniocaudal and mediolateral oblique projection (%RSCC, and %RSMLO) were found to be significantly higher for DCISM compared to DCIS (p = 0.001; p < 0.001; p = 0.008). Additionally, there were noticeable statistical differences in the patterns of enhancement morphological distribution (EMD) and internal enhancement pattern (IEP) between DCIS and DCISM (p = 0.047; p = 0.008). In MRI, only CSAmax (p = 0.012) and IEP (p = 0.020) showed significant statistical differences. The multivariate regression analysis suggested that CSAmax (in CEM or MR) and %RSCC were independent predictors of DCISM (all p < 0.05). The area under the curve (AUC) of CSAmax (CEM), %RSCC (CEM), Logit(P) (CEM), and CSAmax (MR) were 0.764, 0.795, 0.842, and 0.739, respectively. There were no significant differences in DeLong's test for these values (all p > 0.10). DCISM was significantly associated with high nuclear grade, comedo type, high axillary lymph node (ALN) metastasis, and high Ki-67 positivity compared to DCIS (all p < 0.05)., Conclusion: The tumor size (CSAmax), enhancement index (%RS), and internal enhancement pattern (IEP) were highly indicative of DCISM. DCISM tends to express more aggressive pathological features, such as high nuclear grade, comedo-type necrosis, ALN metastasis, and Ki-67 overexpression. As with MRI, CEM has the capability to help predict when DCISM is accompanying DCIS., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Objective: To develop a nomogram that integrates clinical-pathologic and imaging variables to predict ipsilateral breast tumor recurrence (IBTR) in women with ductal carcinoma in situ (DCIS) treated with breast-conserving surgery (BCS)., Materials and Methods: This retrospective study included consecutive women with DCIS who underwent BCS at two hospitals. Patients who underwent BCS between 2003 and 2016 in one hospital and between 2005 and 2013 in another were classified into development and validation cohorts, respectively. Twelve clinical-pathologic variables (age, family history, initial presentation, nuclear grade, necrosis, margin width, number of excisions, DCIS size, estrogen receptor, progesterone receptor, radiation therapy, and endocrine therapy) and six mammography and ultrasound variables (breast density, detection modality, mammography and ultrasound patterns, morphology and distribution of calcifications) were analyzed. A nomogram for predicting 10-year IBTR probabilities was constructed using the variables associated with IBTR identified from the Cox proportional hazard regression analysis in the development cohort. The performance of the developed nomogram was evaluated in the external validation cohort using a calibration plot and 10-year area under the receiver operating characteristic curve (AUROC) and compared with the Memorial Sloan-Kettering Cancer Center (MSKCC) nomogram., Results: The development cohort included 702 women (median age [interquartile range], 50 [44-56] years), of whom 30 (4%) women experienced IBTR. The validation cohort included 182 women (48 [43-54] years), 18 (10%) of whom developed IBTR. A nomogram was constructed using three clinical-pathologic variables (age, margin, and use of adjuvant radiation therapy) and two mammographic variables (breast density and calcification morphology). The nomogram was appropriately calibrated and demonstrated a comparable 10-year AUROC to the MSKCC nomogram (0.73 vs. 0.66, P = 0.534) in the validation cohort., Conclusion: Our nomogram provided individualized risk estimates for women with DCIS treated with BCS, demonstrating a discriminative ability comparable to that of the MSKCC nomogram., Competing Interests: The authors have no potential conflicts of interest to disclose., (Copyright © 2024 The Korean Society of Radiology.)

Background: BreastScreen Australia, the population mammographic screening program for breast cancer, uses two-view digital screening mammography ± ultrasound followed by percutaneous biopsy to detect breast cancer. Secondary breast imaging for further local staging, not performed at BreastScreen, may identify additional clinically significant breast lesions. Staging options include further mammography, bilateral ultrasound, and/or contrast-based imaging (CBI) [magnetic resonance imaging (MRI) or contrast-enhanced mammography (CEM)]. CBI for local staging of screen-detected cancer was introduced at an academic hospital breast service in Melbourne, VIC, Australia. We report findings for otherwise occult disease and resulting treatment changes., Material and Methods: Patients staged using CEM between November 2018 and April 2022 were identified from hospital records. Data were extracted from radiology, pathology, and breast unit databases. CEM-detected abnormalities were documented as true positive (TP) for invasive cancer or ductal carcinoma in situ (DCIS), or otherwise false positive (FP). The impact on surgical decisions was assessed., Results: Of 202 patients aged 44-84 years, 60 (30%) had 74 additional findings [34 (46%) TP, 40 (54%) FP]. These were malignant in 29/202 (14%) patients (79% invasive cancers, 21% DCIS). CEM resulted in surgical changes in 43/202 (21%) patients: wider resection (24/43), conversion to mastectomy (6/43), contralateral breast surgery (6/43), additional ipsilateral excision (5/43), and bracketing (2/43). Additional findings were more common for patients with larger index lesions and for invasive cancer, but there was no significant variation by age, breast density, or index lesion grade., Conclusions: CEM for local staging of screen-detected breast cancers identified occult malignancy in 14% of patients. CEM improves local staging and may facilitate appropriate management of screen-detected breast cancers., (© 2024. Society of Surgical Oncology.)

Aims: Radial sclerosing lesions (RSLs) are benign breast lesions composed of glandular and epithelial proliferations with stellate architecture and fibro-elastotic stroma, which can mimic invasive carcinoma on imaging. Surgical management following a core biopsy diagnosis of RSLs remains controversial., Methods and Results: We retrospectively identified core biopsies with RSLs without atypia who underwent subsequent surgical excision between 2015 and 2021. All core biopsy slides were reviewed to confirm the diagnosis. Imaging was reviewed to determine radiological-pathological concordance. An upgrade was defined as invasive carcinoma or ductal carcinoma in situ (DCIS) in the excision. The final cohort consisted of 130 core biopsies from 124 women (median age = 52 years, range = 27-76). The imaging modality was mammogram in 52 (40%) cases, MRI in 52 (40%) and ultrasound in 26 (20%). One hundred and seven (82%) core biopsies were vacuum-assisted and 23 (18%) were ultrasound-guided without vacuum assistance. The median lesion size on imaging was 9 mm (range = 2-41). Overall, two (1%) cases were upgraded at excision, including one microinvasive lobular carcinoma and one 2 mm focus of invasive mammary carcinoma with associated DCIS. In both cases, the upgraded foci of carcinoma were not closely associated with the biopsy site and were considered incidental upgrades., Conclusions: This study adds to the body of literature supporting observation, rather than routine excision of radial sclerosing lesions without atypia., (© 2024 John Wiley & Sons Ltd.)

Purpose To determine whether time-dependent deep learning models can outperform single time point models in predicting preoperative upgrade of ductal carcinoma in situ (DCIS) to invasive malignancy at dynamic contrast-enhanced (DCE) breast MRI without a lesion segmentation prerequisite. Materials and Methods In this exploratory study, 154 cases of biopsy-proven DCIS (25 upgraded at surgery and 129 not upgraded) were selected consecutively from a retrospective cohort of preoperative DCE MRI in women with a mean age of 59 years at time of diagnosis from 2012 to 2022. Binary classification was implemented with convolutional neural network (CNN)-long short-term memory (LSTM) architectures benchmarked against traditional CNNs without manual segmentation of the lesions. Combinatorial performance analysis of ResNet50 versus VGG16-based models was performed with each contrast phase. Binary classification area under the receiver operating characteristic curve (AUC) was reported. Results VGG16-based models consistently provided better holdout test AUCs than did ResNet50 in CNN and CNN-LSTM studies (multiphase test AUC, 0.67 vs 0.59, respectively, for CNN models [ P = .04] and 0.73 vs 0.62 for CNN-LSTM models [ P = .008]). The time-dependent model (CNN-LSTM) provided a better multiphase test AUC over single time point (CNN) models (0.73 vs 0.67; P = .04). Conclusion Compared with single time point architectures, sequential deep learning algorithms using preoperative DCE MRI improved prediction of DCIS lesions upgraded to invasive malignancy without the need for lesion segmentation. Keywords: MRI, Dynamic Contrast-enhanced, Breast, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2024.

Ductal carcinoma in situ (DCIS) accounts for approximately 30% of new breast cancer diagnoses. However, our understanding of how normal breast tissue evolves into DCIS and invasive cancers remains insufficient. Further, conclusions regarding the mechanisms of disease progression in terms of histopathology, genetics, and radiology are often conflicting and have implications for treatment planning. Moreover, the increase in DCIS diagnoses since the adoption of organized breast cancer screening programs has raised concerns about overdiagnosis and subsequent overtreatment. Active monitoring, a nonsurgical management strategy for DCIS, avoids surgery in favor of close imaging follow-up to de-escalate therapy and provides more treatment options. However, the two major challenges in active monitoring are identifying occult invasive cancer and patients at risk of invasive cancer progression. Subsequently, four prospective active monitoring trials are ongoing to determine the feasibility of active monitoring and refine the patient eligibility criteria and follow-up intervals. Radiologists play a major role in determining eligibility for active monitoring and reviewing surveillance images for disease progression. Trial results published over the next few years would support a new era of multidisciplinary DCIS care., Competing Interests: The author has no potential conflicts of interest to disclose., (Copyright © 2024 The Korean Society of Radiology.)

