BACKGROUND:Previous concordance studies in breast pathology have demonstrated high levels of diagnostic agreement. This study aimed to characterize breast lesions associated with the lowest levels of concordance within the UK NHS Breast Screening Programme external quality assessment (EQA) scheme. METHODS:A total of 263 consecutive breast cases circulated between 2015 and 2025 were reviewed. Each case was assessed by up to 22 coordinators (expert reference panel assessors) and an average of 678 participants (all other pathologists enrolled in the EQA scheme). Cases with <80% concordance among coordinators were analysed in detail. RESULTS:Overall performance was high, with 67.4% of participants achieving 100% concordance across all circulated cases. Fifteen cases (5.7%) failed to meet the ≥80% concordance threshold. Mean diagnostic agreement in these cases was 57% (range 25%-79% among coordinators; 25%-87% among participants). Discordant cases predominantly involved atypical epithelial proliferations and papillary lesions, particularly those situated at diagnostic thresholds between established categories. Persistent challenges included distinguishing among lobular neoplasia subtypes, apocrine atypia, flat epithelial atypia, and related lesions, as well as between in situ and invasive papillary carcinoma. CONCLUSIONS:While overall concordance remains high, a small subset of biologically borderline lesions continues to generate significant diagnostic variability. These findings support further refinement of diagnostic criteria and targeted educational strategies to improve reproducibility in challenging breast lesions.

Ipsilateral breast tumour recurrences (IBTR) that occur after breast-conserving surgery (BCS) and breast radiotherapy may be ‘true recurrences’ or independent ‘new primaries’. Contralateral breast cancers (CBC) are usually assumed to be new primary tumours. Understanding the patterns of occurrence of these entities, and/or being able to reliably distinguish between the two, could have important implications for tailoring patient management at primary presentation and at IBTR/CBC diagnosis. The clonal relationship between index, and ipsilateral and contralateral subsequent breast tumours was assessed in 112 paired samples from two breast radiotherapy trials, IMPORT LOW and IMPORT HIGH, using copy number profiling and targeted sequencing of DNA. The spatial relationships between the index tumour beds, radiotherapy dose distributions and subsequent tumours were analysed via computational co-registration of cross-sectional imaging. In the IMPORT HIGH cohort, where patients were at higher risk of IBTR, 61

The use of progestogens in breast cancer has been controversial. Recent preclinical studies have shown that ligand-bound progesterone receptor interacts directly with the estrogen receptor (ER) and reprograms ER transcriptional activity. Progestogen cotreatment enhances the antitumor activity of antiestrogen therapy in mouse xenografts. We report PIONEER, a 198-participant, three-arm, randomized phase 2b window-of-opportunity study for women with early-stage ER+ breast cancer, which evaluated letrozole with or without megestrol at 40 mg or 160 mg daily. The primary endpoint was the change in tumor proliferation measured by Ki67 immunohistochemistry. Secondary and exploratory endpoints included a comparison of low versus higher dose of megestrol, safety, tolerability and biomarker subgroup analyses. The trial met its primary endpoint, with a greater reduction in proliferation seen when megestrol was added to letrozole. This effect was accompanied by reduced ER genomic binding at canonical binding sites in paired tumor biopsies, indicating reduced ER transcriptional activity. These results support further evaluation of low-dose megestrol, which has two mechanisms for potentially improving breast cancer outcomes in combination with standard antiestrogen therapy: alleviating hot flashes and thereby helping with treatment adherence, as well as a direct antiproliferative effect ( NCT03306472 ). Baird et al. present the phase 2 PIONEER trial findings on the antitumor activity of combining aromatase inhibitor letrozole with megestrol in postmenopausal women with operable estrogen-receptor-positive human epidermal-growth-factor-receptor-2-negative breast cancer.

