Quantitative Image Analysis of Human Epidermal Growth Factor Receptor 2 Immunohistochemistry for Breast Cancer: Guideline From the College of American Pathologists

Global Study on the Accuracy of Human Epidermal Growth Factor Receptor 2-Low Diagnosis in Breast Cancer

CONTEXT.—

Recently, a new type of antibody-drug conjugate, trastuzumab-deruxtecan (T-DXd), has been approved for the treatment of metastatic breast cancer with low level of human epidermal growth factor receptor 2 (HER2) gene expression. Thereby, eligibility relies on an accurate diagnosis of HER2-low status defined by immunohistochemistry IHC 1+/2+ with no gene amplification.

OBJECTIVE.—

To assess pathologists' accuracy and training efficacy in the diagnosis of HER2-low.

DESIGN.—

Agreement rates of HER2-low scoring in breast cancer tissue were assessed between expert consensus and real-world pathologists (n = 77 from 14 countries) before and after a specific 4-hour training program for HER2-low detection. Two assays were evaluated, the Ventana Pathway 4B5 CDx and the Dako HercepTest (polyclonal). Concordance of the pathologists with consensus score and efficacy of training were measured by Cohen κ, overall rater agreement, and receiver operating characteristic (ROC) curve statistics.

RESULTS.—

In the Ventana 4B5 HER2-low category, baseline agreement rates were >80% but <90%. Negative percentage agreement was improved from 80.6% to 91.1% by training. In the HER2-0 category, positive percentage agreement (74.6%) was the only parameter below the 80% benchmark but was significantly improved to 89.2% after training. Training efficacy was confirmed by ROC curve analysis, which shows improvement for the identification of HER2-0 and HER2-low cases. Finally, in-depth examination of cases with discordant HER2 status disclosed specific issues of HER2-low underscoring and overscoring.

CONCLUSIONS.—

The ability of pathologists to achieve acceptable diagnostic accuracy in identifying patients with HER2-low breast cancer could be enhanced by short-term training. Potential routes to improve the quality of HER2-low scoring in clinical practice have been identified.

HER2 testing: evolution and update for a companion diagnostic assay

Human epidermal growth factor receptor 2 (HER2; encoded by ERBB2) testing has been a cornerstone of patient selection for HER2-targeted therapies, principally in breast cancer but also in several other solid tumours. Since the introduction of HercepTest as the original companion diagnostic for trastuzumab, HER2 assessment methods have evolved substantially, incorporating various testing modalities, from western blots, immunohistochemistry and fluorescence in situ hybridization, to early chromogenic quantitative methods and, probably in the future, fully quantitative methods. The advent of highly effective HER2-targeted antibody-drug conjugates with clinical activity at low levels of HER2 expression, such as trastuzumab deruxtecan, has necessitated the re-evaluation of HER2 testing, particularly for HER2-low tumours. In this Review, we provide an in-depth overview of the evolution of HER2 testing, the current clinical guidelines for HER2 testing across various solid tumours, challenges associated with current testing methodologies and the emerging potential of quantitative techniques. We discuss the importance of accurately defining HER2-low expression for therapeutic decision-making and how newer diagnostic approaches, such as quantitative immunofluorescence and RNA-based assays, might address the limitations of traditional immunohistochemistry-based methods. As the use of HER2-targeted therapies continues to expand to a wider range of tumour types, ensuring the precision and accuracy of HER2 testing will be crucial for guiding treatment strategies and improving patient outcomes.

HER2-Low Breast Cancer-Current Knowledge and Future Directions

The concept of binary classification of HER2 status has recently been challenged following the DESTINY-Breast trial data showing a clinically meaningful response to antibody-drug conjugates (ADCs) in invasive breast cancer expressing low levels of HER2. HER2-low breast cancer is defined as an immunohistochemistry (IHC) score of 1+ and 2+ without HER2 gene amplification. While HER2-low breast cancer does not represent a biological entity, it encompasses both hormone receptor-positive and triple-negative breast cancer. Differences exist between this group and HER2-null breast cancer. In this review, we provide an update on HER2-low and HER2-ultralow breast cancer, including background trial data, the evolution of HER2-low expression, current clinical guidelines, quality issues, and future directions.

Perspectives on Reducing Barriers to the Adoption of Digital and Computational Pathology Technology by Clinical Labs

Background/Objectives: Digital and computational pathology (DP/CP) tools have the potential to improve the efficiency and accuracy of the anatomic pathology workflow; however, current adoption among US hospital and reference labs remains low. Methods: To better understand the current utilization of DP/CP technology and barriers to widespread adoption, we conducted a survey among 63 anatomic pathologists and lab directors within the US health system. Results: The survey results indicated that current use cases for DP/CP involve streamlining traditional manual pathology and that labs would have substantial difficulty providing AI-guided image analysis if it were required by physicians today. Among potential catalysts for the broader adoption of DP/CP, pathologists identified clinical guidelines as a key resource for anatomic pathology, whose endorsement of DP/CP would be highly impactful for reducing current barriers. Conclusions: Expanded access to DP/CP may ultimately benefit all major stakeholders-patients, physicians, clinical laboratory professionals, care settings, and payers-and will therefore require collaboration across these groups.

Image analysis for bright-field HER2 in situ hybridization: validation for clinical use

The aim of the present study was to develop and validate a quantitative image analysis (IA) algorithm to aid pathologists in assessing bright-field HER2 in situ hybridization (ISH) tests in solid cancers. A cohort of 80 sequential cases (40 HER2-negative and 40 HER2-positive) were evaluated for HER2 gene amplification with bright-field ISH. We developed an IA algorithm using the ISH Module from HALO software to automatically quantify HER2 and CEP17 copy numbers per cell as well as the HER2/CEP17 ratio. We observed a high correlation of HER2/CEP17 ratio, an average of HER2 and CEP17 copy number per cell between visual and IA quantification (Pearson's correlation coefficient of 0.842, 0.916, and 0.765, respectively). IA was able to count from 124 cells to 47,044 cells (median of 5565 cells). The margin of error for the visual quantification of the HER2/CEP17 ratio and of the average of HER2 copy number per cell decreased from a median of 0.23 to 0.02 and from a median of 0.49 to 0.04, respectively, in IA. Curve estimation regression models showed that a minimum of 469 or 953 invasive cancer cells per case is needed to reach an average margin of error below 0.1 for the HER2/CEP17 ratio or for the average of HER2 copy number per cell, respectively. Lastly, on average, a case took 212.1 s to execute the IA, which means that it evaluates about 130 cells/s and requires 6.7 s/mm2. The concordance of the IA software with the visual scoring was 95%, with a sensitivity of 90% and a specificity of 100%. All four discordant cases were able to achieve concordant results after the region of interest adjustment. In conclusion, this validation study underscores the usefulness of IA in HER2 ISH testing, displaying excellent concordance with visual scoring and significantly reducing margins of error.

Computational pathology in the identification of HER2-low breast cancer: Opportunities and challenges

For the past 2 decades, pathologists have been accustomed to reporting the HER2 status of breast cancer as either positive or negative, based on HER2 IHC. Today, however, there is a clinical imperative to employ a 3-tier approach to interpreting HER2 IHC that can also identify tumours categorised as HER2-low. Meeting this need for a finer degree of discrimination may be challenging, and in this article, we consider the potential for the integration of computational approaches to support pathologists in achieving accurate and reproducible HER2 IHC scoring as well as outlining some of the practicalities involved.

Interobserver Variability in HER-2 Immunostaining Interpretation of Metastatic HER2 Low Breast Cancers in Cytology Specimens

BACKGROUND

Approximately, 55% of breast carcinomas are reported to be HER-2 low breast carcinomas. Trastuzumab-Deruxtecan is a new FDA-approved targeted therapy for HER-2 low metastatic breast carcinomas, making it essential that all efforts are made to identify these tumors in specimens submitted for pathologic examination. Cytology specimens are often the first and only modality of this assessment due to the ease of specimen procurement. This study aimed to determine the variability in HER-2 immunostaining interpretation among observers using cytologic specimens from metastatic sites.

DESIGN

A pathology database search was made to identify metastatic breast carcinoma reported in cytology specimens. A manual search was then done to identify cases of HER-2 low category, H&E cell block and HER-2 neu immunostain slides were retrieved for a total of 50 cases. Reviewer #1 and #2 independently interpreted HER-2 immunostain of all 50 cases. Only discordant cases were sent for reviewer-3 interpretation. All three were blinded by the metastatic site, and original HER-2 interpretation.