Introduction: In the last two decades there has been a paradigm shift with breast conserving surgery (BCS) being applied to larger and more extensive breast malignancies. The aim of this study is to examine the success of BCS being performed in patients with extensive breast malignancies requiring at least 3 wires for localization, and to assess possible risk factors for failure., Materials and Methods: We performed a retrospective single center review of 232 patients who underwent BCS between 2010 and 2020 requiring at least 3 wires for localization, thus comprising the multi-wire group (MWG). The cohort included a control group of 232 single-wire BCS patients (SWG) chronologically matched with the MWG. Patients with either invasive malignancy or ductal carcinoma in situ (DCIS) were included in the study. Clinical, radiological, and pathological data was collected. Proportions of positive surgical margins, re-lumpectomies and conversion to mastectomy were calculated. Survival analysis for locoregional and distant recurrence was performed., Results: Women in the MWG were younger (mean age 57 vs. 63.1, P < 0.001), had larger tumor size (mean size 5.1 cm vs. 1.3 cm, p < 0.001), a higher prevalence of calcifications on mammograms (72 % vs. 17 %, P < 0.001), a higher proportion of positive lymph nodes (75 % vs. 45 %, P = 0.019), and an elevated incidence of a ductal carcinoma in situ (DCIS) component (72 % vs. 38 %, P < 0.001). Positive surgical margins were higher in the MWG (13 % vs 7 %, P = 0.03), which lead to higher proportions of re-lumpectomies or conversion to mastectomies (7 % vs 4 %, P = 0.17). On multivariate analysis of the entire cohort, patients with positive margins were more likely to have a DCIS component (77 % vs 53 %, P = 0.001), an infiltrating lobular carcinoma (ILC) component (15 % vs 9 %, P = 0.013), and positive ER hormonal status (94 % vs 85 %, p = 0.05). The number of wires was not an independent predictor of positive margins. On long-term analysis, the locoregional disease-free survival was similar between the SWG and MWG (P = 0.1). However, the MWG showed higher rates of distant metastasis (12 % vs 4 %, P = 0.006)., Conclusions: BCS requiring 3 or more wires is associated with a slightly higher proportion of positive margins. The increased risk of positive margins appears to be related to the type of tumor (DCIS component, ILC component and ER status) rather than to the number of wires. The number of wires does not significantly impact locoregional disease-free survival., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Rationale and Objectives: To determine the imaging changes and their associated positive predictive value (PPV) for invasive breast cancer in women undergoing active monitoring for ductal carcinoma in situ (DCIS)., Materials and Methods: In this seven-year follow-up retrospective IRB-exempted cohort study, we reviewed patients diagnosed with DCIS who elected active monitoring between 2003 and 2022 at a single academic institution. Imaging characteristics, histopathology at initial diagnosis, and subsequent follow-up were recorded. Low-risk DCIS was defined as low or intermediate grade and hormone receptor (HR) positive (estrogen and/or progesterone receptor positive) disease diagnosed in women at least 40 years of age. Progression was defined as subsequent ipsilateral invasive breast cancer diagnosis., Results: There were 39 patients with a median age of 58.4 years (IQR: 51.1-69.6 years) and a median follow-up of 4.3 years (range: 0.6-16.4 years). Nearly two thirds of patients (64%, 25/39) had stable imaging (range: 0.6-16.4 years) and remained progression-free during active monitoring. Among the remaining 14 patients (36%), there were 24 imaging findings which prompted 22 subsequent core needle biopsies (range: 1-3 biopsies per patient) and two surgical biopsies. The PPV of invasive cancer was 29% (7/24) overall and 38% (3/8) for masses, 33% (3/9) for calcifications, 17% (1/6) for non-mass enhancement, and 0% (0/1) for architectural distortion., Conclusion: Of the radiographic changes prompting an additional biopsy, development of a new mass (38%) and new calcifications (33%) had the highest PPV for invasive progression. Close imaging follow-up should be a critical component for patients undergoing monitoring for DCIS., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Derek L. Nguyen reports financial support was provided by National Institute of Health. Marc D. Ryser reports financial support was provided by National Institute of Health. E Shelley Hwang reports financial support was provided by National Institute of Health. Lars J. Grimm reports financial support was provided by National Institute of Health. Derek L. Nguyen reports a relationship with Hologic Inc that includes: consulting or advisory. Lars J. Grimm reports a relationship with Hologic Inc that includes: consulting or advisory. Lars J. Grimm reports a relationship with Medscape LLC that includes: board membership. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

An intraoperative diagnosis is critical for precise cancer surgery. However, traditional intraoperative assessments based on hematoxylin and eosin (H&E) histology, such as frozen section, are time-, resource-, and labor-intensive, and involve specimen-consuming concerns. Here, we report a near-real-time automated cancer diagnosis workflow for breast cancer that combines dynamic full-field optical coherence tomography (D-FFOCT), a label-free optical imaging method, and deep learning for bedside tumor diagnosis during surgery. To classify the benign and malignant breast tissues, we conducted a prospective cohort trial. In the modeling group (n = 182), D-FFOCT images were captured from April 26 to June 20, 2018, encompassing 48 benign lesions, 114 invasive ductal carcinoma (IDC), 10 invasive lobular carcinoma, 4 ductal carcinoma in situ (DCIS), and 6 rare tumors. Deep learning model was built up and fine-tuned in 10,357 D-FFOCT patches. Subsequently, from June 22 to August 17, 2018, independent tests (n = 42) were conducted on 10 benign lesions, 29 IDC, 1 DCIS, and 2 rare tumors. The model yielded excellent performance, with an accuracy of 97.62%, sensitivity of 96.88% and specificity of 100%; only one IDC was misclassified. Meanwhile, the acquisition of the D-FFOCT images was non-destructive and did not require any tissue preparation or staining procedures. In the simulated intraoperative margin evaluation procedure, the time required for our novel workflow (approximately 3 min) was significantly shorter than that required for traditional procedures (approximately 30 min). These findings indicate that the combination of D-FFOCT and deep learning algorithms can streamline intraoperative cancer diagnosis independently of traditional pathology laboratory procedures., (Copyright © 2024 Science China Press. Published by Elsevier B.V. All rights reserved.)

Objective: To investigate the independent risk variables associated with the potential invasiveness of ductal carcinoma in situ (DCIS) on multi-parametric ultrasonography, and further construct a nomogram for risk assessment., Methods: Consecutive patients from January 2017 to December 2022 who were suspected of having ductal carcinoma in situ (DCIS) based on magnetic resonance imaging or mammography were prospectively enrolled. Histopathological findings after surgical resection served as the gold standard. Grayscale ultrasound, Doppler ultrasound, shear wave elastography (SWE), and contrast-enhanced ultrasound (CEUS) examinations were preoperative performed. Binary logistic regression was used for multifactorial analysis to identify independent risk factors from multi-parametric ultrasonography. The correlation between independent risk factors and pathological prognostic markers was analyzed. The predictive efficacy of DCIS associated with invasiveness was assessed by logistic analysis, and a nomogram was established., Results: A total of 250 DCIS lesions were enrolled from 249 patients, comprising 85 pure DCIS and 165 DCIS with invasion (DCIS-IDC), of which 41 exhibited micro-invasion. The multivariate analysis identified independent risk factors for DCIS with invasion on multi-parametric ultrasonography, including image size (>2cm), Doppler ultrasound RI (≥0.72), SWE's Emax (≥66.4 kPa), hyper-enhancement, centripetal enhancement, increased surrounding vessel, and no contrast agent retention on CEUS. These factors correlated with histological grade, Ki-67, and human epidermal growth factor receptor 2 (HER2) (P < 0.1). The multi-parametric ultrasound approach demonstrated good predictive performance (sensitivity 89.7 %, specificity 73.8 %, AUC 0.903), surpassing single US modality or combinations with SWE or CEUS modalities. Utilizing these factors, a predictive nomogram achieved a respectable performance (AUC of 0.889) for predicting DCIS with invasion. Additionally, a separate nomogram for predicting DCIS with micro-invasion, incorporating independent risk factors such as RI (≥0.72), SWE's Emax (≥65.2 kPa), and centripetal enhancement, demonstrated an AUC of 0.867., Conclusion: Multi-parametric ultrasonography demonstrates good discriminatory ability in predicting both DCIS with invasion and micro-invasion through the analysis of lesion morphology, stiffness, neovascular architecture, and perfusion. The use of a nomogram based on ultrasonographic images offers an intuitive and effective method for assessing the risk of invasion in DCIS. Although the nomogram is not currently considered a clinically applicable diagnostic tool due to its AUC being below the threshold of 0.9, further research and development are anticipated to yield positive outcomes and enhance its viability for clinical utilization., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Objectives: To assess the performance of an artificial intelligence (AI) algorithm in the Australian mammography screening program which routinely uses two independent readers with arbitration of discordant results., Methods: A total of 7533 prevalent round mammograms from 2017 were available for analysis. The AI program classified mammograms into deciles on the basis of breast cancer (BC) risk. BC diagnoses, including invasive BC (IBC) and ductal carcinoma in situ (DCIS), included those from the prevalent round, interval cancers, and cancers identified in the subsequent screening round two years later. Performance was assessed by sensitivity, specificity, positive and negative predictive values, and the proportion of women recalled by the radiologists and identified as higher risk by AI., Results: Radiologists identified 54 women with IBC and 13 with DCIS with a recall rate of 9.7%. In contrast, 51 of 54 of the IBCs and 12/13 cases of DCIS were within the higher AI score group (score 10), a recall equivalent of 10.6% (a difference of 0.9% (CI -0.03 to 1.89%, p = 0.06). When IBCs were identified in the 2017 round, interval cancers classified as false negatives or with minimal signs in 2017, and cancers from the 2019 round were combined, the radiologists identified 54/67 and 59/67 were in the highest risk AI category (sensitivity 80.6% and 88.06 % respectively, a difference that was not different statistically)., Conclusions: As the performance of AI was comparable to that of expert radiologists, future AI roles in screening could include replacing one reader and supporting arbitration, reducing workload and false positive results., Clinical Relevance Statement: AI analysis of consecutive prevalent screening mammograms from the Australian BreastScreen program demonstrated the algorithm's ability to match the cancer detection of experienced radiologists, additionally identifying five interval cancers (false negatives), and the majority of the false positive recalls., Key Points: • The AI program was almost as sensitive as the radiologists in terms of identifying prevalent lesions (51/54 for invasive breast cancer, 63/67 when including ductal carcinoma in situ). • If selected interval cancers and cancers identified in the subsequent screening round were included, the AI program identified more cancers than the radiologists (59/67 compared with 54/67, sensitivity 88.06 % and 80.6% respectively p = 0.24). • The high negative predictive value of a score of 1-9 would indicate a role for AI as a triage tool to reduce the recall rate (specifically false positives)., (© 2023. The Author(s).)

