Significant inter- and intra-laboratory variation in grading of ductal carcinoma in situ of the breast: a nationwide study of 4901 patients in the Netherlands

Invasive recurrence after breast conserving treatment of ductal carcinoma in situ of the breast in the Netherlands: time trends and the association with tumour grade

BACKGROUND

The first aim of this study was to examine trends in the risk of ipsilateral invasive breast cancer (iIBC) after breast-conserving surgery (BCS) of ductal carcinoma in situ (DCIS). A second aim was to analyse the association between DCIS grade and the risk of iIBC following BCS.

PATIENTS AND METHODS

In this population-based, retrospective cohort study, the Netherlands Cancer Registry collected information on 25,719 women with DCIS diagnosed in the period 1989-2021 who underwent BCS. Of these 19,034 received adjuvant radiotherapy (RT). Kaplan-Meier analyses and Cox regression models were used.

RESULTS

A total of 1135 patients experienced iIBC. Ten-year cumulative incidence rates of iIBC for patients diagnosed in the periods 1989-1998, 1999-2008 and 2009-2021 undergoing BCS without RT, were 12.6%, 9.0% and 5.0% (P < 0.001), respectively. For those undergoing BCS with RT these figures were 5.7%, 3.7% and 2.2%, respectively (P < 0.001). In the multivariable analyses, DCIS grade was not associated with the risk of iIBC.

CONCLUSION

Since 1989 the risk of iIBC has decreased substantially and has become even lower than the risk of invasive contralateral breast cancer. No significant association of DCIS grade with iIBC was found, stressing the need for more powerful prognostic factors to guide the treatment of DCIS.

Update on Management of Ductal Carcinoma in Situ

Ductal carcinoma in situ (DCIS) represents 18% to 25% of all diagnosed breast cancers, and is a noninvasive, nonobligate precursor lesion to invasive cancer. The diagnosis of DCIS represents a wide range of disease, including lesions with both low and high risk of progression to invasive cancer and recurrence. Over the past decade, research on the topic of DCIS has focused on the possibility of tailoring treatment for patients according to their risk for progression and recurrence, which is based on clinicopathologic, biomolecular and genetic factors. These efforts are ongoing, with recently completed and continuing clinical trials spanning the continuum of cancer care. We conducted a review to identify recent advances on the topic of diagnosis, risk stratification and management of DCIS. While novel imaging techniques have increased the rate of DCIS diagnosis, questions persist regarding the optimal management of lesions that would not be identified with conventional methods. Additionally, among trials investigating the potential for omission of surgery and use of active surveillance, 2 trials have completed accrual and 2 clinical trials are continuing to enroll patients. Identification of novel genetic patterns is expanding our potential for risk stratification and aiding our ability to de-escalate radiation and systemic therapies for DCIS. These advances provide hope for tailoring of DCIS treatment in the near future.

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.

Pros and cons of artificial intelligence implementation in diagnostic pathology

The rapid introduction of digital pathology has greatly facilitated development of artificial intelligence (AI) models in pathology that have shown great promise in assisting morphological diagnostics and quantitation of therapeutic targets. We are now at a tipping point where companies have started to bring algorithms to the market, and questions arise whether the pathology community is ready to implement AI in routine workflow. However, concerns also arise about the use of AI in pathology. This article reviews the pros and cons of introducing AI in diagnostic pathology.

The natural history of ductal carcinoma in situ (DCIS) in simulation models: A systematic review

OBJECTIVE

Assumptions on the natural history of ductal carcinoma in situ (DCIS) are necessary to accurately model it and estimate overdiagnosis. To improve current estimates of overdiagnosis (0-91%), the purpose of this review was to identify and analyse assumptions made in modelling studies on the natural history of DCIS in women.

METHODS

A systematic review of English full-text articles using PubMed, Embase, and Web of Science was conducted up to February 6, 2023. Eligibility and all assessments were done independently by two reviewers. Risk of bias and quality assessments were performed. Discrepancies were resolved by consensus. Reader agreement was quantified with Cohen's kappa. Data extraction was performed with three forms on study characteristics, model assessment, and tumour progression.

RESULTS

Thirty models were distinguished. The most important assumptions regarding the natural history of DCIS were addition of non-progressive DCIS of 20-100%, classification of DCIS into three grades, where high grade DCIS had an increased chance of progression to invasive breast cancer (IBC), and regression possibilities of 1-4%, depending on age and grade. Other identified risk factors of progression of DCIS to IBC were younger age, birth cohort, larger tumour size, and individual risk.

CONCLUSION

To accurately model the natural history of DCIS, aspects to consider are DCIS grades, non-progressive DCIS (9-80%), regression from DCIS to no cancer (below 10%), and use of well-established risk factors for progression probabilities (age). Improved knowledge on key factors to consider when studying DCIS can improve estimates of overdiagnosis and optimization of screening.