RESULTS

Of 50 cases, 11 cases (22%) were reported as concordant scores between reviewer #1 and reviewer #2 but had a discordant original IHC report. Additionally, 4 cases (8%) had discordant reporting of HER2 IHC stain between reviewer #1 and reviewer #2 making a total of 15 cases (30%) with overall discordant results.

CONCLUSION

This study highlights the interobserver variability of HER-2 immunostain interpretation for HER-2 low category of breast carcinomas. We recommend the need for more robust laboratory techniques including molecular for uniform identification of these unique targetable metastatic breast carcinoma groups.

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.

Fully Automated Artificial Intelligence Solution for Human Epidermal Growth Factor Receptor 2 Immunohistochemistry Scoring in Breast Cancer: A Multireader Study

PURPOSE

The proven efficacy of human epidermal growth factor receptor 2 (HER2) antibody-drug conjugate therapy for treating HER2-low breast cancers necessitates more accurate and reproducible HER2 immunohistochemistry (IHC) scoring. We aimed to validate performance and utility of a fully automated artificial intelligence (AI) solution for interpreting HER2 IHC in breast carcinoma.

MATERIALS AND METHODS

A two-arm multireader study of 120 HER2 IHC whole-slide images from four sites assessed HER2 scoring by four surgical pathologists without and with the aid of an AI HER2 solution. Both arms were compared with high-confidence ground truth (GT) established by agreement of at least four of five breast pathology subspecialists according to ASCO/College of American Pathologists (CAP) 2018/2023 guidelines.

RESULTS

The mean interobserver agreement among GT pathologists across all HER2 scores was 72.4% (N = 120). The AI solution demonstrated high accuracy for HER2 scoring, with 92.1% agreement on slides with high confidence GT (n = 92). The use of the AI tool led to improved performance by readers, interobserver agreement increased from 75.0% for digital manual read to 83.7% for AI-assisted review, and scoring accuracy improved from 85.3% to 88.0%. For the distinction of HER2 0 from 1+ cases (n = 58), pathologists supported by AI showed significantly higher interobserver agreement (69.8% without AI v 87.4% with AI) and accuracy (81.9% without AI v 88.8% with AI).

CONCLUSION

This study demonstrated utility of a fully automated AI solution to aid in scoring HER2 IHC accurately according to ASCO/CAP 2018/2023 guidelines. Pathologists supported by AI showed improvements in HER2 IHC scoring consistency and accuracy, especially for distinguishing HER2 0 from 1+ cases. This AI solution could be used by pathologists as a decision support tool for enhancing reproducibility and consistency of HER2 scoring and particularly for identifying HER2-low breast cancers.

The Performance and Clinical Applicability of HER2 Digital Image Analysis in Breast Cancer: A Systematic Review

This systematic review aims to address the research gap in the performance of computational algorithms for the digital image analysis of HER2 images in clinical settings. While numerous studies have explored various aspects of these algorithms, there is a lack of comprehensive evaluation regarding their effectiveness in real-world clinical applications. We conducted a search of the Web of Science and PubMed databases for studies published from 31 December 2013 to 30 June 2024, focusing on performance effectiveness and components such as dataset size, diversity and source, ground truth, annotation, and validation methods. The study was registered with PROSPERO (CRD42024525404). Key questions guiding this review include the following: How effective are current computational algorithms at detecting HER2 status in digital images? What are the common validation methods and dataset characteristics used in these studies? Is there standardization of algorithm evaluations of clinical applications that can improve the clinical utility and reliability of computational tools for HER2 detection in digital image analysis? We identified 6833 publications, with 25 meeting the inclusion criteria. The accuracy rate with clinical datasets varied from 84.19% to 97.9%. The highest accuracy was achieved on the publicly available Warwick dataset at 98.8% in synthesized datasets. Only 12% of studies used separate datasets for external validation; 64% of studies used a combination of accuracy, precision, recall, and F1 as a set of performance measures. Despite the high accuracy rates reported in these studies, there is a notable absence of direct evidence supporting their clinical application. To facilitate the integration of these technologies into clinical practice, there is an urgent need to address real-world challenges and overreliance on internal validation. Standardizing study designs on real clinical datasets can enhance the reliability and clinical applicability of computational algorithms in improving the detection of HER2 cancer.

Principles of Analytic Validation of Immunohistochemical Assays: Guideline Update

CONTEXT.—

In 2014, the College of American Pathologists developed an evidence-based guideline to address analytic validation of immunohistochemical assays. Fourteen recommendations were offered. Per the National Academy of Medicine standards for developing trustworthy guidelines, guidelines should be updated when new evidence suggests modifications.

OBJECTIVE.—

To assess evidence published since the release of the original guideline and develop updated evidence-based recommendations.

DESIGN.—

The College of American Pathologists convened an expert panel to perform a systematic review of the literature and update the original guideline recommendations using the Grading of Recommendations Assessment, Development and Evaluation approach.

RESULTS.—

Two strong recommendations, 1 conditional recommendation, and 12 good practice statements are offered in this updated guideline. They address analytic validation or verification of predictive and nonpredictive assays, and recommended revalidation procedures following changes in assay conditions.

CONCLUSIONS.—

While many of the original guideline statements remain similar, new recommendations address analytic validation of assays with distinct scoring systems, such as programmed death receptor-1 and analytic verification of US Food and Drug Administration approved/cleared assays; more specific guidance is offered for validating immunohistochemistry performed on cytology specimens.

Early Breast Cancer Risk Assessment: Integrating Histopathology with Artificial Intelligence

Effective risk assessment in early breast cancer is essential for informed clinical decision-making, yet consensus on defining risk categories remains challenging. This paper explores evolving approaches in risk stratification, encompassing histopathological, immunohistochemical, and molecular biomarkers alongside cutting-edge artificial intelligence (AI) techniques. Leveraging machine learning, deep learning, and convolutional neural networks, AI is reshaping predictive algorithms for recurrence risk, thereby revolutionizing diagnostic accuracy and treatment planning. Beyond detection, AI applications extend to histological subtyping, grading, lymph node assessment, and molecular feature identification, fostering personalized therapy decisions. With rising cancer rates, it is crucial to implement AI to accelerate breakthroughs in clinical practice, benefiting both patients and healthcare providers. However, it is important to recognize that while AI offers powerful automation and analysis tools, it lacks the nuanced understanding, clinical context, and ethical considerations inherent to human pathologists in patient care. Hence, the successful integration of AI into clinical practice demands collaborative efforts between medical experts and computational pathologists to optimize patient outcomes.

Color Standardization and Stain Intensity Calibration for Whole Slide Image-Based Immunohistochemistry Assessment

Automated quantification of human epidermal growth factor receptor 2 (HER2) immunohistochemistry (IHC) using whole slide imaging (WSI) is expected to eliminate subjectivity in visual assessment. However, the color intensity in WSI varies depending on the staining process and scanner device. Such variations affect the image analysis results. This paper presents methods to diminish the influence of color variation produced in the staining process using a calibrator slide consisting of peptide-coated microbeads. The calibrator slide is stained along with tissue sample slides, and the 3,3'-diaminobenzidine (DAB) color intensities of the microbeads are used for calibrating the color variation of the sample slides. An off-the-shelf image analysis tool is employed for the automated assessment, in which cells are classified by the thresholds for the membrane staining. We have adopted two methods for calibrating the color variation based on the DAB color intensities obtained from the calibrator slide: (1) thresholds for classifying the DAB membranous intensity are adjusted, and (2) the color intensity of WSI is corrected. In the experiment, the calibrator slides and tissue of breast cancer slides were stained together on different days and used to test our protocol. With the proposed protocol, the discordance in the HER2 evaluation was reduced to one slide out of 120 slides.

High HER2 Intratumoral Heterogeneity Is a Predictive Factor for Poor Prognosis in Early-Stage and Locally Advanced HER2-Positive Breast Cancer

PURPOSE

Breast cancer tumors frequently have intratumoral heterogeneity (ITH). Tumors with high ITH cause therapeutic resistance and have human epidermal growth factor receptor 2 (HER2) heterogeneity in response to HER2-targeted therapies. This study aimed to investigate whether high HER2 heterogeneity levels were clinically related to a poor prognosis for HER2-targeted adjuvant therapy resistance in primary breast cancers.