Objectives: To investigate the influence of preoperative breast MRI on mastectomy and reoperation rates in patients with pure ductal carcinoma in situ (DCIS)., Methods: The MIPA observational study database (7245 patients) was searched for patients aged 18-80 years with pure unilateral DCIS diagnosed at core needle or vacuum-assisted biopsy (CNB/VAB) and planned for primary surgery. Patients who underwent preoperative MRI (MRI group) were matched (1:1) to those who did not receive MRI (noMRI group) according to 8 confounding covariates that drive referral to MRI (age; hormonal status; familial risk; posterior-to-nipple diameter; BI-RADS category; lesion diameter; lesion presentation; surgical planning at conventional imaging). Surgical outcomes were compared between the matched groups with nonparametric statistics after calculating odds ratios (ORs)., Results: Of 1005 women with pure unilateral DCIS at CNB/VAB (507 MRI group, 498 noMRI group), 309 remained in each group after matching. First-line mastectomy rate in the MRI group was 20.1% (62/309 patients, OR 2.03) compared to 11.0% in the noMRI group (34/309 patients, p = 0.003). The reoperation rate was 10.0% in the MRI group (31/309, OR for reoperation 0.40) and 22.0% in the noMRI group (68/309, p < 0.001), with a 2.53 OR of avoiding reoperation in the MRI group. The overall mastectomy rate was 23.3% in the MRI group (72/309, OR 1.40) and 17.8% in the noMRI group (55/309, p = 0.111)., Conclusions: Compared to those going directly to surgery, patients with pure DCIS at CNB/VAB who underwent preoperative MRI had a higher OR for first-line mastectomy but a substantially lower OR for reoperation., Clinical Relevance Statement: When confounding factors behind MRI referral are accounted for in the comparison of patients with CNB/VAB-diagnosed pure unilateral DCIS, preoperative MRI yields a reduction of reoperations that is more than twice as high as the increase in overall mastectomies., Key Points: • Confounding factors cause imbalance when investigating the influence of preoperative MRI on surgical outcomes of pure DCIS. • When patient matching is applied to women with pure unilateral DCIS, reoperation rates are significantly reduced in women who underwent preoperative MRI. • The reduction of reoperations brought about by preoperative MRI is more than double the increase in overall mastectomies., (© 2023. The Author(s).)

Background: Ductal carcinoma in situ (DCIS) is often reclassified as invasive cancer in the final pathology report of the surgical specimen. It is of significant clinical relevance to acknowledge the possibility of underestimating invasive disease when utilizing preoperative biopsies for a DCIS diagnosis. In cases where such histologic upgrades occur, it is imperative to consider them in the preoperative planning process, including the potential inclusion of sentinel lymph node biopsy due to the risk of axillary lymph node metastasis., Purpose: To assess the capability of breast multiparametric magnetic resonance imaging (MP-MRI) in differentiating between pure DCIS and microinvasive carcinoma (MIC)., Material and Methods: Between January 2018 and November 2022, this retrospective study enrolled patients with biopsy-proven DCIS who had undergone preoperative breast MP-MRI. We assessed various MP-MRI features, including size, morphology, margins, internal enhancement pattern, extent of disease, presence of peritumoral edema, time-intensity curve value, diffusion restriction, and ADC value. Subsequently, a logistic regression analysis was conducted to explore the association of these features with the pathological outcome., Results: Of 129 patients with biopsy-proven DCIS, 36 had foci of micro-infiltration on surgical specimens and eight were diagnosed with invasive ductal carcinoma (IDC). The presence of micro-infiltration foci was significantly associated with several MP-MRI features, including tumor size ( P  <0.001), clustered ring enhancement ( P  <0.001), segmental distribution ( P  <0.001), diffusion restriction ( P  = 0.005), and ADC values <1.3 × 10 -3 mm 2 /s ( P  = 0.004)., Conclusion: Breast MP-MRI has the potential to predict the presence of micro-infiltration foci in biopsy-proven DCIS and may serve as a valuable tool for guiding therapeutic planning., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

BACKGROUND. Radial scars are more commonly identified on digital breast tomosynthesis (DBT) than on digital mammography (DM). Nonetheless, universal guidelines for radial scar management in the current era of DBT are lacking. OBJECTIVE. The purpose of this study was to determine the upstaging rates of screening DBT-detected radial scars with and without atypia and to identify features related to upstaging risk. METHODS. This retrospective study included patients who underwent core needle biopsy (CNB) showing a radial scar after screening DBT and DM from January 1, 2013, to December 31, 2020. Patients without surgical excision or at least 2 years of imaging follow-up after CNB were excluded. Rates of upstaging to breast cancer (ductal carcinoma in situ [DCIS] or invasive disease) were compared between radial scars with and without atypia at CNB. Associations of upstaging with patient, imaging, and pathologic variables were explored using standard statistical tests. RESULTS. Of 165 women with 171 radial scars, the final study sample included 153 women (mean age, 56 years; range, 33-83 years) with 159 radial scars that underwent surgical excision (80.5%, 128/159) or at least 2 years of imaging follow-up (19.5%, 31/159). Seven radial scars were upstaged to DCIS and one to invasive disease. Therefore, the up-staging rate of radial scars to cancer was 5.0% (8/159). The upstaging rate of radial scars without atypia at CNB was 1.6% (2/129) and that of radial scars with atypia was 20.0% (6/30) ( p < .001). On multivariable analysis, features associated with higher upstaging risk included a prior breast cancer diagnosis (62.5% vs 4.8%; p = .01) and the presence of atypia at CNB (75.0% vs 15.9%; p = .02). The upstaging rate according to mammographic finding type was 7.1% (1/14) for asymmetries, 12.5% (2/16) for masses, 5.3% (5/95) for architectural distortion, and 0.0% (0/34) for calcifications. CONCLUSION. Screening-detected radial scars without atypia at CNB have a low upstaging rate to breast cancer of 1.6%. CLINICAL IMPACT. Imaging surveillance rather than surgery is a reasonable approach for radial scars without atypia, particularly for those presenting as calcifications.

Background: Patients with a Breast Imaging Reporting and Data System (BI-RADS) 4 mammogram are currently recommended for biopsy. However, 70-80% of the biopsies are negative/benign. In this study, we developed a deep learning classification algorithm on mammogram images to classify BI-RADS 4 suspicious lesions aiming to reduce unnecessary breast biopsies., Materials and Methods: This retrospective study included 847 patients with a BI-RADS 4 breast lesion that underwent biopsy at a single institution and included 200 invasive breast cancers, 200 ductal carcinoma in-situ (DCIS), 198 pure atypias, 194 benign, and 55 atypias upstaged to malignancy after excisional biopsy. We employed convolutional neural networks to perform 4 binary classification tasks: (I) benign vs. all atypia + invasive + DCIS, aiming to identify the benign cases for whom biopsy may be avoided; (II) benign + pure atypia vs. atypia-upstaged + invasive + DCIS, aiming to reduce excision of atypia that is not upgraded to cancer at surgery; (III) benign vs. each of the other 3 classes individually (atypia, DCIS, invasive), aiming for a precise diagnosis; and (IV) pure atypia vs. atypia-upstaged, aiming to reduce unnecessary excisional biopsies on atypia patients., Results: A 95% sensitivity for the "higher stage disease" class was ensured for all tasks. The specificity value was 33% in Task I, and 25% in Task II, respectively. In Task III, the respective specificity value was 30% (vs. atypia), 30% (vs. DCIS), and 46% (vs. invasive tumor). In Task IV, the specificity was 35%. The AUC values for the 4 tasks were 0.72, 0.67, 0.70/0.73/0.72, and 0.67, respectively., Conclusion: Deep learning of digital mammograms containing BI-RADS 4 findings can identify lesions that may not need breast biopsy, leading to potential reduction of unnecessary procedures and the attendant costs and stress., (© 2024. The Author(s).)

