Training pathologists to assess stromal tumour-infiltrating lymphocytes in breast cancer synergises efforts in clinical care and scientific research

A growing body of research supports stromal tumour-infiltrating lymphocyte (TIL) density in breast cancer to be a robust prognostic and predicive biomarker. The gold standard for stromal TIL density quantitation in breast cancer is pathologist visual assessment using haematoxylin and eosin-stained slides. Artificial intelligence/machine-learning algorithms are in development to automate the stromal TIL scoring process, and must be validated against a reference standard such as pathologist visual assessment. Visual TIL assessment may suffer from significant interobserver variability. To improve interobserver agreement, regulatory science experts at the US Food and Drug Administration partnered with academic pathologists internationally to create a freely available online continuing medical education (CME) course to train pathologists in assessing breast cancer stromal TILs using an interactive format with expert commentary. Here we describe and provide a user guide to this CME course, whose content was designed to improve pathologist accuracy in scoring breast cancer TILs. We also suggest subsequent steps to translate knowledge into clinical practice with proficiency testing.

Patient-Reported Outcomes in Patients Undergoing Lumpectomy With and Without Defect Closure

BACKGROUND

The effect of lumpectomy defect repair (a level 1 oncoplastic technique) on patient-reported breast satisfaction among patients undergoing lumpectomy has not yet been investigated.

METHODS

Patients undergoing lumpectomy at our institution between 2018 and 2020 with or without repair of their lumpectomy defect during index operation, comprised our study population. The BREAST-Q quality-of-life questionnaire was administered preoperatively, and at 6 months, 1 year, and 2 years postoperatively. Satisfaction and quality-of-life domains were compared between those who did and did not have closure of their lumpectomy defect, and compared with surgeon-reported outcomes.

RESULTS

A total of 487 patients met eligibility criteria, 206 (42%) had their partial mastectomy defect repaired by glandular displacement. Median breast volume, as calculated from the mammogram, was smaller in patients undergoing defect closure (826 cm3 vs. 895 cm3, p = 0.006). There were no statistically significant differences in satisfaction with breasts (SABTR), physical well-being of the chest (PWB-CHEST), or psychosocial well-being (PsychWB) scores between the two cohorts at any time point. While patients undergoing defect closure had significantly higher sexual well-being (SexWB) scores compared with no closure (66 vs. 59, p = 0.021), there were no predictors of improvement in SexWB scores over time on multivariable analysis. Patients' self-reported scores positively correlated with physician-reported outcomes.

CONCLUSIONS

Despite a larger lumpectomy-to-breast volume ratio among patients undergoing defect repair, satisfaction was equivalent among those whose defects were or were not repaired at 2 years postsurgery. Defect repair was associated with clinically relevant improvement in patient-reported sexual well-being.

Image-based multiplex immune profiling of cancer tissues: translational implications. A report of the International Immuno-oncology Biomarker Working Group on Breast Cancer

Recent advances in the field of immuno-oncology have brought transformative changes in the management of cancer patients. The immune profile of tumours has been found to have key value in predicting disease prognosis and treatment response in various cancers. Multiplex immunohistochemistry and immunofluorescence have emerged as potent tools for the simultaneous detection of multiple protein biomarkers in a single tissue section, thereby expanding opportunities for molecular and immune profiling while preserving tissue samples. By establishing the phenotype of individual tumour cells when distributed within a mixed cell population, the identification of clinically relevant biomarkers with high-throughput multiplex immunophenotyping of tumour samples has great potential to guide appropriate treatment choices. Moreover, the emergence of novel multi-marker imaging approaches can now provide unprecedented insights into the tumour microenvironment, including the potential interplay between various cell types. However, there are significant challenges to widespread integration of these technologies in daily research and clinical practice. This review addresses the challenges and potential solutions within a structured framework of action from a regulatory and clinical trial perspective. New developments within the field of immunophenotyping using multiplexed tissue imaging platforms and associated digital pathology are also described, with a specific focus on translational implications across different subtypes of cancer. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Reproducible Reporting of the Collection and Evaluation of Annotations for Artificial Intelligence Models

This work puts forth and demonstrates the utility of a reporting framework for collecting and evaluating annotations of medical images used for training and testing artificial intelligence (AI) models in assisting detection and diagnosis. AI has unique reporting requirements, as shown by the AI extensions to the Consolidated Standards of Reporting Trials (CONSORT) and Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) checklists and the proposed AI extensions to the Standards for Reporting Diagnostic Accuracy (STARD) and Transparent Reporting of a Multivariable Prediction model for Individual Prognosis or Diagnosis (TRIPOD) checklists. AI for detection and/or diagnostic image analysis requires complete, reproducible, and transparent reporting of the annotations and metadata used in training and testing data sets. In an earlier work by other researchers, an annotation workflow and quality checklist for computational pathology annotations were proposed. In this manuscript, we operationalize this workflow into an evaluable quality checklist that applies to any reader-interpreted medical images, and we demonstrate its use for an annotation effort in digital pathology. We refer to this quality framework as the Collection and Evaluation of Annotations for Reproducible Reporting of Artificial Intelligence (CLEARR-AI).

Recommendations for Performance Evaluation of Machine Learning in Pathology: A Concept Paper From the College of American Pathologists

CONTEXT.—

Machine learning applications in the pathology clinical domain are emerging rapidly. As decision support systems continue to mature, laboratories will increasingly need guidance to evaluate their performance in clinical practice. Currently there are no formal guidelines to assist pathology laboratories in verification and/or validation of such systems. These recommendations are being proposed for the evaluation of machine learning systems in the clinical practice of pathology.

OBJECTIVE.—

To propose recommendations for performance evaluation of in vitro diagnostic tests on patient samples that incorporate machine learning as part of the preanalytical, analytical, or postanalytical phases of the laboratory workflow. Topics described include considerations for machine learning model evaluation including risk assessment, predeployment requirements, data sourcing and curation, verification and validation, change control management, human-computer interaction, practitioner training, and competency evaluation.

DATA SOURCES.—

An expert panel performed a review of the literature, Clinical and Laboratory Standards Institute guidance, and laboratory and government regulatory frameworks.

CONCLUSIONS.—

Review of the literature and existing documents enabled the development of proposed recommendations. This white paper pertains to performance evaluation of machine learning systems intended to be implemented for clinical patient testing. Further studies with real-world clinical data are encouraged to support these proposed recommendations. Performance evaluation of machine learning models is critical to verification and/or validation of in vitro diagnostic tests using machine learning intended for clinical practice.

Initial interactions with the FDA on developing a validation dataset as a medical device development tool

Quantifying tumor-infiltrating lymphocytes (TILs) in breast cancer tumors is a challenging task for pathologists. With the advent of whole slide imaging that digitizes glass slides, it is possible to apply computational models to quantify TILs for pathologists. Development of computational models requires significant time, expertise, consensus, and investment. To reduce this burden, we are preparing a dataset for developers to validate their models and a proposal to the Medical Device Development Tool (MDDT) program in the Center for Devices and Radiological Health of the U.S. Food and Drug Administration (FDA). If the FDA qualifies the dataset for its submitted context of use, model developers can use it in a regulatory submission within the qualified context of use without additional documentation. Our dataset aims at reducing the regulatory burden placed on developers of models that estimate the density of TILs and will allow head-to-head comparison of multiple computational models on the same data. In this paper, we discuss the MDDT preparation and submission process, including the feedback we received from our initial interactions with the FDA and propose how a qualified MDDT validation dataset could be a mechanism for open, fair, and consistent measures of computational model performance. Our experiences will help the community understand what the FDA considers relevant and appropriate (from the perspective of the submitter), at the early stages of the MDDT submission process, for validating stromal TIL density estimation models and other potential computational models. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Ki67 proliferation index in medullary thyroid carcinoma: a comparative study of multiple counting methods and validation of image analysis and deep learning platforms

AIMS

The International Medullary Thyroid Carcinoma Grading System, introduced in 2022, mandates evaluation of the Ki67 proliferation index to assign a histological grade for medullary thyroid carcinoma. However, manual counting remains a tedious and time-consuming task.

METHODS AND RESULTS

We aimed to evaluate the performance of three other counting techniques for the Ki67 index, eyeballing by a trained experienced investigator, a machine learning-based deep learning algorithm (DeepLIIF) and an image analysis software with internal thresholding compared to the gold standard manual counting in a large cohort of 260 primarily resected medullary thyroid carcinoma. The Ki67 proliferation index generated by all three methods correlate near-perfectly with the manual Ki67 index, with kappa values ranging from 0.884 to 0.979 and interclass correlation coefficients ranging from 0.969 to 0.983. Discrepant Ki67 results were only observed in cases with borderline manual Ki67 readings, ranging from 3 to 7%. Medullary thyroid carcinomas with a high Ki67 index (≥ 5%) determined using any of the four methods were associated with significantly decreased disease-specific survival and distant metastasis-free survival.

CONCLUSIONS

We herein validate a machine learning-based deep-learning platform and an image analysis software with internal thresholding to generate accurate automatic Ki67 proliferation indices in medullary thyroid carcinoma. Manual Ki67 count remains useful when facing a tumour with a borderline Ki67 proliferation index of 3-7%. In daily practice, validation of alternative evaluation methods for the Ki67 index in MTC is required prior to implementation.

