Do Eligibility Criteria for Ductal Carcinoma In Situ (DCIS) Active Surveillance Trials Identify Patients at Low Risk for Upgrade to Invasive Carcinoma?

A retrospective study evaluating surgical upstaging rates in low-risk DCIS patients meeting the eligibility criteria for active surveillance trials

BACKGROUND

The management of small low-to-medium grade ductal carcinoma in situ (DCIS) on core biopsy remains controversial. Four international studies are currently recruiting highly selected low-risk DCIS patients to compare active surveillance ( ± hormonal treatment) versus conventional treatment. In this study, we aim to determine the upstaging rate at a tertiary center among low-risk DCIS patients meeting eligibility criteria for active surveillance trials.

METHODS

A retrospective study was undertaken of all patients diagnosed with small (<2 cm) low-medium grade DCIS patients at the European Institute of Oncology, Milan, from 2009 to 2019. All cases were classified as eligible based on the COMET, LORIS, LORD and LORETTA DCIS studies, according to their respective inclusion criteria.

RESULTS

We identified 351 patients from a prospectively maintained database who were diagnosed with G1-G2 DCIS on core biopsy, with a median age of 55 years (range 45-82). The overall upstage/upgrade rate was 23.6 %. Of the 351 patients, sixty-four (18.2 %) were upstaged to invasive disease and nine-teen (5.4 %) were upgraded to grade 3 DCIS. It is worth noting a rate of 7.9 % of patients with >pT1c and 2.3 % of patients with nodal involvement at the time of surgery. On both univariable and multivariable analysis, no specific variable was found to be a statistically significant predictor for upstaging.

CONCLUSION

Over 23 % of patients with low-risk DCIS may be upgraded or upstaged at resection, especially towards invasive carcinoma (18.2 % of cases were staged to invasive cancer at surgical resection). These data suggest that active surveillance is not warranted in this highly selected group of low-risk DCIS patients. Stricter selection criteria must be considered to ensure appropriate treatment of such patients.

Proportion of Patients With Ductal Carcinoma In Situ That Qualify for Observation Criteria Set Forth by Clinical Trials

BACKGROUND

The COMET, LORD, and LORIS clinical trials are investigating the role of active surveillance in low-risk ductal carcinoma in situ (DCIS). The objective of this study was to identify the proportion of patients eligible for these trials amongst a cohort of patients treated at our institution.

METHODS

Retrospective chart review was performed of patients diagnosed with DCIS who were treated from 2013 to 2022. Clinical, tumor, and imaging inclusion and exclusion criteria of the aforementioned observation trials were applied to determine the proportion of patients eligible for each trial. Upgrade rate to invasive cancer were examined across all three groups.

RESULTS

Of 1223 patients diagnosed with DCIS, applying the criteria of each trial, 245 (20%), 238 (19.4%), and 264 (21.6%) patients were eligible for the COMET, LORD, and LORIS trials, respectively. High-grade DCIS and mass on imaging had the largest impact on exclusion. Nineteen (7.8%) of women who qualified for COMET were upgraded to invasive disease at excision, compared to 18 (7.6%) for LORD, and 19 (7.2%) for LORIS.

CONCLUSIONS

One in five patients diagnosed with DCIS at our institution would qualify for observation with current trial eligibility. Observation of DCIS may have limited impact on all DCIS patients.

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.

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.

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.

Surgical De-Escalation for Re-Excision in Patients with a Margin Less Than 2 mm and a Diagnosis of DCIS

The current surgical guidelines recommend an optimal margin width of 2 mm for the management of patients diagnosed with ductal carcinoma in situ (DCIS). However, there are still many controversies regarding re-excision when the optimal margin criteria are not met in the first resection. The purpose of this study is to understand the importance of surgical margin width, re-excision, and treatments to avoid additional surgery on locoregional recurrence (LRR). The study is retrospective and analyzed surgical margins, adjuvant treatments, re-excision, and LRR in patients with DCIS who underwent breast-conserving surgery (BCS). A total of 197 patients were enrolled. Re-operation for a close margin rate was 13.5%, and the 3-year recurrence was 7.6%. No difference in the LRR was reported among the patients subjected to BCS regardless of the margin width (p = 0.295). The recurrence rate according to margin status was not significant (p = 0.484). Approximately 36.9% (n: 79) patients had resection margins < 2 mm. A sub-analysis of patients with margins < 2 mm showed no difference in the recurrence between the patients treated with a second surgery and those treated with radiation (p = 0.091). The recurrence rate according to margin status in patients with margins < 2 mm was not significant (p = 0.161). The margin was not a predictive factor of LRR p = 0.999. Surgical re-excision should be avoided in patients with a focally positive margin and no evidence of the disease at post-surgical imaging.

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.

Ipsilateral tumor recurrence risk in women with ductal carcinoma in situ: application of the Van Nuys Prognostic Index and the Memorial Sloan Kettering Cancer Center nomogram

PURPOSE

To apply the Van Nuys Prognostic Index (VNPI) and the Memorial Sloan Kettering Cancer Center (MSKCC) ductal carcinoma in situ (DCIS) nomogram to DCIS patients with known long-term outcomes.