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., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)

Background: Ductal Carcinoma In Situ (DCIS) can progress to invasive breast cancer, but most DCIS lesions never will. Therefore, four clinical trials (COMET, LORIS, LORETTA, AND LORD) test whether active surveillance for women with low-risk Ductal carcinoma In Situ is safe (E. S. Hwang et al., BMJ Open, 9: e026797, 2019, A. Francis et al., Eur J Cancer. 51: 2296-2303, 2015, Chizuko Kanbayashi et al. The international collaboration of active surveillance trials for low-risk DCIS (LORIS, LORD, COMET, LORETTA),  L. E. Elshof et al., Eur J Cancer, 51, 1497-510, 2015). Low-risk is defined as grade I or II DCIS. Because DCIS grade is a major eligibility criteria in these trials, it would be very helpful to assess DCIS grade on mammography, informed by grade assessed on DCIS histopathology in pre-surgery biopsies, since surgery will not be performed on a significant number of patients participating in these trials., Objective: To assess the performance and clinical utility of a convolutional neural network (CNN) in discriminating high-risk (grade III) DCIS and/or Invasive Breast Cancer (IBC) from low-risk (grade I/II) DCIS based on mammographic features. We explored whether the CNN could be used as a decision support tool, from excluding high-risk patients for active surveillance., Methods: In this single centre retrospective study, 464 patients diagnosed with DCIS based on pre-surgery biopsy between 2000 and 2014 were included. The collection of mammography images was partitioned on a patient-level into two subsets, one for training containing 80% of cases (371 cases, 681 images) and 20% (93 cases, 173 images) for testing. A deep learning model based on the U-Net CNN was trained and validated on 681 two-dimensional mammograms. Classification performance was assessed with the Area Under the Curve (AUC) receiver operating characteristic and predictive values on the test set for predicting high risk DCIS-and high-risk DCIS and/ or IBC from low-risk DCIS., Results: When classifying DCIS as high-risk, the deep learning network achieved a Positive Predictive Value (PPV) of 0.40, Negative Predictive Value (NPV) of 0.91 and an AUC of 0.72 on the test dataset. For distinguishing high-risk and/or upstaged DCIS (occult invasive breast cancer) from low-risk DCIS a PPV of 0.80, a NPV of 0.84 and an AUC of 0.76 were achieved., Conclusion: For both scenarios (DCIS grade I/II vs. III, DCIS grade I/II vs. III and/or IBC) AUCs were high, 0.72 and 0.76, respectively, concluding that our convolutional neural network can discriminate low-grade from high-grade DCIS., (© 2024. The Author(s).)

Objective: The aim of this study was to determine surgical and clinical outcomes of lobular neoplasia (LN) diagnosed by magnetic resonance imaging (MRI) biopsy, including upgrade to malignancy, and to assess for characteristics associated with upgrade., Method: A single-institution retrospective study, between 2013 and 2022, of patients with histopathological findings of LN via MRI-guided biopsy was performed using an institutional database and review of the electronic medical records. Decision for excision or surveillance was made by a multidisciplinary team per institutional practice. Patient demographics and imaging characteristics were summarized using descriptive analyses. Upgrade was defined as upgrade to cancer on surgical pathology for patients treated with excision or the development of cancer at the biopsy site during surveillance. The Wilcoxon rank-sum test and Fisher's exact test were used to compare features of the upgraded cohort with the remainder of the group., Results: Ninety-four MRI biopsies diagnosing LN were included. Median age was 57 years (range 37-78 years). Forty-six lesions underwent excision while 48 lesions were surveilled. The upgrade rate was 7.4% (7/94). Upgrades in the excised cohort consisted of pleomorphic lobular carcinoma in situ (LCIS; n = 1), ductal carcinoma in situ (DCIS; n = 3) and invasive lobular carcinoma (ILC; n = 2), while one interval development of DCIS was observed at the site of biopsy in the surveillance cohort. No MRI or patient variables were associated with upgrade., Conclusions: In this contemporary cohort of MRI-detected LNs, the upgrade rate was low. Omission of surgery for MRI-detected LNs in carefully selected patients may be considered in a shared decision-making capacity between the patient and the treatment team. Larger cohorts are needed to determine factors predictive of upgrade risk., (© 2023. Society of Surgical Oncology.)

Aims: The aims of this study were to investigate the ultrasound features of non-mass-type ductal carcinoma in situ (DCIS) of the breast and conduct a pathological analysis., Material and Methods: Ultrasound images of 32 cases of non-mass-type DCIS of the breast, collected between September 2014 and June 2016, were analyzed. The characteristics of the lesions, including border, internal echogenicity, local glandular hyperplasia, micro-calcification, and intra-tumoral blood flow resistance index (RI), were analyzed, and a concurrent pathological analysis was conducted., Results: Obvious local glandular hyperplasia was commonly observed in the 32 cases of non-mass-type DCIS of the breast. The internal echogenicity varied in intensity, exhibiting a "leopard pattern" or "zebra pattern." Color Doppler imaging revealed abundant blood flow signals within the lesion with an RI of >0.7. Isolated duct dilatation and micro-calcifications were occasionally observed within the lesions. High-grade DCIS was the predominant pathological type of non-mass-type DCIS., Conclusions: Non-mass-type DCIS of the breast often presents with obvious local glandular hyperplasia and varying internal echogenicity. High-grade DCIS is the frequent pathological type. Color Doppler imaging and RI measurement can assist in diagnosing non-mass-type DCIS of the breast., (Copyright © 2024 Copyright: © 2024 Journal of Cancer Research and Therapeutics.)

Background: The risk of ductal carcinoma in situ (DCIS) identified by biopsy often increases during surgery. Therefore, confirming the DCIS grade preoperatively is necessary for clinical decision-making., Purpose: To train a three-classification deep learning (DL) model based on ultrasound (US), combining clinical data, mammography (MG), US, and core needle biopsy (CNB) pathology to predict low-grade DCIS, intermediate-to-high-grade DCIS, and upstaged DCIS., Materials and Methods: Data of 733 patients with 754 DCIS cases confirmed by biopsy were retrospectively collected from May 2013 to June 2022 (N1), and other data (N2) were confirmed by biopsy as low-grade DCIS. The lesions were randomly divided into training (n=471), validation (n=142), and test (n = 141) sets to establish the DCIS-Net. Information on the DCIS-Net, clinical (age and sign), US (size, calcifications, type, breast imaging reporting and data system [BI-RADS]), MG (microcalcifications, BI-RADS), and CNB pathology (nuclear grade, architectural features, and immunohistochemistry) were collected. Logistic regression and random forest analyses were conducted to develop Multimodal DCIS-Net to calculate the specificity, sensitivity, accuracy, receiver operating characteristic curve, and area under the curve (AUC)., Results: In the test set of N1, the accuracy and AUC of the multimodal DCIS-Net were 0.752-0.766 and 0.859-0.907 in the three-classification task, respectively. The accuracy and AUC for discriminating DCIS from upstaged DCIS were 0.751-0.780 and 0.829-0.861, respectively. In the test set of N2, the accuracy and AUC of discriminating low-grade DCIS from upstaged low-grade DCIS were 0.769-0.987 and 0.818-0.939, respectively. DL was ranked from one to five in the importance of features in the multimodal-DCIS-Net., Conclusion: By developing the DCIS-Net and integrating it with multimodal information, diagnosing low-grade DCIS, intermediate-to high-grade DCIS, and upstaged DCIS is possible. It can also be used to distinguish DCIS from upstaged DCIS and low-grade DCIS from upstaged low-grade DCIS, which could pave the way for the DCIS clinical workflow., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

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.

Background: A malignancy might be found at surgery in cases of atypical ductal hyperplasia (ADH) diagnosed via US-guided core needle biopsy (CNB). The objective of this study was to investigate the diagnostic performance of contrast-enhanced ultrasound (CEUS) in predicting ADH diagnosed by US-guided CNB that was upgraded to malignancy after surgery., Methods: In this retrospective study, 110 CNB-diagnosed ADH lesions in 109 consecutive women who underwent US, CEUS, and surgery between June 2018 and June 2023 were included. CEUS was incorporated into US BI-RADS and yielded a CEUS-adjusted BI-RADS. The diagnostic performance of US BI-RADS and CEUS-adjusted BI-RADS for ADH were analyzed and compared., Results: The mean age of the 109 women was 49.7 years ± 11.6 (SD). The upgrade rate of ADH at CNB was 48.2% (53 of 110). The sensitivity, specificity, positive predictive value, and negative predictive value of CEUS for identification of malignant upgrading were 96.2%, 66.7%,72.9%, and 95.0%, respectively, based on BI-RADS category 4B threshold. The two false-negative cases were low-grade ductal carcinoma in situ. Compared with the US, CEUS-adjusted BI-RADS had better specificity for lesions smaller than 2 cm (76.7% vs. 96.7%, P = 0.031). After CEUS, 16 (10 malignant and 6 nonmalignant) of the 45 original US BI-RADS category 4A lesions were up-classified to BI-RADS 4B, and 3 (1 malignant and 2 nonmalignant) of the 41 original US BI-RADS category 4B lesions were down-classified to BI-RADS 4A., Conclusions: CEUS is helpful in predicting malignant upgrading of ADH, especially for lesions smaller than 2 cm and those classified as BI-RADS 4A and 4B on ultrasound., (© 2024. The Author(s).)