Pathologic complete response of ductal carcinoma in situ to neoadjuvant systemic therapy in HER2-positive invasive breast cancer patients: a nationwide analysis

PURPOSE

Ductal carcinoma in situ (DCIS) is present in more than half of HER2-positive invasive breast cancer (IBC). Recent studies show that DCIS accompanying HER2-positive IBC can be completely eradicated by neoadjuvant systemic therapy (NST). Our aim was to determine the percentage of pathologic complete response of the DCIS component in a nationwide cohort and to assess associated clinicopathologic variables. Furthermore, the impact on surgical treatment after NST was investigated.

METHODS

Women diagnosed with HER2-positive IBC, treated with NST and surgery, between 2010 and 2020, were selected from the Netherlands Cancer Registry. Pre-NST biopsy and postoperative pathology reports were obtained from the Dutch Nationwide Pathology Databank and assessed for the presence of DCIS. Clinicopathologic factors associated with DCIS response were assessed using logistic regression analyses.

RESULTS

A DCIS component was present in the pre-NST biopsy in 1403 (25.1%) of 5598 included patients. Pathologic complete response of the DCIS component was achieved in 730 patients (52.0%). Complete response of DCIS occurred more frequently in case of complete response of IBC (63.4% versus 33.8%, p < 0.001). ER-negative IBC (OR 1.79; 95%CI 1.33-2.42) and more recent years of diagnosis (2014-2016 OR 1.60; 95%CI 1.17-2.19, 2017-2019 OR 1.76; 95%CI 1.34-2.34) were associated with DCIS response. Mastectomy rates were higher in IBC+DCIS compared to IBC (53.6% versus 41.0%, p < 0.001).

CONCLUSION

Pathologic complete response of DCIS occurred in 52.0% of HER2-positive IBC patients and was associated with ER-negative IBC and more recent years of diagnosis. Future studies should investigate imaging evaluation of DCIS response to improve surgical decision making.

CONFIDENT-trial protocol: a pragmatic template for clinical implementation of artificial intelligence assistance in pathology

INTRODUCTION

Artificial intelligence (AI) has been on the rise in the field of pathology. Despite promising results in retrospective studies, and several CE-IVD certified algorithms on the market, prospective clinical implementation studies of AI have yet to be performed, to the best of our knowledge. In this trial, we will explore the benefits of an AI-assisted pathology workflow, while maintaining diagnostic safety standards.

METHODS AND ANALYSIS

This is a Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence compliant single-centre, controlled clinical trial, in a fully digital academic pathology laboratory. We will prospectively include prostate cancer patients who undergo prostate needle biopsies (CONFIDENT-P) and breast cancer patients who undergo a sentinel node procedure (CONFIDENT-B) in the University Medical Centre Utrecht. For both the CONFIDENT-B and CONFIDENT-P trials, the specific pathology specimens will be pseudo-randomised to be assessed by a pathologist with or without AI assistance in a pragmatic (bi-)weekly sequential design. In the intervention group, pathologists will assess whole slide images (WSI) of the standard hematoxylin and eosin (H&E)-stained sections assisted by the output of the algorithm. In the control group, pathologists will assess H&E WSI according to the current clinical workflow. If no tumour cells are identified or when the pathologist is in doubt, immunohistochemistry (IHC) staining will be performed. At least 80 patients in the CONFIDENT-P and 180 patients in the CONFIDENT-B trial will need to be enrolled to detect superiority, allocated as 1:1. Primary endpoint for both trials is the number of saved resources of IHC staining procedures for detecting tumour cells, since this will clarify tangible cost savings that will support the business case for AI.

ETHICS AND DISSEMINATION

The ethics committee (MREC NedMec) waived the need of official ethical approval, since participants are not subjected to procedures nor are they required to follow rules. Results of both trials (CONFIDENT-B and CONFIDENT-P) will be published in scientific peer-reviewed journals.

Clinical validity and clinical utility of Ki67 in early breast cancer

Ki67 represents an immunohistochemical nuclear localized marker that is widely used in surgical pathology. Nuclear immunoreactivity for Ki67 indicates that cells are cycling and are in G1- to S-phase. The percentage of Ki67-positive tumor cells (Ki67 index) therefore provides an estimate of the growth fraction in tumor specimens. In breast cancer (BC), tumor cell proliferation rate is one of the most relevant prognostic markers and Ki67 is consequently helpful in prognostication similar to histological grading and mRNA profiling-based BC risk stratification. In BCs treated with short-term preoperative endocrine therapy, Ki67 dynamics enable distinguishing between endocrine sensitive and resistant tumors. Despite its nearly universal use in pathology laboratories worldwide, no internationally accepted consensus has yet been achieved for some methodological details related to Ki67 immunohistochemistry (IHC). Controversial issues refer to choice of IHC antibody clones, scoring methods, inter-laboratory reproducibility, and the potential value of computer-assisted imaging analysis and/or artificial intelligence for Ki67 assessment. Prospective clinical trials focusing on BC treatment have proven that Ki67, as determined by standardized central pathology assessment, is of clinical validity. Clinical utility has been demonstrated in huge observational studies.