Digital pathology systems enabling quality patient care

Pathology laboratories are undergoing digital transformations, adopting innovative technologies to enhance patient care. Digital pathology systems impact clinical, education, and research use cases where pathologists use digital technologies to perform tasks in lieu of using glass slides and a microscope. Pathology professional societies have established clinical validation guidelines, and the US Food and Drug Administration have also authorized digital pathology systems for primary diagnosis, including image analysis and machine learning systems. Whole slide images, or digital slides, can be viewed and navigated similar to glass slides on a microscope. These modern tools not only enable pathologists to practice their routine clinical activities, but can potentially enable digital computational discovery. Assimilation of whole slide images in pathology clinical workflow can further empower machine learning systems to support computer assisted diagnostics. The potential enrichment these systems can provide is unprecedented in the field of pathology. With appropriate integration, these clinical decision support systems will allow pathologists to increase the delivery of quality patient care. This review describes the digital pathology transformation process, applicable clinical use cases, incorporation of image analysis and machine learning systems in the clinical workflow, as well as future technologies that may further disrupt pathology modalities to deliver quality patient care.

Future Practices of Breast Pathology Using Digital and Computational Pathology

Pathology clinical practice has evolved by adopting technological advancements initially regarded as potentially disruptive, such as electron microscopy, immunohistochemistry, and genomic sequencing. Breast pathology has a critical role as a medical domain, where the patient's pathology diagnosis has significant implications for prognostication and treatment of diseases. The advent of digital and computational pathology has brought about significant advancements in the field, offering new possibilities for enhancing diagnostic accuracy and improving patient care. Digital slide scanning enables to conversion of glass slides into high-fidelity digital images, supporting the review of cases in a digital workflow. Digitization offers the capability to render specimen diagnoses, digital archival of patient specimens, collaboration, and telepathology. Integration of image analysis and machine learning-based systems layered atop the high-resolution digital images offers novel workflows to assist breast pathologists in their clinical, educational, and research endeavors. Decision support tools may improve the detection and classification of breast lesions and the quantification of immunohistochemical studies. Computational biomarkers may help to contribute to patient management or outcomes. Furthermore, using digital and computational pathology may increase standardization and quality assurance, especially in areas with high interobserver variability. This review explores the current landscape and possible future applications of digital and computational techniques in the field of breast pathology.

Quality Management System in Clinical Digital Pathology Operations at a Tertiary Cancer Center

Digital pathology workflows can improve pathology operations by allowing reliable and fast retrieval of digital images, digitally reviewing pathology slides, enabling remote work and telepathology, use of computer-aided tools, and sharing of digital images for research and educational purposes. The need for quality systems is a prerequisite for successful clinical-grade digital pathology adoption and patient safety. In this article, we describe the development of a structured digital pathology laboratory quality management system (QMS) for clinical digital pathology operations at Memorial Sloan Kettering Cancer Center (MSK). This digital pathology-specific QMS development stemmed from the gaps that were identified when MSK integrated digital pathology into its clinical practice. The digital scan team in conjunction with the Department of Pathology and Laboratory Medicine quality team developed a QMS tailored to the scanning operation to support departmental and institutional needs. As a first step, systemic mapping of the digital pathology operations identified the prescan, scan, and postscan processes; instrumentation; and staffing involved in the digital pathology operation. Next, gaps identified in quality control and quality assurance measures led to the development of standard operating procedures and training material for the different roles and workflows in the process. All digital pathology-related documents were subject to regulatory review and approval by departmental leadership. The quality essentials were developed into an extensive Digital Pathology Quality Essentials framework to specifically address the needs of the growing clinical use of digital pathology technologies. Using the unique digital experience gained at MSK, we present our recommendations for QMS for large-scale digital pathology operations in clinical settings.

Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer

The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Spatial analyses of immune cell infiltration in cancer: current methods and future directions: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer

Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector-based). We then provide a compendium of spatial immune cell metrics that have been reported in the literature, summarizing prognostic associations in the context of a variety of cancers. We conclude by discussing two well-described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, and describe investigative opportunities to improve clinical utility of these spatial biomarkers. © 2023 The Pathological Society of Great Britain and Ireland.

Digital pathology operations at a tertiary cancer center: Infrastructure requirements and operational cost

Whole slide imaging is revolutionizing the field of pathology and is currently being used for clinical, educational, and research initiatives by an increasing number of institutions. Pathology departments have distinct needs for digital pathology systems, yet the cost of digital workflows is cited as a major barrier for widespread adoption by many organizations. Memorial Sloan Kettering Cancer Center (MSK) is an early adopter of whole slide imaging with incremental investments in resources that started more than 15 years ago. This experience and the large-scale scan operations led to the identification of required framework components of digital pathology operations. The cost of these components for the 2021 digital pathology operations at MSK were studied and calculated to enable an understanding of the operation and benchmark the accompanying costs. This paper describes the unique infrastructure cost and the costs associated with the digital pathology clinical operation use cases in a large, tertiary cancer center. These calculations can serve as a blueprint for other institutions to provide the necessary concepts and offer insights towards the financial requirements for digital pathology adoption by other institutions.

Changes in the Diagnoses of Breast Core Needle Biopsies on Second Review at a Tertiary Care Center: Implications for Surgical Management

Core needle biopsy (CNB) of breast lesions is routine for diagnosis and treatment planning. Despite refinement of diagnostic criteria, the diagnosis of breast lesions on CNB can be challenging. At many centers, including ours, confirmation of diagnoses rendered in other laboratories is required before treatment planning. We identified CNBs first diagnosed elsewhere that were reviewed in our department over the course of 1 year because the patients sought care at our center and in which a change in diagnosis had been recorded. The outside and in-house CNB diagnoses were then classified based on Breast WHO Fifth Edition diagnostic categories. The impact of the change in diagnosis was estimated based on the subsequent surgical management. Findings in follow-up surgical excisions (EXCs) were used for validation. In 2018, 4950 outside cases with CNB were reviewed at our center. A total of 403 CNBs diagnoses were discrepant. Of these, 147 had a change in the WHO diagnostic category: 80 (54%) CNBs had a more severe diagnosis and 44 (30%) a less severe diagnosis. In 23 (16%) CNBs, the change of diagnostic category had no impact on management. Intraductal proliferations (n=54), microinvasive carcinoma (n=18), and papillary lesions (n=35) were the most disputed diagnoses. The in-house CNB diagnosis was confirmed in most cases with available excisions. Following CNB reclassification, 22/147 (15%) lesions were not excised. A change affecting the surgical management at our center occurred in 2.5% of all CNBs. Our results support routine review of outside breast CNB as a clinically significant practice before definitive treatment.

Efficient Visualization of Whole Slide Images in Web-based Viewers for Digital Pathology

CONTEXT.—

Wide adoption of digital pathology requires efficient visualization and navigation in Web-based digital slide viewers, which is poorly defined.

OBJECTIVE.—

To define and quantify relevant performance metrics for efficient visualization of cases and slides in digital slide viewers.

DESIGN.—

With a universal slide viewer used in clinical routine diagnostics, we evaluated the impact of slide caching, compression type, tile, and block size of whole slide images generated from Philips, Leica, and 3DHistech scanners on streaming performance on case, slide, and field of view levels.

RESULTS.—

Two hundred thirty-nine pathologists routinely reviewed 60 080 whole slide images over 3 months. The median time to open a case's slides from the laboratory information system was less than 4 seconds, the time to change to a slide within the case was less than 1 second, and the time to render the adjacent field of view when navigating the slide was less than one-quarter of a second. A whole slide image's block size and a viewer tile size of 1024 pixels showed best performance to display a field of view and was preferrable over smaller tiles due to fewer mosaic effects. For Philips, fastest median slide streaming pace was 238 ms per field of view and for 3DHistech, 125 ms. For Leica, the fastest pace of 108 ms per field of view was established with block serving without decompression.

CONCLUSIONS.—

This is the first study to systematically assess user-centric slide visualization performance metrics for digital viewers, including time to open a case, time to change a slide, and time to change a field of view. These metrics help to improve the viewer's configuration, leading to an efficient visualization baseline that is widely accepted among pathologists using routine digital pathology.

Expression of novel neuroendocrine markers in breast carcinomas: a study of INSM1, ASCL1, and POU2F3

INSM1, ASCL1, and POU2F3 are novel transcription factors involved in neuroendocrine (NE) differentiation of neoplasms in several organs, but data on their expression in breast carcinomas (BCs) are limited. We retrospectively evaluated the expression of these markers in a series of 97 BCs (58 with NE morphology and 39 with otherwise uncommon morphology) tested prospectively using immunohistochemistry (IHC). Nuclear staining in >50% of the cells was used as the positive cut-off. Thirty-two of the 97 BCs (33%) were INSM1-positive. INSM1-positivity correlated significantly with histologic type and presence of stromal mucin. INSM1 also correlated with synaptophysin and chromogranin, established markers of NE differentiation (P < .0001 and P = .0023, respectively). In BC with NE morphology, the expression of INSM1 supported NE differentiation, and INSM1 was more specific than synaptophysin and more sensitive and specific than chromogranin. INSM1 was the most expressed NE marker in 17 BCs. INSM1-positive BCs included 56% of solid papillary BCs, 88% of BCs with solid papillary features, and 75% of high-grade NE carcinomas. Of 35 BCs tested for POU2F3 and ASCL1, only 1 and 4 cases were positive, respectively. Our results show that INSM1 is a sensitive marker of NE differentiation in BC and should be included with synaptophysin and chromogranin in the IHC panel used to evaluate NE differentiation in BC with NE morphology. ASCL1 and POU2F3 are uncommon in BC and their routine assessment is not warranted.