Background: Involved resection margins after breast conserving surgery (BCS) often require a re-operation with increased patient anxiety and risk of impaired cosmesis. We investigated the number of re-operations due to involved resection margins after BCS comparing digital breast tomosynthesis(DBT) with X-ray for intraoperative margin evaluation. Furthermore, we assessed the diagnostic accuracy of these methods to predict histopathological margin status. Finally, we evaluated risk factors for re-operation., Methods: In this randomized, non-blinded study, 250 invasive breast cancer patients were randomized (1:1), whereof 241 were analyzed intraoperatively with either DBT (intervention, n = 119) or X-ray (standard, n = 122). Pearson's chi-squared test, Fisher's exact test, t-test, logistic and ordinal regression analysis was used as appropriate., Results: No difference was found in the number of re-operations between the DBT and X-ray group (16.8 % vs 19.7 %, p = 0.57), or in diagnostic accuracy to predict histopathological margin status (77.5 %, CI: 68.6-84.9 %) and (67.3 %, CI: 57.7-75.9 %), respectively. We evaluated 5 potential risk factors for re-operation: Ductal carcinoma in situ (DCIS) outside tumor, OR = 9.4 (CI: 4.3-20.6, p < 0.001); high mammographic breast density, OR = 6.1 (CI: 1.0-38.1, p = 0.047); non-evaluable margins on imaging, OR = 3.8 (CI: 1.3-10.8, p = 0.016); neoadjuvant chemotherapy, OR = 3.0 (CI: 1.0-8.8, p = 0.048); and T2 tumor-size, OR = 2.6 (CI: 1.0-6.4, p = 0.045)., Conclusions: No difference was found in the number of re-operations or in diagnostic accuracy to predict histopathological margin status between DBT and X-ray groups. DCIS outside the tumor showed the highest risk of re-operation. Intraoperative methods with improved visualization of DCIS are needed to obtain tumor free margins in BCS., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Introduction: Thanks to mammographic screening and the improvement of breast cancer diagnostics, the detection of precancers is also increasing. They are defined as morphological changes of the mammary gland which are more likely to cause cancer. The evaluated precancers are atypical ductal hyperplasia (ADH), lobular carcinoma in situ (LCIS) and radial scar., Methodology: In the period 1. 1. 2018-31. 12. 2022, we performed 1,302 planned operations for breast disease at the Surgical Clinic of Teaching Hospital Plzeň, of which 30 (2%) were precancer operations. ADH was confirmed 11×, LCIS 8×, and a radical scar 11×. The average age of the patients in all three groups was 56 years (27-85). Precancer was diagnosed 8× only by sonography, 3× by mammography and 19× by a combination of both methods. Subsequently, a puncture biopsy was always completed. We performed 28 tumor excisions with intraoperative biopsy and 2 mastectomies., Results: In the case of ADH from puncture biopsy, ADH was confirmed intraoperatively 8×, DCIS was diagnosed 2×, and mucinous carcinoma 1×. In LCIS, no tumor was found by intraoperative biopsy 4×, LCIS was confirmed 1×, lobular invasive carcinoma was diagnosed 1×, mastectomy was performed 2× without intraoperative biopsy. In the radial scar, ADH was diagnosed 3×, sclerosing adenosis 6×, DCIS 1×, invasive carcinoma 1×. After the final histological processing of the samples, there was an increase in diagnosed carcinomas. In ADH, DCIS was confirmed 3×, DIC 2×, and mucinous carcinoma 1×. In LCIS, LIC was diagnosed 3×. In the radial scar, DCIS was confirmed 1×, and invasive carcinoma remain 1×. Thus, carcinoma was diagnosed in 11 patients (37%) thanks to the surgical solution. No patient underwent axillary node surgery. All 11 patients subsequently underwent oncological treatment, always a combination of radiotherapy and hormone therapy. All patients are alive, 10 patients are in complete remission of the disease, one with DCIS experienced a local recurrence after 4 years., Conclusion: Surgical treatment of precancers of the breast makes sense, DCIS or even invasive cancer is often hidden in addition to precancer. Thanks to the surgical solution, the cancer was detected in time.

Background: Supplemental screening with breast MRI is recommended annually for patients who have greater than 20% lifetime risk for breast cancer. While there is robust data regarding features of mammographic screen-detected breast cancers, there is limited data regarding MRI-screen-detected cancers., Patients and Methods: Screening breast MRIs performed between August 1, 2016 and July 30, 2022 identified 50 screen-detected breast cancers in 47 patients. Clinical and imaging features of all eligible cancers were recorded., Results: During the study period, 50 MRI-screen detected cancers were identified in 47 patients. The majority of MRI-screen detected cancers (32/50, 64%) were invasive. Pathology revealed ductal carcinoma in situ (DCIS) in 36% (18/50), invasive ductal carcinoma (IDC) in 52% (26/50), invasive lobular carcinoma in 10% (5/50), and angiosarcoma in 2% (1/50). The majority of patients (43/47, 91%) were stage 0 or 1 at diagnosis and there were no breast cancer-related deaths during the follow-up periods. Cancers presented as masses in 50% (25/50), nonmass enhancement in 48% (25/50), and a focus in 2% (1/50). DCIS was more likely to present as nonmass enhancement (94.4%, 17/18), whereas invasive cancers were more likely to present as masses (75%, 24/32) (P < .001). All cancers that were stage 2 at diagnosis were detected either on a baseline exam or more than 4 years since the prior MRI exam., Conclusion: MRI screen-detected breast cancers were most often invasive cancers. Cancers detected by MRI screening had an excellent prognosis in our study population. Invasive cancers most commonly presented as a mass., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Rationale: Breast cancer represents a prevalent malignancy that primarily impacts women, with pronounced consequences on their overarching health. The major therapeutic approach, encompassing surgical procedures, can often culminate in mastectomy, potentially inciting psychological turmoil and disorders., Patient Concerns: A patient was admitted to our facility on May 5, 2023, precipitated by the discovery of bilateral breast masses during a routine physical examination conducted 3 days before admission., Diagnosis: The breasts were symmetric, with the right nipple inverted and a palpable mass in the upper outer quadrant of the right breast, measuring approximately 5 cm × 4 cm. The mass was firm with indistinct borders, relatively regular morphology, poor mobility, and no tenderness. Outpatient color Doppler ultrasound revealed heterogeneous echogenicity in the right breast, classified as Breast Imaging Reporting and Data System (BI-RADS) category 0, along with multiple ductal dilatations. The left breast exhibited a hypoechoic area (BI-RADS 3), indicative of proliferative changes. Radiographic mammography confirmed diffuse changes in the right breast (BI-RADS 0) and proliferative signs in the left breast (BI-RADS 2). Biopsy results reveal significant atypical ductal hyperplasia consistent with intermediate-grade ductal carcinoma in situ. This patient was diagnosed as ductal carcinoma in situ of the right breast (cTisN0M0 and Stage 0), accompanied by a left breast mass., Interventions: On May 15, 2023, the patient was readmitted for further surgical intervention. Following relevant auxiliary examinations, the patient underwent nipple-areola complex-sparing radical mastectomy for the right breast, sentinel lymph node biopsy in the right axillary area, prosthesis-based breast reconstruction for the right breast, and microrotatotomy of the left breast mass on the left side on May 17., Outcomes: The patient made a successful recovery under scrupulous perioperative supervision and was discharged 7 days post-surgery., Lessons: The axillary approach for endoscopic mammary gland excision and immediate implant reconstruction permits patients to preserve the esthetics of the female form while undergoing conventional medical treatment. This methodology considerably enhances the psychophysical health of the patients, thereby marking it as an advantageous practice worthy of broad dissemination in the medical community., Competing Interests: The authors have no funding and conflict of interest to disclose., (Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc.)

Objective: This study aimed to investigate the feasibility of ultrafast magnetic resonance imaging (MRI) and radiomic features derived from breast MRI for predicting the upstaging of ductal carcinoma in situ (DCIS) diagnosed using percutaneous needle biopsy., Materials and Methods: Between August 2018 and June 2020, 95 patients with 98 DCIS lesions who underwent preoperative breast MRI, including an ultrafast sequence, and subsequent surgery were included. Four ultrafast MRI parameters were analyzed: time-to-enhancement, maximum slope (MS), area under the curve for 60 s after enhancement, and time-to-peak enhancement. One hundred and seven radiomic features were extracted for the whole tumor on the first post-contrast T1WI and T2WI using PyRadiomics. Clinicopathological characteristics, ultrafast MRI findings, and radiomic features were compared between the pure DCIS and DCIS with invasion groups. Prediction models, incorporating clinicopathological, ultrafast MRI, and radiomic features, were developed. Receiver operating characteristic curve analysis and area under the curve (AUC) were used to evaluate model performance in distinguishing between the two groups using leave-one-out cross-validation., Results: Thirty-six of the 98 lesions (36.7%) were confirmed to have invasive components after surgery. Compared to the pure DCIS group, the DCIS with invasion group had a higher nuclear grade ( P < 0.001), larger mean lesion size ( P = 0.038), larger mean MS ( P = 0.002), and different radiomic-related characteristics, including a more extensive tumor volume; higher maximum gray-level intensity; coarser, more complex, and heterogeneous texture; and a greater concentration of high gray-level intensity. No significant differences in AUCs were found between the model incorporating nuclear grade and lesion size (0.687) and the models integrating additional ultrafast MRI and radiomic features (0.680-0.732)., Conclusion: High nuclear grade, larger lesion size, larger MS, and multiple radiomic features were associated with DCIS upstaging. However, the addition of MS and radiomic features to the prediction model did not significantly improve the prediction performance., Competing Interests: Sang Joon Park is founder and CEO of MEDICALIP, however, this do not affect the publication of this manuscript. All remaining authors have declared no conflicts of interest., (Copyright © 2023 The Korean Society of Radiology.)

