ESMO Real World Data and Digital Oncology最新文献

{"title":"Age and frailty in anticancer drug regulatory assessment: a comprehensive cohort study of European marketing authorisations 2012-2023","authors":"O. Tenhunen ,&nbsp;K. Penttinen ,&nbsp;C. Voltz ,&nbsp;H. Kuitunen ,&nbsp;M. Turpeinen","doi":"10.1016/j.esmorw.2025.100663","DOIUrl":"10.1016/j.esmorw.2025.100663","url":null,"abstract":"<div><h3>Background</h3><div>Prevalence of cancer increases with age, but older adults remain underrepresented in pivotal clinical trials. We analysed the representation of older adults and poor performance score patients in initial marketing authorisation (MA) of new anticancer medicines in the European Union (EU).</div></div><div><h3>Materials and methods</h3><div>Medicines with an anticancer indication authorised in the EU during the years 2012-2023 were analysed based on public assessment reports (EPARs). We identified original clinical studies and datasets based on the classification in the EPARs.</div></div><div><h3>Results</h3><div>One hundred and thirty-seven products and MAs, corresponding with 153 efficacy datasets fulfilled the inclusion criteria. Most studies had enrolled subjects in age groups 65-74, 75-84, and ≥85 years of age; however, the proportion of individuals in these age groups was low. The majority of cases analysed efficacy in patient subgroups of &gt;65 years of age, whereas a minority of studies evaluated efficacy for groups older than 70 years. No differences were observed between time cohorts from 2012-2017 and 2018-2023. Fifty-one percent of datasets allowed inclusion of patients with an Eastern Cooperative Oncology Group performance status of ≥2, whereas the median proportion of these patients was 1.8%. Safety was specifically analysed in subjects of &gt;65 years of age in 92% of MAs. A dedicated discussion on age and/or frailty was identified in 45% of the MAs. Age- or frailty-related Annex II conditions were found in 10 cases.</div></div><div><h3>Conclusion</h3><div>Older adults and patients with higher performance scores have been included in the pivotal MA data and analyses, but their proportions have remained low. An opportunity for structured communication on age-related uncertainties is identified.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"11 ","pages":"Article 100663"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How artificial intelligence applied to digital pathology could guide treatment personalization in breast cancer","authors":"T. Ruelle ,&nbsp;T. Grinda ,&nbsp;L. Del Mastro ,&nbsp;M. Lacroix-Triki ,&nbsp;B. Pistilli ,&nbsp;G. Gessain","doi":"10.1016/j.esmorw.2025.100662","DOIUrl":"10.1016/j.esmorw.2025.100662","url":null,"abstract":"<div><h3>Introduction</h3><div>The global increase in breast cancer incidence is determining substantial augment of pathologists’ workload. Digital pathology (DP), which consists of digitizing haematoxylin–eosin (H&amp;E)-stained slides into whole-slide images, enables new workflows and horizons. The integration of artificial intelligence (AI) with DP led to the emergence of computational pathology (CP), which has the potential of offering valuable diagnostic, prognostic and predictive tools. The aim of this narrative review is to provide an overview of the state of the art in this rapidly evolving field.</div></div><div><h3>Diagnostic, predictive and prognostic application</h3><div>Several AI tools demonstrated enhanced performances as compared with visual evaluation of pathologists, such as identification of invasive breast cancers, sentinel lymph node metastasis, histological grading, Ki-67, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor 2 (HER2) and programmed death-ligand 1 (PD-L1) quantification. Due to this, most of them are currently authorized in the European market. Beyond diagnosis, CP has shown strong potential in predicting, from a simple H&amp;E digital slide, treatment response (e.g. neoadjuvant chemotherapy, anti-HER2 therapy and endocrine therapy), immunohistochemistry status (ER, PR, HER2, PD-L1), molecular alterations (e.g. <em>BRCA1/2</em> and homologous recombination deficiency status), genomic signatures (e.g. Oncotype DX) and ultimately the patient’s risk of recurrence.