Ductal carcinoma in situ at core-needle biopsy: meta-analysis of underestimation and predictors of invasive breast cancer

Presurgical Upgrade Prediction of DCIS to Invasive Ductal Carcinoma Using Time-dependent Deep Learning Models with DCE MRI

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.

Radiology for Ductal Carcinoma In Situ of the Breast: Updates on Invasive Cancer Progression and Active Monitoring

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.

Potential Overtreatment of DCIS in Patients with Limited Life Expectancy

INTRODUCTION

As the benefits of intensive locoregional therapy for ductal carcinoma in situ (DCIS) are realized over time in older adults, life expectancy may help to guide treatment decisions. We examined whether life expectancy was associated with extent of locoregional therapy in this population.

PATIENTS AND METHODS

Women ≥ 70 years old with < 5 cm of DCIS diagnosed 2010-2015 were identified in the Surveillance, Epidemiology, and End Results (SEER)-Medicare dataset and categorized by a life expectancy ≤ 5 or > 5 years, defined by a validated claims-based measure. Differences in locoregional therapy (mastectomy + axillary surgery, mastectomy-only, lumpectomy + radiation therapy (RT) + axillary surgery, lumpectomy + RT, lumpectomy-only, and no treatment) by life expectancy were assessed using Pearson chi-squared tests. Generalized linear mixed models were used to identify factors associated with receipt of lumpectomy-only.

RESULTS

Of 5346 women (median age of 75 years, range 70-97 years), 927 (17.3%) had a life expectancy ≤ 5 years. Of the 4041 patients who underwent lumpectomy, 710 (13.3%) underwent axillary surgery. More patients with life expectancy ≤ 5 years underwent lumpectomy-only (39.4% versus 27%), mastectomy-only (8.1% versus 5.3%), or no treatment (5.8% versus 3.2%; p < 0.001). On multivariable analysis, women with life expectancy ≤ 5 years had a significantly greater likelihood of undergoing lumpectomy-only [OR 1.90, 95% CI (1.63-2.22)].

CONCLUSIONS

Life expectancy is associated with lower-intensity locoregional therapy for older women with DCIS, yet a large proportion of patients with a life expectancy ≤ 5 years received RT and axillary surgery, highlighting potential overtreatment and opportunities to de-escalate locoregional therapy in older adults.

Could breast multiparametric MRI discriminate between pure ductal carcinoma in situ and microinvasive carcinoma?

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 mm2/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.

Interpretable Radiomic Signature for Breast Microcalcification Detection and Classification

Breast microcalcifications are observed in 80% of mammograms, and a notable proportion can lead to invasive tumors. However, diagnosing microcalcifications is a highly complicated and error-prone process due to their diverse sizes, shapes, and subtle variations. In this study, we propose a radiomic signature that effectively differentiates between healthy tissue, benign microcalcifications, and malignant microcalcifications. Radiomic features were extracted from a proprietary dataset, composed of 380 healthy tissue, 136 benign, and 242 malignant microcalcifications ROIs. Subsequently, two distinct signatures were selected to differentiate between healthy tissue and microcalcifications (detection task) and between benign and malignant microcalcifications (classification task). Machine learning models, namely Support Vector Machine, Random Forest, and XGBoost, were employed as classifiers. The shared signature selected for both tasks was then used to train a multi-class model capable of simultaneously classifying healthy, benign, and malignant ROIs. A significant overlap was discovered between the detection and classification signatures. The performance of the models was highly promising, with XGBoost exhibiting an AUC-ROC of 0.830, 0.856, and 0.876 for healthy, benign, and malignant microcalcifications classification, respectively. The intrinsic interpretability of radiomic features, and the use of the Mean Score Decrease method for model introspection, enabled models' clinical validation. In fact, the most important features, namely GLCM Contrast, FO Minimum and FO Entropy, were compared and found important in other studies on breast cancer.

Digital Mammography (DM) vs. Dynamic Contrast Enhancement-Magnetic Resonance Imaging (DCE-MRI) in Microcalcifications Assessment: A Radiological-Pathological Comparison

The aim of this study was to compare the characteristics of breast microcalcification on digital mammography (DM) with the histological and molecular subtypes of breast cancer and to identify the predictive value of DM and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in assessing microcalcifications for radiologic-pathologic correlation. We relied on our prospectively maintained database of suspicious microcalcifications on DM, from which data were retrospectively collected between January 2020 and April 2023. We enrolled 158 patients, all of whom were subjected to biopsy. Additionally, 63 patients underwent breast DCE-MRI. Microcalcifications with a linear branched morphology were correlated with malignancies (p < 0.001), among which an association was highlighted between triple negatives (TNs) and segmental distribution (p < 0.001). Amorphous calcifications were correlated with atypical ductal hyperplasia (ADH) (p = 0.013), coarse heterogeneous (p < 0.001), and fine-pleomorphic (p = 0.008) with atypical lobular hyperplasia (ALH) and fine pleomorphic (p = 0.009) with flat epithelial atypia (FEA). Regarding DCE-MRI, no statistical significance was observed between non-mass lesions and ductal carcinoma in situ (DCIS). Concerning mass lesions, three were identified as DCIS and five as invasive ductal carcinoma (IDC). In conclusion, microcalcifications assessed in DM exhibit promising predictive characteristics concerning breast lesion subtypes, leading to a reduction in diagnostic times and further examination costs, thereby enhancing the clinical management of patients.

Deep-learning model to improve histological grading and predict upstaging of atypical ductal hyperplasia / ductal carcinoma in situ on breast biopsy

AIMS

Risk stratification of atypical ductal hyperplasia (ADH) and ductal carcinoma in situ (DCIS), diagnosed using breast biopsy, has great clinical significance. Clinical trials are currently exploring the possibility of active surveillance for low-risk lesions, whereas axillary lymph node staging may be considered during surgical planning for high-risk lesions. We aimed to develop a machine-learning algorithm based on whole-slide images of breast biopsy specimens and clinical information to predict the risk of upstaging to invasive breast cancer after wide excision.

METHODS AND RESULTS

Patients diagnosed with ADH/DCIS on breast biopsy were included in this study, comprising 592 (740 slides) and 141 (198 slides) patients in the development and independent testing cohorts, respectively. Histological grading of the lesions was independently evaluated by two pathologists. Clinical information, including biopsy method, lesion size, and Breast Imaging Reporting and Data System (BI-RADS) classification of ultrasound and mammograms, were collected. Deep DCIS consisted of three deep neural networks to evaluate nuclear grade, necrosis, and stromal reactivity. Deep DCIS output comprised five parameters: total patches, lesion extent, Deep Grade, Deep Necrosis, and Deep Stroma. Deep DCIS highly correlated with the pathologists' evaluations of both slide- and patient-level labels. All five parameters of Deep DCIS were significantly associated with upstaging to invasive carcinoma in subsequent wide excisional specimens. Using multivariate logistic regression, Deep DCIS predicted upstaging to invasive carcinoma with an area under the curve (AUC) of 0.81, outperforming pathologists' evaluation (AUC, 0.71 and 0.69). After including clinical and hormone receptor status information, performance further improved (AUC, 0.87). This combined model retained its predictive power in two subgroup analyses: the first subgroup included unequivocal DCIS (excluding cases of ADH and DCIS suspicious for microinvasion) (AUC, 0.83), while the second excluded cases of high-grade DCIS (AUC, 0.81). The model was validated in an independent testing cohort (AUC, 0.81).

CONCLUSION

This study demonstrated that deep-learning models can refine histological evaluation of ADH and DCIS on breast biopsies, which may help guide future treatment planning.

AI analytics can be used as imaging biomarkers for predicting invasive upgrade of ductal carcinoma in situ

OBJECTIVES

To evaluate whether the quantitative abnormality scores provided by artificial intelligence (AI)-based computer-aided detection/diagnosis (CAD) for mammography interpretation can be used to predict invasive upgrade in ductal carcinoma in situ (DCIS) diagnosed on percutaneous biopsy.