Due to limited biopsy samples, ~20% of DCIS lesions confirmed by biopsy are upgraded to invasive ductal carcinoma (IDC) upon surgical resection. Avoiding underestimation of IDC when diagnosing DCIS has become an urgent challenge in an era discouraging overtreatment of DCIS. In this study, the metabolic profiles of 284 fresh frozen breast samples, including tumor tissues and adjacent benign tissues (ABTs) and distant surrounding tissues (DSTs), were analyzed using desorption electrospray ionization-mass spectrometry (DESI-MS) imaging. Metabolomics analysis using DESI-MS data revealed significant differences in metabolite levels, including small-molecule antioxidants, long-chain polyunsaturated fatty acids (PUFAs) and phospholipids between pure DCIS and IDC. However, the metabolic profile in DCIS with invasive carcinoma components clearly shifts to be closer to adjacent IDC components. For instance, DCIS with invasive carcinoma components showed lower levels of antioxidants and higher levels of free fatty acids compared to pure DCIS. Furthermore, the accumulation of long-chain PUFAs and the phosphatidylinositols (PIs) containing PUFA residues may also be associated with the progression of DCIS. These distinctive metabolic characteristics may offer valuable indications for investigating the malignant potential of DCIS. By combining DESI-MS data with machine learning (ML) methods, various breast lesions were discriminated. Importantly, the pure DCIS components were successfully distinguished from the DCIS components in samples with invasion in postoperative specimens by a Lasso prediction model, achieving an AUC value of 0.851. In addition, pixel-level prediction based on DESI-MS data enabled automatic visualization of tissue properties across whole tissue sections. Summarily, DESI-MS imaging on histopathological sections can provide abundant metabolic information about breast lesions. By analyzing the spatial metabolic characteristics in tissue sections, this technology has the potential to facilitate accurate diagnosis and individualized treatment of DCIS by inferring the presence of IDC components surrounding DCIS lesions. © 2023 The Pathological Society of Great Britain and Ireland., (© 2023 The Pathological Society of Great Britain and Ireland.)

Purpose: To predict ductal carcinoma in situ with microinvasion (DCISMI) based on clinicopathologic, conventional breast magnetic resonance imaging (MRI), and dynamic contrast enhanced MRI (DCE-MRI) radiomics signatures in women with biopsy-confirmed ductal carcinoma in situ (DCIS)., Methods: Eighty-six women with eighty-seven biopsy-proven DCIS who underwent preoperative MRI and underwent surgery were retrospectively identified. Clinicopathologic, conventional MRI, DCE-MRI radiomics, combine (based on conventional MRI and DCE-MRI radiomics), traditional (based on clinicopathologic and conventional MRI) and mixed (based on clinicopathologic, conventional MRI and DCE-MRI radiomics) models were constructed by logistic regression (LR) with a 3-fold cross-validation, all evaluated using receiver operating characteristic (ROC) curve analysis. A clinical radiomics nomogram was then built by incorporating the Radiomics score, significant clinicopathologic and conventional MRI features of mixed model., Results: The area under the curves (AUCs) of clinicopathologic, conventional MRI, DCE-MRI radiomics, traditional, combine, and mixed model were 0.76 (95% confidence interval [CI] 0.59-0.94), 0.77 (95%CI 0.59-0.95), 0.74 (95%CI 0.55-0.93), 0.87 (95%CI 0.73-1), 0.8 (95%CI 0.63-0.96), and 0.93 (95%CI 0.84-1) in the validation cohort, respectively. The clinical radiomics nomogram based on mixed model showed higher AUCs than both clinicopathologic and DCE-MRI radiomics models in training/test (all P < 0.05) set and showed the greatest overall net benefit for upstaging according to decision curve analysis (DCA)., Conclusion: A nomogram constructed by combining clinicopathologic, conventional MRI features and DCE-MRI radiomics signatures may be useful in predicting DCISMI from DICS preoperatively., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Rationale and Objectives: Accurate preoperative differentiation between ductal carcinoma in situ with microinvasion (DCISM) and ductal carcinoma in situ (DCIS) could facilitate treatment optimization and individualized risk assessment. The present study aims to build and validate a radiomics nomogram based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) that could distinguish DCISM from pure DCIS breast cancer., Materials and Methods: MR images of 140 patients obtained between March 2019 and November 2022 at our institution were included. Patients were randomly divided into a training (n = 97) and a test set (n = 43). Patients in both sets were further split into DCIS and DCISM subgroups. The independent clinical risk factors were selected by multivariate logistic regression to establish the clinical model. The optimal radiomics features were chosen by the least absolute shrinkage and selection operator, and a radiomics signature was built. The nomogram model was constructed by integrating the radiomics signature and independent risk factors. The discrimination efficacy of our nomogram was assessed by using calibration and decision curves., Results: Six features were selected to construct the radiomics signature for distinguishing DCISM from DCIS. The radiomics signature and nomogram model exhibited better calibration and validation performance in the training (AUC 0.815, 0.911, 95% confidence interval [CI], 0.703-0.926, 0.848-0.974) and test (AUC 0.830, 0.882, 95% CI, 0.672-0.989, 0.764-0.999) sets than in the clinical factor model (AUC 0.672, 0.717, 95% CI, 0.544-0.801, 0.527-0.907). The decision curve also demonstrated that the nomogram model exhibited good clinical utility., Conclusion: The proposed noninvasive MRI-based radiomics nomogram model showed good performance in distinguishing DCISM from DCIS., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

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., (© 2023 The Royal Australian and New Zealand College of Radiologists.)

Although nonrecurrent and recurrent forms of ductal carcinoma in situ (DCIS) of the breast are observed, no evidence-based test can make this distinction. The current retrospective case-control study used archival DCIS samples stained with anti-phospho-Ser226-glucose transporter type 1 and anti-phosphofructokinase type L antibodies. Immunofluorescence micrographs were used to create machine learning models of recurrent and nonrecurrent biomarker patterns, which were evaluated in cross-validation studies. Clinical performance was assessed by holdout studies using patients whose data were not used in training. Micrographs were stratified according to the recurrence probability of each image. Recurrent patients were defined by at least one image with a probability of recurrence ≥98%, whereas nonrecurrent patients had none. These studies found no false-negatives, identified true-positives, and uniquely identified true-negatives. Roughly 20% of the microscope fields of recurrent lesions were computationally recurrent. Strong prognostic results were obtained for both white and African-American women. This machine tool provides the first means to accurately predict recurrent and nonrecurrent patient outcomes. Data indicate that at least some false-positive findings were true-positive findings that benefited from surgical intervention. The intracellular locations of phospho-Ser226-glucose transporter type 1 and phosphofructokinase type L likely participate in cancer recurrences by accelerating glucose flux, a key feature of the Warburg effect., (Copyright © 2023 American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved.)

Background: About 20%-40% of patients diagnosed with ductal carcinoma in situ (DCIS) by core needle biopsy (CNB) will develop invasive cancer at the time of excision. Improving the preoperative diagnosis of DCIS is important for surgical planning., Purpose: To establish an MRI-based radiomics nomogram for preoperatively evaluating the upstaging of DCIS patients and help with risk stratification., Study Type: Retrospective., Population: A total of 227 patients (50.5 ± 9.7 years; 67 upstaged DCIS) were divided into training (n = 109), internal (n = 47), and external (n = 71) validation cohort., Field Strength/sequence: 1.5-T or 3-T, dynamic contrast-enhanced (DCE) imaging, and diffusion-weighted imaging (DWI)., Assessment: DCIS lesions were manually segmented using ITK-SNAP software and 1304 radiomic features were extracted from DCE, DWI, and apparent diffusion coef-ficient (ADC) maps, respectively. A radscore was calculated by a random forest algo-rithm based on DCIS upstaging-related radiomic features, which selected by a coarse-to-fine method including interclass correlation coefficient, single-factor anal-ysis, and the least absolute shrinkage and selection operator (LASSO) method. Uni-variate and multivariate logistic regression was used to analyze the independent risk factors, including age, location, lesion size, estrogen receptor (ER) status, and other clinico-pathologic factors. Finally, Mann-Whitney U tests were performed to com-pare the differences in radscore between low/intermediate and high nuclear grade groups for pure DCIS patients., Statistical Tests: Student's t-tests or Mann-Whitney U tests, chi-square-tests, or Fisher's-tests, univariate and multivariate logistic regression analysis, calibration curve, Youden index, the area under the curve (AUC), Delong test, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) analyses., Results: Eight important radiomic features (two from ADC, three from DWI, and three from DCE) were selected for calculating radscore. Clinical model including age and ER was established with AUCs of 0.747 and 0.738 in the internal and external validation cohorts, respectively. A combined model integrating age, estrogen receptor (ER), and radscore were also constructed with AUCs of 0.887 and 0.881. Further subgroup analysis showed that pure DCIS patients with different nuclear grade have significant differences in radscore., Data Conclusion: Multisequence MRI radiomics may preoperatively evaluate the upstaging of DCIS and might provide personalized image-based clinical decision support., Evidence Level: 4., Technical Efficacy: Stage 2., (© 2022 International Society for Magnetic Resonance in Medicine.)