Supplementary Figures 1-20 and Supplementary Tables 1-10 Legends

Ki-67 is a well-established marker of tumour proliferation and an important prognostic and predictive biomarker in breast cancer, particularly in hormone receptor-positive (HR-positive), HER2-negative disease. Despite its biological relevance, clinical implementation has been limited by the reported interobserver and interlaboratory variability. Recent therapeutic advances have created an increased need for accurate and reproducible Ki-67 assessment in clinical practice. This review summarizes the biological basis for the use of Ki-67 as a marker of proliferation, technical requirements for reliable immunohistochemistry and the influence of pre-analytical and analytical variables on staining performance. We evaluate established and emerging scoring approaches and provide scoring recommendations for practising pathologists. A simplified calibrated global assessment method is presented as an alternative to exhaustive visual quantification that preserves its accuracy while substantially reducing scoring time and avoiding the variability of estimated methods. Image analysis/artificial intelligence (AI) using validated algorithms is recommended where available. We also review the role of Ki-67 in predicting response to neoadjuvant endocrine and chemotherapy, its integration into prognostic models such as the PEPI score, and its utility in selecting patients for adjuvant CDK4/6 inhibition. The limitations of fixed cut-off values are discussed, together with the potential advantages of tiered classification and continuous modelling. Finally, we outline the growing role of digital pathology and AI, which have demonstrated improved reproducibility, reduced turnaround time, and prognostic performance superior to manual scoring. Ki-67 is a clinically meaningful biomarker, the value of which can only be fully realized through rigorous standardization, validated scoring approaches and close communication between pathologists and oncologists. This guidance provides a practical framework for high-quality Ki-67 assessment and supports its safe and effective integration into contemporary breast cancer management.

Borderline breast lesions (B3 lesions, also termed lesions of uncertain malignant potential or high-risk lesions) represent a heterogeneous group of entities associated with variable risks of malignancy. While the management of screen-detected B3 lesions has become increasingly standardised, no dedicated international recommendations exist for symptomatic B3 lesions, despite them posing a distinct clinical challenge. Symptomatic lesions differ from screen-detected lesions in their mode of presentation, lesion characteristics, biopsy techniques, and diagnostic objectives; consequently, management strategies derived from screening populations may not be directly applicable. This review summarises the current evidence and proposes a pragmatic management framework for B3 lesions encountered in the symptomatic setting (defined as those presenting with breast symptoms outside population-based screening programmes). This is particularly relevant for patients under 50 years of age, where the primary objective is to exclude malignancy at the index site rather than solely to stratify long-term cancer risk. Clinical-radiological-pathological concordance and multidisciplinary assessment are central to management decisions. Lesions presenting as palpable abnormalities (which are typically larger) or those demonstrating radiological-pathological discordance warrant a lower threshold for additional sampling or excision. Conversely, selected concordant lesions without atypia may be managed conservatively following adequate sampling. The long-term cancer risk associated with epithelial atypia should also be considered with subsequent risk-based surveillance implemented where appropriate. Overall, management requires a risk-adapted, multidisciplinary approach integrating pathological, radiological, and clinical factors alongside patient symptoms and quality-of-life considerations. Given the limited evidence, current recommendations rely on expert consensus and a proposed practical framework. They underscore the need for large-scale, high-quality studies with long-term follow-up to establish formal clinical guidelines.

The extent of residual disease after neoadjuvant chemotherapy (NAC) in patients with breast cancer (BC) holds prognostic value. However, current practices for reporting post-NAC BC specimens according to the ypTNM classification vary. This study aimed to map these practices and provide recommendations for standardization. A survey was developed and globally circulated to pathologists with a special interest in BC through personal networks and working group mailing lists. The survey included general questions about tumor diameter assessment, as well as graphical scenarios presenting different distributions of tumor cells. We did not provide definitions mentioned in reporting guidelines to capture unbiased current real-world practices. A total of 208 pathologists from 35 countries completed the survey. Almost all responding pathologists (97.1%) reported the ypTNM in daily practice. Despite self-reported strict adherence to the eighth edition of the international ypTNM classification, we found substantial variation in practice concerning the application of this staging system, particularly in cases with an uneven distribution of scattered residual disease. Notably, 57.2% of respondents reported measuring the largest "continuous cluster of tumor cells," but the interpretation of this definition varied widely. This international survey identifies the challenges and practice heterogeneity in the current application of the ypTNM staging system, which hampers the value of ypTNM reporting in daily practice. To enhance reproducibility and to provide more reliable post-NAC risk stratification, we recommend adopting standardized reporting with clearer pattern-based definitions of the ypTNM guidelines, supplemented with the elements of the residual cancer burden system.