METHODS

This study included patients with primary breast cancer (n = 251) treated with adjuvant HER2-targeted therapies. HER2 heterogeneity was manifested by the shape of HER2 fluorescence in situ hybridization amplification (FISH) distributed histograms with the HER2 gene copy number within a tumor sample. Each tumor was classified into a biphasic grade graph (high heterogeneity [HH]) group or a monophasic grade graph (low heterogeneity [LH]) group based on heterogeneity. Both groups were evaluated for disease-free survival (DFS) and overall survival (OS) for a median of ten years of annual follow-up.

RESULTS

Of 251 patients with HER2-positive breast cancer, 46 (18.3%) and 205 (81.7%) were classified into the HH and LH groups, respectively. The HH group had more distant metastases and a poorer prognosis than the LH group (DFS: p < 0.001 (HH:63% vs. LH:91% at 10 years) and for the OS: p = 0.012 (HH:78% vs. LH:95% at 10 years).

CONCLUSIONS

High HER2 heterogeneity is a poor prognostic factor in patients with HER2-positive breast cancer. A novel approach to heterogeneity, which is manifested by the shape of HER2 FISH distributions, might be clinically useful in the prognosis prediction of patients after HER2 adjuvant therapy.

Invisible for a few but essential for many: the role of Histotechnologists in the establishment of digital pathology

Digital pathology (DP) is indisputably the future for histopathology laboratories. The process of digital implementation requires deep workflow reorganisation which involves an interdisciplinary team. This transformation may have the greatest impact on the Histotechnologist (HTL) profession. Our review of the literature has clearly revealed that the role of HTLs in the establishment of DP is being unnoticed and guidance is limited. This article aims to bring HTLs from behind-the-scenes into the spotlight. Our objective is to provide them guidance and practical recommendations to successfully contribute to the implementation of a new digital workflow. Furthermore, it also intends to contribute for improvement of study programs, ensuring the role of HTL in DP is addressed as part of graduate and post-graduate education. In our review, we report on the differences encountered between workflow schemes and the limitations observed in this process. The authors propose a digital workflow to achieve its limitless potential, focusing on the HTL's role. This article explores the novel responsibilities of HTLs during specimen gross dissection, embedding, microtomy, staining, digital scanning, and whole slide image quality control. Furthermore, we highlight the benefits and challenges that DP implementation might bring the HTLs career. HTLs have an important role in the digital workflow: the responsibility of achieving the perfect glass slide.

Digital cytology part 2: artificial intelligence in cytology: a concept paper with review and recommendations from the American Society of Cytopathology Digital Cytology Task Force

Digital cytology and artificial intelligence (AI) are gaining greater adoption in the cytology laboratory. However, peer-reviewed real-world data and literature are lacking in regard to the current clinical landscape. The American Society of Cytopathology in conjunction with the International Academy of Cytology and the Digital Pathology Association established a special task force comprising 20 members with expertise and/or interest in digital cytology. The aim of the group was to investigate the feasibility of incorporating digital cytology, specifically cytology whole slide scanning and AI applications, into the workflow of the laboratory. In turn, the impact on cytopathologists, cytologists (cytotechnologists), and cytology departments were also assessed. The task force reviewed existing literature on digital cytology, conducted a worldwide survey, and held a virtual roundtable discussion on digital cytology and AI with multiple industry corporate representatives. This white paper, presented in 2 parts, summarizes the current state of digital cytology and AI practice in global cytology practice. Part 1 of the white paper is presented as a separate paper which details a review and best practice recommendations for incorporating digital cytology into practice. Part 2 of the white paper presented here provides a comprehensive review of AI in cytology practice along with best practice recommendations and legal considerations. Additionally, the cytology global survey results highlighting current AI practices by various laboratories, as well as current attitudes, are reported.

Algorithm-assisted diagnosis of Hirschsprung's disease - evaluation of robustness and comparative image analysis on data from various labs and slide scanners

BACKGROUND

Differences in the preparation, staining and scanning of digital pathology slides create significant pre-analytic variability. Algorithm-assisted tools must be able to contend with this variability in order to be applicable in clinical practice. In a previous study, a decision support algorithm was developed to assist in the diagnosis of Hirschsprung's disease. In the current study, we tested the robustness of this algorithm while assessing for pre-analytic factors which may affect its performance.

METHODS

The decision support algorithm was used on digital pathology slides obtained from four different medical centers (A-D) and scanned by three different scanner models (by Philips, Hamamatsu and 3DHISTECH). A total of 192 cases and 1782 slides were used in this study. RGB histograms were constructed to compare images from the various medical centers and scanner models and highlight the differences in color and contrast.

RESULTS

The algorithm was able to correctly identify ganglion cells in 99.2% of cases, from all medical centers (All scanned by the Philips slide scanner) as well as 95.5% and 100% of the slides scanned by the 3DHISTECH and Hamamatsu brand slide scanners, respectively. The total error rate for center D was lower than the other medical centers (3.9% vs 7.1%, 10.8% and 6% for centers A-C, respectively), the vast majority of errors being false positives (3.45% vs 0.45% false negatives). The other medical centers showed a higher rate of false negatives in relation to false positives (6.81% vs 0.29%, 9.8% vs 1.2% and 5.37% vs 0.63% for centers A-C, respectively). The total error rates for the Philips, Hamamatsu and 3DHISTECH brand scanners were 3.9%, 3.2% and 9.8%, respectively. RGB histograms demonstrated significant differences in pixel value distribution between the four medical centers, as well as between the 3DHISTECH brand scanner when compared to the Philips and Hamamatsu brand scanners.

CONCLUSIONS

The results reported in this paper suggest that the algorithm-based decision support system has sufficient robustness to be applicable for clinical practice. In addition, the novel method used in its development - Hierarchial-Contexual Analysis (HCA) may be applicable to the development of algorithm-assisted tools in other diseases, for which available datasets are limited. Validation of any given algorithm-assisted support system should nonetheless include data from as many medical centers and scanner models as possible.

High contrast breast cancer biomarker semi-quantification and immunohistochemistry imaging using upconverting nanoparticles

Breast cancer is the second leading cause of cancer death in women. Current clinical treatment stratification practices open up an avenue for significant improvements, potentially through advancements in immunohistochemistry (IHC) assessments of biopsies. We report a high contrast upconverting nanoparticles (UCNP) labeling to distinguish different levels of human epidermal growth factor receptor 2 (HER2) in HER2 control pellet arrays (CPAs) and HER2-positive breast cancer tissue. A simple Fourier transform algorithm trained on CPAs was sufficient to provide a semi-quantitative HER2 assessment tool for breast cancer tissues. The UCNP labeling had a signal-to-background ratio of 40 compared to the negative control.

Emerging Landscape of Targeted Therapy of Breast Cancers With Low Human Epidermal Growth Factor Receptor 2 Protein Expression

CONTEXT.—

Human epidermal growth factor receptor 2 (HER2) status in breast cancer is currently classified as negative or positive for selecting patients for anti-HER2 targeted therapy. The evolution of the HER2 status has included a new HER2-low category defined as an HER2 immunohistochemistry score of 1+ or 2+ without gene amplification. This new category opens the door to a targetable HER2-low breast cancer population for which new treatments may be effective.

OBJECTIVE.—

To review the current literature on the emerging category of breast cancers with low HER2 protein expression, including the clinical, histopathologic, and molecular features, and outline the clinical trials and best practice recommendations for identifying HER2-low-expressing breast cancers by immunohistochemistry.

DATA SOURCES.—

We conducted a literature review based on peer-reviewed original articles, review articles, regulatory communications, ongoing and past clinical trials identified through ClinicalTrials.gov, and the authors' practice experience.

CONCLUSIONS.—

The availability of new targeted therapy potentially effective for patients with breast cancers with low HER2 protein expression requires multidisciplinary recognition. In particular, pathologists need to recognize and identify this category to allow the optimal selection of patients for targeted therapy.

Quantitative Imaging Analysis Fluorescence In Situ Hybridization Validation for Clinical HER2 Testing in Breast Cancer

CONTEXT.—

Quantitative imaging is a promising tool that is gaining wide use across several areas of pathology. Although there has been increasing adoption of morphologic and immunohistochemical analysis, the adoption of evaluation of fluorescence in situ hybridization (FISH) on formalin-fixed, paraffin-embedded tissue has been limited because of complexity and lack of practice guidelines.