METHODS

Four hundred forty DCIS in 420 women (mean age, 52.8 years) diagnosed via percutaneous biopsy from January 2015 to December 2019 were included. Mammographic characteristics were assessed based on imaging features (mammographically occult, mass/asymmetry/distortion, calcifications only, and combined mass/asymmetry/distortion with calcifications) and BI-RADS assessments. Routine pre-biopsy 4-view digital mammograms were analyzed using AI-CAD to obtain abnormality scores (AI-CAD score, ranging 0-100%). Multivariable logistic regression was performed to identify independent predictive mammographic variables after adjusting for clinicopathological variables. A subgroup analysis was performed with mammographically detected DCIS.

RESULTS

Of the 440 DCIS, 117 (26.6%) were upgraded to invasive cancer. Three hundred forty-one (77.5%) DCIS were detected on mammography. The multivariable analysis showed that combined features (odds ratio (OR): 2.225, p = 0.033), BI-RADS 4c or 5 assessments (OR: 2.473, p = 0.023 and OR: 5.190, p < 0.001, respectively), higher AI-CAD score (OR: 1.009, p = 0.007), AI-CAD score ≥ 50% (OR: 1.960, p = 0.017), and AI-CAD score ≥ 75% (OR: 2.306, p = 0.009) were independent predictors of invasive upgrade. In mammographically detected DCIS, combined features (OR: 2.194, p = 0.035), and higher AI-CAD score (OR: 1.008, p = 0.047) were significant predictors of invasive upgrade.

CONCLUSION

The AI-CAD score was an independent predictor of invasive upgrade for DCIS. Higher AI-CAD scores, especially in the highest quartile of ≥ 75%, can be used as an objective imaging biomarker to predict invasive upgrade in DCIS diagnosed with percutaneous biopsy.

CRITICAL RELEVANCE STATEMENT

Noninvasive imaging features including the quantitative results of AI-CAD for mammography interpretation were independent predictors of invasive upgrade in lesions initially diagnosed as ductal carcinoma in situ via percutaneous biopsy and therefore may help decide the direction of surgery before treatment.

KEY POINTS

• Predicting ductal carcinoma in situ upgrade is important, yet there is a lack of conclusive non-invasive biomarkers. • AI-CAD scores-raw numbers, ≥ 50%, and ≥ 75%-predicted ductal carcinoma in situ upgrade independently. • Quantitative AI-CAD results may help predict ductal carcinoma in situ upgrade and guide patient management.

Development of a Multi-Parametric ultrasonography nomogram for prediction of invasiveness in ductal carcinoma in situ

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.

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

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

Classification performance bias between training and test sets in a limited mammography dataset

OBJECTIVES

To assess the performance bias caused by sampling data into training and test sets in a mammography radiomics study.

METHODS

Mammograms from 700 women were used to study upstaging of ductal carcinoma in situ. The dataset was repeatedly shuffled and split into training (n = 400) and test cases (n = 300) forty times. For each split, cross-validation was used for training, followed by an assessment of the test set. Logistic regression with regularization and support vector machine were used as the machine learning classifiers. For each split and classifier type, multiple models were created based on radiomics and/or clinical features.

RESULTS

Area under the curve (AUC) performances varied considerably across the different data splits (e.g., radiomics regression model: train 0.58-0.70, test 0.59-0.73). Performances for regression models showed a tradeoff where better training led to worse testing and vice versa. Cross-validation over all cases reduced this variability, but required samples of 500+ cases to yield representative estimates of performance.

CONCLUSIONS

In medical imaging, clinical datasets are often limited to relatively small size. Models built from different training sets may not be representative of the whole dataset. Depending on the selected data split and model, performance bias could lead to inappropriate conclusions that might influence the clinical significance of the findings.

ADVANCES IN KNOWLEDGE

Performance bias can result from model testing when using limited datasets. Optimal strategies for test set selection should be developed to ensure study conclusions are appropriate.

Rates of Occult Invasive Disease in Patients With Biopsy-Proven Oral Cavity Squamous Cell Carcinoma in Situ

Importance

The likelihood that an oral cavity lesion harbors occult invasive disease after biopsy demonstrating carcinoma in situ (CIS) is unknown. While de-escalated treatment strategies may be appealing in the setting of CIS, knowing whether occult invasive disease may be present and its association with survival outcomes would lead to more informed management decisions.

Objective

To evaluate rate of occult invasive disease and clinical outcomes in patients with oral cavity CIS.

Design, Setting, and Participants

This was a retrospective population-based cohort study using the National Cancer Database and included adults with biopsy-proven oral cavity CIS as the first diagnosis of cancer between 2004 and 2020. Data were analyzed from October 10, 2022, to June 25, 2023.

Exposures

Surgical resection vs no surgery.

Main Outcomes and Measures

Analyses calculated the rate of occult invasive disease identified on resection of a biopsy-proven CIS lesion. Univariate and multivariate logistic regression with odds ratios and 95% CIs were used to identify significant demographic and clinical characteristics associated with risk of occult invasion (age, year of diagnosis, sex, race and ethnicity, oral cavity subsite, and comorbidity status). Kaplan-Meier curves for overall survival (OS) were calculated for both unresected and resected cohorts (stratified by presence of occult invasive disease).

Results

A total of 1856 patients with oral cavity CIS were identified, with 122 who did not undergo surgery (median [range] age, 65 [26-90] years; 48 female individuals [39.3%] and 74 male individuals [60.7%]) and 1458 who underwent surgical resection and had documented pathology (median [range] age, 62 [21-90] years; 490 female individuals [33.6%] and 968 male individuals [66.4%]). Of the 1580 patients overall, 52 (3.3%) were Black; 39 (2.5%), Hispanic; 1365 (86.4%), White; and 124 (7.8%), other, not specified. Among those who proceeded with surgery with documented pathology, 408 patients (28.0%) were found to have occult invasive disease. Higher-risk features were present in 45 patients (11.0%) for final margin positivity, 16 patients (3.9%) for lymphovascular invasion, 13 patients (3.2%) for high-grade invasive disease, and 14 patients (3.4%) for nodal involvement. For those patients with occult disease, staging according to the American Joint Committee on Cancer's AJCC Cancer Staging Manual, eighth edition, was pT1 in 341 patients (83.6%), pT2 in 41 (10.0%), and pT3 or pT4 disease in 26 (6.4%). Factors associated with greater odds of occult invasive disease at resection were female sex, Black race, and alveolar ridge, vestibule, and retromolar subsite. With median 66-month follow-up, 5-year OS was 85.9% in patients who proceeded with surgical resection vs 59.7% in patients who did not undergo surgery (difference, 26.2%; 95% CI, 19.0%-33.4%).

Conclusions and Relevance

This cohort study assessed the risk of concurrent occult invasion with biopsy-proven CIS of the oral cavity, demonstrating that 28.0% had invasive disease at resection. Reassuringly, even in the setting of occult invasion, high-risk disease features were rare, and 5-year OS was nearly 80% with resection. The findings support the practice of definitive resection if feasible following biopsy demonstrating oral cavity CIS.

Ultrafast MRI and T1 and T2 Radiomics for Predicting Invasive Components in Ductal Carcinoma in Situ Diagnosed With Percutaneous Needle Biopsy

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.

Prediction of Surgical Upstaging Risk of Ductal Carcinoma In Situ Using Machine Learning Models

Objective

The purpose of this study was to build machine learning models to predict surgical upstaging risk of ductal carcinoma in situ (DCIS) to invasive cancer and to compare model performance to eligibility criteria used by the Comparison of Operative versus Monitoring and Endocrine Therapy (COMET) active surveillance trial.

Methods

Medical records were retrospectively reviewed of all women with DCIS at core-needle biopsy who underwent surgery from 2007 to 2016 at an academic medical center. Multivariable regression and machine learning models were developed to evaluate upstaging-related features and their performance was compared with that achieved using the COMET trial eligibility criteria.