Pilot study to evaluate tools to collect pathologist annotations for validating machine learning algorithms

Purpose: Validation of artificial intelligence (AI) algorithms in digital pathology with a reference standard is necessary before widespread clinical use, but few examples focus on creating a reference standard based on pathologist annotations. This work assesses the results of a pilot study that collects density estimates of stromal tumor-infiltrating lymphocytes (sTILs) in breast cancer biopsy specimens. This work will inform the creation of a validation dataset for the evaluation of AI algorithms fit for a regulatory purpose. Approach: Collaborators and crowdsourced pathologists contributed glass slides, digital images, and annotations. Here, "annotations" refer to any marks, segmentations, measurements, or labels a pathologist adds to a report, image, region of interest (ROI), or biological feature. Pathologists estimated sTILs density in 640 ROIs from hematoxylin and eosin stained slides of 64 patients via two modalities: an optical light microscope and two digital image viewing platforms. Results: The pilot study generated 7373 sTILs density estimates from 29 pathologists. Analysis of annotations found the variability of density estimates per ROI increases with the mean; the root mean square differences were 4.46, 14.25, and 26.25 as the mean density ranged from 0% to 10%, 11% to 40%, and 41% to 100%, respectively. The pilot study informs three areas of improvement for future work: technical workflows, annotation platforms, and agreement analysis methods. Upgrades to the workflows and platforms will improve operability and increase annotation speed and consistency. Conclusions: Exploratory data analysis demonstrates the need to develop new statistical approaches for agreement. The pilot study dataset and analysis methods are publicly available to allow community feedback. The development and results of the validation dataset will be publicly available to serve as an instructive tool that can be replicated by developers and researchers.

Excitability properties of mouse and human skeletal muscle fibres compared by muscle velocity recovery cycles

Mouse models of skeletal muscle channelopathies are not phenocopies of human disease. In some cases (e.g. Myotonia Congenita) the phenotype is much more severe, whilst in others (e.g. Hypokalaemic periodic paralysis) rodent physiology is protective. This suggests a species' difference in muscle excitability properties. In humans these can be measured indirectly by the post-impulse changes in conduction velocity, using Muscle Velocity Recovery Cycles (MVRCs). We performed MVRCs in mice and compared their muscle excitability properties with humans. Mouse Tibialis Anterior MVRCs (n = 70) have only one phase of supernormality (increased conduction velocity), which is smaller in magnitude (p = 9 × 10-21), and shorter in duration (p = 3 × 10-24) than human (n = 26). This abbreviated supernormality is followed by a period of late subnormality (reduced velocity) in mice, which overlaps in time with the late supernormality seen in human MVRCs. The period of late subnormality suggests increased t-tubule Na+/K+-pump activity. The subnormal phase in mice was converted to supernormality by blocking ClC-1 chloride channels, suggesting relatively higher chloride conductance in skeletal muscle. Our findings help explain discrepancies in phenotype between mice and humans with skeletal muscle channelopathies and potentially other neuromuscular disorders. MVRCs are a valuable new tool to compare in vivo muscle membrane properties between species and will allow further dissection of the molecular mechanisms regulating muscle excitability.

Abstract PD11-02: Subtyping invasive carcinomas and high-risk lesions for machine learning based breast pathology

Background. The female mammary gland can develop a myriad of epithelial proliferative lesions including, high risk lesions, in-situ and invasive carcinomas. Identification of these pre-neoplastic and neoplastic conditions in biopsy specimens is crucial for proper patient management and may sometimes pose diagnostic challenges for pathologists. Recent research has shown that machine learning algorithms applied to whole slide images (WSI) can accurately detect and grade various cancers; herein, we devise and test a system that classifies the most common preneoplastic and neoplastic conditions of the female breast from WSIs. Design. De-identified slides were scanned on Leica AT2 whole slide scanners (20x; 0.5 µm/pixel) from MSK database were retrieved. Clinical diagnostic metadata were extracted from the pathology reports. Using a multi-label multiple-instance learning (ML-MIL) approach, a SE-ResNet50 Convolutional Neural Network (CNN) was trained to classify atypical lobular hyperplasia (ALH), atypical ductal hyperplasia (ADH), lobular carcinoma in situ (LCIS), ductal carcinoma in situ (DCIS), invasive lobular carcinoma (ILC), invasive ductal carcinoma (IDC). In additional morphological subtypes including apocrine, mucinous, solid papillary, micropapillary, and tubular carcinoma were trained. The system uses the WSI as an input and outputs a slide level class and heatmap for the presence of the trained classes. A validation dataset separate from the training set was used to assess performance of the trained model. Results. The CNN was trained on 9,751 surgical specimens (biopsy, 6,289; excision, 3,462) comprising 40,637 slides. The system was validated on 3,183 breast specimens (biopsy, 1,934; excision, 1,249) comprising 11,447 digital slides that were not included in the training of the CNN model. Validation performance in terms of Area Under Receiver Operating Characteristic Curve (AUROC) for each class is shown in Table 1. Conclusion. The trained CNN had a high performance in identifying the presence of ADH, ALH, DCIS, IDC, ILC, LCIS, and, apocrine, micropapillary, mucinous, solid papillary, and tubular carcinomas. Further studies expanding classes to include all clinically relevant lesions and morphologies are underway. In addition, the same approach can be used to detect microinvasions and calcifications in breast tissue.

Table 1.Area Under Receiver Operating Characteristic Curve for Breast Lesion ClassesClassNum. Positive (specimens)Num. Negative (specimens)AUROCADH24718640.903ALH20918860.950LCIS17520080.958DCIS81920920.956IDC52126620.956ILC7131120.934Apocrine2431590.931Micropapillary17230110.927Mucinous1231710.994Solid Papillary1531680.908Tubular carcinoma831750.990

Citation Format: Matthew G Hanna, Patricia Raciti, Alican Bozkurt, Ran Godrich, Julian Viret, Donghun Lee, Philippe Mathieu, Matthew Lee, Eugene Vorontsov, Tomer Sabo, Felipe C Geyer, Jorge S Reis-Filho, Leo Grady, Thomas Fuchs, Christopher Kanan. Subtyping invasive carcinomas and high-risk lesions for machine learning based breast pathology [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr PD11-02.

Abstract PD11-01: An artificial intelligence-based predictor of CDH1 biallelic mutations and invasive lobular carcinoma

Introduction: Invasive lobular carcinoma (ILC) is the most frequent special histologic subtype of breast cancer (BC). ILC is identifiable by pathologic assessment given its distinctive discohesive growth pattern, largely caused by CDH1 inactivation. Compared to common forms of BC, ILCs display lower responses to chemotherapy and selective estrogen receptor modulators. The low interobserver agreement for the diagnosis of ILC, however, renders the inclusion of histologic subtyping in therapeutic decision-making challenging. Artificial intelligence (AI)-based algorithms hold promise for improving pathologic diagnosis; their performance, however, depends on the ground truth labeling used. Here, we seek to develop an AI-based methodology for detection of ILC using ‘CDH1 biallelic mutations’ (i.e., mutation + loss-of-heterozygosity of the wild-type allele or two pathogenic somatic mutations) as ground truth, reasoning that in BC, &gt;95% of CDH1 bi-allelic inactivation is found in ILCs.Materials and methods: We developed a convolutional neural network system to detect CDH1 biallelic genetic inactivation (AI-CDH1) using whole slide images (WSI) of 1,100 primary BCs with available targeted sequencing data. The model was trained using a 10-fold cross-validation method to detect biallelic mutations. The mean number of positive and negative samples in the training set was 85.2 (SD=2.57) and 562.8 (SD=10.51) per fold, respectively. The evaluation set consisted of a mean of 14.2 (SD=2.04) positive and 93.8 (SD=9.13) negative samples. We evaluated the performance of the AI-CDH1 classifier to predict the lobular phenotype and CDH1 status using original and revised labels, following a histopathologic re-review of the histologic type and CDH1 status curation. The latter was conducted by incorporating information on biallelic CDH1 inactivation beyond CDH1 mutations (homozygous deletions, deleterious structural rearrangements, and loss-of-heterozygosity and gene promoter methylation).Results: The AI-CDH1 classifier predicted biallelic CDH1 mutations with an area under the curve (AUC)=0.944 (95 CI: 0.925-0.963), sensitivity=91.6% and specificity=85.9%, PPV=49.8%, NPV=98.5% and accuracy=86.7%, and the original ‘lobular phenotype’ with an AUC=0.941 (95 CI: 0.922-0.960), sensitivity=89%, specificity=86.7%, PPV=55.6%, NPV=97.7% and accuracy=87.1%. Review of the CDH1 gene status revealed that 7/957 BCs lacking CDH1 biallelic mutations harbored biallelic CDH1 inactivation by promoter methylation, homozygous deletions or structural rearrangements. The AI-CDH1 classifier detected all seven reclassified BCs and predicted the revised CDH1 biallelic inactivation with an AUC=0.948 (95 CI: 0.930-0.966), sensitivity=92%, specificity=86.5%, PPV=52.3%, NPV=98.5% and accuracy=87.2%. Upon histologic re-review, which resulted in reclassification of 36/927 non-lobular BCs as ‘lobular’ and 5/173 ‘lobular’ BCs as ‘non-lobular’, the AI-CDH1 classifier detected the ‘lobular phenotype’ with an AUC=0.953 (95 CI: 0.935-0.971), sensitivity=90.7%, specificity=89.7%, PPV=66.8%, NPV=97.7% and accuracy=89.9%. Using the revised histologic re-classification and CDH1 biallelic inactivation status labels, the AI-CDH1 classifier predicted the lobular phenotype irrespective of CDH1 status (P&gt;0.05).Conclusions: By training a machine learning system to detect ‘CDH1 biallelic mutations’, as ground truth rather than histologic diagnosis of lobular carcinoma, which might be confounded by human subjectivity, we developed an AI-based system that can detect ILCs accurately, providing a new paradigm for the development of AI-based cancer classification systems.