A coclinical trial framework reveals concordant patient–PDTX drug responses. A, Experimental framework (consisting of two trial designs) and associated analytical approach, with modeling metrics used to assess drug response. B and C, TV growth curves displaying linear mixed model fits of trial designs 1 (B) and 2 (C) over treatment duration. Treatment arm for each PDTX model corresponds to the clinical treatment of the matched patient. D and E, Analytical metrics derived from mathematical modeling (as in A). Change in growth rate (top) and estimated difference in the AUC (bottom) for trial design 1 (D) and growth rate under treatment (top) and predicted volume at treatment end (bottom) for trial design 2 (E). F, Box plots displaying growth rate under treatment (top) and predicted volume at treatment end (bottom) for trial design 2 between pCR and non-pCR models. Statistical significance is calculated using the Wilcoxon test.

Fibroepithelial lesions (FELs) of the breast represent a diverse group of biphasic tumors with varying morphologies and clinical behavior. The classification of FELs is mainly based on a constellation of diagnostic criteria, and intralesional heterogeneity is not uncommon. Therefore, reporting FELs in a core needle biopsy (CNB) with limited tissue material can be challenging as not all the features may be represented for assessment. Differentiating a classic fibroadenoma from a well-sampled phyllodes tumor (PT) is generally straightforward. However, cellular fibroadenoma, morphologically heterogeneous benign PT, and myoid hamartoma can overlap histologically. Accurate grading of PT is also challenging on CNB and carries significant management implications. In this article, we provide an overview and propose a pragmatic approach to reporting FELs on CNB, particularly for lesions with overlapping features. Guidance using the UK/European "B" classification of FELs alongside descriptive reporting of the various lesions, is also presented to aid in management decisions.