Implementation of Artificial Intelligence in Diagnostic Practice as a Next Step after Going Digital: The UMC Utrecht Perspective

Building on a growing number of pathology labs having a full digital infrastructure for pathology diagnostics, there is a growing interest in implementing artificial intelligence (AI) algorithms for diagnostic purposes. This article provides an overview of the current status of the digital pathology infrastructure at the University Medical Center Utrecht and our roadmap for implementing AI algorithms in the next few years.

Screen-detected ductal carcinoma in situ, 2008-2020: An observational study

OBJECTIVES

The purpose of this study was to evaluate the grade distribution of screen-detected ductal carcinoma in situ (DCIS) diagnosed in Ireland, in the context of the clinical trials currently underway to determine if active surveillance is a feasible management option for low-risk DCIS.

SETTING

BreastCheck is the national breast screening programme in Ireland, offering screening to women aged 50 to 69 every two years.

METHODS

This study was a secondary analysis of data collected by BreastCheck on all screen-detected DCIS diagnosed in the 12 years of nationwide screening. Incidence and detection rates were calculated. Descriptive analysis of the cases was performed and, for comparative analysis, grade of DCIS was analysed as a binary variable (high vs. low/intermediate) in keeping with the inclusion criteria for active surveillance trials. Analysis was performed in IBM Statistical Package for Social Sciences, version 26.

RESULTS

Between 2008 and 2020, 2240 women were diagnosed with DCIS through BreastCheck; 876 (39.1%) were low/intermediate-grade. The overall incidence rate has remained relatively stable during this period. Women with low/intermediate-grade DCIS were younger than women with high-grade DCIS (56 (interquartile range: 56-61) years v 57 (interquartile range: 53-61) years; p < 0.001). They were also more likely to have been diagnosed at an initial screening episode compared with those who had high-grade lesions (42.5% v 29.0%; p < 0.001).

CONCLUSION

If current clinical trials recommend active surveillance as a feasible option for DCIS, up to 40% of women with screen-detected DCIS may be eligible. These women are younger and often diagnosed on initial screening episode, so may require longer active follow-up.

A deep learning model for breast ductal carcinoma in situ classification in whole slide images

The pathological differential diagnosis between breast ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) is of pivotal importance for determining optimum cancer treatment(s) and clinical outcomes. Since conventional diagnosis by pathologists using microscopes is limited in terms of human resources, it is necessary to develop new techniques that can rapidly and accurately diagnose large numbers of histopathological specimens. Computational pathology tools which can assist pathologists in detecting and classifying DCIS and IDC from whole slide images (WSIs) would be of great benefit for routine pathological diagnosis. In this paper, we trained deep learning models capable of classifying biopsy and surgical histopathological WSIs into DCIS, IDC, and benign. We evaluated the models on two independent test sets (n= 1382, n= 548), achieving ROC areas under the curves (AUCs) up to 0.960 and 0.977 for DCIS and IDC, respectively.

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.

Long-term risk of subsequent ipsilateral lesions after surgery with or without radiotherapy for ductal carcinoma in situ of the breast

BACKGROUND

Radiotherapy (RT) following breast-conserving surgery (BCS) for ductal carcinoma in situ (DCIS) reduces ipsilateral breast event rates in clinical trials. This study assessed the impact of DCIS treatment on a 20-year risk of ipsilateral DCIS (iDCIS) and ipsilateral invasive breast cancer (iIBC) in a population-based cohort.

METHODS

The cohort comprised all women diagnosed with DCIS in the Netherlands during 1989-2004 with follow-up until 2017. Cumulative incidence of iDCIS and iIBC following BCS and BCS + RT were assessed. Associations of DCIS treatment with iDCIS and iIBC risk were estimated in multivariable Cox models.

RESULTS

The 20-year cumulative incidence of any ipsilateral breast event was 30.6% (95% confidence interval (CI): 28.9-32.6) after BCS compared to 18.2% (95% CI 16.3-20.3) following BCS  +  RT. Women treated with BCS compared to BCS + RT had higher risk of developing iDCIS and iIBC within 5 years after DCIS diagnosis (for iDCIS: hazard ratio (HR)_age < 50 3.2 (95% CI 1.6-6.6); HR_{age ≥ 50} 3.6 (95% CI 2.6-4.8) and for iIBC: HR_age<50 2.1 (95% CI 1.4-3.2); HR_{age ≥ 50} 4.3 (95% CI 3.0-6.0)). After 10 years, the risk of iDCIS and iIBC no longer differed for BCS versus BCS + RT (for iDCIS: HR_age < 50 0.7 (95% CI 0.3-1.5); HR_{age ≥ 50} 0.7 (95% CI 0.4-1.3) and for iIBC: HR_age < 50 0.6 (95% CI 0.4-0.9); HR_{age ≥ 50} 1.2 (95% CI 0.9-1.6)).