Citation Format: Jorge S Reis-Filho, Fresia Pareja, Fatemeh Derakhshan, David N Brown, Jillian Sue, Pier Selenica, Yi Kan Wang, Arnaud Da Cruz Paula, Monami Banerjee, Zahra Ebrahimzadeh, Manuel Isava, Matthew Lee, Ran Godrich, Adam Casson, Ruben Padron, George Shaikovski, Alexander van Eck, Antonio Marra, Higinio Dopeso, Hannah Y Wen, Edi Brogi, Matthew G Hanna, Chris Kanan, Jeremy D Kunz, Felipe C Geyer, Carla Leibowitz, David Klimstra, Leo Grady, Thomas J Fuchs. An artificial intelligence-based predictor of CDH1 biallelic mutations and invasive lobular carcinoma [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr PD11-01.

Integrating digital pathology into clinical practice

The field of anatomic pathology has been evolving in the last few decades and the advancements have been largely fostered by innovative technology. Immunohistochemistry enabled a paradigm shift in discovery and diagnostic evaluation, followed by booming genomic advancements which allowed for submicroscopic pathologic characterization, and now the field of digital pathology coupled with machine learning and big data acquisition is paving the way to revolutionize the pathology medical domain. Whole slide imaging (WSI) is a disruptive technology where glass slides are digitized to produce on-screen whole slide images. Specifically, in the past decade, there have been significant advances in digital pathology systems that have allowed this technology to promote integration into clinical practice. Whole slide images (WSI), or digital slides, can be viewed and navigated comparable to glass slides on a microscope, as digital files. Whole slide imaging has increased in adoption among pathologists, pathology departments, and scientists for clinical, educational, and research initiatives. Integration of digital pathology systems requires a coordinated effort with numerous stakeholders, not only within the pathology department, but across the entire enterprise. Each pathology department has distinct needs, use cases and blueprints, however the framework components and variables for successful clinical integration can be generalized across any organization seeking to undergo a digital transformation at any scale. This article will review those components and considerations for integrating digital pathology systems into clinical practice.

Digital validation of breast biomarkers (ER, PR, AR, and HER2) in cytology specimens using three different scanners

Progression in digital pathology has yielded new opportunities for a remote work environment. We evaluated the utility of digital review of breast cancer immunohistochemical prognostic markers (IHC) using whole slide images (WSI) from formalin fixed paraffin embedded (FFPE) cytology cell block specimens (CB) using three different scanners.CB from 20 patients with breast cancer diagnosis and available IHC were included. Glass slides including 20 Hematoxylin and eosin (H&E), 20 Estrogen Receptor (ER), 20 Progesterone Receptor (PR), 16 Androgen Receptor (AR), and 20 Human Epidermal Growth Factor Receptor 2 (HER2) were scanned on 3 different scanners. Four breast pathologists reviewed the WSI and recorded their semi-quantitative scoring for each marker. Kappa concordance was defined as complete agreement between glass/digital pairs. Discordances between microscopic and digital reads were classified as a major when a clinically relevant change was seen. Minor discordances were defined as differences in scoring percentages/staining pattern that would not have resulted in a clinical implication. Scanner precision was tabulated according to the success rate of each scan on all three scanners.In total, we had 228 paired glass/digital IHC reads on all 3 scanners. There was strong concordance kappa ≥0.85 for all pathologists when comparing paired microscopic/digital reads. Strong concordance (kappa ≥0.86) was also seen when comparing reads between scanners.Twenty-three percent of the WSI required rescanning due to barcode detection failures, 14% due to tissue detection failures, and 2% due to focus issues. Scanner 1 had the best average precision of 92%. HER2 IHC had the lowest intra-scanner precision (64%) among all stains.This study is the first to address the utility of WSI in breast cancer IHC in CB and to validate its reporting using 3 different scanners. Digital images are reliable for breast IHC assessment in CB and offer similar reproducibility to microscope reads.

A Pathologist-Annotated Dataset for Validating Artificial Intelligence: A Project Description and Pilot Study

Purpose:

Validating artificial intelligence algorithms for clinical use in medical images is a challenging endeavor due to a lack of standard reference data (ground truth). This topic typically occupies a small portion of the discussion in research papers since most of the efforts are focused on developing novel algorithms. In this work, we present a collaboration to create a validation dataset of pathologist annotations for algorithms that process whole slide images. We focus on data collection and evaluation of algorithm performance in the context of estimating the density of stromal tumor-infiltrating lymphocytes (sTILs) in breast cancer.

Methods:

We digitized 64 glass slides of hematoxylin- and eosin-stained invasive ductal carcinoma core biopsies prepared at a single clinical site. A collaborating pathologist selected 10 regions of interest (ROIs) per slide for evaluation. We created training materials and workflows to crowdsource pathologist image annotations on two modes: an optical microscope and two digital platforms. The microscope platform allows the same ROIs to be evaluated in both modes. The workflows collect the ROI type, a decision on whether the ROI is appropriate for estimating the density of sTILs, and if appropriate, the sTIL density value for that ROI.

Results:

In total, 19 pathologists made 1645 ROI evaluations during a data collection event and the following 2 weeks. The pilot study yielded an abundant number of cases with nominal sTIL infiltration. Furthermore, we found that the sTIL densities are correlated within a case, and there is notable pathologist variability. Consequently, we outline plans to improve our ROI and case sampling methods. We also outline statistical methods to account for ROI correlations within a case and pathologist variability when validating an algorithm.

Conclusion:

We have built workflows for efficient data collection and tested them in a pilot study. As we prepare for pivotal studies, we will investigate methods to use the dataset as an external validation tool for algorithms. We will also consider what it will take for the dataset to be fit for a regulatory purpose: study size, patient population, and pathologist training and qualifications. To this end, we will elicit feedback from the Food and Drug Administration via the Medical Device Development Tool program and from the broader digital pathology and AI community. Ultimately, we intend to share the dataset, statistical methods, and lessons learned.

Integrated digital pathology at scale: A solution for clinical diagnostics and cancer research at a large academic medical center

OBJECTIVE

Broad adoption of digital pathology (DP) is still lacking, and examples for DP connecting diagnostic, research, and educational use cases are missing. We blueprint a holistic DP solution at a large academic medical center ubiquitously integrated into clinical workflows; researchapplications including molecular, genetic, and tissue databases; and educational processes.

MATERIALS AND METHODS

We built a vendor-agnostic, integrated viewer for reviewing, annotating, sharing, and quality assurance of digital slides in a clinical or research context. It is the first homegrown viewer cleared by New York State provisional approval in 2020 for primary diagnosis and remote sign-out during the COVID-19 (coronavirus disease 2019) pandemic. We further introduce an interconnected Honest Broker for BioInformatics Technology (HoBBIT) to systematically compile and share large-scale DP research datasets including anonymized images, redacted pathology reports, and clinical data of patients with consent.

RESULTS

The solution has been operationally used over 3 years by 926 pathologists and researchers evaluating 288 903 digital slides. A total of 51% of these were reviewed within 1 month after scanning. Seamless integration of the viewer into 4 hospital systems clearly increases the adoption of DP. HoBBIT directly impacts the translation of knowledge in pathology into effective new health measures, including artificial intelligence-driven detection models for prostate cancer, basal cell carcinoma, and breast cancer metastases, developed and validated on thousands of cases.

CONCLUSIONS

We highlight major challenges and lessons learned when going digital to provide orientation for other pathologists. Building interconnected solutions will not only increase adoption of DP, but also facilitate next-generation computational pathology at scale for enhanced cancer research.