METHODS

A retrospective review was performed of consecutive patients diagnosed with DCIS from 2007 to 2014. Included patients underwent breast-conserving surgery (BCS) and were followed with imaging for at least five years. For each patient, the VNPI and MSKCC nomogram risk estimates were determined. In addition, variables used in both models were compared between women with and without recurrences using the Wilcoxon signed-rank test and the Pearson's chi-squared test.

RESULTS

Over the eight-year period, 456 women (average age 57 years, range 30-87) underwent BCS for DCIS. Thirty-one (6.8%) experienced an ipsilateral recurrence. The average VNPI scores were 7 (range 5-9) and 7 (range 4-10) for women with and without a recurrence (p = 0.14), respectively, with 4-6, 7-9, and 10-12 being the low, moderate, and high-risk groups, respectively. Per the MSKCC nomogram, the average five-year recurrence risks were 5% (range 1-12%) and 4% (range 1-38%) for women with and without a recurrence (p = 0.09), respectively. The recurrence risk-related variables were younger patient age, need for one or more re-excision surgeries, and use of endocrine therapy for 0 to less than five years after surgery.

CONCLUSION

Ipsilateral tumor recurrence risk estimates based on the VNPI and MSKCC nomogram are similar between women with DCIS who did and did not have a recurrence, suggesting that more robust prognostic models are needed.

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.

De-Escalating the Management of In Situ and Invasive Breast Cancer

Simple Summary

De-escalation of breast cancer treatment reduces morbidity and toxicity for patients. De-escalation is safe if cancer outcomes, such as recurrence and survival, remain unaffected compared to more radical regimens. This review provides an overview on treatment de-escalation for ductal carcinoma in situ (DCIS), local treatment of breast cancer, and surgery after neoadjuvant systemic therapy. Improvements in understanding the natural history and biology of breast cancer, imaging modalities, and adjuvant treatments have facilitated de-escalation of treatment over time.

Abstract

It is necessary to identify appropriate areas of de-escalation in breast cancer treatment to minimize morbidity and maximize patients’ quality of life. Less radical treatment modalities, or even no treatment, have been reconsidered if they offer the same oncologic outcomes as standard therapies. Identifying which patients benefit from de-escalation requires particular care, as standard therapies will continue to offer adequate cancer outcomes. We provide an overview of the literature on the de-escalation of treatment of ductal carcinoma in situ (DCIS), local treatment of breast cancer, and surgery after neoadjuvant systemic therapy. De-escalation of breast cancer treatment is a key area of investigation that will continue to remain a priority. Improvements in understanding the natural history and biology of breast cancer, imaging modalities, and adjuvant treatments will expand this even further. Future efforts will continue to challenge us to consider the true role of various treatment modalities.

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.

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.

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.

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

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

Contralateral breast cancer after curative-intent treatment for ductal carcinoma in situ: Rate and associated clinicopathological and imaging risk factors

PURPOSE

Patients who have ductal carcinoma in situ (DCIS) are undergoing bilateral mastectomy at increasing rates. One of the reasons is to minimize contralateral breast cancer (CBC) risk. The purpose of this study is to determine the rate of and risk factors associated with CBC in women treated for DCIS.

METHODS

A retrospective study was performed of women with DCIS at surgery from 2007 to 2014 who had at least five-year follow-up. Patient attributes, imaging findings, histopathology results, and surgical and long-term outcomes were collected. Features associated with a CBC were assessed with multivariable logistic regression models.

RESULTS

613 women (mean 56 years, range 30-87) with DCIS underwent breast-conserving surgery (BCS) (n = 426), unilateral mastectomy (n = 101), or bilateral mastectomy (n = 86), with mean follow-up of 7.9 years. Of the 527 women who had BCS or unilateral mastectomy, 7.4% (n = 39) developed a CBC (DCIS in 12 and invasive cancer in 27). 4.1% (5/122) of women treated with adjuvant endocrine therapy developed a CBC, compared to 8.4% (34/405) who were not treated (p = .11). Features associated with CBC risk were younger age at menarche (adjusted odds ratio [aOR] of 0.76, p = .03) and low nuclear grade of DCIS (aOR of 5.43 for grade 1 versus 3, p = .01).

CONCLUSION

In women treated for DCIS, the overall rate of CBC was low at 7.4%. Younger age at menarche and low nuclear grade of DCIS had significant associations with higher CBC risk.

Axillary evaluation in ductal cancer in situ of the breast: challenging the diagnostic accuracy of clinical practice guidelines

BACKGROUND

Staging of the axilla is not routine in ductal cancer in situ (DCIS) although invasive cancer is observed in 20-25 per cent of patients at final pathology. Upfront sentinel lymph node dissection (SLND) is advocated in clinical practice guidelines in certain situations. These include expected challenges in subsequent SLN detection and when the risk for invasion is high. Clinical practice guidelines are, however, inconsistent and lead to considerable practice variability.