CONCLUSION

RT is associated with lower iDCIS and iIBC risk up to 10 years after BCS, but this effect wanes thereafter.

Significant Inter- and Intralaboratory Variation in Gleason Grading of Prostate Cancer: A Nationwide Study of 35,258 Patients in The Netherlands

Simple Summary

Gleason grading of prostate cancer is essential for treatment strategies and patient prognosis. Previous studies showed grading variation between pathologists when grading prostate cancer. Our study analyzed the presence and extent of grading variation between and within pathology laboratories in The Netherlands. In our nationwide retrospective study, we analyzed prostate needle biopsy reports of 35,258 patients in The Netherlands graded by 40 pathology laboratories. We found a considerable variation between and within pathology laboratories, as over half of the laboratories graded significantly different from the national mean. This likely affects treatment strategy and prognosis assessment of prostate cancer patients.

Abstract

Purpose: Our aim was to analyze grading variation between pathology laboratories and between pathologists within individual laboratories using nationwide real-life data. Methods: We retrieved synoptic (n = 13,397) and narrative (n = 29,377) needle biopsy reports from the Dutch Pathology Registry and prostate-specific antigen values from The Netherlands Cancer Registration for prostate cancer patients diagnosed between January 2017 and December 2019. We determined laboratory-specific proportions per histologic grade and unadjusted odds ratios (ORs) for International Society of Urological Pathologists Grades 1 vs. 2–5 for 40 laboratories due to treatment implications for higher grades. Pathologist-specific proportions were determined for 21 laboratories that consented to this part of analysis. The synoptic reports of 21 laboratories were used for analysis of case-mix correction for PSA, age, year of diagnosis, number of biopsies and positive cores. Results: A total of 38,321 reports of 35,258 patients were included. Grade 1 ranged between 19.7% and 44.3% per laboratory (national mean = 34.1%). Out of 40 laboratories, 22 (55%) reported a significantly deviant OR, ranging from 0.48 (95% confidence interval (CI) 0.39–0.59) to 1.54 (CI 1.22–1.93). Case-mix correction was performed for 10,294 reports, altering the status of 3/21 (14%) laboratories, but increasing the observed variation (20.8% vs. 17.7%). Within 15/21 (71%) of laboratories, significant inter-pathologist variation existed. Conclusion: Substantial variation in prostate cancer grading was observed between and within Dutch pathology laboratories. Case-mix correction did not explain the variation. Better standardization of prostate cancer grading is warranted to optimize and harmonize treatment.

Considerable interlaboratory variation in PD-L1 positivity in a nationwide cohort of non-small cell lung cancer patients

OBJECTIVES

Immunohistochemical expression of programmed death-ligand 1 (PD-L1) is used as a predictive biomarker for prescription of immunotherapy to non-small cell lung cancer (NSCLC) patients. Accurate assessment of PD-L1 expression is therefore crucial. In this study, the extent of interlaboratory variation in PD-L1 positivity in the Netherlands was assessed, using real-world clinical pathology data.

MATERIALS AND METHODS

Data on all NSCLC patients in the Netherlands with a mention of PD-L1 testing in their pathology report from July 2017 to December 2018 were extracted from PALGA, the nationwide network and registry of histo- and cytopathology in the Netherlands. PD-L1 positivity rates were determined for each laboratory that performed PD-L1 testing, with separate analyses for histological and cytological material. Two cutoffs (1% and 50%) were used to determine PD-L1 positivity. Differences between laboratories were assessed using funnel plots with 95% confidence limits around the overall mean.

RESULTS

6,354 patients from 30 laboratories were included in the analysis of histology data. At the 1% cutoff, maximum interlaboratory variation was 39.1% (32.7%-71.8%) and ten laboratories (33.3%) differed significantly from the mean. Using the 50% cutoff, four laboratories (13.3%) differed significantly from the mean and maximum variation was 23.1% (17.2%-40.3%). In the analysis of cytology data, 1,868 patients from 23 laboratories were included. Eight laboratories (34.8%) differed significantly from the mean in the analyses of both cutoffs. Maximum variation was 41.2% (32.2%-73.4%) and 29.2% (14.7%-43.9%) using the 1% and 50% cutoffs, respectively.

CONCLUSION

Considerable interlaboratory variation in PD-L1 positivity was observed. Variation was largest using the 1% cutoff. At the 50% cutoff, analysis of cytology data demonstrated a higher degree of variation than the analysis of histology data.

