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

Quality improvement of biomarker assessment in breast cancer

Breast cancer treatment is largely guided by the results of tumor tissue analyses, i.e. evaluation of so called biomarkers, by pathologists. These biomarkers determine whether the tumor is sensitive to treatment with hormone therapy or treatment against the HER2-protein. Histologic grade represents the degree of aggressiveness and (co)determines whether additional treatment, for example with chemotherapy, is required. Given the importance of these biomarkers with regard to breast cancer treatment, we investigated whether there are differences in the results of biomarker assessment between pathology laboratories and between pathologists within individual laboratories. These analyses were performed with data from over 33.000 patients treated for breast cancer in the Netherlands between 2013 and 2016. Variation in biomarker assessment was limited, with exception of variation in grading, which was substantial, both between laboratories and between pathologists within the same laboratory. Realizing that grade is decisive for the indications for additional chemotherapy and hormone therapy in 35% and 30% of breast cancer patients, it seems likely that variation in grading lead to variation in treatment. This could potentially affect survival of breast cancer patients, which is still under investigation. To promote uniform grading, these data were reported back to the laboratories by means of feedback reports. An e-learning module, in which pathologists were trained in grading of breast cancer, was also developed. Both initiatives showed promising results. Additional initiatives, such as artificial intelligence, should be developed with priority to limit the variation in grading as much as possible

Quality improvement of biomarker assessment in breast cancer

Breast cancer treatment is largely guided by the results of tumor tissue analyses, i.e. evaluation of so called biomarkers, by pathologists. These biomarkers determine whether the tumor is sensitive to treatment with hormone therapy or treatment against the HER2-protein. Histologic grade represents the degree of aggressiveness and (co)determines whether additional treatment, for example with chemotherapy, is required. Given the importance of these biomarkers with regard to breast cancer treatment, we investigated whether there are differences in the results of biomarker assessment between pathology laboratories and between pathologists within individual laboratories. These analyses were performed with data from over 33.000 patients treated for breast cancer in the Netherlands between 2013 and 2016. Variation in biomarker assessment was limited, with exception of variation in grading, which was substantial, both between laboratories and between pathologists within the same laboratory. Realizing that grade is decisive for the indications for additional chemotherapy and hormone therapy in 35% and 30% of breast cancer patients, it seems likely that variation in grading lead to variation in treatment. This could potentially affect survival of breast cancer patients, which is still under investigation. To promote uniform grading, these data were reported back to the laboratories by means of feedback reports. An e-learning module, in which pathologists were trained in grading of breast cancer, was also developed. Both initiatives showed promising results. Additional initiatives, such as artificial intelligence, should be developed with priority to limit the variation in grading as much as possible

Hormone- and HER2-receptor assessment in 33,046 breast cancer patients : a nationwide comparison of positivity rates between pathology laboratories in the Netherlands

Purpose: Patient management of invasive breast cancer (IBC) is to a large extent based on hormone- and HER2-receptor assessment. High-quality, reliable receptor assessment is of key importance as false results may lead to under- or overtreatment of patients. Surveillance of case-mix adjusted positivity rates has been suggested as a tool to identify laboratories with insufficient testing assays, as this covers the whole process of receptor assessment and enables laboratories to benchmark their positivity rates against other laboratories. We studied laboratory-specific variation in hormone- and HER2 positivity rates of 33,046 breast cancer patients using real-life nationwide data. Methods: All synoptic pathology reports of IBC resection-specimens, obtained between 2013 and 2016, were retrieved from the nationwide Dutch pathology registry (PALGA). Absolute and case-mix adjusted receptor positivity rates were compared to the mean national proportion and presented in funnel plots in separate analyses for estrogen (ER), progesterone (PR) and HER2. Case-mix adjustment was performed by multivariable logistic regression. Results: 33,794 IBC lesions from 33,046 patients of 39 pathology laboratories were included. After case-mix adjustment, mean positivity rates were 87.2% for ER (range 80.4–94.3), 71.3% for PR (62.5–77.5%), and 9.9% for HER2 (5.5–12.7%). Overall, 14 (35.9%), 17 (43.6%) and 11 (28.2%) laboratories showed positivity rates outside the 95% confidence interval for ER, PR and HER2, respectively. Conclusion: This nationwide study shows that absolute variation in hormone- and HER2-receptor positivity rates between Dutch pathology laboratories is limited. Yet, the considerable number of outlying laboratories shows that there is still need for improvement. Continuous monitoring and benchmarking of positivity rates may help to realize this