Objective: HER2 expression is a key prognostic and treatment-influencing factor in breast cancer and is assessed for all invasive breast carcinoma (BC). As with all immunohistochemistry (IHC) staining, visual interpretation of HER2 expression is subjective, which leads to intra- and inter-pathologist variability. This study aims to evaluate the clinical utility (concordance, accuracy, and user feedback) of artificial intelligence (AI)-aided HER2 scoring solution on whole slide digital images of HER2 IHCs of breast samples. Methods: The cohort included biopsies and excisions from 1,997 patients from 12 US, EU, and UK clinical laboratories, including academic medical centers and reference/private laboratories. HER2 slides of diverse BC subtypes from primary and metastatic tumors were stained with anti-HER2 antibody (4B5, VENTANA) at each laboratory and scanned with different scanners (Leica GT450DX, Philips UFS, Aperio AT2). This observational two-arm multi-reader study compared the performance of 26 pathologists (“readers”) on HER2 scoring (each reviewed 50-200 slides) unassisted vs. aided by AI HER2 solution (Ibex Breast HER2®), which detects the invasive tumor area and on slide control, classifies tumor cells based on their staining pattern, and derives a slide-level HER2 score by applying 2018 ASCO/CAP guidelines. Both study arms were compared to ground truth (GT), established as majority score of three breast pathologists (“experts”) who reviewed the slides manually. Results: Experts’ overall inter-observer agreement on all HER2 scores was 73.9% (95%CI: 72.6%,75.2%) and for 0/1+/2+/3+ was 80.8%/ 66.2%/ 63.7%/ 94.3%, respectively. Readers’ overall inter-observer agreement was significantly higher when assisted by AI, 87.5% (85.9%,89.0%) vs. 74.3% (72.3%,76.3%) without AI, p <0.05. Moreover, reader's accuracy for all HER2 scores (agreement with GT) was significantly higher with AI 80.9% (79.7%,82.2%) vs without AI, 76.6% (75.2%,77.9%) (p <0.05). For 0/1+ vs 2+/3+ cutoff, readers with AI showed significantly higher inter-observer agreement 93.1% (91.8%,94.2%) vs. without AI 86.8% (85.2%,88.3%), p <0.05, and significantly higher accuracy, 91.9% (91.0%,92.8%) with AI vs 88.8% (87.7%,89.8%) (p <0.05) without AI. For the HER2-low cutoff of 0 vs. 1+/2+/3+, readers with AI showed significantly higher inter-observer agreement of 95.0% (93.9%,95.9%) vs. 88.8% (87.3%,90.2%), p <0.05, with slightly higher accuracy (89.8% (88.8%,90.7%) with AI vs 88.5% (87.4%,89.4%) without AI). The standalone automatic AI solution demonstrated high accuracy for HER2 scoring of 89.4% (88.0%,90.7%), and 91.2% (89.8%,92.3%) for the respective clinical cutoffs of 0 vs. 1+/2+/3+, 0/1+ vs. 2+/3+. Feedback from reader pathologists' user survey indicates an increased confidence in their HER2 scoring accuracy and consistency. Additionally, 77% of the pathologists expressed a preference for HER2 scoring supported by AI over manual scoring. Conclusions: This study reports a large multi-site validation of a fully automated AI solution for HER2 scoring in BC. Pathologists supported by AI showed significant improvements in HER2 scoring consistency, evidenced by inter-reader agreement, and accuracy overall and for other clinical cutoffs. The AI solution demonstrated high accuracy and generalizability to multiple different laboratories (pre-analytics and staining protocols) and scanners. AI solutions, such as the one investigated here, could be used as decision-support tools for pathologists in routine clinical practice, enhancing the reproducibility and consistency of HER2 scoring, thus enabling optimal treatment pathways and improved patient outcomes. Citation Format: Savitri Krishnamurthy, Stuart Schnitt, Anne Vincent-Salomon, Elena Provenzano, Rita Canas-Marques, Laurent Arnould, Gaetan Mac Grogan, Elisabeth Shearon, Derek Welch, Pranil Chandra, Piotr Borkowski, Sabine Declercq, Joseph Loane, Anu Gunavardhan, Luca Di Tommaso, Vitor Krauss, Jeanne Thomassin, Marie Brevet, Maya Grinwald, Dana Mevorach, Sevde Etoz, Raz Ziv, Shai stein, Giuseppe Mallel, Judith Sandbank, Chaim Linhart, Manuela Vecsler. Improved Standardization and Accuracy of HER2 Score with AI support in Breast Cancer: Large Multicenter Study [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P1-07-03.

The intertumor and intratumor heterogeneity of triple-negative breast cancers, which is reflected in diverse drug responses, interplays with tumor evolution. In this study, we developed a preclinical experimental and analytical framework using patient-derived tumor xenografts (PDTX) from patients with treatment-naïve triple-negative breast cancers to test their predictive value in personalized cancer treatment approaches. Patients and their matched PDTXs exhibited concordant drug responses to neoadjuvant therapy using two trial designs and dosing schedules. This platform enabled analysis of nongenetic mechanisms involved in relapse dynamics. Treatment resulted in permanent phenotypic changes, with functional and therapeutic consequences. High-throughput drug screening methods in ex vivo PDTX cells revealed patient-specific drug response changes dependent on first-line therapy. This was validated in vivo, as exemplified by a change in olaparib sensitivity in tumors previously treated with clinically relevant cycles of standard-of-care chemotherapy. In summary, PDTXs provide a robust tool to test patient drug responses and therapeutic regimens and to model evolutionary trajectories. However, high intermodel variability and permanent nongenomic transcriptional changes constrain their use for personalized cancer therapy. This work highlights important considerations associated with preclinical drug response modeling and potential uses of the platform to identify efficacious and preferential sequential therapeutic regimens. Significance: Patient-derived tumor xenografts from treatment-naïve breast cancer samples can predict patient drug responses and model treatment-induced phenotypic and functional evolution, making them valuable preclinical tools.