Results

Of 1387 women (mean age, 57 years; range, 27-89 years), the upstaging rate of DCIS was 17% (235/1387). On multivariable analysis, upstaging-associated features were presentation of DCIS as a palpable area of concern, imaging finding of a mass, and nuclear grades 2 or 3 at biopsy (P < 0.05). If COMET trial eligibility criteria were applied to our study cohort, then 496 women (42%, 496/1175) would have been eligible for the trial, with an upstaging rate of 12% (61/496). Of the machine learning models, none had a significantly lower upstaging rate than 12%. However, if using the models to determine eligibility, then a significantly larger proportion of women (56%-87%) would have been eligible for active surveillance.

Conclusion

Use of machine learning models to determine eligibility for the COMET trial identified a larger proportion of women eligible for surveillance compared with current eligibility criteria while maintaining similar upstaging rates.

Validation and Clinical Utility of a Prediction Model for the Risk of Upstaging to Invasive Breast Cancer After a Biopsy Diagnosis Ductal Carcinoma In Situ

BACKGROUND

This study aimed to validate the DCIS-upstage model, a previously developed model to predict the risk of upstaging to invasive breast cancer in patients with biopsy-proven ductal carcinoma in situ (DCIS) in a more recent cohort and to assess the model's clinical utility.

METHODS

The model was validated in a registry cohort (n = 2269) and in an institution cohort (n = 302). A calibration plot was made, followed by a decision curve analysis (DCA). The model's area under the curve (AUC) was compared with the AUC of another published model and with the AUCs of new models using the risk factors of the DCIS-upstage model and additional risk factors.

RESULTS

The DCIS-upstage model had an AUC of 0.67 at development; in the validation, the AUC was 0.65 in the registry cohort and 0.73 in the institution cohort. The DCA showed that the model has clinical utility. The other published model had an AUC of 0.66 in the institution cohort. Adding risk factors to the DCIS-upstage model slightly increased the AUC.

CONCLUSIONS

The DCIS-upstage prediction model is valid in other cohorts. The model has clinical utility and may be used to select patients with biopsy-proven DCIS for sentinel lymph node biopsy.

Usefulness of COL11A1 as a Prognostic Marker of Tumor Infiltration

BACKGROUND

Determining the infiltration of carcinomas is essential for the proper follow-up and treatment of cancer patients. However, it continues to be a diagnostic challenge for pathologists in multiple types of tumors. In previous studies (carried out in surgical specimens), the protein COL11A1 has been postulated as an infiltration marker mainly expressed in the extracellular matrix (ECM). We hypothesized that a differential expression of COL11A1 may exist in the peritumoral stroma of tumors that have acquired infiltrating properties and that it may be detected in the small biopsies usually available in normal clinical practice.

MATERIAL AND METHODS

In our study, we performed immunohistochemical staining in more than 350 invasive and noninvasive small samples obtained via core needle biopsy (CNB), colonoscopy, or transurethral resection of bladder tumor (TURBT) of breast, colorectal, bladder, and ovarian cancer.

RESULTS

Our results revealed that COL11A1 immunostaining had a sensitivity to classify the samples into infiltrative vs. noninfiltrative tumors of 94% (breast), 97% (colorectal), >90% (bladder), and 74% (ovarian); and a specificity of 97% (breast), 100% (colorectal), and >90% (bladder). In ovarian cancer, the negative predictive value (0.59) did not present improvement over the usual histopathological markers. In all samples tested, the cumulative sensitivity was 86% and the specificity 96% (p < 0.0001).

CONCLUSIONS

COL11A1-positive immunostaining in small biopsies of breast, colon, bladder and ovarian cancer is an accurate predictive marker of tumor infiltration that can be easily implemented in daily clinical practice.

Preoperative diagnosis of ductal carcinoma in situ of the breast over a 24-year period

AIMS

The method of diagnosis of ductal carcinoma in situ (DCIS) has changed since the 1980s. The aim of this audit was to assess changes in the preoperative diagnosis of DCIS since the introduction of needle core biopsy, particularly the proportion with a preoperative biopsy diagnosis of DCIS.

METHODS AND RESULTS

The preoperative diagnoses of patients with a final diagnosis of DCIS in the surgical specimen were reviewed (i) in 809 patients who presented through breast screening from 1997 to 2021, and (ii) in all patients in 5 individual years at 5-year intervals from 2000 to 2020 (254 in total). For screening-detected DCIS the proportion with a preoperative diagnosis of DCIS increased from 75% to 98% over the study period. In a detailed analysis of all cases of DCIS in 5 separate years the proportion with a preoperative diagnosis of DCIS increased from 68% in 2000 to 96% in 2020. For high-grade DCIS the proportion increased from 87% to 97%, and for low- or intermediate-grade DCIS from 48% to 93%. The proportion of women who had vacuum-assisted biopsy increased from 7% in 2000 to 58% in 2015. There was a small increase in the number of biopsies that had basal cytokeratin and oestrogen receptor immunohistochemistry to aid diagnosis.

CONCLUSION

There has been an increase in the preoperative diagnosis of DCIS, particularly of low- or intermediate-grade, over the last two decades. The increasing use of vacuum-assisted biopsy is likely to be a major contributory factor to this increase.

Frozen Sections in Decision-Making Regarding the Axillary Procedures in Breast Conserving Surgery for Intraductal Carcinoma at Preoperative Diagnosis

BACKGROUND

Axillary evaluation is unnecessary for pure ductal carcinoma in situ (DCIS); however, it is performed because of the risk of upstaging to invasive cancer. We assessed the role of intraoperative frozen section (IOF) biopsy in reducing invasive cancer upstaging and axillary evaluation in preoperative DCIS patients.

METHODS

We reviewed patients with preoperative DCIS who underwent breast-conserving surgery (BCS) with IOF biopsy. Positive IOF biopsy findings were defined as the presence of invasive or micro-invasive cancer. The IOF biopsy and permanent pathology findings were compared.

RESULTS

Seventy-eight patients underwent BCS with IOF biopsy. Six patients showed positive IOF biopsy findings; five of these patients showed concordant permanent pathology findings. Sentinel lymph node biopsy (SLNB) was positive in one patient. Thirteen patients with invasive breast cancer were missed by IOF biopsy; they underwent SLNB during the second surgery. None of them had metastatic lymph nodes. The sensitivity and specificity of IOF biopsy were 27.7% and 98.3%, respectively, with 82.1% accuracy. None of the other factors showed statistically significant relationships with the permanent pathology findings, except for the IOF biopsy findings.

CONCLUSION

IOF evaluation can aid in detecting the invasiveness of tumors in patients with preoperative DCIS.

Active Surveillance for Atypical Ductal Hyperplasia and Ductal Carcinoma In Situ

Atypical ductal hyperplasia (ADH) and ductal carcinoma in situ (DCIS) are relatively common breast lesions on the same spectrum of disease. Atypical ductal hyperblasia is a nonmalignant, high-risk lesion, and DCIS is a noninvasive malignancy. While a benefit of screening mammography is early cancer detection, it also leads to increased biopsy diagnosis of noninvasive lesions. Previously, treatment guidelines for both entities included surgical excision because of the risk of upgrade to invasive cancer after surgery and risk of progression to invasive cancer for DCIS. However, this universal management approach is not optimal for all patients because most lesions are not upgraded after surgery. Furthermore, some DCIS lesions do not progress to clinically significant invasive cancer. Overtreatment of high-risk lesions and DCIS is considered a burden on patients and clinicians and is a strain on the health care system. Extensive research has identified many potential histologic, clinical, and imaging factors that may predict ADH and DCIS upgrade and thereby help clinicians select which patients should undergo surgery and which may be appropriate for active surveillance (AS) with imaging. Additionally, multiple clinical trials are currently underway to evaluate whether AS for DCIS is feasible for a select group of patients. Recent advances in MRI, artificial intelligence, and molecular markers may also have an important role to play in stratifying patients and delineating best management guidelines. This review article discusses the available evidence regarding the feasibility and limitations of AS for ADH and DCIS, as well as recent advances in patient risk stratification.