Pathology Informatics Education during the COVID-19 Pandemic at Memorial Sloan Kettering Cancer Center (MSKCC)

This review details the development and structure of a four-week rotation in pathology informatics for a resident trainee at Memorial Sloan Kettering Cancer Center (MSKCC) in New York City so that other programs interested in such a rotation can refer to. The role of pathology informatics is exponentially increasing in research and clinical practice. With an ever-expanding role, training in pathology informatics is paramount as pathology training programs and training accreditation bodies recognize the need for pathology informatics in training future pathologists. However, due to its novelty, many training programs are unfamiliar with implementing pathology informatics training. The rotation incorporates educational resources for pathology informatics, guidance in the development, and general topics relevant to pathology informatics training. Informatics topics include anatomic pathology related aspects such as whole slide imaging, laboratory information systems, image analysis, and molecular pathology associated issues such as the bioinformatics pipeline and data processing. Additionally, we highlight how the rotation pivoted to meet the department's informatics needs while still providing an educational experience during the onset of the COVID-19 pandemic. CONCLUSION: As pathology informatics continues to grow and integrate itself into practice, informatics education must also grow to meet the future needs of pathology. As informatics programs develop across institutions, such as the one detailed in this paper, these programs will better equip future pathologists with informatics to approach disease and pathology.

Metastasis and Immune Evasion from Extracellular cGAMP Hydrolysis

Cytosolic DNA is characteristic of chromosomally unstable metastatic cancer cells, resulting in constitutive activation of the cGAS-STING innate immune pathway. How tumors co-opt inflammatory signaling while evading immune surveillance remains unknown. Here, we show that the ectonucleotidase ENPP1 promotes metastasis by selectively degrading extracellular cGAMP, an immune-stimulatory metabolite whose breakdown products include the immune suppressor adenosine. ENPP1 loss suppresses metastasis, restores tumor immune infiltration, and potentiates response to immune checkpoint blockade in a manner dependent on tumor cGAS and host STING. Conversely, overexpression of wild-type ENPP1, but not an enzymatically weakened mutant, promotes migration and metastasis, in part through the generation of extracellular adenosine, and renders otherwise sensitive tumors completely resistant to immunotherapy. In human cancers, ENPP1 expression correlates with reduced immune cell infiltration, increased metastasis, and resistance to anti-PD-1/PD-L1 treatment. Thus, cGAMP hydrolysis by ENPP1 enables chromosomally unstable tumors to transmute cGAS activation into an immune-suppressive pathway. SIGNIFICANCE: Chromosomal instability promotes metastasis by generating chronic tumor inflammation. ENPP1 facilitates metastasis and enables tumor cells to tolerate inflammation by hydrolyzing the immunotransmitter cGAMP, preventing its transfer from cancer cells to immune cells.This article is highlighted in the In This Issue feature, p. 995.

Clinical and genetic spectrum of a Chinese cohort with SCN4A gene mutations

Skeletal muscle sodium channelopathies due to SCN4A gene mutations have a broad clinical spectrum. However, each phenotype has been reported in few cases of Chinese origin. We present detailed phenotype and genotype data from a cohort of 40 cases with SCN4A gene mutations seen in neuromuscular diagnostic service in Huashan hospital, Fudan University. Cases were referred from 6 independent provinces from 2010 to 2018. A questionnaire covering demographics, precipitating factors, episodes of paralysis and myotonia was designed to collect the clinical information. Electrodiagnostic studies and muscle MRI were retrospectively analyzed. The clinical spectrum of patients included: 6 Hyperkalemic periodic paralysis (15%), 18 Hypokalemic periodic paralysis (45%), 7 sodium channel myotonia (17.5%), 4 paramyotonia congenita (10%) and 5 heterozygous asymptomatic mutation carriers (12.5%). Review of clinical information highlights a significant delay to diagnosis (median 15 years), reports of pain and myalgia in the majority of patients, male predominance, circadian rhythm and common precipitating factors. Electrodiagnostic studies revealed subclinical myotonic discharges and a positive long exercise test in asymptomatic carriers. Muscle MRI identified edema and fatty infiltration in gastrocnemius and soleus. A total of 13 reported and 2 novel SCN4A mutations were identified with most variants distributed in the transmembrane helix S4 to S6, with a hotspot mutation p.Arg675Gln accounting for 32.5% (13/40) of the cohort. Our study revealed a higher proportion of periodic paralysis in SCN4A-mutated patients compared with cohorts from England and the Netherlands. It also highlights the importance of electrodiagnostic studies in diagnosis and segregation studies.

Dissecting the Business Case for Adoption and Implementation of Digital Pathology: A White Paper from the Digital Pathology Association

We believe the switch to a digital pathology (DP) workflow is imminent and it is essential to understand the economic implications of conversion. Many aspects of the adoption of DP will be disruptive and have a direct financial impact, both in short term costs, such as investment in equipment and personnel, and long term revenue potential, such as improved productivity and novel tests. The focus of this whitepaper is to educate pathologists, laboratorians and other stakeholders about the business and monetary considerations of converting to a digital pathology workflow. The components of a DP business plan will be thoroughly summarized, and guidance will be provided on how to build a case for adoption and implementation as well as a roadmap for transitioning from an analog to a digital pathology workflow in various laboratory settings. It is important to clarify that this publication is not intended to list prices although some financials will be mentioned as examples. The authors encourage readers who are evaluating conversion to a DP workflow to use this paper as a foundational guide for conducting a thorough and complete assessment while incorporating in current market pricing. Contributors to this paper analyzed peer-reviewed literature and data collected from various institutions, some of which are mentioned. Digital pathology will change the way we practice through facilitating patient access to expert pathology services and enabling image analysis tools and assays to aid in diagnosis, prognosis, risk stratification and therapeutic selection. Together, they will result in the delivery of valuable information from which to make better decisions and improve the health of patients.

Deep Multi-Magnification Networks for multi-class breast cancer image segmentation

Pathologic analysis of surgical excision specimens for breast carcinoma is important to evaluate the completeness of surgical excision and has implications for future treatment. This analysis is performed manually by pathologists reviewing histologic slides prepared from formalin-fixed tissue. In this paper, we present Deep Multi-Magnification Network trained by partial annotation for automated multi-class tissue segmentation by a set of patches from multiple magnifications in digitized whole slide images. Our proposed architecture with multi-encoder, multi-decoder, and multi-concatenation outperforms other single and multi-magnification-based architectures by achieving the highest mean intersection-over-union, and can be used to facilitate pathologists' assessments of breast cancer.

Abstract PD6-03: Clinical-grade detection of breast cancer in biopsies and excisions using machine learning

Background: Pathologists reviewing breast tissue slides must identify the presence of many salient features within each slide, including invasive and in situ breast cancer as well as various forms of atypia. In breast pathology particularly, the large volume of slides poses significant challenges for workload management and pathologist productivity (Johnson et al. 2019). The shift to a digital workflow in pathology, augmented by machine learning algorithms, has the potential to increase the efficiency, and productivity of pathologists by identifying cancer and pre-cancerous lesions in digitized slides. While there has been extensive work using machine learning algorithms to detect breast cancer metastasis in lymph nodes (Liu et al. 2018; Steiner et al. 2018), almost no research has been done on using such systems to detect breast cancer in biopsies and excisions.

Methods: We created and assessed Paige Breast Alpha, a machine learning system for the detection of breast cancer in hematoxylin and eosin (H&E) stained whole slide images (WSIs) of glass slides. The system is a binary classifier, intended to draw a pathologist’s attention to concerning features. Concerning features (the positive category) consisted of invasive breast cancer, in situ breast cancer, and various forms of atypia (atypical ductal hyperplasia, atypical lobular hyperplasia, etc. ). The deep learning system is based on the method proposed in Campanella et al. (2019). It learns directly from diagnosis using multiple instance learning, without the need for pixel-wise annotations. Paige Breast Alpha was trained on 17354 images from 3378 patients, and was assessed on 7921 images from 2443 patients. All slides were scanned on a Leica Aperio AT2.

Results: For detecting invasive or in situ cancer at the part level, the system achieved an overall sensitivity of 97.3% and a specificity of 98.0% in biopsies and 96.1% sensitivity and 91.5% specificity in excisions. Each part had between 1—10 slides.

Conclusions: We hypothesized that a machine learning system trained to detect predefined types of breast cancer and pre-cancerous lesions could be applied to a range of breast biopsy and resection WSIs to detect for the presence of these lesions. Herein we showed that the presence of these predefined features can be detected with high accuracy. Future studies are being initiated to assess the potential benefits of such a system when used by a pathologist. Additional systems are under development that would have the capability of subtyping the lesion present, in addition to acting as an overall binary classifier.

Citation Format: Matthew G Hanna, Patricia Raciti, Ran Godrich, Adam Casson, Julian Viret, Donghun Lee, Matthew Lee, Alican Bozkurt, Jillian Sue, Belma Dogdas, Brandon Rothrock, Leo Grady, Christopher Kanan, Thomas Fuchs. Clinical-grade detection of breast cancer in biopsies and excisions using machine learning [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PD6-03.