Deep learning-based grading of ductal carcinoma in situ in breast histopathology images

Ductal carcinoma in situ (DCIS) is a non-invasive breast cancer that can progress into invasive ductal carcinoma (IDC). Studies suggest DCIS is often overtreated since a considerable part of DCIS lesions may never progress into IDC. Lower grade lesions have a lower progression speed and risk, possibly allowing treatment de-escalation. However, studies show significant inter-observer variation in DCIS grading. Automated image analysis may provide an objective solution to address high subjectivity of DCIS grading by pathologists. In this study, we developed and evaluated a deep learning-based DCIS grading system. The system was developed using the consensus DCIS grade of three expert observers on a dataset of 1186 DCIS lesions from 59 patients. The inter-observer agreement, measured by quadratic weighted Cohen's kappa, was used to evaluate the system and compare its performance to that of expert observers. We present an analysis of the lesion-level and patient-level inter-observer agreement on an independent test set of 1001 lesions from 50 patients. The deep learning system (dl) achieved on average slightly higher inter-observer agreement to the three observers (o1, o2 and o3) (κo1,dl = 0.81, κo2,dl = 0.53 and κo3,dl = 0.40) than the observers amongst each other (κo1,o2 = 0.58, κo1,o3 = 0.50 and κo2,o3 = 0.42) at the lesion-level. At the patient-level, the deep learning system achieved similar agreement to the observers (κo1,dl = 0.77, κo2,dl = 0.75 and κo3,dl = 0.70) as the observers amongst each other (κo1,o2 = 0.77, κo1,o3 = 0.75 and κo2,o3 = 0.72). The deep learning system better reflected the grading spectrum of DCIS than two of the observers. In conclusion, we developed a deep learning-based DCIS grading system that achieved a performance similar to expert observers. To the best of our knowledge, this is the first automated system for the grading of DCIS that could assist pathologists by providing robust and reproducible second opinions on DCIS grade.

Patterns of treatment and outcome of ductal carcinoma in situ in the Netherlands

Purpose

To spare DCIS patients from overtreatment, treatment de-escalated over the years. This study evaluates the influence of these developments on the patterns of care in the treatment of DCIS with particular interest in the use of breast conserving surgery (BCS), radiotherapy following BCS and the use and type of axillary staging.

Methods

In this large population-based cohort study all women, aged 50–74 years diagnosed with DCIS from January 1989 until January 2019, were analyzed per two-year cohort.

Results

A total of 30,417 women were diagnosed with DCIS. The proportion of patients undergoing BCS increased from 47.7% in 1995–1996 to 72.7% in 2017–2018 (p < 0.001). Adjuvant radiotherapy following BCS increased from 28.9% (1995–1996) to 89.6% (2011–2012) and subsequently decreased to 74.9% (2017–2018; p < 0.001). Since its introduction, the use of sentinel lymph node biopsy (SLNB) increased to 63.1% in 2013–2014 and subsequently decreased to 52.8% in 2017–2018 (p < 0.001). Axillary surgery is already omitted in 55.8% of the patients undergoing BCS nowadays. The five-year invasive relapse-free survival (iRFS) for BCS with adjuvant radiotherapy in the period 1989–2010, was 98.7% [CI 98.4% – 99.0%], compared to 95.0% [CI 94.1% –95.8%] for BCS only (p < 0.001). In 2011–2018, this was 99.3% [CI 99.1% – 99.5%] and 98.8% [CI 98.2% – 99.4%] respectively (p = 0.01).

Conclusions

This study shows a shift toward less extensive treatment. DCIS is increasingly treated with BCS and less often followed by additional radiotherapy. The absence of radiotherapy still results in excellent iRFS. Axillary surgery is increasingly omitted in DCIS patients.

Pathological features of 11,337 patients with primary ductal carcinoma in situ (DCIS) and subsequent events: results from the UK Sloane Project

Background

The Sloane audit compares screen-detected ductal carcinoma in situ (DCIS) pathology with subsequent management and outcomes.

Methods

This was a national, prospective cohort study of DCIS diagnosed during 2003–2012.

Results

Among 11,337 patients, 7204 (64%) had high-grade DCIS. Over time, the proportion of high-grade disease increased (from 60 to 65%), low-grade DCIS decreased (from 10 to 6%) and mean size increased (from 21.4 to 24.1 mm). Mastectomy was more common for high-grade (36%) than for low-grade DCIS (15%). Few (6%) patients treated with breast-conserving surgery (BCS) had a surgical margin <1 mm. Of the 9191 women diagnosed in England (median follow-up 9.4 years), 7% developed DCIS or invasive malignancy in the ipsilateral and 5% in the contralateral breast. The commonest ipsilateral event was invasive carcinoma (n = 413), median time 62 months, followed by DCIS (n = 225), at median 37 months. Radiotherapy (RT) was most protective against recurrence for high-grade DCIS (3.2% for high-grade DCIS with RT compared to 6.9% without, compared with 2.3 and 3.0%, respectively, for low/intermediate-grade DCIS). Ipsilateral DCIS events lessened after 5 years, while the risk of ipsilateral invasive cancer remained consistent to beyond 10 years.

Conclusion

DCIS pathology informs patient management and highlights the need for prolonged follow-up of screen-detected DCIS.