OBJECTIVE.—

To perform human epidermal growth factor receptor 2 (HER2) FISH validation in breast carcinoma in accordance with the American Society of Clinical Oncology/College of American Pathologists (ASCO/CAP) 2018 guideline.

DESIGN.—

Clinical validation of HER2 FISH was performed using the US Food and Drug Administration-approved dual-probe HER2 IQFISH (Dako, Carpinteria, California) with digital scanning performed on a PathFusion (Applied Spectral Imaging, Carlsbad, California) system. Validation parameters evaluated included z-stacking, classifier, accuracy, precision, software, and hardware settings. Finally, we evaluated the performance of digital enumeration on clinical samples in a real-world setting.

RESULTS.—

The accuracy samples showed a final concordance of 95.3% to 100% across HER2 groups 1 to 5. During clinical implementation for HER2 groups 2, 3, and 4, we achieved a final concordance of 76% (95 of 125). Of these cases, only 8% (10 of 125) had discordances with clinical impact that could be identified algorithmically and triaged for manual review.

CONCLUSIONS.—

Digital FISH enumeration is a useful tool to improve the efficacy of HER2 FISH enumeration and capture genetic heterogeneity across HER2 signals. Excluding cases with high background or poor image quality and manual review of cases with ASCO/CAP group discordances can further improve the efficiency of digital HER2 FISH enumeration.

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.

Improved concordance of challenging human epidermal growth factor receptor 2 dual in-situ hybridisation cases with the use of a digital image analysis algorithm in breast cancer

AIMS

Accurate assessment of human epidermal growth factor receptor 2 (HER2) expression by HER2 immunohistochemistry and in-situ hybridisation (ISH) is critical for the management of patients with breast cancer. The revised 2018 ASCO/CAP guidelines define 5 groups based on HER2 expression and copy number. Manual pathologist quantification by light microscopy of equivocal and less common HER2 ISH groups (groups 2-4) can be challenging, and there are no data on interobserver variability in reporting of these cases. We sought to determine whether a digital algorithm could improve interobserver variability in the assessment of difficult HER2 ISH cases.

METHODS AND RESULTS

HER2 ISH was evaluated in a cohort enriched for less common HER2 patterns using standard light microscopy versus analysis of whole slide images using the Roche uPath HER2 dual ISH image analysis algorithm. Standard microscopy demonstrated significant interobserver variability with a Fleiss's kappa value of 0.471 (fair-moderate agreement) improving to 0.666 (moderate-good) with the use of the algorithm. For HER2 group designation (groups 1-5), there was poor-moderate reliability between pathologists by microscopy [intraclass correlation coefficient (ICC) = 0.526], improving to moderate-good agreement (ICC = 0.763) with the use of the algorithm. In subgroup analysis, the algorithm improved concordance particularly in groups 2, 4 and 5. Time to enumerate cases was also significantly reduced.

CONCLUSION

This work demonstrates the potential of a digital image analysis algorithm to improve the concordance of pathologist HER2 amplification status reporting in less common HER2 groups. This has the potential to improve therapy selection and outcomes for patients with HER2-low and borderline HER2-amplified breast cancers.

Applications of Artificial Intelligence in Breast Pathology

CONTEXT.—

Increasing implementation of whole slide imaging together with digital workflow and advances in computing capacity enable the use of artificial intelligence (AI) in pathology, including breast pathology. Breast pathologists often face a significant workload, with diagnosis complexity, tedious repetitive tasks, and semiquantitative evaluation of biomarkers. Recent advances in developing AI algorithms have provided promising approaches to meet the demand in breast pathology.

OBJECTIVE.—

To provide an updated review of AI in breast pathology. We examined the success and challenges of current and potential AI applications in diagnosing and grading breast carcinomas and other pathologic changes, detecting lymph node metastasis, quantifying breast cancer biomarkers, predicting prognosis and therapy response, and predicting potential molecular changes.

DATA SOURCES.—

We obtained data and information by searching and reviewing literature on AI in breast pathology from PubMed and based our own experience.

CONCLUSIONS.—

With the increasing application in breast pathology, AI not only assists in pathology diagnosis to improve accuracy and reduce pathologists' workload, but also provides new information in predicting prognosis and therapy response.

Lessons Learned, Challenges Taken, and Actions Made for "Precision" Immunohistochemistry. Analysis and Perspectives From the NordiQC Proficiency Testing Program

Immunohistochemistry (IHC) has for decades been an integrated method within pathology applied to gain diagnostic, prognostic, and predictive information. However, the multimodality of the analytical phase of IHC is a challenge to ensure the reproducibility of IHC, which has been documented by external quality assessment (EQA) programs for many biomarkers. More than 600 laboratories participate in the Nordic immunohistochemical Quality Control EQA program for IHC. In the period, 2017-2021, 65 different biomarkers were assessed and a total of 31,967 results were evaluated. An overall pass rate of 79% was obtained being an improvement compared with 71% for the period, 2003-2015. The pass rates for established predictive biomarkers (estrogen receptor, progesterone receptor, and HER2) for breast carcinoma were most successful showing mean pass rates of 89% to 92%. Diagnostic IHC biomarkers as PAX8, SOX10, and different cytokeratins showed a wide spectrum of pass rates ranging from 37% to 95%, mean level of 75%, and attributed to central parameters as access to sensitive and specific antibodies but also related to purpose of the IHC test and validation performed accordingly to this. Seven new diagnostic biomarkers were introduced, and all showed inferior pass rates compared with the average level for diagnostic biomarkers emphasizing the challenge to optimize, validate, and implement new IHC biomarkers. Nordic immunohistochemical Quality Control operates by "Fit-For-Purpose" EQA principles and for programmed death-ligand 1, 2 segments are offered aligned to the "3-dimensional" approach-bridging diagnostic tests, drugs to be offered, and diseases addressed. Mean pass rates of 65% and 79% was obtained in the 2 segments for programmed death-ligand 1.

HER2 Intratumoral Heterogeneity in Breast Cancer, an Evolving Concept

Amplification and/or overexpression of human epidermal growth factor receptor 2 (HER2) in breast cancer is associated with an adverse prognosis. The introduction of anti-HER2 targeted therapy has dramatically improved the clinical outcomes of patients with HER2-positive breast cancer. Unfortunately, a significant number of patients eventually relapse and develop distant metastasis. HER2 intratumoral heterogeneity (ITH) has been reported to be associated with poor prognosis in patients with anti-HER2 targeted therapies and was proposed to be a potential mechanism for anti-HER2 resistance. In this review, we described the current definition, common types of HER2 ITH in breast cancer, the challenge in interpretation of HER2 status in cases showing ITH and the clinical applications of anti-HER2 agents in breast cancer showing heterogeneous HER2 expression. Digital image analysis has emerged as an objective and reproducible scoring method and its role in the assessment of HER2 status with ITH remains to be demonstrated.

UK recommendations for HER2 assessment in breast cancer: an update

The last UK breast cancer (BC) human epidermal growth factor receptor 2 (HER2) testing guideline recommendations were published in 2015. Since then, new data and therapeutic strategies have emerged. The American Society of Clinical Oncology (ASCO)/College of American Pathologists (CAP) published a focused update in 2018 that reclassified in situ hybridisation (ISH) Group 2 (immunohistochemistry (IHC) score 2+and HER2/chromosome enumeration probe 17 (CEP17) ratio ≥2.0 and HER2 copy number <4.0 signals/cell), as well as addressed other concerns raised by previous guidelines. The present article further refines UK guidelines, with specific attention to definitions of HER2 status focusing on eight key areas: (1) HER2 equivocal (IHC 2+) and assignment of the ASCO/CAP ISH group 2 tumours; (2) the definition of the group of BCs with low IHC scores for HER2 with emphasis on the distinction between IHC score 1+ (HER2-Low) from HER2 IHC score 0 (HER2 negative); (3) reporting cases showing HER2 heterogeneity; (4) HER2 testing in specific settings, including on cytological material; (5) repeat HER2 testing, (6) HER2 testing turnaround time targets; (7) the potential role of next generation sequencing and other diagnostic molecular assays for routine testing of HER2 status in BC and (8) use of image analysis to score HER2 IHC. The two tiered system of HER2 assessment remains unchanged, with first line IHC and then ISH limited to IHC equivocal cases (IHC score 2+) but emerging data on the relationship between IHC scores and levels of response to anti-HER2 therapy are considered. Here, we present the latest UK recommendations for HER2 status evaluation in BC, and where relevant, the differences from other published guidelines.