Introduction to Artificial Intelligence and Machine Learning for Pathology

CONTEXT.—

Recent developments in machine learning have stimulated intense interest in software that may augment or replace human experts. Machine learning may impact pathology practice by offering new capabilities in analysis, interpretation, and outcomes prediction using images and other data. The principles of operation and management of machine learning systems are unfamiliar to pathologists, who anticipate a need for additional education to be effective as expert users and managers of the new tools.

OBJECTIVE.—

To provide a background on machine learning for practicing pathologists, including an overview of algorithms, model development, and performance evaluation; to examine the current status of machine learning in pathology and consider possible roles and requirements for pathologists in local deployment and management of machine learning systems; and to highlight existing challenges and gaps in deployment methodology and regulation.

DATA SOURCES.—

Sources include the biomedical and engineering literature, white papers from professional organizations, government reports, electronic resources, and authors' experience in machine learning. References were chosen when possible for accessibility to practicing pathologists without specialized training in mathematics, statistics, or software development.

CONCLUSIONS.—

Machine learning offers an array of techniques that in recent published results show substantial promise. Data suggest that human experts working with machine learning tools outperform humans or machines separately, but the optimal form for this combination in pathology has not been established. Significant questions related to the generalizability of machine learning systems, local site verification, and performance monitoring remain to be resolved before a consensus on best practices and a regulatory environment can be established.

Interobserver Variation of PD-L1 SP142 Immunohistochemistry Interpretation in Breast Carcinoma: A Study of 79 Cases Using Whole Slide Imaging

CONTEXT.—

The Ventana programmed death ligand-1 (PD-L1) SP142 immunohistochemical assay (IHC) is approved by the US Food and Drug Administration as the companion diagnostic assay to identify patients with locally advanced or metastatic triple-negative breast cancer for immunotherapy with atezolizumab, a monoclonal antibody targeting PD-L1.

OBJECTIVE.—

To determine interobserver variability in PD-L1 SP142 IHC interpretation in invasive breast carcinoma.

DESIGN.—

The pathology database was interrogated for all patients diagnosed with primary invasive, locally recurrent, or metastatic breast carcinoma on which PD-L1 SP142 IHC was performed from November 2018 to June 2019 at our institution. A subset of cases was selected using a computerized random-number generator. PD-L1 IHC was evaluated in stromal tumor-infiltrating immune cells using the IMpassion130 trial criteria, with positive cases defined as immunoreactivity in immune cells 1% or more of the tumor area. IHC was interpreted on whole slide images by staff pathologists with breast pathology expertise. Interobserver variability was calculated using unweighted κ.

RESULTS.—

A total of 79 cases were assessed by 8 pathologists. Interobserver agreement was substantial (κ = 0.727). There was complete agreement among all 8 pathologists in 62% (49 of 79) of cases, 7 pathologists or more in 84% (66 of 79) of cases and 6 pathologists or more in 92% (73 of 79) of cases. In 4% (3 of 79) of cases, all of which were small biopsies, pathologists' interpretations were evenly split between scores of positive and negative.

CONCLUSIONS.—

The findings show substantial agreement in PD-L1 SP142 IHC assessment of breast carcinoma cases among 8 pathologists at a single institution. Further study is warranted to define the basis for discrepant results.

Digital Pathology Operations at an NYC Tertiary Cancer Center During the First 4 Months of COVID-19 Pandemic Response

Implementation of an infrastructure to support digital pathology began in 2006 at Memorial Sloan Kettering Cancer Center. The public health emergency and COVID-19 pandemic regulations in New York City required a novel workflow to sustain existing operations. While regulatory enforcement discretions offered faculty workspace flexibility, a substantial portion of laboratory and digital pathology workflows require on-site presence of staff. Maintaining social distancing and offering staggered work schedules. Due to a decrease in patients seeking health care at the onset of the pandemic, a temporary decrease in patient specimens was observed. Hospital and travel regulations impacted onsite vendor technical support. Digital glass slide scanning activities onsite proceeded without interruption throughout the pandemic, with challenges including staff who required quarantine due to virus exposure, unrelated illness, family support, or lack of public transportation. During the public health emergency, we validated digital pathology systems for a remote pathology operation. Since March 2020, the departmental digital pathology staff were able to maintain scanning volumes of over 100 000 slides per month. The digital scanning team reprioritized archival slide scanning and participated in a remote sign-out validation and successful submission of New York State approval for a laboratory developed test. Digital pathology offers a health care delivery model where pathologists can perform their sign out duties at remote location and prevent disruptions to critical pathology services for patients seeking care at our institution during emergencies. Development of standard operating procedures to support digital workflows will maintain turnaround times and enable clinical operations during emergency or otherwise unanticipated events.

HER2 Immunohistochemistry in Invasive Micropapillary Breast Carcinoma: Complete Assessment of an Incomplete Pattern

CONTEXT.—

Invasive micropapillary carcinoma (IMPC) is a rare variant of breast carcinoma, composed of avascular morula-like tumor clusters surrounded by stromal spaces, which can affect the HER2 immunohistochemical (IHC) staining pattern. The 2013 American Society of Clinical Oncology/College of American Pathologists (ASCO/CAP) HER2 testing guideline suggests moderate to intense but incomplete (basolateral) staining be considered equivocal.

OBJECTIVES.—

To perform a detailed assessment of HER2 IHC staining patterns in IMPC.

DESIGN.—

Hematoxylin-eosin and HER2 IHC slides were retrospectively reviewed to assess the morphology and HER2 IHC characteristics of IMPC. The 2018 ASCO/CAP guideline was applied.

RESULTS.—

The cohort consisted of 187 IMPCs from 181 patients with median age of 58 years. Homogeneous (≥90%) micropapillary component was found in 40% (75 of 187) of cases. Receptor profile was as follows: 75% (140 of 187) ER+ HER2-, 19% (37 of 187) ER+ HER2+, 4% (7 of 187) ER- HER2+, and 2% (3 of 187) ER- HER2-. Of 26 cases with HER2 IHC 3+, 65% (17 of 26) showed a basolateral staining pattern with strong intensity. HER2 fluorescence in situ hybridization (FISH) showed amplification in 26% (17 of 66) of HER2 IHC equivocal cases: 76% (13 of 17) showed basolateral staining pattern and 24% (4 of 17) complete staining, with weak to moderate (2), moderate (14), or moderate to strong (1) intensity.

CONCLUSIONS.—

The most frequent staining pattern was basolateral, seen in 49% of cases, including 65% HER2 IHC positive and 76% HER2 IHC equivocal/FISH amplified. If a basolateral pattern and weak to moderate staining is observed in IMPC, alternative testing should be performed to confirm the HER2 status.

(Re) Defining the High-Power Field for Digital Pathology

BACKGROUND

The microscope high-power field (HPF) is the cornerstone for histopathology diagnostic evaluation such as the quantification of mitotic figures, lymphocytes, and tumor grading. With traditional light microscopy, HPFs are typically evaluated by quantifying histologic events in 10 fields of view at × 400 magnification. In the era of digital pathology, new variables are introduced that may affect HPF evaluation. The aim of this study was to determine the parameters that influence HPF in whole slide images (WSIs).

MATERIALS AND METHODS

Glass slides scanned on various devices (Leica's Aperio GT450, AT2, and ScanScope XT; Philips UltraFast Scanner; Hamamatsu's Nanozoomer 2.0HT; and 3DHistech's P1000) were compared to acquired digital slides reviewed on each vendor's respective WSI viewer software (e.g., Aperio ImageScope, ImageScope DX, Philips IMS, 3DHistech CaseViewer, and Hamamatsu NDP.view) and an in-house developed vendor-agnostic viewer. WSIs were reviewed at "×40" equivalent HPF on different sized monitors with varying display resolutions (1900 × 1080-4500 × 3000) and aspect ratios (e.g., Food and Drug Administration [FDA]-cleared 27" Philips PS27QHDCR, FDA-cleared 24" Dell MR2416, 24" Hewlett Packard Z24n G2, and 28" Microsoft Surface Studio). Digital and microscopic HPF areas were calculated and compared.

RESULTS

A significant variation of HPF area occurred between differing monitor size and display resolutions with minor differences between WSI viewers. No differences were identified by scanner or WSIs scanned at different resolutions (e.g., 0.5, 0.25, 0.24, and 0.12 μm/pixel).

CONCLUSION

Glass slide HPF at × 400 magnification with conventional light microscopy was not equivalent to "×40" digital HPF areas. Digital HPF quantification may vary due to differences in the tissue area displayed by monitor sizes, display resolutions, and WSI viewers but not by scanner or scanning resolution. These findings will need to be further clinically validated with potentially new digital metrics for evaluation.