Interobserver Variability in Ductal Carcinoma In Situ of the Breast

OBJECTIVES

Since most patients with ductal carcinoma in situ (DCIS) of the breast are treated upon diagnosis, evidence on its natural progression to invasive carcinoma is limited. It is estimated that around half of the screen-detected DCIS lesions would have remained indolent if they had never been detected. Many patients with DCIS are therefore probably overtreated. Four ongoing randomized noninferiority trials explore active surveillance as a treatment option. Eligibility for these trials is mainly based on histopathologic features. Hence, the call for reproducible histopathologic assessment has never sounded louder.

METHODS

Here, the available classification systems for DCIS are discussed in depth.

RESULTS

This comprehensive review illustrates that histopathologic evaluation of DCIS is characterized by significant interobserver variability. Future digitalization of pathology, combined with development of deep learning algorithms or so-called artificial intelligence, may be an innovative solution to tackle this problem. However, implementation of digital pathology is not within reach for each laboratory worldwide. An alternative classification system could reduce the disagreement among histopathologists who use "conventional" light microscopy: the introduction of dichotomous histopathologic assessment is likely to increase interobserver concordance.

CONCLUSIONS

Reproducible histopathologic assessment is a prerequisite for robust risk stratification and adequate clinical decision-making. Two-tier histopathologic assessment might enhance the quality of care.

Prognostic value of histopathological DCIS features in a large-scale international interrater reliability study

PURPOSE

For optimal management of ductal carcinoma in situ (DCIS), reproducible histopathological assessment is essential to distinguish low-risk from high-risk DCIS. Therefore, we analyzed interrater reliability of histopathological DCIS features and assessed their associations with subsequent ipsilateral invasive breast cancer (iIBC) risk.

METHODS

Using a case-cohort design, reliability was assessed in a population-based, nationwide cohort of 2767 women with screen-detected DCIS diagnosed between 1993 and 2004, treated by breast-conserving surgery with/without radiotherapy (BCS ± RT) using Krippendorff's alpha (KA) and Gwet's AC2 (GAC2). Thirty-eight raters scored histopathological DCIS features including grade (2-tiered and 3-tiered), growth pattern, mitotic activity, periductal fibrosis, and lymphocytic infiltrate in 342 women. Using majority opinion-based scores for each feature, their association with subsequent iIBC risk was assessed using Cox regression.

RESULTS

Interrater reliability of grade using various classifications was fair to moderate, and only substantial for grade 1 versus 2 + 3 when using GAC2 (0.78). Reliability for growth pattern (KA 0.44, GAC2 0.78), calcifications (KA 0.49, GAC2 0.70) and necrosis (KA 0.47, GAC2 0.70) was moderate using KA and substantial using GAC2; for (type of) periductal fibrosis and lymphocytic infiltrate fair to moderate estimates were found and for mitotic activity reliability was substantial using GAC2 (0.70). Only in patients treated with BCS-RT, high mitotic activity was associated with a higher iIBC risk in univariable analysis (Hazard Ratio (HR) 2.53, 95% Confidence Interval (95% CI) 1.05-6.11); grade 3 versus 1 + 2 (HR 2.64, 95% CI 1.35-5.14) and a cribriform/solid versus flat epithelial atypia/clinging/(micro)papillary growth pattern (HR 3.70, 95% CI 1.34-10.23) were independently associated with a higher iIBC risk.

CONCLUSIONS

Using majority opinion-based scores, DCIS grade, growth pattern, and mitotic activity are associated with iIBC risk in patients treated with BCS-RT, but interrater variability is substantial. Semi-quantitative grading, incorporating and separately evaluating nuclear pleomorphism, growth pattern, and mitotic activity, may improve the reliability and prognostic value of these features.

The effect of an e-learning module on grading variation of (pre)malignant breast lesions

Histologic grade is a biomarker that is widely used to guide treatment of invasive breast cancer (IBC) and ductal carcinoma in situ of the breast (DCIS). Yet, currently, substantial grading variation between laboratories and pathologists exists in daily pathology practice. This study was conducted to evaluate whether an e-learning may be a feasible tool to decrease grading variation of (pre)malignant breast lesions. An e-learning module, representing the key-concepts of grading (pre)malignant breast lesions through gold standard digital images, was designed. Pathologists and residents could take part in either or both the separate modules on DCIS and IBC. Variation in grading of a digital set of lesions before and after the e-learning was compared in a fully-crossed study-design. Multiple outcome measures were assessed: inter-rater reliability (IRR) by Light's kappa, the number of images graded unanimously, the number of images with both extreme scores (i.e., grade I and grade III), and the average number of discrepancies from expert-consensus. Participants were included as they completed both the pre- and post-e-learning set (DCIS-module: n = 36, IBC-module: n = 21). For DCIS, all outcome measures improved after e-learning, with the IRR improving from fair (kappa: 0.532) to good (kappa: 0.657). For IBC, all outcome measures for the subcategories tubular differentiation and mitosis improved, with >90% of participants agreeing on almost 90% of the images after the e-learning. In contrast, the IRR for the subcategory of nuclear pleomorphism remained fair (kappa: 0.523 vs. kappa: 0.571). This study shows that an e-learning module, in which pathologists and residents are trained in histologic grading of DCIS and IBC, is a feasible and promising tool to decrease grading variation of (pre)malignant breast lesions. This is highly relevant given the important role of histologic grading in clinical decision making of (pre)malignant breast lesions.