The Approach of Artificial Intelligence in Neuroendocrine Carcinomas of the Breast: A Next Step towards Precision Pathology?—A Case Report and Review of the Literature

Primary neuroendocrine tumors (NETs) of the breast are considered a rare and undervalued subtype of breast carcinoma that occur mainly in postmenopausal women and are graded as G1 or G2 NETs or an invasive neuroendocrine carcinoma (NEC) (small cell or large cell). To establish a final diagnosis of breast carcinoma with neuroendocrine differentiation, it is essential to perform an immunohistochemical profile of the tumor, using antibodies against synaptophysin or chromogranin, as well as the MIB-1 proliferation index, one of the most controversial markers in breast pathology regarding its methodology in current clinical practice. A standardization error between institutions and pathologists regarding the evaluation of the MIB-1 proliferation index is present. Another challenge refers to the counting process of MIB-1′s expressiveness, which is known as a time-consuming process. The involvement of AI (artificial intelligence) automated systems could be a solution for diagnosing early stages, as well. We present the case of a post-menopausal 79-year-old woman diagnosed with primary neuroendocrine carcinoma of the breast (NECB). The purpose of this paper is to expose the interpretation of MIB-1 expression in our patient’ s case of breast neuroendocrine carcinoma, assisted by artificial intelligence (AI) software (HALO—IndicaLabs), and to analyze the associations between MIB-1 and common histopathological parameters.

HER2 Expression in Bladder Cancer: A Focused View on Its Diagnostic, Prognostic, and Predictive Role

Bladder cancer (BC) is a heterogeneous disease from a molecular, morphological, and clinical standpoint. HER2 is a known oncogene involved in bladder carcinogenesis. Assessing HER2 overexpression as a result of its molecular changes in a routine pathology practice using immunohistochemistry might be a useful adjunct in several scenarios, namely (1) to correctly identify flat urothelial lesions and inverted urothelial lesions in the diagnostic setting; (2) to provide prognostic hints in both non-muscle invasive (NMI) and muscle invasive (MI) tumors, thus supplementing risk stratification tools, especially when evaluating higher-risk tumors such as those with variant morphology; (3) to improve antibody panels as a surrogate marker of BC molecular subtyping. Furthermore, the potential of HER2 as a therapeutic target has been only partly explored so far, in light of the ongoing development of novel target therapies.

Digital image analysis and assisted reading of the HER2 score display reduced concordance: pitfalls in the categorisation of HER2-low breast cancer

AIMS

Digital image analysis (DIA) is used increasingly as an assisting tool to evaluate biomarkers, including human epidermal growth factor receptor 2 (HER2) in invasive breast cancer (BC). DIA can assist pathologists in HER2 evaluation by presenting quantitative information about the HER2 staining in APP assisted reading (AR). Concurrently, the HER2-low category (HER2-1+/2+ without HER2 gene amplification) has gained prominence due to newly developed antibody-drug conjugates. However, major inter- and intraobserver variability have been observed for the entity. The present quality assurance study investigated the concordance between DIA and AR in clinical use, especially concerning the HER2-low category.

METHODS AND RESULTS

HER2 immunohistochemistry (IHC) in 761 tumours from 727 patients was evaluated in tissue microarray (TMA) cores by DIA (Visiopharm HER2-CONNECT) and AR. Overall concordance between HER2-scores were 73% (n = 552, weighted-κ: 0.66), and 88% (n = 669, weighted-κ: 0.70), when combining HER2-0/1+. A total of 205 scores were discordant by one category, while four were discordant by two categories. A heterogeneous HER2 pattern was relatively common in the discordant cases and a pitfall in the categorisation of HER2-low BC. AR more commonly reassigned a lower HER2 score (from HER2-1+ to HER2-0) within the HER2-low subgroup (n = 624) compared with DIA.

CONCLUSION

DIA and AR display moderate agreement with heterogeneous and aberrant staining, representing a source of discordance and a pitfall in the evaluation of HER2.

Determining HER2 Status by Artificial Intelligence: An Investigation of Primary, Metastatic, and HER2 Low Breast Tumors

The expression of human epidermal growth factor receptor 2 (HER2) protein or gene transcripts is critical for therapeutic decision making in breast cancer. We examined the performance of a digitalized and artificial intelligence (AI)-assisted workflow for HER2 status determination in accordance with the American Society of Clinical Oncology (ASCO)/College of Pathologists (CAP) guidelines. Our preliminary cohort consisted of 495 primary breast carcinomas, and our study cohort included 67 primary breast carcinomas and 30 metastatic deposits, which were evaluated for HER2 status by immunohistochemistry (IHC) and in situ hybridization (ISH). Three practicing breast pathologists independently assessed and scored slides, building the ground truth. Following a washout period, pathologists were provided with the results of the AI digital image analysis (DIA) and asked to reassess the slides. Both rounds of assessment from the pathologists were compared to the AI results and ground truth for each slide. We observed an overall HER2 positivity rate of 15% in our study cohort. Moderate agreement (Cohen's κ 0.59) was observed between the ground truth and AI on IHC, with most discrepancies occurring between 0 and 1+ scores. Inter-observer agreement amongst pathologists was substantial (Fleiss´ κ 0.77) and pathologists' agreement with AI scores was 80.6%. Substantial agreement of the AI with the ground truth (Cohen´s κ 0.80) was detected on ISH-stained slides, and the accuracy of AI was similar for the primary and metastatic tumors. We demonstrated the feasibility of a combined HER2 IHC and ISH AI workflow, with a Cohen's κ of 0.94 when assessed in accordance with the ASCO/CAP recommendations.

Artificial intelligence in cancer research and precision medicine: Applications, limitations and priorities to drive transformation in the delivery of equitable and unbiased care

Artificial intelligence (AI) has experienced explosive growth in oncology and related specialties in recent years. The improved expertise in data capture, the increased capacity for data aggregation and analytic power, along with decreasing costs of genome sequencing and related biologic "omics", set the foundation and need for novel tools that can meaningfully process these data from multiple sources and of varying types. These advances provide value across biomedical discovery, diagnosis, prognosis, treatment, and prevention, in a multimodal fashion. However, while big data and AI tools have already revolutionized many fields, medicine has partially lagged due to its complexity and multi-dimensionality, leading to technical challenges in developing and validating solutions that generalize to diverse populations. Indeed, inner biases and miseducation of algorithms, in view of their implementation in daily clinical practice, are increasingly relevant concerns; critically, it is possible for AI to mirror the unconscious biases of the humans who generated these algorithms. Therefore, to avoid worsening existing health disparities, it is critical to employ a thoughtful, transparent, and inclusive approach that involves addressing bias in algorithm design and implementation along the cancer care continuum. In this review, a broad landscape of major applications of AI in cancer care is provided, with a focus on cancer research and precision medicine. Major challenges posed by the implementation of AI in the clinical setting will be discussed. Potentially feasible solutions for mitigating bias are provided, in the light of promoting cancer health equity.

Using Whole Slide Gray Value Map to Predict HER2 Expression and FISH Status in Breast Cancer

Accurate detection of HER2 expression through immunohistochemistry (IHC) is of great clinical significance in the treatment of breast cancer. However, manual interpretation of HER2 is challenging, due to the interobserver variability among pathologists. We sought to explore a deep learning method to predict HER2 expression level and gene status based on a Whole Slide Image (WSI) of the HER2 IHC section. When applied to 228 invasive breast carcinoma of no special type (IBC-NST) DAB-stained slides, our GrayMap+ convolutional neural network (CNN) model accurately classified HER2 IHC level with mean accuracy 0.952 ± 0.029 and predicted HER2 FISH status with mean accuracy 0.921 ± 0.029. Our result also demonstrated strong consistency in HER2 expression score between our system and experienced pathologists (intraclass correlation coefficient (ICC) = 0.903, Cohen's κ = 0.875). The discordant cases were found to be largely caused by high intra-tumor staining heterogeneity in the HER2 IHC group and low copy number in the HER2 FISH group.