A Regulatory Science Initiative to Harmonize and Standardize Digital Pathology and Machine Learning Processes to Speed up Clinical Innovation to Patients

Unlocking the full potential of pathology data by gaining computational access to histological pixel data and metadata (digital pathology) is one of the key promises of computational pathology. Despite scientific progress and several regulatory approvals for primary diagnosis using whole-slide imaging, true clinical adoption at scale is slower than anticipated. In the U.S., advances in digital pathology are often siloed pursuits by individual stakeholders, and to our knowledge, there has not been a systematic approach to advance the field through a regulatory science initiative. The Alliance for Digital Pathology (the Alliance) is a recently established, volunteer, collaborative, regulatory science initiative to standardize digital pathology processes to speed up innovation to patients. The purpose is: (1) to account for the patient perspective by including patient advocacy; (2) to investigate and develop methods and tools for the evaluation of effectiveness, safety, and quality to specify risks and benefits in the precompetitive phase; (3) to help strategize the sequence of clinically meaningful deliverables; (4) to encourage and streamline the development of ground-truth data sets for machine learning model development and validation; and (5) to clarify regulatory pathways by investigating relevant regulatory science questions. The Alliance accepts participation from all stakeholders, and we solicit clinically relevant proposals that will benefit the field at large. The initiative will dissolve once a clinical, interoperable, modularized, integrated solution (from tissue acquisition to diagnostic algorithm) has been implemented. In times of rapidly evolving discoveries, scientific input from subject-matter experts is one essential element to inform regulatory guidance and decision-making. The Alliance aims to establish and promote synergistic regulatory science efforts that will leverage diverse inputs to move digital pathology forward and ultimately improve patient care.

Outcome of Everolimus-Based Therapy in Hormone-Receptor-Positive Metastatic Breast Cancer Patients After Progression on Palbociclib

Background

Despite the approval of mTOR inhibitor everolimus and CDK4/6 inhibitors in the management of hormone-receptor-positive HER2 non-amplified metastatic breast cancer (HR+ HER2-MBC), the optimal sequence of therapy is unclear. There are no clinical data on efficacy of everolimus in HR+ HER2-MBC after cancer progresses on CDK4/6 inhibitors.

Objective

The objective of this study is to find the efficacy of everolimus in HR+ HER2-MBC after they progress on a CDK4/6 inhibitor palbociclib.

Methods

This is a retrospective, 2-institute review of HR+ HER2-MBC from Jan 2015 to March 2018 treated with everolimus after progression on palbociclib. Primary end point was median progression-free survival (PFS), secondary end points objective response rate (ORR), clinical benefit ratio (CBR), and overall survival (OS).

Results

Out of 41 women with median age 61 years (33, 87) enrolled, 66% had received adjuvant systemic therapy, 61% had visceral disease, and 95% had prior nonsteroidal aromatase inhibitors. About 83% women had 3 or more chemotherapy or hormonal therapies prior to everolimus. Kaplan-Meier estimates showed a median PFS of 4.2 months (95% confidence interval [CI]: 3.2-6.2). The median OS was 18.7 months (95% CI 9.5 to not reached). Objective response rate and CBR were both 17.1%.

Conclusion

Everolimus was associated with modest PFS and ORR in HR+ HER2-MBCs postprogression on palbociclib.

Pitfalls in assessing stromal tumor infiltrating lymphocytes (sTILs) in breast cancer

Stromal tumor-infiltrating lymphocytes (sTILs) are important prognostic and predictive biomarkers in triple-negative (TNBC) and HER2-positive breast cancer. Incorporating sTILs into clinical practice necessitates reproducible assessment. Previously developed standardized scoring guidelines have been widely embraced by the clinical and research communities. We evaluated sources of variability in sTIL assessment by pathologists in three previous sTIL ring studies. We identify common challenges and evaluate impact of discrepancies on outcome estimates in early TNBC using a newly-developed prognostic tool. Discordant sTIL assessment is driven by heterogeneity in lymphocyte distribution. Additional factors include: technical slide-related issues; scoring outside the tumor boundary; tumors with minimal assessable stroma; including lymphocytes associated with other structures; and including other inflammatory cells. Small variations in sTIL assessment modestly alter risk estimation in early TNBC but have the potential to affect treatment selection if cutpoints are employed. Scoring and averaging multiple areas, as well as use of reference images, improve consistency of sTIL evaluation. Moreover, to assist in avoiding the pitfalls identified in this analysis, we developed an educational resource available at www.tilsinbreastcancer.org/pitfalls.

Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group

Assessment of tumor-infiltrating lymphocytes (TILs) is increasingly recognized as an integral part of the prognostic workflow in triple-negative (TNBC) and HER2-positive breast cancer, as well as many other solid tumors. This recognition has come about thanks to standardized visual reporting guidelines, which helped to reduce inter-reader variability. Now, there are ripe opportunities to employ computational methods that extract spatio-morphologic predictive features, enabling computer-aided diagnostics. We detail the benefits of computational TILs assessment, the readiness of TILs scoring for computational assessment, and outline considerations for overcoming key barriers to clinical translation in this arena. Specifically, we discuss: 1. ensuring computational workflows closely capture visual guidelines and standards; 2. challenges and thoughts standards for assessment of algorithms including training, preanalytical, analytical, and clinical validation; 3. perspectives on how to realize the potential of machine learning models and to overcome the perceptual and practical limits of visual scoring.

Abstract P5-02-09: The prevalence of invasive lobular carcinomas with unconventional immunohistochemical phenotypes

Background The prognosis of invasive lobular carcinoma (ILC) is controversial. Some studies report equivalent or better outcomes than invasive ductal carcinoma, likely due to ILC’s favorable prognostic phenotype. Classic ILC (CL) is typically of low histologic grade, estrogen receptor (ER) positive and human epidermal growth factor receptor-2 (HER2) negative. However, worse long term outcomes have been observed because ILC tends to be multicentric, bilateral, and to show higher rates of late metastases in uncommon sites. Additionally, subtypes of ILC, including solid (SOL) and pleomorphic (PL), have been reported to display more aggressive behavior. Yet current treatment paradigms are distinguished by hormone receptor profile and stage, not by tumor morphology. This study aimed to evaluate the clinicopathologic and immunohistochemical (IHC) characteristics of ILC, particularly those with unusual patterns.

Methods

From 2009-2018, consecutive in-house biopsy and resection specimens with the diagnosis of ILC were identified by a retrospective review of our institutional database. Demographic, morphologic, and IHC data were obtained from pathology reports. Cases with equivocal HER2 IHC results are sent for fluorescence in situ hybridization analysis at our Center. CL ILC was defined by small cells with low nuclear grade and discohesive growth arranged in single file or cords. PL ILC is composed of larger, more atypical cells, which still exhibit discohesion typical of lobular phenotype. SOL ILC consists of tumors growing in large sheets with minimal intervening stroma.

Results

Over the nine year study period, a total of 3,028 ILCs from 2,322 patients were identified. Most (74%, 2229/3028) cases were classified as CL while 22% (665/3028) were PL and 4% (134/3028) SOL (Table 1). The median patient age was 60 years (range 32-95). The median tumor size was larger for PL (2.3 cm) and SOL (2.4 cm) subtypes than CL (1.1 cm). IHC results were available for 896 specimens. The majority of all ILCs showed an expected phenotype: ER positive and HER2 negative (99% CL, 84% PL, 97% SOL). A smaller subset of ER positive ILC were also HER2 positive (1% CL, 8% PL, 3% SOL). No CL or SOL ILC were ER negative; in contrast 8% (21/270) of PL ILC were ER negative. Twelve (5%) PL ER negative cases were also HER2 negative. Overall, 15% of ILC were HER2 positive, with the PL variant showing the highest incidence (11%, 30/270). Lymph node (LN) metastases were seen in 22% (66/291) of CL cases, 55% (53/96) PL and 29% (17/58) SOL. Metastases in 10 or more LN (N3) was seen only in PL and SOL cases (13% and 5%, respectively).

Conclusions

In this study, up to 8% of ILC cases were found to be ER negative, a finding unique to the PL variant. No CL or SOL ILC was ER negative. HER2 positivity, not typically expected in ILC, was seen in 15% of cases, most commonly in the PL type. ILC that was both negative for ER and HER2 was observed in 5% of cases, and only within the PL subgroup. PL ILC showed higher median tumor size at presentation than CL and nodal metastases were also more common in PL than in CL or SOL ILC (55% vs 22% and 29%, respectively). Our findings are in keeping with prior observations that compared to CL ILC. PL ILC exhibits features associated with unfavorable clinicopathologic features, such as higher tumor and nodal stage at presentation. Additionally, this is the first study to systematically examine the prevalence of unconventional IHC phenotypes of ILC (ER negativity and/or HER2 positivity) in a large series from one institution.

Table 1. Summary of Clinicopathologic FeaturesClassicPleomorphicSolidILC types (n=3028)2229665134Median age, (range)60 yrs (32-94)61 yrs (31-95)66 yrs (44-93)Median size, cm 1.12.32.4Immunophenotype (n=896)ER+/HER2-99% (551/559)84% (228/270)97% (65/67)ER+/HER2+1% (8/559)8% (21/270)3% (2/67)ER-/HER2+0% (0/559)3% (9/270)0% (0/67)ER-/HER2-0% (0/559)5% (12/270)0% (0/67)Lymph node status (n=445)N077% (225/291)45% (43/96)71% (41/58)N120% (59/291)31% (30/96)19% (11/58)N22% (7/291)11% (11/96)5% (3/58)N30% (0/291)13% (12/96)5% (3/58)

Citation Format: Matthew G Hanna, Anne Grabenstetter, Dara S Ross, Lee K Tan. The prevalence of invasive lobular carcinomas with unconventional immunohistochemical phenotypes [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P5-02-09.