Grading variation in 2,934 patients with ductal carcinoma in situ of the breast: the effect of laboratory- and pathologist-specific feedback reports

BACKGROUND

Histologic grade of ductal carcinoma in situ of the breast (DCIS) may become the single biomarker that decides whether patients will be treated. Yet, evidence shows that grading variation in daily practice is substantial. To facilitate quality improvement, feedback reports, in which laboratory-specific case-mix adjusted proportions per grade were benchmarked against other laboratories, were sent to the individual laboratories by March 1, 2018. One year later, the effect of these feedback reports on inter-laboratory variation was studied.

METHODS

Synoptic pathology reports of all pure DCIS resection specimens between March 1, 2017 and March 1, 2019 were retrieved from PALGA (the nationwide Dutch pathology registry). Laboratory-specific proportions per grade were compared to the overall proportion in the year before and after feedback. The absolute deviation for all three grades at once, represented by the overall deviation score (ODS), was calculated as the sum of deviations from the grade-specific overall proportions. Case-mix adjusted, laboratory-specific odds ratios (ORs) for high- (grade III) versus low-grade (grade I-II) DCIS were obtained by multivariable logistic regression.

RESULTS

Overall, 2954 DCIS reports from 31 laboratories were included. After feedback, the range between laboratories decreased by 22 and 6.5% for grades II and III, while an increase of 6.2% was observed for grade I. Both the mean ODS (27.2 to 24.1%) and maximum ODS (87.7 to 59.6%) decreased considerably. However, the range of case-mix adjusted ORs remained fairly stable and substantial (0.39 (95% CI: 0.18-0.86) to 3.69 (95% CI: 1.30-10.51)).

CONCLUSION

A promising decrease in grading variation was observed after laboratory-specific feedback for DCIS grades II-III, while this was not observed for DCIS grade I. Overall, grading variation remained substantial which needs to be addressed considering its clinical implications. Nationwide consensus on a classification, and training of (expert breast) pathologists, for example by e-learning, may help to further improve grading standardization.

Interobserver variability in upfront dichotomous histopathological assessment of ductal carcinoma in situ of the breast: the DCISion study

Histopathological assessment of ductal carcinoma in situ, a nonobligate precursor of invasive breast cancer, is characterized by considerable interobserver variability. Previously, post hoc dichotomization of multicategorical variables was used to determine the "ideal" cutoffs for dichotomous assessment. The present international multicenter study evaluated interobserver variability among 39 pathologists who performed upfront dichotomous evaluation of 149 consecutive ductal carcinomas in situ. All pathologists independently assessed nuclear atypia, necrosis, solid ductal carcinoma in situ architecture, calcifications, stromal architecture, and lobular cancerization in one digital slide per lesion. Stromal inflammation was assessed semiquantitatively. Tumor-infiltrating lymphocytes were quantified as percentages and dichotomously assessed with a cutoff at 50%. Krippendorff's alpha (KA), Cohen's kappa and intraclass correlation coefficient were calculated for the appropriate variables. Lobular cancerization (KA = 0.396), nuclear atypia (KA = 0.422), and stromal architecture (KA = 0.450) showed the highest interobserver variability. Stromal inflammation (KA = 0.564), dichotomously assessed tumor-infiltrating lymphocytes (KA = 0.520), and comedonecrosis (KA = 0.539) showed slightly lower interobserver disagreement. Solid ductal carcinoma in situ architecture (KA = 0.602) and calcifications (KA = 0.676) presented with the lowest interobserver variability. Semiquantitative assessment of stromal inflammation resulted in a slightly higher interobserver concordance than upfront dichotomous tumor-infiltrating lymphocytes assessment (KA = 0.564 versus KA = 0.520). High stromal inflammation corresponded best with dichotomously assessed tumor-infiltrating lymphocytes when the cutoff was set at 10% (kappa = 0.881). Nevertheless, a post hoc tumor-infiltrating lymphocytes cutoff set at 20% resulted in the highest interobserver agreement (KA = 0.669). Despite upfront dichotomous evaluation, the interobserver variability remains considerable and is at most acceptable, although it varies among the different histopathological features. Future studies should investigate its impact on ductal carcinoma in situ prognostication. Forthcoming machine learning algorithms may be useful to tackle this substantial diagnostic challenge.