Upgrade Rate of Ductal Carcinoma In Situ to Invasive Carcinoma and the Clinicopathological Factors Predicting the Upgrade Following a Mastectomy: A Retrospective Study

Background The rate of upgrading ductal carcinoma in situ (DCIS) to invasive cancer varies widely in the literature with no consensus regarding sentinel lymph node biopsy (SLNB) for DCIS; however, some guidelines do recommend it in the event of a mastectomy. The primary aim of this study was to determine the upgrade rate of DCIS to invasive carcinoma (IC) in patients undergoing mastectomy for DCIS and identify the clinicopathological predicting factors for the upgrade. The secondary aim was to determine the SLNB positivity rate. Methodology We retrospectively analysed consecutive patients with DCIS diagnosed through a biopsy who then underwent mastectomy over a 10-year period (2010 to 2020). Clinical, radiological, and histological variables were collected from medical records. Results We studied 143 women (mean age = 57.4 years, range = 26-85 years) who underwent mastectomy for DCIS identified on biopsy. Almost two-thirds (62.9%, 90/143) of the patients were detected on screening mammography, while 35.6% (51/143) were diagnosed following presentation with either an area of palpable concern or nipple discharge. The most common mammographic presentation of DCIS was calcification (83.9%, 120/143), and, in 85.9% of the patients, the mammographic lesion was more than 20 mm. High-grade DCIS was noted in 76.9% of preoperative biopsy results, while the rest was either low or intermediate-grade DCIS. Overall, 24.5% (35/143) were upgraded to IC (upgraded group) on postoperative histology, whereas 108/143 remained DCIS postoperatively (pure DCIS group). The positivity rate of SLNB was 4.8%. Multifocality was the only significant predictor of IC on multivariate analyses of clinicopathological predictors (odds ratio = 3.0, 95% confidence interval = 1.0-8.7). The presence of comedonecrosis was higher in the upgraded group compared to the pure DCIS group (42.9% vs. 27.8%), but this was not statistically significant. Conclusions In our study cohort, nearly one in four (24.5%) patients were upgraded from DCIS to IC on postoperative histology, with an SLNB positivity rate of 4.8%. This is important when counselling patients regarding the risk of coincident occult IC and the importance of SLNB at the time of mastectomy. Multifocality on preoperative imaging was the only significant predictive factor. Based on this result, we recommend that SLNB should also be considered if patients have multifocal DCIS and planned for oncoplastic breast-conserving surgery. However, further studies are required to investigate the association between multifocal DCIS and the risk of upgrading to IC.

A novel nomogram for the preoperative prediction of sentinel lymph node metastasis in breast cancer

BACKGROUND OR PURPOSE

A practical noninvasive method to identify sentinel lymph node (SLN) status in breast cancer patients, who had a suspicious axillary lymph node (ALN) at ultrasound (US), but a negative clinical physical examination is needed. To predict SLN metastasis using a nomogram based on US and biopsy-based pathological features, this retrospective study investigated associations between clinicopathological features and SLN status.

METHODS

Patients treated with SLN dissection at four centers were apportioned to training, internal, or external validation sets (n = 472, 175, and 81). Lymph node ultrasound and pathological characteristics were compared using chi-squared and t-tests. A nomogram predicting SLN metastasis was constructed using multivariate logistic regression models.

RESULTS

In the training set, statistically significant factors associated with SLN+ were as follows: histology type (p < 0.001); progesterone receptor (PR: p = 0.003); Her-2 status (p = 0.049); and ALN-US shape (p = 0.034), corticomedullary demarcation (CMD: p < 0.001), and blood flow (p = 0.001). With multivariate analysis, five independent variables (histological type, PR status, ALN-US shape, CMD, and blood flow) were integrated into the nomogram (C-statistic 0.714 [95% CI: 0.688-0.740]) and validated internally (0.816 [95% CI: 0.784-0.849]) and externally (0.942 [95% CI: 0.918-0.966]), with good predictive accuracy and clinical applicability.

CONCLUSION

This nomogram could be a direct and reliable tool for individual preoperative evaluation of SLN status, and therefore aids decisions concerning ALN dissection and adjuvant treatment.

High rate of postoperative upstaging of ductal carcinoma in situ when prioritizing ultrasound evaluation of mammography-detected lesions: a single-center retrospective cohort study

BACKGROUND

The initial diagnosis of ductal carcinoma in situ (DCIS) can be upstaged to invasive cancer after definitive surgery. This study aimed to identify risk factors for DCIS upstaging using routine breast ultrasonography and mammography (MG) and to propose a prediction model.

METHODS

In this single-center retrospective study, patients initially diagnosed with DCIS (January 2016-December 2017) were enrolled (final sample size = 272 lesions). Diagnostic modalities included ultrasound-guided core needle biopsy (US-CNB), MG-guided vacuum-assisted breast biopsy, and wire-localized surgical biopsy. Breast ultrasonography was routinely performed for all patients. US-CNB was prioritized for lesions visible on ultrasound. Lesions initially diagnosed as DCIS on biopsy with a final diagnosis of invasive cancer at definitive surgery were defined as "upstaged."

RESULTS

The postoperative upstaging rates were 70.5%, 9.7%, and 4.8% in the US-CNB, MG-guided vacuum-assisted breast biopsy, and wire-localized surgical biopsy groups, respectively. US-CNB, ultrasonographic lesion size, and high-grade DCIS were independent predictive factors for postoperative upstaging, which were used to construct a logistic regression model. Receiver operating characteristic analysis showed good internal validation (area under the curve = 0.88).

CONCLUSIONS

Supplemental screening breast ultrasonography possibly contributes to lesion stratification. The low upstaging rate for ultrasound-invisible DCIS diagnosed by MG-guided procedures suggests that it is unnecessary to perform sentinel lymph node biopsy for lesions invisible on ultrasound. Case-by-case evaluation of DCIS detected by US-CNB can help surgeons determine if repeating biopsy with vacuum-assisted breast biopsy is necessary or if sentinel lymph node biopsy should accompany breast-preserving surgery.

TRIAL REGISTRATION

This single-center retrospective cohort study was conducted with the approval of the institutional review board of our hospital (approval number 201610005RIND). As this was a retrospective review of clinical data, it was not registered prospectively.

Predicting Underestimation of Invasive Cancer in Patients with Core-Needle-Biopsy-Diagnosed Ductal Carcinoma In Situ Using Deep Learning Algorithms

The prediction of an occult invasive component in ductal carcinoma in situ (DCIS) before surgery is of clinical importance because the treatment strategies are different between pure DCIS without invasive component and upgraded DCIS. We demonstrated the potential of using deep learning models for differentiating between upgraded versus pure DCIS in DCIS diagnosed by core-needle biopsy. Preoperative axial dynamic contrast-enhanced magnetic resonance imaging (MRI) data from 352 lesions were used to train, validate, and test three different types of deep learning models. The highest performance was achieved by Recurrent Residual Convolutional Neural Network using Regions of Interest (ROIs) with an accuracy of 75.0% and area under the receiver operating characteristic curve (AUC) of 0.796. Our results suggest that the deep learning approach may provide an assisting tool to predict the histologic upgrade of DCIS and provide personalized treatment strategies to patients with underestimated invasive disease.

The Effect of Black Cohosh on Ki67 expression and Tumor Volume: A Pilot Study of Ductal Carcinoma in Situ Patients

BACKGROUND

Black cohosh (BC) (Cimicifuga racemosa) may prevent and treat breast cancer through anti-proliferative, pro-apoptotic, anti-estrogenic, and anti-inflammatory effects. This study sought to evaluate the effect of BC on tumor cellular proliferation, measured by Ki67 expression, in a pre-operative window trial of ductal carcinoma in situ (DCIS) patients.