Development and Validation of a Digital Analysis Method to Quantify CD3-immunostained T Lymphocytes in Whole Slide Images of Crohn's Disease Biopsies

The T-lymphocyte-mediated inflammation in Crohn's disease can be assessed by quantifying CD3-positive T-lymphocyte counts in colonic sections. We developed and validated a process to reliably quantify immunohistochemical marker-positive cells in a high-throughput setting using whole slide images (WSIs) of CD3-immunostained colonic and ileal tissue sections. In regions of interest (ROIs) and/or whole tissue sections of 40 WSIs from 36 patients with Crohn's disease, CD3-positive cells were quantified by an expert gastrointestinal pathologist (gold standard) and by image analysis algorithms developed with software from 3 independent vendors. Semiautomated quantification of CD3-positive cell counts estimated in 1 ROI per section were accurate when compared with manual analysis (Pearson correlation coefficient, 0.877 to 0.925). Biological variability was acceptable in digitally determined CD3-positive cell measures between 2 to 5 ROIs annotated on the same tissue section (coefficient of variation <25%). Results from computer-aided analysis of CD3-positive T lymphocytes in a whole tissue section and the average of results from 2 to 5 ROIs per tissue section lacked reliability (overestimation or underestimation and systematic bias), suggesting that absolute quantification of CD3-positive T lymphocytes in a whole tissue section may be more accurate. Semiautomated image analysis in WSIs demonstrated reproducible CD3-positive cell measures across 3 independent algorithms. A computer-aided digital image analysis method was developed and validated to quantify CD3-positive T lymphocytes in colonic and ileal biopsy sections from patients with Crohn's disease. Results support consideration of this digital analysis method for use in future Crohn's disease clinical studies.

Update on prognostic and predictive biomarkers of breast cancer

Breast cancer represents a heterogeneous group of human cancer at both histological and molecular levels. Estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) are the most commonly used biomarkers in clinical practice for making treatment plans for breast cancer patients by oncologists. Recently, PD-L1 testing plays an important role for immunotherapy for triple-negative breast cancer. With the increased understanding of the molecular characterization of breast cancer and the emergence of novel targeted therapies, more potential biomarkers are needed for the development of more personalized treatments. In this review, we summarized several main prognostic and predictive biomarkers in breast cancer at genomic, transcriptomic and proteomic levels, including hormone receptors, HER2, Ki67, multiple gene expression assays, PD-L1 testing, mismatch repair deficiency/microsatellite instability, tumor mutational burden, PIK3CA, ESR1 andNTRK and briefly introduced the roles of digital imaging analysis in breast biomarker evaluation.

Quantitative Image Analysis as an Adjunct to Manual Scoring of ER, PgR, and HER2 in Invasive Breast Carcinoma

OBJECTIVES

Biomarker expression evaluation for estrogen receptor (ER), progesterone receptor (PgR), and human epidermal growth factor receptor 2 (HER2) is an essential prognostic and predictive parameter for breast cancer and critical for guiding hormonal and neoadjuvant therapy. This study compared quantitative image analysis (QIA) with pathologists' scoring for ER, PgR, and HER2.

METHODS

A retrospective analysis was undertaken of 1,367 invasive breast carcinomas, including all histopathology subtypes, for which ER, PgR, and HER2 were analyzed by manual scoring and QIA. The resulting scores were compared, and in a subset of HER2 cases (n = 373, 26%), scores were correlated with available fluorescence in situ hybridization (FISH) results.

RESULTS

Concordance between QIA and manual scores for ER, PgR, and HER2 was 93%, 96%, and 90%, respectively. Discordant cases had low positive scores (1%-10%) for ER (n = 33), were due to nonrepresentative region selection (eg, ductal carcinoma in situ) or tumor heterogeneity for PgR (n = 43), and were of one-step difference (negative to equivocal, equivocal to positive, or vice versa) for HER2 (n = 90). Among HER2 cases where FISH results were available, only four (1.0%) showed discordant QIA and FISH results.

CONCLUSIONS

QIA is a computer-aided diagnostic support tool for pathologists. It significantly improves ER, PgR, and HER2 scoring standardization. QIA demonstrated excellent concordance with pathologists' scores. To avoid pitfalls, pathologist oversight of representative region selection is recommended.

Digital pathology and artificial intelligence in translational medicine and clinical practice

Traditional pathology approaches have played an integral role in the delivery of diagnosis, semi-quantitative or qualitative assessment of protein expression, and classification of disease. Technological advances and the increased focus on precision medicine have recently paved the way for the development of digital pathology-based approaches for quantitative pathologic assessments, namely whole slide imaging and artificial intelligence (AI)-based solutions, allowing us to explore and extract information beyond human visual perception. Within the field of immuno-oncology, the application of such methodologies in drug development and translational research have created invaluable opportunities for deciphering complex pathophysiology and the discovery of novel biomarkers and drug targets. With an increasing number of treatment options available for any given disease, practitioners face the growing challenge of selecting the most appropriate treatment for each patient. The ever-increasing utilization of AI-based approaches substantially expands our understanding of the tumor microenvironment, with digital approaches to patient stratification and selection for diagnostic assays supporting the identification of the optimal treatment regimen based on patient profiles. This review provides an overview of the opportunities and limitations around implementing AI-based methods in biomarker discovery and patient selection and discusses how advances in digital pathology and AI should be considered in the current landscape of translational medicine, touching on challenges this technology may face if adopted in clinical settings. The traditional role of pathologists in delivering accurate diagnoses or assessing biomarkers for companion diagnostics may be enhanced in precision, reproducibility, and scale by AI-powered analysis tools.

Artificial intelligence applied to breast pathology

The convergence of digital pathology and computer vision is increasingly enabling computers to perform tasks performed by humans. As a result, artificial intelligence (AI) is having an astoundingly positive effect on the field of pathology, including breast pathology. Research using machine learning and the development of algorithms that learn patterns from labeled digital data based on "deep learning" neural networks and feature-engineered approaches to analyze histology images have recently provided promising results. Thus far, image analysis and more complex AI-based tools have demonstrated excellent success performing tasks such as the quantification of breast biomarkers and Ki67, mitosis detection, lymph node metastasis recognition, tissue segmentation for diagnosing breast carcinoma, prognostication, computational assessment of tumor-infiltrating lymphocytes, and prediction of molecular expression as well as treatment response and benefit of therapy from routine H&E images. This review critically examines the literature regarding these applications of AI in the area of breast pathology.

Hormone Receptor Subtype in Ductal Carcinoma in Situ: Prognostic and Predictive Roles of the Progesterone Receptor

Background

We investigated the prognostic and predictive roles of the hormone receptor (HRc) subtype in patients with ductal carcinoma in situ (DCIS). We focused on identifying the roles of the progesterone receptor (PR) independent of estrogen receptor (ER) status.

Methods

Nationwide data of 12,508 female patients diagnosed with DCIS with a mean follow‐up period of 60.7 months were analyzed. HRc subtypes were classified as ER−/PR−, ER−/PR+, ER+/PR−, and ER+/PR+ based on ER and PR statuses. The Cox proportional hazards model was used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs).

Results

The ER+/PR+ group showed better prognoses than the ER+/PR− and ER−/PR− groups in the patients who received tamoxifen therapy (p = .001 and p = .031, respectively). HRc subtype was an independent prognostic factor (p = .028). The tamoxifen therapy group showed better survival than the patients who did not receive tamoxifen, but only in the ER+/PR+ subgroup (p = .002). Tamoxifen therapy was an independent prognostic factor (HR, 0.619; 95% CI, 0.423 − 0.907; p = .014). PR status was a favorable prognostic factor in patients with DCIS who received tamoxifen therapy (p < .001), and it remained a prognostic factor independent of ER status (HR, 0.576; 95% CI, 0.349 − 0.951; p = .031).

Conclusion

The HRc subtype can be used as both a prognostic and predictive marker in patients with newly diagnosed DCIS. Tamoxifen therapy can improve overall survival in the ER+/PR+ subtype. PR status has significant prognostic and predictive roles independent of ER status. Testing for the PR status in addition to the ER status is routinely recommended in patients with DCIS to determine the HRc subtype in clinical settings.