Diagnostic pathology is inherently interpretative and subject to interobserver variability. Although diagnostic concordance is a critical quality metric, distinguishing between acceptable variation, diagnostic error, and professional negligence is essential for both clinical care and medicolegal clarity. This review highlights the difference between interobserver variability (diagnostic disagreement/discordance) that remains within acceptable professional limits, diagnostic error (a deviation from expected standards due to cognitive, technical, or systemic factors), and negligence (a repeated, reckless, or unjustified deviation from established standards). Errors in pathology often reflect systemic vulnerabilities, such as workflow inefficiencies, inadequate quality control, or limited biopsy sampling, rather than individual performance alone. They may occur at any stage of the diagnostic pathway (preanalytical, analytical, or postanalytical) and arise from specimen misidentification, contamination or loss, inadequate sampling, or incomplete documentation. Pathologist-related errors encompass failure to recognize significant pathology, misinterpretation, omission of appropriate ancillary studies, insufficient workup of complex cases, including failure to seek a second opinion, or substandard reporting. Medicolegal implications are heightened when such errors result in delayed diagnosis or major misclassification, leading to patient harm. In breast pathology, interobserver variation in the classification of borderline lesions (eg, grading of phyllodes tumors) and in the interpretation of overlapping entities (eg, atypical apocrine lesions) is well recognized. Although such differences may influence management, they should be regarded as acceptable professional variability, rather than error or negligence. To minimize diagnostic risk and uphold standards, structured reporting, vigilance in complex cases, participation in quality assurance, explicit documentation of uncertainty, active multidisciplinary team engagement, and laboratory accreditation are strongly recommended. Supporting pathologists as diagnosticians and patient safety advocates, within a culture of openness, shared learning, and institutional support, remains central to diagnostic accuracy, transparency, and medicolegal defensibility.

Data from clinical trials (CTs) drive advancements in clinical practice. Despite most CTs now incorporating extensive translational portfolios, the diverse modalities of clinical and sample data they generate often remain disconnected and underutilised. SYNERGIA is a resource designed to integrate multi-modal data from multiple trials in a comparable format, that will be appropriately accessible to clinicians and researchers. The aims of SYNERGIA are to:1) Develop a comprehensive, multi-modal data repository that integrates longitudinal CT data (>5 years), with genomics (for example, whole-genome sequencing, genome wide association data, circulating tumour DNA, transcriptomics, and spatial- and single-cell- omics), radiomics (for example, mammograms, MRIs, and ultrasounds), and digitalised pathology images from five UK-based breast cancer CTs/studies involving up to 5,000 patients. 2) Facilitate the development and validation of multi-modal machine learning tools, including models predicting response to neo-adjuvant treatment, risk of disease recurrence or death, radiology segmentation tools, and pathology tools for assessing residual cancer burden or cellular biomarkers. 3) Inform future trial designs, support research applications and grants, and establish standard operating procedures for the collection, storage, and management of extensive datasets, samples, and imaging resources. The repository will enable integration across data modalities, ensuring that legacy research data continues to have meaningful impact in future research. SYNERGIA’s tools may support the personalisation of treatment regimens for individual patients, particularly for higher risk sub-types such as human epidermal growth factor receptor 2 positive and triple negative breast cancers. The platform offers the potential to differentiate high risk from low-risk breast cancers and assess in detail which data features contribute to risk. It may enable identification of the most critical data modalities/features at each timepoint in a patient’s cancer journey. The multi-modal approach to cancer biomarker discovery and predictive/prognostic tool development promises to enhance patient stratification to the most appropriate care pathways and advance precision breast cancer medicine. Integrating diverse datasets generated from individual patients may provide new insights into long-standing clinical questions such as identifying early indicators of relapse and distinguishing between lethal and non-lethal breast cancers. By enabling data-driven insights, the SYNERGIA platform aims to support the development of rational data-informed clinical research and trials that improve outcomes while reducing unnecessary toxicities and costs. Amy Riddell, Joanna R. Worley, Fatima Begum-Miah, Steven Bell, Samuel Casford, Alexander J. Fulton, Melis O. Irfan, Justine Kane, Ollie Kane, Charlotte King, Zac Kinsella, Jonathan Lay, Bin Liu, Zoe Matthews, Meena Murthy, Claudia Pallucca, Karen Pinilla, Leah Prowse, Nikola Simidjievski, Aris Sionakidis, Deborah Whitehorn, Katrina Xian, Elena Provenzano, Philip C. Schouten, Mateja Jamnik, Pietro Liò, Silvia Tarantino, Akanksha Anand, Kui Hua, Clare A. Rebbeck, Ramona Woitek, Iris Allajbeu, Gregory J. Hannon, Jean E. Abraham. SYNERGIA Breast Cancer - Revolutionizing breast cancer care with multi-modal data integration for personalised treatment and future trials [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_2):Abstract nr LB339.