Automatic quantification of HER2 gene amplification in invasive breast cancer from chromogenic in situ hybridization whole slide images

Human epidermal growth factor receptor 2 (HER2), a transmembrane tyrosine kinase receptor encoded by the ERBB2 gene on chromosome 17q12, is a predictive and prognostic biomarker in invasive breast cancer (BC). Approximately 20% of BC are HER2-positive as a result of ERBB2 gene amplification and overexpression of the HER2 protein. Quantification of HER2 is performed routinely on all invasive BCs, to assist in clinical decision making for prognosis and treatment for HER2-positive BC patients by manually counting gene signals. We propose an automated system to quantify the HER2 gene status from chromogenic in situ hybridization (CISH) whole slide images (WSI) in invasive BC. The proposed method selects untruncated and nonoverlapped singular nuclei from the cancer regions using color unmixing and machine learning techniques. Then, HER2 and chromosome enumeration probe 17 (CEP17) signals are detected based on the RGB intensity and counted per nucleus. Finally, the HER2-to-CEP17 signal ratio is calculated to determine the HER2 amplification status following the ASCO/CAP 2018 guidelines. The proposed method reduced the labor and time for the quantification. In the experiment, the correlation coefficient between the proposed automatic CISH quantification method and pathologist manual enumeration was 0.98. The p -values larger than 0.05 from the one-sided paired t -test ensured that the proposed method yields statistically indifferent results to the reference method. The method was established on WSI scanned by two different scanners. Through the experiments, the capability of the proposed system has been demonstrated.

Clinical-grade computational pathology using weakly supervised deep learning on whole slide images

The development of decision support systems for pathology and their deployment in clinical practice have been hindered by the need for large manually annotated datasets. To overcome this problem, we present a multiple instance learning-based deep learning system that uses only the reported diagnoses as labels for training, thereby avoiding expensive and time-consuming pixel-wise manual annotations. We evaluated this framework at scale on a dataset of 44,732 whole slide images from 15,187 patients without any form of data curation. Tests on prostate cancer, basal cell carcinoma and breast cancer metastases to axillary lymph nodes resulted in areas under the curve above 0.98 for all cancer types. Its clinical application would allow pathologists to exclude 65-75% of slides while retaining 100% sensitivity. Our results show that this system has the ability to train accurate classification models at unprecedented scale, laying the foundation for the deployment of computational decision support systems in clinical practice.

Feasibility of using the Omnyx digital pathology system for cytology practice

INTRODUCTION

Whole slide imaging systems have focused mostly on surgical pathologic evaluation. However, for pathology laboratories to become fully digital, cytology slides will also need to be digitized, managed, viewed, and analyzed. Our aim was to determine the feasibility of using the Omnyx whole slide imaging system for various purposes in cytology.

MATERIALS AND METHODS

Our institution implemented the Omnyx digital pathology system, which was tested for feasibility and not implemented for clinical use in cytology. Glass slides (n = 18), scanned using various whole slide scanners, were uploaded into the Omnyx system. The system was evaluated for its feasibility with cytology case management, digital slide navigation and annotation, telecytology, and cytologic-histologic correlation.

RESULTS

The Omnyx software was able to manage cases similar to a laboratory information system. Users were able to electronically distribute, search, and sort the clinical cases. A graphic dashboard approach and virtual slide tray is available for end users to evaluate cases. The ability to create custom folders and drag-and-drop images into these folders fulfilled clinical, quality assurance, education, and research needs. Innovative tools for digital slide navigation and annotation (eg, auto-pan, adding text to annotation, hiding annotations, image coregistration) offered innovative methods to work with slides. Omnyx also provided a mechanism for sharing images with others to perform teleconsultation.

CONCLUSIONS

We have demonstrated the feasibility of using the Omnyx whole slide imaging system for various purposes in cytology practice. The application supported, not only case management, but also the ability to perform cytologic-histologic correlation and telecytology. The viewer offered many features that improved digital slide navigation and annotation.

Validation of mitotic cell quantification via microscopy and multiple whole-slide scanners

BACKGROUND

The establishment of whole-slide imaging (WSI) as a medical diagnostic device allows that pathologists may evaluate mitotic activity with this new technology. Furthermore, the image digitalization provides an opportunity to develop algorithms for automatic quantifications, ideally leading to improved reproducibility as compared to the naked eye examination by pathologists. In order to implement them effectively, accuracy of mitotic figure detection using WSI should be investigated. In this study, we aimed to measure pathologist performance in detecting mitotic figures (MFs) using multiple platforms (multiple scanners) and compare the results with those obtained using a brightfield microscope.

METHODS

Four slides of canine oral melanoma were prepared and digitized using 4 WSI scanners. In these slides, 40 regions of interest (ROIs) were demarcated, and five observers identified the MFs using different viewing modes: microscopy and WSI. We evaluated the inter- and intra-observer agreements between modes with Cohen's Kappa and determined "true" MFs with a consensus panel. We then assessed the accuracy (agreement with truth) using the average of sensitivity and specificity.

RESULTS

In the 40 ROIs, 155 candidate MFs were detected by five pathologists; 74 of them were determined to be true MFs. Inter- and intra-observer agreement was mostly "substantial" or greater (Kappa = 0.594-0.939). Accuracy was between 0.632 and 0.843 across all readers and modes. After averaging over readers for each modality, we found that mitosis detection accuracy for 3 of the 4 WSI scanners was significantly less than that of the microscope (p = 0.002, 0.012, and 0.001).

CONCLUSIONS

This study is the first to compare WSIs and microscopy in detecting MFs at the level of individual cells. Our results suggest that WSI can be used for mitotic cell detection and offers similar reproducibility to the microscope, with slightly less accuracy.

Implementation of Digital Pathology Offers Clinical and Operational Increase in Efficiency and Cost Savings

CONTEXT.—

Digital pathology (DP) implementations vary in scale, based on aims of intended operation. Few laboratories have completed a full-scale DP implementation, which may be due to high overhead costs that disrupt the traditional pathology workflow. Neither standardized criteria nor benchmark data have yet been published showing practical return on investment after implementing a DP platform.

OBJECTIVE.—

To provide benchmark data and practical metrics to support operational efficiency and cost savings in a large academic center.

DESIGN.—

Metrics reviewed include archived pathology asset retrieval; ancillary test request for recurrent/metastatic disease; cost analysis and turnaround time (TAT); and DP experience survey.

RESULTS.—

Glass slide requests from the department slide archive and an off-site surgery center showed a 93% and 97% decrease, respectively. Ancillary immunohistochemical orders, compared in 2014 (52%)-before whole slide images (WSIs) were available in the laboratory information system-and 2017 (21%) showed $114 000/y in anticipated savings. Comprehensive comparative cost analysis showed a 5-year $1.3 million savings. Surgical resection cases with prior WSIs showed a 1-day decrease in TAT. A DP experience survey showed 80% of respondents agreed WSIs improved their clinical sign-out experience.

CONCLUSIONS.—

Implementing a DP operation showed a noteworthy increase in efficiency and operational utility. Digital pathology deployments and operations may be gauged by the following metrics: number of glass slide requests as WSIs become available, decrease in confirmatory testing for patients with metastatic/recurrent disease, long-term decrease in off-site pathology asset costs, and faster TAT. Other departments may use our benchmark data and metrics to enhance patient care and demonstrate return on investment to justify adoption of DP.

Urogenital symptoms in mitochondrial disease: overlooked and undertreated

Background and purpose

Bowel symptoms are well documented in mitochondrial disease. However, data concerning other pelvic organs is limited. A large case–control study has therefore been undertaken to determine the presence of lower urinary tract symptoms (LUTS) and sexual dysfunction in adults with genetically confirmed mitochondrial disease.

Methods

Adults with genetically confirmed mitochondrial disease and control subjects were recruited from a specialist mitochondrial clinic. The presence and severity of LUTS and their impact on quality of life, in addition to sexual dysfunction and bowel symptoms, were captured using four validated questionnaires. Subgroup analysis was undertaken in patients harbouring the m.3243A>G MT‐TL1 mitochondrial DNA mutation. A subset of patients underwent urodynamic studies to further characterize their LUTS.

Results

Data from 58 patients and 19 controls (gender and age matched) were collected. Adults with mitochondrial disease had significantly more overactive bladder (81.5% vs. 56.3%, P = 0.039) and low stream (34.5% vs. 5.3%, P = 0.013) urinary symptoms than controls. Urodynamic studies in 10 patients confirmed that bladder storage symptoms predominate. Despite high rates of LUTS, none of the patient group was receiving treatment. Female patients and those harbouring the m.3243A>G MT‐TL1 mutation experienced significantly more sexual dysfunction than controls (53.1% vs. 11.1%, P = 0.026, and 66.7% vs. 26.3%, P = 0.011, respectively).

Conclusions

Lower urinary tract symptoms are common but undertreated in adult mitochondrial disease, and female patients and those harbouring the m.3243A>G MT‐TL1 mutation experience sexual dysfunction. Given their impact on quality of life, screening for and treating LUTS and sexual dysfunction in adults with mitochondrial disease are strongly recommended.