METHODS

Patients were treated pre-operatively for 2 to 6 weeks with BC extract. Eligible subjects were those who had DCIS on core biopsy. Ki67 was measured using automated quantitative immunofluorescence (AQUA) pre/post-operatively. Ki67, tumor volume, and hormone changes were assessed with 2-sided Wilcoxon signed-rank tests, α = .05.

RESULTS

Thirty-one patients were treated for an average of 24.5 days (median 25; range 15-36). Ki67 decreased non-significantly (n = 26; P = .20; median pre-treatment 1280, post-treatment 859; range pre-treatment 175-7438, post-treatment 162-3370). Tumor volume, estradiol, and FSH did not change significantly. No grade 3 or 4 adverse events were reported.

CONCLUSIONS

BC use showed no significant impact on cellular proliferation, tumor volume, or invasive disease upgrade rates in DCIS patients. It was well-tolerated, with no observed significant toxicities. Further study is needed to elucidate BC's role in breast cancer treatment and prevention.ClinicalTrials.gov Identifier: NCT01628536https://clinicaltrials.gov/ct2/show/NCT01628536.

Prediction of the histologic upgrade of ductal carcinoma in situ using a combined radiomics and machine learning approach based on breast dynamic contrast-enhanced magnetic resonance imaging

Objective

To investigate whether support vector machine (SVM) trained with radiomics features based on breast magnetic resonance imaging (MRI) could predict the upgrade of ductal carcinoma in situ (DCIS) diagnosed by core needle biopsy (CNB) after surgical excision.

Materials and methods

This retrospective study included a total of 349 lesions from 346 female patients (mean age, 54 years) diagnosed with DCIS by CNB between January 2011 and December 2017. Based on histological confirmation after surgery, the patients were divided into pure (n = 198, 56.7%) and upgraded DCIS (n = 151, 43.3%). The entire dataset was randomly split to training (80%) and test sets (20%). Radiomics features were extracted from the intratumor region-of-interest, which was semi-automatically drawn by two radiologists, based on the first subtraction images from dynamic contrast-enhanced T1-weighted MRI. A least absolute shrinkage and selection operator (LASSO) was used for feature selection. A 4-fold cross validation was applied to the training set to determine the combination of features used to train SVM for classification between pure and upgraded DCIS. Sensitivity, specificity, accuracy, and area under the receiver-operating characteristic curve (AUC) were calculated to evaluate the model performance using the hold-out test set.

Results

The model trained with 9 features (Energy, Skewness, Surface Area to Volume ratio, Gray Level Non Uniformity, Kurtosis, Dependence Variance, Maximum 2D diameter Column, Sphericity, and Large Area Emphasis) demonstrated the highest 4-fold mean validation accuracy and AUC of 0.724 (95% CI, 0.619–0.829) and 0.742 (0.623–0.860), respectively. Sensitivity, specificity, accuracy, and AUC using the test set were 0.733 (0.575–0.892) and 0.7 (0.558–0.842), 0.714 (0.608–0.820) and 0.767 (0.651–0.882), respectively.

Conclusion

Our study suggested that the combined radiomics and machine learning approach based on preoperative breast MRI may provide an assisting tool to predict the histologic upgrade of DCIS.

Ductal carcinoma in situ of the male breast: clinical radiological features and management in a cancer referral center

Purpose

To present an overview of the management of male patients with Ductal Carcinoma In Situ of the breast (male DCIS).

Methods

We retrospectively studied all male patients with a diagnosis of pure DCIS from January 1999 to December 2018: 20 patients were identified in our cancer referral center. We collected data regarding clinical presentation, age of onset, radiological features, receptor status of the neoplasm, histological type, and the follow-up of those patients.

Results

The median age was 62 years (range 21–80). All patients underwent surgery, in 15/20 (75%) cases a mastectomy was carried out. Two patients (10%) underwent endocrine treatment and 1/20 (5%) underwent radiotherapy. The receptor status for 15/20 patients was documented: 13/15 patients were ER+/Pr+. In 3 cases the Ki 67% was positive (i.e., > 20%). All cases were negative for Her2. The median follow-up time was 9.0 years (IQR 4.0–13.7). Only one patient had an ipsilateral recurrence with the finding of an infiltrating carcinoma in the same breast after 14 years. The 5-year disease-free survival was 92.9%.

Conclusion

Pure DCIS in men is an extremely rare disease: proper diagnosis and management allow an excellent prognosis.

Does pre-operative breast MRI have an impact on surgical outcomes in high-grade DCIS?

OBJECTIVES

High-grade DCIS (HG DCIS) is associated with upgrade to invasive disease but few studies evaluate the role of MRI in this subset of DCIS. This study compared surgical outcomes of females with HG DCIS on biopsy who had pre-operative MRI with those that proceeded directly to surgery.

METHODS

This single-centre retrospective, observational study identified patients with pure HG DCIS on pre-operative biopsy from the pathology database. Surgical outcomes, clinicopathological and radiological features were obtained for all patients.

RESULTS

From August 2015 to February 2020, 217 patients had HG DCIS on biopsy. Pre-operative MRI was performed in 40 (MRI group) and not in 88 (No MRI group) patients. Initial mastectomy was performed in 25/40 (63%) women in the MRI group and 20/88 (23%) women in the no MRI group (p < 0.0001). No difference was observed in re-operation rate between the two groups, 15% in MRI group vs 22% in No MRI group (p = 0.4749). Mean tumour size on histology was larger in mastectomy cases in the MRI group (73.4 mm, range 6-140 mm), than the total MRI group, (58.3 mm, range 0-140 mm) or no MRI group (30.7 mm, range 0-130 mm) (p < 0.0001).

CONCLUSIONS

Pre-operative MRI in HG DCIS is associated with higher mastectomy rates, possibly due to patient selection for MRI, as tumours on final histology were significantly larger. Fewer re-operations were observed in the MRI group although this was not significant.

ADVANCES IN KNOWLEDGE

Breast MRI performed pre-operatively in HG DCIS is associated with higher mastectomy rates and fewer re-operation rates.

Quantitative classification of invasive and noninvasive breast cancer using dynamic magnetic resonance imaging of the mammary gland

Objectives

Breast cancers are classified as invasive or noninvasive based on histopathological findings. Although time-intensity curve (TIC) analysis using magnetic resonance imaging (MRI) can differentiate benign from malignant disease, its diagnostic ability to quantitatively distinguish between invasive and noninvasive breast cancers has not been determined. In this study, we evaluated the ability of TIC analysis of dynamic MRI data (MRI-TIC) to distinguish between invasive and noninvasive breast cancers.

Material and Methods

We collected and analyzed data for 429 cases of epithelial invasive and noninvasive breast carcinomas. TIC features were extracted in washout areas suggestive of malignancy.

Results

The graph determining the positive diagnosis rate for invasive and noninvasive cases revealed that the cut-off θ_i/ni value was 21.6° (invasive: θ_w > 21.6°, noninvasive: θ_w ≤ 21.6°). Tissues were classified as invasive or noninvasive using this cut-off value, and each result was compared with the histopathological diagnosis. Using this method, the accuracy of tissue classification by MRI-TIC was 88.6% (380/429), which was higher than that using ultrasound (73.4%, 315/429).

Conclusion

MRI-TIC is effective for the classification of invasive vs. noninvasive breast cancer.