Objectives: In approximately 45% of invasive breast cancer (IBC) patients treated with neoadjuvant systemic therapy (NST), ductal carcinoma in situ (DCIS) is present. Recent studies suggest response of DCIS to NST. The aim of this systematic review and meta-analysis was to summarise and examine the current literature on imaging findings for different imaging modalities evaluating DCIS response to NST. More specifically, imaging findings of DCIS pre- and post-NST, and the effect of different pathological complete response (pCR) definitions, will be evaluated on mammography, breast MRI, and contrast-enhanced mammography (CEM)., Methods: PubMed and Embase databases were searched for studies investigating NST response of IBC, including information on DCIS. Imaging findings and response evaluation of DCIS were assessed for mammography, breast MRI, and CEM. A meta-analysis was conducted per imaging modality to calculate pooled sensitivity and specificity for detecting residual disease between pCR definition no residual invasive disease (ypT0/is) and no residual invasive or in situ disease (ypT0)., Results: Thirty-one studies were included. Calcifications on mammography are related to DCIS, but can persist despite complete response of DCIS. In 20 breast MRI studies, an average of 57% of residual DCIS showed enhancement. A meta-analysis of 17 breast MRI studies confirmed higher pooled sensitivity (0.86 versus 0.82) and lower pooled specificity (0.61 versus 0.68) for detection of residual disease when DCIS is considered pCR (ypT0/is). Three CEM studies suggest the potential benefit of simultaneous evaluation of calcifications and enhancement., Conclusions and Clinical Relevance: Calcifications on mammography can remain despite complete response of DCIS, and residual DCIS does not always show enhancement on breast MRI and CEM. Moreover, pCR definition effects diagnostic performance of breast MRI. Given the lack of evidence on imaging findings of response of the DCIS component to NST, further research is demanded., Key Points: • Ductal carcinoma in situ has shown to be responsive to neoadjuvant systemic therapy, but imaging studies mainly focus on response of the invasive tumour. • The 31 included studies demonstrate that after neoadjuvant systemic therapy, calcifications on mammography can remain despite complete response of DCIS and residual DCIS does not always show enhancement on MRI and contrast-enhanced mammography. • The definition of pCR has impact on the diagnostic performance of MRI in detecting residual disease, and when DCIS is considered pCR, pooled sensitivity was slightly higher and pooled specificity slightly lower., (© 2023. The Author(s).)

Introduction: The standard of care for imaging of breast pathology has historically been mammography and sonography. MRI is a modern adjunct in the surgeon's toolkit. We looked to examine the differences in imaging modalities and their ability to predict the size in relation to the pathologic size after excision with focus on pathologic subtypes., Methods: We analyzed patient records across a 4-year period from 2017 to 2021 who were treated surgically for breast cancer at our facility. We used a retrospective chart review to collect measurements that were recorded of the tumors by the radiologist for available mammography, ultrasound, and MRI which were compared to pathology report measurements of the final specimens. We subdivided the results by pathologic subtypes including invasive ductal carcinoma (IDC), invasive lobular carcinoma (ILC), and ductal carcinoma in situ (DCIS)., Results: 658 total patients met criteria for analysis. Mammography overestimated specimens with DCIS by 1.93 mm ( P = .15), US underestimated by .56 (.55), and MRI overestimated by 5.77 mm ( P < .01). There was no statistically significant difference in any modalities with IDC. With specimens of ILC, all 3 imaging modalities underestimated tumor size, with only US being significant., Discussion: Mammography and MRI consistently overestimated tumor size with the exception of ILC while US underestimated tumor size on all pathologic subtypes. MRI significantly overestimated tumor size in DCIS by 5.77 mm. Mammography was the most accurate imaging modality for all pathologic subtypes and never had a statistically significant difference from actual tumor size.

Background: When needle core biopsy (NCB) of the breast yields atypical ductal hyperplasia (ADH), excision is typically recommended. The natural history of ADH undergoing active surveillance (AS) is not well described. We investigate the rates of upgrade to malignancy of excised ADH and the rates of radiographic progression under AS., Materials and Methods: We retrospectively reviewed records of 220 cases of ADH on NCB. Of patients who had surgery within 6 months of NCB, we examined the malignancy upgrade rate. In the AS cohort, we examined rates of radiographic progression on interval imaging., Results: The malignancy upgrade rate among patients who underwent immediate excision (n = 185) was 15.7%: 14.1% (n = 26) ductal carcinoma in situ (DCIS) and 1.6% (n = 3) invasive ductal carcinoma (IDC). Upgrade to malignancy was less common in lesions <4 mm in size (0%) or with focal ADH (5%), and more common among lesions presenting with a radiographic mass (26%). Among the 35 patients who underwent AS, median follow-up was 20 months. Two lesions progressed on imaging (incidence 3.8% at 2 years). One patient without radiographic progression was found to have IDC at delayed surgery. The remaining lesions remained stable (46%), decreased in size (11%), or resolved (37%)., Conclusions: Our findings suggest that AS is a safe approach to managing ADH on NCB for most patients. This could spare many patients with ADH from unnecessary surgery. Given that AS is being investigated for low-risk DCIS in multiple international prospective trials, these results suggest that AS should also be investigated for ADH., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Objective: The objective was to evaluate outcomes of mammographic architectural distortion (AD) with and without MRI and US correlates., Methods: A retrospective review of unexplained mammographic AD with subsequent MRI from January 1, 2007 to September 30, 2017 was performed using a reader-based study design. Mammographic, MRI, and US features and outcomes were documented. Truth was based on biopsy results or minimum two-year imaging follow-up. Measures of diagnostic accuracy were calculated., Results: Fifty-six cases of AD were included: 29 (51.8%) detected on 2D mammogram and 27 (48.2%) detected on digital breast tomosynthesis. Of 35.7% (20/56) with MRI correlate, 40.0% (8/20) were enhancing masses, 55.0% (11/20) were non-mass enhancement (NME), and 5.0% (1/20) were nonenhancing AD. Of eight enhancing masses, 75.0% (6/8) were invasive cancers, and 25.0% (2/8) were high-risk lesions. Of 11 NME, 18.2% (2/11) were ductal carcinoma in situ, 36.4% (4/11) were high-risk lesions, and 45.4% (5/11) were benign. Of 64.3% (36/56) without MRI correlate, 94.4% (34/36) were benign by pathology or follow-up, one (2.8%, 1/36) was a 4-mm focus of invasive cancer with US correlate, and one (1/36, 2.8%) was a high-risk lesion. Of cases without MRI and US correlates, one (3.0%, 1/33) was a high-risk lesion and 97.0% (32/33) were benign. The negative predictive value of mammographic AD without MRI correlate was 97.2% (35/36) and without both MRI and US correlates was 100.0% (33/33)., Conclusion: Mammographic AD without MRI or US correlate was not cancer in our small cohort and follow-up could be considered, reducing interventions., (© Society of Breast Imaging 2023. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Some breast carcinomas detected at screening, especially ductal carcinoma in situ, may have limited potential for progression to symptomatic disease. To determine non-progression is a challenge, but if all screening-detected breast tumors eventually reach a clinical stage, the cumulative incidence at a reasonably high age would be similar for women with or without screening, conditional on the women being alive., Methods: Using high-quality population data with 24 years of follow-up from the gradually introduced BreastScreen Norway program, we studied whether all breast carcinomas detected at mammography screening 50-69 years of age would progress to clinical symptoms within 85 years of age. First, we estimated the incidence rates of breast carcinomas by age in scenarios with or without screening, based on an extended age-period-cohort incidence model. Next, we estimated the frequency of non-progressive tumors among screening-detected cases, by calculating the difference in the cumulative rate of breast carcinomas between the screening and non-screening scenarios at 85 years of age., Results: Among women who attended BreastScreen Norway from the age of 50 to 69 years, we estimated that 1.1% of the participants were diagnosed with a breast carcinoma without the potential to progress to symptomatic disease by 85 years of age. This proportion of potentially non-progressive tumors corresponded to 15.7% [95% CI 3.3, 27.1] of breast carcinomas detected at screening., Conclusions: Our findings suggest that nearly one in six breast carcinomas detected at screening may be non-progressive., (© 2023. The Author(s).)

Intraductal carcinoma of the salivary gland (IDC) is a rare in situ neoplasm of the salivary gland with similar features to the ductal carcinoma in situ of the breast. This report aims to present the clinical presentation and histological features of IDC. The authors present a 90-year-old gentleman with an indurated and painless tumor within the right parotid. Preoperative diagnostics, including fine needle aspiration cytology, ultrasound scan, and magnetic resonance imaging, were suggestive of Warthin tumor. The tumour was excised by extracapsular dissection. The patient is disease free within the follow-up period of 33 months. Intraductal carcinoma is an indolent phenotype with only a few reported cases with nodal metastases, and to the best of our knowledge, no cases have been reported with distant metastases. Complete surgical excision is recommended to prevent a recurrence. The knowledge of this underreported salivary gland malignancy is important to prevent misdiagnosis and insufficient treatment., Competing Interests: The authors report no conflicts of interest., (Copyright © 2023 by Mutaz B. Habal, MD.)