Implications for Practice

The hormone receptor (HRc) subtype was an independent prognostic factor, and the estrogen receptor (ER)+/progesterone receptor (PR) + subtype showed a better survival in patients with ductal carcinoma in situ (DCIS) who received tamoxifen therapy. PR was an independent prognostic factor independent of ER, and PR was a favorable prognostic factor in patients with DCIS who received tamoxifen therapy. The HRc subtype could be used as both a prognostic and predictive marker in patients with newly diagnosed DCIS. Testing of PR status in addition to ER status is routinely recommended for patients with DCIS to determine the HRc subtype in clinical settings.

This study investigated the prognostic and predictive roles of the hormone receptor subtype in patients with newly diagnosed ductal carcinoma in situ, focusing on the prognostic and predictive values of progesterone receptor status independent of estrogen receptor status. The prognostic effect of tamoxifen therapy was also investigated

The design and fabrication of nanoengineered platinum needles with laser welded carbon nanotubes (CNTs) for the electrochemical biosensing of cancer lymph nodes

A new biosensor for detecting cancer involved sentinel lymph nodes has been developed via the electrochemical tracing of fatty acid oxidation as a distinct metabolism of malignant cells invading lymph nodes (LNs). The system included integrated platinum needle electrodes that were decorated by carbon nanotubes (as hydrophobic agents) through laser-assisted nanowelding. It was applied to record the dielectric spectroscopy data from LN contents via electrochemical impedance spectroscopy. The system was applied for dielectric spectroscopy of LN contents via electrochemical impedance approach. The reduced lipid content of involved LNs, due to fat metabolism by invasive cancer cells, would decrease the charge transfer resistance (RCT) of the LNs with respect to their normal counterparts. Multi-walled carbon nanotubes (MWCNTs) with superhydrophobic properties were used to enhance the interaction of Pt needle electrodes with the lipidic contents of lymph nodes. This is the first time that a fatty acid metabolism-based sensing approach has been introduced to detect involved LNs. Moreover, a novel electrode decorating method was applied to enhance the interfacial contact of this lipid detection probe (LDP). In order to avoid doubt about the biocompatibility of ferrocyanide, [Fe(CN)6]4- and ferricyanide, [Fe(CN)6]3-, a biocompatible injectable metal ion-based material, ferric carboxymaltose, was selected and applied as the electrolyte for the first time. Rabbit LNs were tested using the LDP in the animal model phase. The system was then used in vitro on 122 dissected human LNs in the operating room. Calibration of the results showed an excellent match between the dielectric response of the LDP (known as charge transfer resistance (RCT)) and the final pathological diagnoses. The LDP may have a promising future after further clinical investigations for intra-operative distinction between normal and cancerous LNs.

Quantitative Image Analysis for Tissue Biomarker Use: A White Paper From the Digital Pathology Association

Tissue biomarkers have been of increasing utility for scientific research, diagnosing disease, and treatment response prediction. There has been a steady shift away from qualitative assessment toward providing more quantitative scores for these biomarkers. The application of quantitative image analysis has thus become an indispensable tool for in-depth tissue biomarker interrogation in these contexts. This white paper reviews current technologies being employed for quantitative image analysis, their application and pitfalls, regulatory framework demands, and guidelines established for promoting their safe adoption in clinical practice.

Can AI-assisted microscope facilitate breast HER2 interpretation? A multi-institutional ring study

The level of human epidermal growth factor receptor-2 (HER2) protein and gene expression in breast cancer is an essential factor in judging the prognosis of breast cancer patients. Several investigations have shown high intraobserver and interobserver variability in the evaluation of HER2 staining by visual examination. In this study, we aim to propose an artificial intelligence (AI)-assisted microscope to improve the HER2 assessment accuracy and reliability. Our AI-assisted microscope was equipped with a conventional microscope with a cell-level classification-based HER2 scoring algorithm and an augmented reality module to enable pathologists to obtain AI results in real time. We organized a three-round ring study of 50 infiltrating duct carcinoma not otherwise specified (NOS) cases without neoadjuvant treatment, and recruited 33 pathologists from 6 hospitals. In the first ring study (RS1), the pathologists read 50 HER2 whole-slide images (WSIs) through an online system. After a 2-week washout period, they read the HER2 slides using a conventional microscope in RS2. After another 2-week washout period, the pathologists used our AI microscope for assisted interpretation in RS3. The consistency and accuracy of HER2 assessment by the AI-assisted microscope were significantly improved (p < 0.001) over those obtained using a conventional microscope and online WSI. Specifically, our AI-assisted microscope improved the precision of immunohistochemistry (IHC) 3 + and 2 + scoring while ensuring the recall of fluorescent in situ hybridization (FISH)-positive results in IHC 2 + . Also, the average acceptance rate of AI for all pathologists was 0.90, demonstrating that the pathologists agreed with most AI scoring results.

A narrative review of digital pathology and artificial intelligence: focusing on lung cancer

The emergence of whole slide imaging technology allows for pathology diagnosis on a computer screen. The applications of digital pathology are expanding, from supporting remote institutes suffering from a shortage of pathologists to routine use in daily diagnosis including that of lung cancer. Through practice and research large archival databases of digital pathology images have been developed that will facilitate the development of artificial intelligence (AI) methods for image analysis. Currently, several AI applications have been reported in the field of lung cancer; these include the segmentation of carcinoma foci, detection of lymph node metastasis, counting of tumor cells, and prediction of gene mutations. Although the integration of AI algorithms into clinical practice remains a significant challenge, we have implemented tumor cell count for genetic analysis, a helpful application for routine use. Our experience suggests that pathologists often overestimate the contents of tumor cells, and the use of AI-based analysis increases the accuracy and makes the tasks less tedious. However, there are several difficulties encountered in the practical use of AI in clinical diagnosis. These include the lack of sufficient annotated data for the development and validation of AI systems, the explainability of black box AI models, such as those based on deep learning that offer the most promising performance, and the difficulty in defining the ground truth data for training and validation owing to inherent ambiguity in most applications. All of these together present significant challenges in the development and clinical translation of AI methods in the practice of pathology. Additional research on these problems will help in resolving the barriers to the clinical use of AI. Helping pathologists in developing knowledge of the working and limitations of AI will benefit the use of AI in both diagnostics and research.

CDH2/N-cadherin and early diagnosis of invasion in patients with ductal carcinoma in situ

PURPOSE

This proof-of-concept study investigates gene expression in core needle biopsies (CNB) to predict whether individuals diagnosed with ductal carcinoma in situ (DCIS) on CNB were affected by invasion at the time of diagnosis.

METHODS

Using a QuantiGene Plex 2.0 assay, 14 gene expression profiling was performed in 303 breast tissue samples. Preoperative diagnostic performance of a gene was measured by area under receiver-operating characteristic curve (AUC) with 95% confidence interval (CI). The gene mRNA positivity cutoff was computed using Gaussian mixture model (GMM); protein expression was measured by immunohistochemistry; DNA methylation was evaluated by targeted bisulfite sequencing.

RESULTS

mRNA from 69% (34/49) mammoplasties, 72% (75/104) CNB DCIS, and 89% (133/150) invasive breast cancers (IBC) were analyzed. Based on pre-and post-surgery DCIS chart reviews, 21 cases were categorized as DCIS synchronous with invasion and 54 DCIS were pure DCIS without pathologic evidence of invasive disease. The ectopic expression of neuronal cadherin CDH2 was probable in 0% mammoplasties, 6% pure DCIS, 29% synchronous DCIS, and 26% IBC. The CDH2 mRNA positivity in preoperative biopsies showing pure DCIS was predictive of a final diagnosis of invasion (AUC = 0.67; 95% CI 0.53-0.80; P = 0.029). Site-specific methylation of the CDH2 promoter (AUC = 0.76; 95% CI 0.54-0.97; P = 0.04) and measurements of N-cadherin, a pro-invasive cell-cell adhesion receptor encoded by CDH2 (AUC = 0.8; 95% CI 0.66-0.99; P < 0.005) had a discriminating power allowing for discernment of CDH2-positive biopsy.

CONCLUSIONS

Evidence of CDH2/N-cadherin expression, predictive of invasion synchronous with DCIS, may help to clarify a diagnosis and direct the course of therapy earlier in a patient's care.