Significant inter- and intra-laboratory variation in grading of invasive breast cancer: A nationwide study of 33,043 patients in the Netherlands

Accurate, consistent and reproducible grading by pathologists is of key-importance for identification of individual patients with invasive breast cancer (IBC) that will or will not benefit from adjuvant systemic treatment. We studied the laboratory-specific grading variation using nationwide real-life data to create insight and awareness in grading variation. Synoptic pathology reports of all IBC resection-specimens, obtained between 2013 and 2016, were retrieved from the nationwide Dutch Pathology Registry (PALGA). Absolute differences in laboratory-proportions of Grades I-III were compared to the national reference. Multivariable logistic regression provided laboratory-specific odds ratios (ORs) for high- vs. low-grade IBC. 33,792 IBC pathology reports of 33,043 patients from 39 laboratories were included, of which 28.1% were reported as Grade I (range between laboratories 16.3-43.3%), 47.6% as Grade II (38.4-57.8%), and 24.3% as Grade III (15.5-34.3%). Based on national guidelines, the indication for adjuvant chemotherapy was dependent on histologic grade in 29.9% of patients. After case-mix correction, 20 laboratories (51.3%) showed a significantly deviant OR. Significant grading differences were also observed among pathologists within laboratories. In this cohort of 33,043 breast cancer patients, we observed substantial inter- and intra-laboratory variation in histologic grading. It can be anticipated that this has influenced outcome including exposure to unnecessary toxicity, since choice of adjuvant chemotherapy was dependent on grade in nearly a third of patients. Better standardization and training seems warranted.

Grading Ductal Carcinoma In Situ (DCIS) of the Breast - What's Wrong with It?

Ductal carcinoma in situ of the breast is a non-obligate precursor of invasive breast cancer, and at its lower risk end might not need treatment, a hypothesis tested in several currently running randomized clinical trials. This review describes the heterogeneity of grading ductal carcinoma in situ (DCIS). First it considers differences between low and high grade DCIS, and then it looks at several grading schemes and highlights how different these are, not only in the features considered for defining a given grade but also in their wording of a given variable seen in the grade in question. Rather than being fully comprehensive, the review aims to illustrate the inconsistencies. Reproducibility studies on grading mostly suggestive of moderate agreement on DCIS differentiation are also illustrated. The need for a well structured, more uniform and widely accepted language for grading DCIS is urged to avoid misunderstanding based misclassifications and improper treatment selection.

Low-grade screen-detected ductal carcinoma in situ progresses more slowly than high-grade lesions: evidence from an international multi-centre study

PURPOSE

Nuclear grade is an important indicator of the biological behaviour of ductal carcinoma in situ (DCIS). De-escalation of treatment has been suggested for low-grade DCIS. Our aim is to estimate the relative rate of progression of DCIS by nuclear grade by analysing the distribution of nuclear grade by detection at initial or subsequent screening.

METHODS

We asked International Cancer Screening Network sites to complete, based on their screening and clinical databases, an aggregated data file on DCIS detection, diagnosis and treatment.

RESULTS

Eleven screening programs reported 5068 screen-detected pure DCIS in nearly 7 million screening tests in women 50-69 years of age. For all programs combined, low-grade DCIS were 20.1% (range 11.4-31.8%) of graded DCIS, intermediate grade 31.0% and high grade 48.9%. Detection rates decreased more steeply from initial to subsequent screening in low compared to high-grade DCIS: the ratios of subsequent to initial detection rates were 0.39 for low grade, 0.51 for intermediate grade, and 0.75 for high grade (p < 0.001).

CONCLUSIONS

These results suggest that the duration of the preclinical detectable phase is longer for low than for high-grade DCIS. The findings from this large multi-centre, international study emphasize that the management of low-grade DCIS should be carefully scrutinized in order to minimize overtreatment of screen-detected slow-growing or indolent lesions. The high variation by site in the proportion of low grade suggests that further pathology standardization and training would be beneficial.

A retrospective alternative for active surveillance trials for ductal carcinoma in situ of the breast

Ductal carcinoma in situ (DCIS) of the breast is a nonobligate precursor of invasive breast cancer, accounting for 20 % of screen-detected breast cancers. Little is known about the natural progression of DCIS because most patients undergo surgery upon diagnosis. Many DCIS patients are likely being overtreated, as it is believed that only around 50 % of DCIS will progress to invasive carcinoma. Robust prognostic markers for progression to invasive carcinoma are lacking. In the past, studies have investigated women who developed a recurrence after breast-conserving surgery (BCS) and compared them with those who did not. However, where there is no recurrence, the patient has probably been adequately treated. The present narrative review advocates a new research strategy, wherein only those patients with a recurrence are studied. Approximately half of the recurrences are invasive cancers, and half are DCIS. So-called "recurrences" are probably most often the result of residual disease. The new approach allows us to ask: why did some residual DCIS evolve to invasive cancers and others not? This novel strategy compares the group of patients that developed in situ recurrence with the group of patients that developed invasive recurrence after BCS. The differences between these groups could then be used to develop a robust risk stratification tool. This tool should estimate the risk of synchronous and metachronous invasive carcinoma when DCIS is diagnosed in a biopsy. Identification of DCIS patients at low risk for developing invasive carcinoma will individualize future therapy and prevent overtreatment.