The Role of Surgical Axillary Staging Prior to Immediate Breast Reconstruction in the Era of De-Escalation of Axillary Management in Early Breast Cancer

Postmastectomy radiotherapy (PMRT) following immediate breast reconstruction (IBR) is associated with postoperative complications. Although the incidence of node-positive breast cancer is declining, a separate sentinel lymph node biopsy (SLNB) is still performed before mastectomy when IBR is planned, in order to evaluate nodal status and the need for PMRT. This study assessed the impact of staged SLNB on the breast reconstructive planning, and presents common clinicopathological characteristics of breast cancer with macrometastatic nodal spread where staged SLNB would be beneficial to indicate PMRT. Medical records of breast cancer patients scheduled for mastectomy and IBR at Skåne University Hospital, Sweden, from November 2014 to February 2020, were reviewed. Of 92 patients, node-positive disease was present in 15 (16%). Fifty-three patients underwent staged SLNB before mastectomy and IBR, and 10 (19%) presented with nodal metastasis. All patients with macrometastatic sentinel nodes were presented with palpable, multifocal, ER+ breast carcinoma of no special type with tumor size > 17.0 mm. Overall, four women received PMRT after verified metastasis by staged SLNB, and IBR was cancelled for three patients. These findings question the benefit of routine staged SLNB before mastectomy and IBR in breast cancer populations within established mammography screening programs with low risk of nodal metastasis.

Overuse of Axillary Surgery in Patients with Ductal Carcinoma In Situ: Opportunity for De-escalation

BACKGROUND

Ductal carcinoma in situ (DCIS) is noninvasive breast cancer and therefore nodal staging is not routinely recommended. We evaluated the use of and factors associated with axillary surgery in DCIS in the National Cancer Database (NCDB).

METHODS

DCIS cases were identified from the NCDB 2012-2018. Use of axillary surgery was evaluated over time, and factors associated with axillary surgery were assessed for breast-conserving surgery (BCS) and mastectomy groups.

RESULTS

We identified 178,762 patients, median age of 60 years. Majority of DCIS (87%) was ER-positive, and 14% low, 43% intermediate, and 44% high grade. Median DCIS size was 1.1 cm. BCS was performed in 72%, whereas 28% had mastectomy. Overall axillary surgery was performed in 38% and was higher in patients undergoing mastectomy compared with patients undergoing BCS (88% vs. 19%, p < 0.001). At axillary surgery, the vast majority (92%) had 1-5 nodes examined, whereas 8% had >5 nodes examined. Over time, axillary surgery decreased in BCS patients (21% in 2012 to 17% in 2018, p < 0.001) but increased slightly in mastectomy patients (86% in 2012 to 90% in 2018, p < 0.001). On multivariable analysis, factors significantly associated with axillary surgery were younger patient age, larger tumor size, higher grade, and ER-negative status.

CONCLUSIONS

Factors associated with axillary surgery reflect higher risk disease for upstage to invasive cancer, indicating surgeon judgment. However, despite axillary surgery being overtreatment of DCIS, it is common in mastectomy and is performed for one in five patients undergoing BCS. This provides opportunity for improvement in breast cancer care delivery.

Diffusion-weighted Breast MRI in Prediction of Upstaging in Women with Biopsy-proven Ductal Carcinoma in Situ

Background Accurate preoperative prediction of upstaging in women with biopsy-proven ductal carcinoma in situ (DCIS) is important for surgical planning, but published models using predictive MRI features remain lacking. Purpose To develop and validate a predictive model based on preoperative breast MRI to predict upstaging in women with biopsy-proven DCIS and to select high-risk women who may benefit from sentinel lymph node biopsy at initial surgery. Materials and methods Consecutive women with biopsy-proven DCIS who underwent preoperative 3.0-T breast MRI including dynamic contrast-enhanced (DCE) MRI and diffusion-weighted imaging (DWI) and who underwent surgery between June 2019 and March 2020 were retrospectively identified (development set) from an academic medical center. The apparent diffusion coefficients of lesions from DWI, lesion size and morphologic features on DCE MRI scans, mammographic findings, age, symptoms, biopsy method, and DCIS grade at biopsy were collected. The presence of invasive cancer and axillary metastases was determined with surgical pathology. A predictive model for upstaging was developed by using multivariable logistic regression and validated in a subsequent prospective internal validation set recruited between July 2020 and April 2021. Results Fifty-seven (41%) of 140 women (mean age, 53 years ± 11 [SD]) in the development set and 43 (41%) of 105 women (mean age, 53 years ± 10) in the validation set were upstaged after surgery. The predictive model combining DWI and clinical-pathologic factors showed the areas under the receiver operating characteristic curve at 0.87 (95% CI: 0.80, 0.92) in the development set and 0.76 (95% CI: 0.67, 0.84) in the validation set. The predicted probability of invasive cancer showed good interobserver agreement (intraclass correlation coefficient, 0.79); the positive predictive value was 85% (28 of 33), and the negative predictive value was 92% (22 of 24). Conclusion A predictive model based on diffusion-weighted breast MRI identified women at high risk of upstaging. © RSNA, 2022 Online supplemental material is available for this article See also the editorial by Baltzer in this issue.

Oncological Safety of Skin-Sparing Mastectomy and Immediate Breast Reconstruction in Extensive Ductal Carcinoma In Situ

INTRODUCTION

Skin-sparing mastectomy (SSM) with immediate breast reconstruction is the ideal treatment for interested and suitable patients with extensive ductal carcinoma in situ (DCIS). There is no guideline to indicate on how large DCIS the procedure can be performed safely. The primary target of this study was to define the oncological safety of SSM in extensive pure DCIS. The secondary target was to find predictive factors for DCIS upstaging to invasive disease.

MATERIALS AND METHODS

A total of 71 consecutive patients with extensive pure DCIS and undergoing SSM with immediate latissimus dorsi (LD) breast reconstruction were retrospectively evaluated.

RESULTS

The median size of DCIS lesion in preoperative imaging was 60 mm, the median weight of mastectomy specimen was 350 g, and the median resection margin (RM) was 2.0 mm. A total of 20 patients (28%) had an RM less than 0.5 mm and nine patients (13%) had ink positive margins. Six patients having positive RM underwent reoperation. A total of 29 patients (41%) presented invasive cancer foci in final histopathological assessment and nine patients (13%) had an axillary metastasis. Adjuvant therapy was given to 23 patients presenting invasive cancer. There were no local recurrences or distant metastases (0%, 95% confidence interval 0-0.051) during the mean follow-up of 71 mo. None of the factors evaluated predicted upstaging to invasive disease.

CONCLUSIONS

SSM with immediate breast reconstruction in patients with extensive DCIS is oncologically safe even when the margins are close or positive. Additional invasive foci and solitary axillary lymph node metastases are frequent but do not worsen the outcome.

Surgeons’ preferences for using sentinel lymph node biopsy in patients with ductal carcinoma in situ

Background

There is a large variation between Dutch hospitals in the use of Sentinel Lymph Node Biopsy (SLNB) in patients with a biopsy diagnosis of Ductal Carcinoma in Situ. The aim of our study was to investigate whether this variation might be explained by preferences of surgeons, organisational factors or the influence of patients preferences.

Methods

A cross-sectional web survey was conducted among 260 Dutch oncological/breast surgeons. Preferences of surgeons and the influence of the patients’ preferences were determined by means of best-worst scaling (BWS) of profile case scenarios and by ranking risk factors. The survey also explored organisational questions, the reported use of diagnostic techniques and influences on the decision.

Results

The BWS scenarios were completed by 57 surgeons. The most important reasons for performing SLNB were a suspected invasive component and DCIS grade 3. In the ranking, these were also the first and second most important factor, followed by the size of the lesion and a mass on mammogram. In 58% to 70% of the scenarios, the surgeons would not change their decisions on the use of SLNB if the patient’s chose differed. No organisational factor was significantly associated with the reported use of SLNB.

Conclusion

The inter-hospital variation in the use of SLNB could not be attributed to organisational factors or surgeons’ preferences for risk factors. The risk factors that most surgeons reported as reasons for performing SLNB are consistent with the factors described in the Dutch treatment guideline for the use of SLNB.

Breast Cancer, Version 3.2022, NCCN Clinical Practice Guidelines in Oncology

The therapeutic options for patients with noninvasive or invasive breast cancer are complex and varied. These NCCN Clinical Practice Guidelines for Breast Cancer include recommendations for clinical management of patients with carcinoma in situ, invasive breast cancer, Paget disease, phyllodes tumor, inflammatory breast cancer, and management of breast cancer during pregnancy. The content featured in this issue focuses on the recommendations for overall management of ductal carcinoma in situ and the workup and locoregional management of early stage invasive breast cancer. For the full version of the NCCN Guidelines for Breast Cancer, visit NCCN.org.