Clinical Application of Image Analysis in Pathology

Quantitative biomarkers are key prognostic and predictive factors in the diagnosis and treatment of cancer. In the clinical laboratory, the majority of biomarker quantitation is still performed manually, but digital image analysis (DIA) methods have been steadily growing and account for around 25% of all quantitative immunohistochemistry (IHC) testing performed today. Quantitative DIA is primarily employed in the analysis of breast cancer IHC biomarkers, including estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2/neu; more recently clinical applications have expanded to include human epidermal growth factor receptor 2/neu in gastroesophageal adenocarcinomas and Ki-67 in both breast cancer and gastrointestinal and pancreatic neuroendocrine tumors. Evidence in the literature suggests that DIA has significant benefits over manual quantitation of IHC biomarkers, such as increased objectivity, accuracy, and reproducibility. Despite this fact, a number of barriers to the adoption of DIA in the clinical laboratory persist. These include difficulties in integrating DIA into clinical workflows, lack of standards for integrating DIA software with laboratory information systems and digital pathology systems, costs of implementing DIA, inadequate reimbursement relative to those costs, and other factors. These barriers to adoption may be overcome with international standards such as Digital Imaging and Communications in Medicine (DICOM), increased adoption of routine digital pathology workflows, the application of artificial intelligence to DIA, and the emergence of new clinical applications for DIA.

Advances in tissue-based imaging: impact on oncology research and clinical practice

INTRODUCTION

Tissue-based imaging has emerged as a critical tool in translational cancer research and is rapidly gaining traction within a clinical context. Significant progress has been made in the digital pathology arena, particularly in respect of brightfield and fluorescent imaging. Critically, the cellular context of molecular alterations occurring at DNA, RNA, or protein level within tumor tissue is now being more fully appreciated. Moreover, the emergence of novel multi-marker imaging approaches can now provide unprecedented insights into the tumor microenvironment, including the potential interplay between various cell types.

AREAS COVERED

This review summarizes the recent developments within the field of tissue-based imaging, centering on the application of these approaches in oncology research and clinical practice.

EXPERT OPINION

Significant advances have been made in digital pathology during the last 10 years. These include the use of quantitative image analysis algorithms, predictive artificial intelligence (AI) on large datasets of H&E images, and quantification of fluorescence multiplexed tissue imaging data. We believe that new methodologies that can integrate AI-derived histologic data with omic data, together with other forms of imaging data (such as radiologic image data), will enhance our ability to deliver better diagnostics and treatment decisions to the cancer patient.

Estrogen and Progesterone Receptor Testing in Breast Cancer: American Society of Clinical Oncology/College of American Pathologists Guideline Update

PURPOSE.—

To update key recommendations of the American Society of Clinical Oncology/College of American Pathologists estrogen receptor (ER) and progesterone receptor (PgR) testing in breast cancer guideline.

METHODS.—

A multidisciplinary international Expert Panel was convened to update the clinical practice guideline recommendations informed by a systematic review of the medical literature.

RECOMMENDATIONS.—

The Expert Panel continues to recommend ER testing of invasive breast cancers by validated immunohistochemistry as the standard for predicting which patients may benefit from endocrine therapy, and no other assays are recommended for this purpose. Breast cancer samples with 1% to 100% of tumor nuclei positive should be interpreted as ER positive. However, the Expert Panel acknowledges that there are limited data on endocrine therapy benefit for cancers with 1% to 10% of cells staining ER positive. Samples with these results should be reported using a new reporting category, ER Low Positive, with a recommended comment. A sample is considered ER negative if < 1% or 0% of tumor cell nuclei are immunoreactive. Additional strategies recommended to promote optimal performance, interpretation, and reporting of cases with an initial low to no ER staining result include establishing a laboratory-specific standard operating procedure describing additional steps used by the laboratory to confirm/adjudicate results. The status of controls should be reported for cases with 0% to 10% staining. Similar principles apply to PgR testing, which is used primarily for prognostic purposes in the setting of an ER-positive cancer. Testing of ductal carcinoma in situ (DCIS) for ER is recommended to determine potential benefit of endocrine therapies to reduce risk of future breast cancer, while testing DCIS for PgR is considered optional. Additional information can be found at www.asco.org/breast-cancer-guidelines .

Review of the current state of digital image analysis in breast pathology

Advances in digital image analysis have the potential to transform the practice of breast pathology. In the near future, a move to a digital workflow offers improvements in efficiency. Coupled with artificial intelligence (AI), digital pathology can assist pathologist interpretation, automate time-consuming tasks, and discover novel morphologic patterns. Opportunities for digital enhancements abound in breast pathology, from increasing reproducibility in grading and biomarker interpretation, to discovering features that correlate with patient outcome and treatment. Our objective is to review the most recent developments in digital pathology with clear impact to breast pathology practice. Although breast pathologists currently undertake limited adoption of digital methods, the field is rapidly evolving. Care is needed to validate emerging technologies for effective patient care.

Estrogen and Progesterone Receptor Testing in Breast Cancer: ASCO/CAP Guideline Update

PURPOSE

To update key recommendations of the American Society of Clinical Oncology/College of American Pathologists estrogen (ER) and progesterone receptor (PgR) testing in breast cancer guideline.

METHODS

A multidisciplinary international Expert Panel was convened to update the clinical practice guideline recommendations informed by a systematic review of the medical literature.

RECOMMENDATIONS

The Expert Panel continues to recommend ER testing of invasive breast cancers by validated immunohistochemistry as the standard for predicting which patients may benefit from endocrine therapy, and no other assays are recommended for this purpose. Breast cancer samples with 1% to 100% of tumor nuclei positive should be interpreted as ER positive. However, the Expert Panel acknowledges that there are limited data on endocrine therapy benefit for cancers with 1% to 10% of cells staining ER positive. Samples with these results should be reported using a new reporting category, ER Low Positive, with a recommended comment. A sample is considered ER negative if < 1% or 0% of tumor cell nuclei are immunoreactive. Additional strategies recommended to promote optimal performance, interpretation, and reporting of cases with an initial low to no ER staining result include establishing a laboratory-specific standard operating procedure describing additional steps used by the laboratory to confirm/adjudicate results. The status of controls should be reported for cases with 0% to 10% staining. Similar principles apply to PgR testing, which is used primarily for prognostic purposes in the setting of an ER-positive cancer. Testing of ductal carcinoma in situ (DCIS) for ER is recommended to determine potential benefit of endocrine therapies to reduce risk of future breast cancer, while testing DCIS for PgR is considered optional. Additional information can be found at www.asco.org/breast-cancer-guidelines.

A Validation Study of Human Epidermal Growth Factor Receptor 2 Immunohistochemistry Digital Imaging Analysis and its Correlation with Human Epidermal Growth Factor Receptor 2 Fluorescence In situ Hybridization Results in Breast Carcinoma

Background:

The Visiopharm human epidermal growth factor receptor 2 (HER2) digital imaging analysis (DIA) algorithm assesses digitized HER2 immunohistochemistry (IHC) by measuring cell membrane connectivity. We aimed to validate this algorithm for clinical use by comparing with pathologists’ scoring and correlating with HER2 fluorescence in situ hybridization (FISH) results.

Materials and Methods:

The study cohort consisted of 612 consecutive invasive breast carcinoma specimens including 395 biopsies and 217 resections. HER2 IHC slides were scanned using Philips IntelliSite Scanners, and the digital images were analyzed using Visiopharm HER2-CONNECT App to obtain the connectivity values (0–1) and scores (0, 1+, 2+, and 3+). HER2 DIA scores were compared with Pathologists’ manual scores, and HER2 connectivity values were correlated with HER2 FISH results.

Results:

The concordance between HER2 DIA scores and pathologists’ scores was 87.3% (534/612). All discordant cases (n = 78) were only one-step discordant (negative to equivocal, equivocal to positive, or vice versa). Five cases (0.8%) showed discordant HER2 IHC DIA and HER2 FISH results, but all these cases had relatively low HER2 copy numbers (between 4 and 6). HER2 IHC connectivity showed significantly better correlation with HER2 copy number than HER2/CEP17 ratio.

Conclusions:

HER2 IHC DIA demonstrates excellent concordance with pathologists’ scores and accurately discriminates between HER2 FISH positive and negative cases. HER2 IHC connectivity has better correlation with HER2 copy number than HER2/CEP17 ratio, suggesting HER2 copy number may be more important in predicting HER2 protein expression, and response to anti-HER2-targeted therapy.