METHODS

Clinical practice guidelines for upfront SLND in DCIS were identified and applied to patients included in the prospective SentiNot study. These patients were evaluated by six independent, blinded raters. Agreement statistics were performed to assess agreement and concordance. Receiver operating characteristic curves were constructed, to assess guideline accuracy in identifying patients with underlying invasion.

RESULTS

Eight guidelines with relevant recommendations were identified. Interobserver agreement varied greatly (kappa: 0.23-0.9) and the interpretation as to whether SLND should be performed ranged from 40-90 per cent and with varying concordance (32-88 per cent). The diagnostic accuracy was low with area under the curve ranging from 0.45 to 0.55. Fifty to 90 per cent of patients with pure DCIS would undergo unnecessary SLNB, whereas 10-50 per cent of patients with invasion were not identified as 'high risk'. Agreement across guidelines was low (kappa = 0.24), meaning that different patients had a similar risk of being treated inaccurately.

CONCLUSION

Available guidelines are inaccurate in identifying patients with DCIS who would benefit from upfront SLNB. Guideline refinement with detailed preoperative work-up and novel techniques for SLND identification could address this challenge and avoid overtreatment.

Breast cancer

Breast cancer is still the most common cancer worldwide. But the way breast cancer is viewed has changed drastically since its molecular hallmarks were extensively characterised, now including immunohistochemical markers (eg, ER, PR, HER2 [ERBB2], and proliferation marker protein Ki-67 [MKI67]), genomic markers (eg, BRCA1, BRCA2, and PIK3CA), and immunomarkers (eg, tumour-infiltrating lymphocytes and PD-L1). New biomarker combinations are the basis for increasingly complex diagnostic algorithms. Neoadjuvant combination therapy, often including targeted agents, is a standard of care (especially in HER2-positive and triple-negative breast cancer), and the basis for de-escalation of surgery in the breast and axilla and for risk-adapted post-neoadjuvant strategies. Radiotherapy remains an important cornerstone of breast cancer therapy, but de-escalation schemes have become the standard of care. ER-positive tumours are treated with 5-10 years of endocrine therapy and chemotherapy, based on an individual risk assessment. For metastatic breast cancer, standard therapy options include targeted approaches such as CDK4 and CDK6 inhibitors, PI3K inhibitors, PARP inhibitors, and anti-PD-L1 immunotherapy, depending on tumour type and molecular profile. This range of treatment options reflects the complexity of breast cancer therapy today.

Discriminating low-grade ductal carcinoma in situ (DCIS) from non-low-grade DCIS or DCIS upgraded to invasive carcinoma: effective texture features on ultrafast dynamic contrast-enhanced magnetic resonance imaging

PURPOSE

To investigate effective model composed of features from ultrafast dynamic contrast-enhanced magnetic resonance imaging (UF-MRI) for distinguishing low- from non-low-grade ductal carcinoma in situ (DCIS) lesions or DCIS lesions upgraded to invasive carcinoma (upgrade DCIS lesions) among lesions diagnosed as DCIS on pre-operative biopsy.

MATERIALS AND METHODS

Eighty-six consecutive women with 86 DCIS lesions diagnosed by biopsy underwent UF-MRI including pre- and 18 post-contrast ultrafast scans (temporal resolution of 3 s/phase). The last phase of UF-MRI was used to perform 3D segmentation. The time point at 6 s after the aorta started to enhance was used to obtain subtracted images. From the 3D segmentation and subtracted images, enhancement, shape, and texture features were calculated and compared between low- and non-low-grade or upgrade DCIS lesions using univariate analysis. Feature selection by least absolute shrinkage and selection operator (LASSO) algorithm and k-fold cross-validation were performed to evaluate the diagnostic performance.

RESULTS

Surgical specimens revealed 16 low-grade DCIS lesions, 37 non-low-grade lesions and 33 upgrade DCIS lesions. In univariate analysis, five shape and seven texture features were significantly different between low- and non-low-grade lesions or upgrade DCIS lesions, whereas enhancement features were not. The six features including surface/volume ratio, irregularity, diff variance, uniformity, sum average, and variance were selected using LASSO algorism and the mean area under the receiver operating characteristic curve for training and validation folds were 0.88 and 0.88, respectively.

CONCLUSION

The model with shape and texture features of UF-MRI could effectively distinguish low- from non-low-grade or upgrade DCIS lesions.

Ductal Carcinoma In Situ and Progression to Invasive Cancer: A Review of the Evidence

Ductal carcinoma in situ (DCIS), breast cancer confined to the milk ducts, is a heterogeneous entity. The question of how and when a case of DCIS will extend beyond the ducts to become invasive breast cancer has implications for both patient prognosis and optimal treatment approaches. The natural history of DCIS has been explored through a variety of methods, from mouse models to biopsy specimen reviews to population-based screening data to modeling studies. This article will review the available evidence regarding progression pathways and will also summarize current trials designed to assess DCIS progression.