There have been several investigations of non-mass-like (NML) lesions on ultrasound (US) since Uematsu first described this approach, and it is a relatively new concept for breast examination. However, the results have varied, and there have been only a few studies related to the detailed histopathology of NML lesions on US. Here, we review the histopathology of NML lesions. NML lesions are pathologically benign, atypical, or malignant. There are two major findings of NML lesions on US: architectural distortion and calcifications. Architectural distortion pathologically indicates a fibrous change with ductal proliferation, invasive breast carcinoma, and carcinoma in situ. Histopathologically, microcalcifications are seen in both benign and malignant lesions, and it is important to distinguish between these lesions among NML lesions, particularly fibrocystic changes including adenosis and hyperplasia in the case of benign lesions and carcinoma in situ (ductal and lobular) in the case of malignant lesions. The differential major points may be whether NML lesions are associated with abundant hyperechoic foci, which indicate comedo necrosis on histology. They are usually high-grade carcinoma in situ that may be positive for HER2 or triple negativity. A recent report indicated that low-grade carcinoma in situ showed better survival than higher-grade carcinoma in situ, which is often accompanied by comedo necrosis on histology, reflecting visible microcalcification on US. NML lesions are considered to include a certain rate of low-grade carcinoma in situ. Therefore, more caution may be needed when detecting and managing NML lesions to avoid overdiagnosis and overtreatment as a result of this recent "low-risk ductal carcinoma in situ" concept., (© 2023. The Author(s).)

Introduction: Imaging guidelines for post-neoadjuvant chemotherapy (NAC) breast cancer patients lack specificity on appropriateness and utility of individual modalities for surgical planning. Microcalcifications confound mammographic interpretation. We examined the correlation between the mammographic extent of microcalcifications present post-NAC, corresponding magnetic resonance imaging (MRI) lesions, and definitive surgical pathology., Methods: In this retrospective cohort study, patients with calcifications on mammography were collected from a database of consecutive breast cancer patients receiving NAC. The primary objective was to determine the correlation between maximum dimension of post-NAC calcifications with surgical pathology (invasive disease, tumor bed, and ductal carcinoma in situ [DCIS]), stratified by tumor receptor subgroup. Secondarily, we examined the correlation of residual disease with MRI mass enhancement (ME) and non-ME (NME). Pearson's correlation coefficient was used to evaluate statistical significance (strong: R 2 ≥70%; moderate: R 2 =25-70%; weak: R 2 ≤25%)., Results: Overall, 186 patients met the inclusion criteria. Mammographic calcifications correlated poorly with invasive disease (R 2  = 10.8%), overestimating by 57%. In patients with calcifications on mammography, MRI ME and NME correlated weakly with the maximum dimension of invasive disease and DCIS. In triple-negative breast cancer (TNBC) patients, invasive disease correlated strongly with the maximum dimension of calcifications (R 2  = 83%) and moderately with ME (R 2  = 37.7%) and NME (R 2  = 28.4%)., Conclusion: Overall, current imaging techniques correlate poorly and overestimate final surgical pathology. This poor correlation may lead to uncertainty in the extent of required surgical excision and the exclusion of potential candidates for non-surgical management in ongoing trials. TNBCs would be good candidates for these trials given the stronger observed correlations between pathology and imaging., (© 2023. Society of Surgical Oncology.)

BACKGROUND. Biopsy-proven ductal carcinoma in situ (DCIS) lesions are often upgraded to invasive cancer at surgery. Therefore, accurate prediction of the likelihood of invasion is helpful for surgical planning, including the need for sentinel lymph node biopsy (SLNB). OBJECTIVE. The purpose of the present study was to investigate whether kinetic features of clinically available ultrafast MRI (UF-MRI) can predict upgrade of biopsy-proven DCIS to invasive cancer at surgical excision. METHODS. Consecutive patients with biopsy-proven pure DCIS lesions who underwent UF-MRI with conventional dynamic contrast-enhanced MRI (DCE-MRI) and subsequently underwent surgery between August 2019 and January 2021 were identified. Patient and lesion characteristics, biopsy method and pathology, and lesion features on mammography, ultrasound, DCE-MRI, and UF-MRI were assessed to determine predictors of upgrade to invasive cancer. The Fisher exact test and Kruskal-Wallis test were used for association analysis. RESULTS. In 68 patients (median age, 52.0 years; range, 31-79 years) with 68 biopsy-proven pure DCIS lesions, 26 lesions (38%) were upgraded from in situ to invasive cancer. An upgrade of DCIS to invasive cancer was significantly associated with a shorter time to enhancement (TTE) on preoperative UF-MRI ( p = .03), with a threshold of 11 seconds providing maximum specificity (50%) and sensitivity (76%) for upgrade. Larger lesion size on DCE-MRI ( p = .001) and mammography ( p = .04) was also significantly associated with upgrade; an optimal predictive threshold of 4.4 cm on DCE-MRI yielded sensitivity of 88% and specificity of 56%. No other specific variables were significantly associated with upgrade after surgery. Logistic regression of selected features combined with TTE produced a higher AUC (0.85) in predicting upgrade to invasive disease than did each factor alone, but this result was not statistically significant. CONCLUSION. Preoperative UF-MRI TTE and lesion size on DCE-MRI and mammography show potential in predicting upgrade of DCIS to invasive cancer at surgery. CLINICAL IMPACT. UF-MRI provides useful information that can be used in surgical planning, including determination of the need to perform SLNB.

Ultrasound images of ductal carcinoma in situ (DCIS) show a wide range of variations from mass to non-mass lesions. This article describes the characteristics of ultrasound images of DCIS based on the BC-02 study conducted by The Japanese Association of Breast and Thyroid Sonology (JABTS). In the BC-02 study, ultrasound images of 705 DCIS cases were classified by imaging findings. The results showed that non-mass abnormalities accounted for 60% of all lesions and masses for 40%. Looking at each subclassification, hypoechoic areas in the mammary gland were the most common (50% of the total), followed by solid masses (31%), mixed masses (9%), and abnormalities of the ducts (8%). These four classifications accounted for 98% of the total. Echogenic foci without a hypoechoic area, architectural distortion, and clustered microcysts were very rare, accounting for about 1% of the total. The ultrasound images of DCIS were characterized by a wide range of variations from masses to non-masses abnormalities, with hypoechoic areas in the mammary gland being the most common, followed by solid masses., (© 2023. The Author(s).)

Ductal carcinoma in-situ (DCIS) accounts for 20-25% of all new breast cancer diagnoses. DCIS has an uncertain risk of progression to invasive breast cancer and a lack of predictive biomarkers may result in relatively high levels (~ 75%) of overtreatment. To identify unique prognostic biomarkers of invasive progression, crystallographic and chemical features of DCIS microcalcifications have been explored. Samples from patients with at least 5-years of follow up and no known recurrence (174 calcifications in 67 patients) or ipsilateral invasive breast cancer recurrence (179 microcalcifications in 57 patients) were studied. Significant differences were noted between the two groups including whitlockite relative mass, hydroxyapatite and whitlockite crystal maturity and, elementally, sodium to calcium ion ratio. A preliminary predictive model for DCIS to invasive cancer progression was developed from these parameters with an AUC of 0.797. These results provide insights into the differing DCIS tissue microenvironments, and how these impact microcalcification formation., (© 2023. The Author(s).)

Differentiation of ductal carcinoma in situ (DCIS, a precancerous lesion of the breast) from fibroadenoma (FA) using ultrasonography is significant for the early prevention of malignant breast tumors. Radiomics-based artificial intelligence (AI) can provide additional diagnostic information but usually requires extensive labeling efforts by clinicians with specialized knowledge. This study aims to investigate the feasibility of differentially diagnosing DCIS and FA using ultrasound radiomics-based AI techniques and further explore a novel approach that can reduce labeling efforts without sacrificing diagnostic performance. We included 461 DCIS and 651 FA patients, of whom 139 DCIS and 181 FA patients constituted a prospective test cohort. First, various feature engineering-based machine learning (FEML) and deep learning (DL) approaches were developed. Then, we designed a difference-based self-supervised (DSS) learning approach that only required FA samples to participate in training. The DSS approach consists of three steps: (1) pretraining a Bootstrap Your Own Latent (BYOL) model using FA images, (2) reconstructing images using the encoder and decoder of the pretrained model, and (3) distinguishing DCIS from FA based on the differences between the original and reconstructed images. The experimental results showed that the trained FEML and DL models achieved the highest AUC of 0.7935 (95% confidence interval, 0.7900-0.7969) on the prospective test cohort, indicating that the developed models are effective for assisting in differentiating DCIS from FA based on ultrasound images. Furthermore, the DSS model achieved an AUC of 0.8172 (95% confidence interval, 0.8124-0.8219), indicating that our model outperforms the conventional radiomics-based AI models and is more competitive., (© 2023. International Association of Scientists in the Interdisciplinary Areas.)