Phyllodes tumours (PTs) of the breast present diagnostic challenges due to their complex histological features and potential for malignant behaviour. The World Health Organisation (WHO) classification requires the presence of five adverse histological criteria to categorise PTs as malignant, aiming to avoid overdiagnosis and improve diagnostic consistency. However, emerging evidence suggests that these strict criteria may underdiagnose tumours with metastatic potential and histological features that would otherwise be considered malignant in soft tissue tumours, leading to significant implications for prognosis and treatment. Recent studies have highlighted cases where tumours classified as borderline PT by WHO criteria exhibited metastatic behaviour, emphasising the need to refine the diagnostic framework. Microscopic criteria used to classify PT also vary among reporting pathologists, resulting in suboptimal reproducibility. This review examines the histological parameters utilised in the classification of malignant PT, highlights existing evidence gaps and analyses international breast pathologist survey data to propose a pragmatic diagnostic approach. We recommend redefining malignant PTs to include cases meeting four of the five WHO criteria, supplemented by comprehensive sampling and clinical context. This approach balances the risk of underdiagnosis with the need for standardised, reproducible diagnostic practices. Future collaborative efforts should focus upon developing evidence-based, biologically relevant classification systems and leveraging technological advancements to enhance diagnostic precision. These efforts aim to refine classification, improve prognostic accuracy and optimise patient management strategies.

Assessment of axillary lymph nodes in breast cancer patients following neoadjuvant chemotherapy (NACT) is a crucial part of the clinical and pathological assessment of the disease and has prognostic and management implications. This, however, currently lacks standardisation and focuses only on the number of lymph nodes with metastases still present, the largest metastasis, and the presence of pathological complete response. Potential changes in any residual disease or within the lymph node parenchyma are not examined. Novel methods of more nuanced approaches are rare in the literature, even when considering multiple cancer types, but can offer an insight into the potential additional information to be gained and improvement in patient stratification. Given how common NACT is as the backbone of cancer therapy, there is a surprising lack of research into the lymph node response and determination of the biological factors driving what is seen histologically. Furthermore, with NACT now being administered alongside immunotherapy, there is an increasing need to understand the functional and architectural changes induced in the lymph nodes by metastatic tumour and systemic therapies. This review summarises current approaches, with breast cancer as an exemplar, and discusses the literature investigating a possible more granular approach to lymph node assessment after NACT. Translating these multiple carcinoma studies to breast cancer patients may prompt tissue-based research and, with clinical validation studies, changes to the reporting of lymph node response, for example percentage of viable tumour and immunological architectural features such as germinal centres. © 2025 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.