Is conservative management of ductal carcinoma in situ risky?

Nonsurgical management of ductal carcinoma in situ is controversial and little is known about the long-term consequences of this approach. In this study, we aimed to determine the risk of (a) upstaging to invasive carcinoma at excision and (b) ipsilateral breast cancer events in patients who might have been eligible for nonsurgical management of DCIS trials. Data from women aged 20 years or older with a biopsy diagnosis of DCIS between January 1, 2010 to December 31, 2014 were collated. The women underwent biopsy and surgical resection (lumpectomy or mastectomy) and were treated with radiation or endocrine therapy as per treating physicians’ choice. The development of ipsilateral breast cancer events (IBEs) was analyzed in patients with at least 5 years of follow-up after standard of care therapy for DCIS. Subset-analysis was undertaken to identify the incidence of IBEs in patients eligible for nonsurgical management trials. The study population consisted of 378 patients with matched cases of biopsy and surgical excision. The overall upstaging rate to IBC was 14.3 and 12.9% for COMET, 8.8% for LORIS, and 10.7% for LORD trial “eligible” patients. At 5 years of follow-up, ~11.5% of overall and trial eligible patients developed IBEs of which approximately half were invasive IBEs. In conclusion, women with DCIS who would have been eligible for nonsurgical management trials have a significantly high risk of developing ipsilateral breast events within 5 years of diagnosis. Better selection criteria are needed to identify DCIS patients who are at very low risk for the development of IBC.

Sentinel Lymph Node Methods in Breast Cancer

Breast cancer is the most frequent cancer diagnosed in women worldwide. Accurate lymph node staging is essential for both prognosis (of early-stage disease) and treatment (for regional control of disease) in patients with breast cancer. The sentinel lymph nodes are the regional nodes that directly drain lymph from the primary tumor. No imaging modality is accurate enough to detect lymph node metastases when a primary breast cancer is at an early stage (I or II), but sentinel lymph node biopsy is a highly reliable method for screening axillary nodes and for identifying metastatic (including micro-metastatic) disease in regional lymph nodes. Despite the widespread use of sentinel lymph node biopsy for early-stage breast cancer, relevant variations have been described regarding practical aspects of the procedure, and some variability has initially been reported regarding the rates of intraoperative sentinel lymph node identification and of false-negative findings, most likely because of differences in the size of the populations being investigated and in lymphatic mapping techniques. Nevertheless, using adequate learning curves and once a multidisciplinary team is experienced with the procedure, improved levels of accuracy are achieved.

US, Mammography, and Histopathologic Evaluation to Identify Low Nuclear Grade Ductal Carcinoma in Situ

Background Low nuclear grade ductal carcinoma in situ (DCIS) identified at biopsy can be upgraded to intermediate to high nuclear grade DCIS at surgery. Methods that confirm low nuclear grade are needed to consider nonsurgical approaches for these patients. Purpose To develop a preoperative model to identify low nuclear grade DCIS and to evaluate factors associated with low nuclear grade DCIS at biopsy that was not upgraded to intermediate to high nuclear grade DCIS at surgery. Materials and Methods In this retrospective study, 470 women (median age, 50 years; interquartile range, 44-58 years) with 477 pure DCIS lesions at surgical histopathologic evaluation were included (January 2010 to December 2015). Patients were divided into the training set (n = 330) or validation set (n = 147) to develop a preoperative model to identify low nuclear grade DCIS. Features at US (mass, nonmass) and at mammography (morphologic characteristics, distribution of microcalcification) were reviewed. The upgrade rate of low nuclear grade DCIS was calculated, and multivariable regression was used to evaluate factors for associations with low nuclear grade DCIS that was not upgraded later. Results A preoperative model that included lesions manifesting as a mass at US without microcalcification and no comedonecrosis at biopsy was used to identify low nuclear grade DCIS, with a high area under the receiver operating characteristic curve of 0.97 (95% CI: 0.94, 1.00) in the validation set. The upgrade rate of low nuclear grade DCIS at biopsy was 38.8% (50 of 129). Ki-67 positivity (odds ratio, 0.04; 95% CI: 0.0003, 0.43; P = .005) was inversely associated with constant low nuclear grade DCIS. Conclusion The upgrade rate of low nuclear grade ductal carcinoma in situ (DCIS) at biopsy to intermediate to high nuclear grade DCIS at surgery occurred in more than a third of patients; low nuclear grade DCIS at final histopathologic evaluation could be identified if the mass was viewed at US without microcalcifications and had no comedonecrosis at histopathologic evaluation of biopsy. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Rahbar in this issue.

Pathological underestimation and biomarkers concordance rates in breast cancer patients diagnosed with ductal carcinoma in situ at preoperative biopsy

Ductal carcinoma in situ (DCIS) often upgrade to invasive breast cancer at surgery. The current study aimed to identify factors associated with pathological underestimation and evaluate concordance rates of biomarkers between biopsy and surgery. Patients diagnosed with DCIS at needle biopsy from 2009 to 2020 were retrospectively reviewed. Univariate and multivariate analyses were performed to identify factors associated with pathological underestimation. Concordance rates between paired biopsy samples and surgical specimens were evaluated. A total of 735 patients with pure DCIS at biopsy were included, and 392 patients (53.3%) underwent pathological underestimation at surgery. Multivariate analysis demonstrated that tumor size > 5.0 cm [odds ratio (OR) 1.79], MRI BI-RADS ≥ 5 categories (OR 2.03), and high nuclear grade (OR 2.01) were significantly associated with pathological underestimation. Concordance rates of ER, PR, HER2 status and Ki-67 between biopsy and surgery were 89.6%, 91.9%, 94.8%, and 76.4% in lesions without pathological underestimation, and were 86.4%, 93.2%, 98.2% and 76.3% for in situ components in lesions with pathological underestimation. Meanwhile, in situ components and invasive components at surgery had concordance rates of 92.9%, 93.8%, 97.4%, and 86.5% for those biomarkers, respectively. In conclusion, lesions diagnosed as DCIS at biopsy have a high rate of pathological underestimation, which was associated with larger tumor size, higher MRI BI-RADS category, and higher nuclear grade. High concordances were found in terms of ER, PR, and HER2 status evaluation between biopsy and surgery, regardless of the pathological underestimation.

Updates on treating ductal carcinoma in situ: what's to know in 2021

PURPOSE OF REVIEW

Ductal carcinoma in situ (DCIS) is a noninvasive stage of disease but understood to be a nonobligate precursor to invasive breast cancer. As such, women with DCIS are routinely recommended for standard breast cancer treatment to prevent progression to invasive disease. DCIS, however, represents a heterogeneous group of lesions that differs in its biologic behavior and risk of progression. Thus, optimal treatment is unclear. This review presents the clinical trials evaluating the de-escalation of therapy, attempts at risk stratification, and future directions in the management of this disease.

RECENT FINDINGS

The de-escalation of therapy for patients with DCIS is being actively explored. Although no group of patients based on clinicopathologic features has yet been identified as suitable for omission of therapy, molecular tests appear better able to stratify patients at low risk for whom omission of radiation may be considered. Trials considering omission of surgery are ongoing, and the use of Herceptin and vaccine therapy are also being explored.

SUMMARY

The current review provides a centralized summary enabling the clinician to better understand the complexity of DCIS and the controversies over the optimal management of this disease. It highlights the need for better risk stratification to individualize patient care.

VIDEO ABSTRACT

http://links.lww.com/COOG/A77.