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

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

Purpose: A considerable part of ductal carcinoma in situ (DCIS) lesions may never progress into invasive breast cancer. However, standard treatment consists of surgical excision. Trials aim to identify a subgroup of low-risk DCIS patients that can safely forgo surgical treatment based on histologic grade, which highlights the importance of accurate grading. Using real-life nationwide data, we aimed to create insight and awareness in grading variation of DCIS in daily clinical practice. Methods: All synoptic pathology reports of pure DCIS resection specimens between 2013 and 2016 were retrieved from PALGA, the nationwide Dutch Pathology Registry. Absolute differences in proportions of grade I-III were visualized using funnel plots. Multivariable analysis was performed by logistic regression to correct for case-mix, providing odds ratios and 95% confidence intervals for high-grade (III) versus low-grade (I–II) DCIS. Results: 4952 DCIS reports from 36 laboratories were included, of which 12.5% were reported as grade I (range 6.1–24.4%), 39.5% as grade II (18.2–57.6%), and 48.0% as grade III (30.2–72.7%). After correction for case-mix, 14 laboratories (38.9%) reported a significantly lower (n = 4) or higher (n = 10) proportion of high-grade DCIS than the reference laboratory. Adjusted ORs (95%CI) ranged from 0.52 (0.31–0.87) to 3.83 (1.42–10.39). Significant grading differences were also observed among pathologists within laboratories. Conclusion: In this cohort of 4901 patients, we observed substantial inter- and intra-laboratory variation in DCIS grading, not explained by differences in case-mix. Therefore, there is an urgent need for nationwide standardization of grading practices, especially since the future management of DCIS may alter significantly depending on histologic grade

Nationwide differences in cytology fixation and processing methods and their impact on interlaboratory variation in PD-L1 positivity

Programmed death ligand-1 (PD-L1) immunostaining, which aids clinicians in decision-making on immunotherapy for non-small cell lung cancer (NSCLC) patients, is sometimes performed on cytological specimens. In this study, differences in cytology fixation and cell block (CB) processing between pathology laboratories were assessed, and the influence of these differences on interlaboratory variation in PD-L1 positivity was investigated. Questionnaires on cytology processing were sent to all Dutch laboratories. Information gathered from the responses was added to data on all Dutch NSCLC patients with a mention of PD-L1 testing in their cytopathology report from July 2017 to December 2018, retrieved from PALGA (the nationwide network and registry of histo- and cytopathology in the Netherlands). Case mix-adjusted PD-L1 positivity rates were determined for laboratories with known fixation and CB method. The influence of differences in cytology processing on interlaboratory variation in PD-L1 positivity was assessed by comparing positivity rates adjusted for differences in the variables fixative and CB method with positivity rates not adjusted for differences in these variables. Twenty-eight laboratories responded to the survey and reported 19 different combinations of fixation and CB method. Interlaboratory variation in PD-L1 positivity was assessed in 19 laboratories. Correcting for differences in the fixative and CB method resulted in a reduction (from eight (42.1%) to five (26.3%)) in the number of laboratories that differed significantly from the mean in PD-L1 positivity. Substantial variation in cytology fixation and CB processing methods was observed between Dutch pathology laboratories, which partially explains the existing considerable interlaboratory variation in PD-L1 positivity

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