Apparent Diffusion Coefficient-Based Convolutional Neural Network Model Can Be Better Than Sole Diffusion-Weighted Magnetic Resonance Imaging to Improve the Differentiation of Invasive Breast Cancer From Breast Ductal Carcinoma In Situ

BACKGROUND AND PURPOSE

Breast ductal carcinoma in situ (DCIS) has no metastatic potential, and has better clinical outcomes compared with invasive breast cancer (IBC). Convolutional neural networks (CNNs) can adaptively extract features and may achieve higher efficiency in apparent diffusion coefficient (ADC)-based tumor invasion assessment. This study aimed to determine the feasibility of constructing an ADC-based CNN model to discriminate DCIS from IBC.

METHODS

The study retrospectively enrolled 700 patients with primary breast cancer between March 2006 and June 2019 from our hospital, and randomly selected 560 patients as the training and validation sets (ratio of 3 to 1), and 140 patients as the internal test set. An independent external test set of 102 patients during July 2019 and May 2021 from a different scanner of our hospital was selected as the primary cohort using the same criteria. In each set, the status of tumor invasion was confirmed by pathologic examination. The CNN model was constructed to discriminate DCIS from IBC using the training and validation sets. The CNN model was evaluated using the internal and external tests, and compared with the discriminating performance using the mean ADC. The area under the curve (AUC), sensitivity, specificity, and accuracy were calculated to evaluate the performance of the previous model.

RESULTS

The AUCs of the ADC-based CNN model using the internal and external test sets were larger than those of the mean ADC (AUC: 0.977 vs. 0.866, P = 0.001; and 0.926 vs. 0.845, P = 0.096, respectively). Regarding the internal test set and external test set, the ADC-based CNN model yielded sensitivities of 0.893 and 0.873, specificities of 0.929 and 0.894, and accuracies of 0.907 and 0.902, respectively. Regarding the two test sets, the mean ADC showed sensitivities of 0.845 and 0.818, specificities of 0.821 and 0.829, and accuracies of 0.836 and 0.824, respectively. Using the ADC-based CNN model, the prediction only takes approximately one second for a single lesion.

CONCLUSION

The ADC-based CNN model can improve the differentiation of IBC from DCIS with higher accuracy and less time.

Influence of rurality on lymph node assessment among women diagnosed with ductal carcinoma in situ and treated with mastectomy, SEER 2000-2015

PURPOSE

Despite recommendations from national organizations supporting the use of lymph node assessment (LNA) among patients with ductal carcinoma in situ (DCIS) at time of mastectomy, variation in practice patterns across the United States has been observed. However, few studies have evaluated LNA differences and rurality.

METHODS

Data from the SEER Patterns of Care studies were used to identify women who underwent mastectomy for newly diagnosed DCIS during 2000, 2005, 2010, and 2015. Weighted multivariate logistic regression was used to evaluate the association between rural-urban residence and the use of LNA. A subgroup analysis was performed comparing the use of axillary lymph node dissection (ALND) versus sentinel lymph node biopsy (SLNB).

RESULTS

Of the 504 patients included in the analysis, approximately 81% underwent LNA at time of mastectomy with lower rates of use observed among rural patients (66%) versus urban patients (82%). In multivariate analysis, LNA increased over time (p < 0.0001), and rural patients were less likely to receive LNA compared to urban patients [adjusted odds ratio (aOR) = 0.19; 95% confidence interval (CI) 0.06-0.66]. However, the likelihood of undergoing ALND relative to SLNB was lower among rural compared to urban patients (aOR = 0.16; 95% CI 0.03-0.73).

CONCLUSIONS

Over time, the use of LNA with mastectomy has increased among DCIS patients. However, significant rural-urban differences in the use and type of LNA persist. The findings of this study highlight the importance of continued research aimed at examining the impact of rurality on the receipt of high-quality cancer care.

Unresected screen-detected ductal carcinoma in situ: Outcomes of 311 women in the Forget-Me-Not 2 study

BACKGROUND AND AIM

The natural history of ductal carcinoma in situ (DCIS) is poorly understood. The aim of this cohort study was to determine the outcomes of women who had no surgery for screen-detected DCIS in the 6 months following diagnosis.

METHODS

English breast screening databases were retrospectively searched for women diagnosed with DCIS without invasive cancer at screening and who had no record of surgery within 6 months of diagnosis. These were cross-referenced with cancer registry data. Details of the potentially eligible women were sent to the relevant breast screening units for verification and for completion of data forms detailing clinical, radiological and pathological findings, non-surgical treatment and subsequent clinical course.

RESULTS

Data for 311 eligible women (median age 62 years) were available. 60 women developed invasive cancer, 56 ipsilateral and 4 contralateral. Ipsilateral invasion risk increased approximately linearly with time for at least 10 years. The 10-year cumulative risk of ipsilateral invasion was 9% (95% CI 4-21%), 39% (24-58%) and 36% (24-50%) for low, intermediate and high grade DCIS respectively and was higher in younger women, in those with larger DCIS lesions and in those with microinvasion. Most invasive cancers that developed were grade 2 or 3.

CONCLUSION

The findings suggest that active surveillance may be a reasonable alternative to surgery in patients with low grade DCIS but that women with intermediate or high grade disease should continue to be offered surgery. This highlights the importance of reproducible grading of DCIS to ensure patients receive appropriate treatment.

Preneoplastic Low-Risk Mammary Ductal Lesions (Atypical Ductal Hyperplasia and Ductal Carcinoma In Situ Spectrum): Current Status and Future Directions

Intraepithelial mammary ductal neoplasia is a spectrum of disease that varies from atypical ductal hyperplasia (ADH), low-grade (LG), intermediate-grade (IG), to high-grade (HG) ductal carcinoma in situ (DCIS). While ADH has the lowest prognostic significance, HG-DCIS carries the highest risk. Due to widely used screening mammography, the number of intraepithelial mammary ductal neoplastic lesions has increased. The consequence of this practice is the increase in the number of patients who are overdiagnosed and, therefore, overtreated. The active surveillance (AS) trials are initiated to separate lesions that require active treatment from those that can be safely monitored and only be treated when they develop a change in the clinical/radiologic characteristics. At the same time, the natural history of these lesions can be evaluated. This review aims to evaluate ADH/DCIS as a spectrum of intraductal neoplastic disease (risk and histomorphology); examine the controversies of distinguishing ADH vs. DCIS and the grading of DCIS; review the upgrading for both ADH and DCIS with emphasis on the variation of methods of detection and the definitions of upgrading; and evaluate the impact of all these variables on the AS trials.

Ductal carcinoma in situ: a risk prediction model for the underestimation of invasive breast cancer

Patients with a biopsy diagnosis of ductal carcinoma in situ (DCIS) may be diagnosed with invasive breast cancer after excision. We evaluated the preoperative clinical and imaging predictors of DCIS that were associated with an upgrade to invasive carcinoma on final pathology and also compared the diagnostic performance of various statistical models. We reviewed the medical records; including mammography, ultrasound (US), and magnetic resonance imaging (MRI) findings; of 644 patients who were preoperatively diagnosed with DCIS and who underwent surgery between January 2012 and September 2018. Logistic regression and three machine learning methods were applied to predict DCIS underestimation. Among 644 DCIS biopsies, 161 (25%) underestimated invasive breast cancers. In multivariable analysis, suspicious axillary lymph nodes (LNs) on US (odds ratio [OR], 12.16; 95% confidence interval [CI], 4.94-29.95; P < 0.001) and high nuclear grade (OR, 1.90; 95% CI, 1.24-2.91; P = 0.003) were associated with underestimation. Cases with biopsy performed using vacuum-assisted biopsy (VAB) (OR, 0.42; 95% CI, 0.27-0.65; P < 0.001) and lesion size <2 cm on mammography (OR, 0.45; 95% CI, 0.22-0.90; P = 0.021) and MRI (OR, 0.29; 95% CI, 0.09-0.94; P = 0.037) were less likely to be upgraded. No significant differences in performance were observed between logistic regression and machine learning models. Our results suggest that biopsy device, high nuclear grade, presence of suspicious axillary LN on US, and lesion size on mammography or MRI were independent predictors of DCIS underestimation.