</div></div><div><h3>Conclusion</h3><div>Despite challenges such as high validation costs, initial economic investments and potential AI biases, CP holds great promise. Continued effort to address these barriers and improve AI reliability are crucial. Emerging AI tools, including agent-driven systems, point towards the potential integration of CP into routine clinical workflow practice, even though clinical validation has not yet been established.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"11 ","pages":"Article 100662"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Balancing clinical relevance, legal boundaries, and technological solutions: a case-based analysis of secondary use of electronic health records in Sweden","authors":"Z. Dóczi ,&nbsp;A. Valachis","doi":"10.1016/j.esmorw.2025.100661","DOIUrl":"10.1016/j.esmorw.2025.100661","url":null,"abstract":"<div><h3>Background</h3><div>The secondary use of electronic health records (EHRs) poses legal challenges, particularly when the responsibility for managing EHRs lies with local or regional authorities. This article presents a case-based analysis of the secondary use of EHR data in contexts where data privacy responsibilities are managed regionally in Sweden.</div></div><div><h3>Methods and results</h3><div>Using two distinct purposes for the secondary use of the digital tool Patient Overview Breast Cancer: (i) assessing the uptake of new treatment strategies in a real-world setting for quality assurance, and (ii) evaluating the effectiveness of these strategies in specific patient subgroups with limited evidence for research purposes, the study explored the distinctions between research and quality assurance, the legal implications of each framework, and the potential role of federated learning as a privacy-preserving technological solution.</div></div><div><h3>Conclusions</h3><div>Federated learning offers a promising approach to overcome legal and organizational barriers to secondary use of regional EHRs in Sweden, enabling scalable, clinically meaningful insights for cancer care. However, its effective implementation requires a unified national framework that balances personal integrity with patient safety, supported by regulatory sandboxes.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"11 ","pages":"Article 100661"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predictors of acute lymphopenia after radiotherapy for prostate cancer including pelvic node irradiation: results of a real-world prospective multi-centric study","authors":"V. Vavassori ,&nbsp;M. Pavarini ,&nbsp;L. Alborghetti ,&nbsp;S. Aimonetto ,&nbsp;A. Maggio ,&nbsp;V. Landoni ,&nbsp;P. Ferrari ,&nbsp;A. Bianculli ,&nbsp;E. Petrucci ,&nbsp;A. Cicchetti ,&nbsp;B. Farina ,&nbsp;M.G. Ubeira-Gabellini ,&nbsp;P. Salmoiraghi ,&nbsp;E. Moretti ,&nbsp;B. Avuzzi ,&nbsp;T. Giandini ,&nbsp;F. Munoz ,&nbsp;A. Magli ,&nbsp;B. Noris Chiorda ,&nbsp;G. Sanguineti ,&nbsp;C. Cozzarini","doi":"10.1016/j.esmorw.2025.100680","DOIUrl":"10.1016/j.esmorw.2025.100680","url":null,"abstract":"<div><h3>Background</h3><div>Acute lymphopenia (AL) is a clinically relevant concern in patients undergoing pelvic lymph-node irradiation (PNI) for prostate cancer (PCa) and reliable predictive models are not available. The purpose of the current analysis was to develop a predictive model of AL after PNI for PCa combining dosimetric and clinical information in a large, prospectively followed cohort.</div></div><div><h3>Materials and methods</h3><div>Clinical/dosimetry/blood test data from a multi-centric prospective study were available, including absolute lymphocyte count (ALC) at baseline, mid-point and radiotherapy (RT) end. Dose–volume histograms (DVHs) of the body and of pelvic bones were extracted, as well as the integral dose (ID) to the body. Lymph-nodal planning target volume (LN-PTV) and its cranial limit were also recovered. The current analysis focused on acute CTCAEv4.03 grade ≥ 3 (G3+) lymphopenia (ALC &lt; 500/μl), defined as the lowest count between baseline and mid-point or RT end. The patient population was split into training and validation cohorts, and a multivariable logistic regression model combining DVHs and clinical information was trained and validated.</div></div><div><h3>Results</h3><div>700/887 patients with full 3D planning data and available baseline, mid-point and RT end counts were considered. 290 patients (41.4%) experienced acute G3+ lymphopenia. Both ID and pelvic bone DVH parameters were significantly associated with the endpoint. The two best resulting models included baseline ALC (OR = 0.999, <em>P</em> &lt; 0.001) and ID (Gy∗l) (OR = 1.003, <em>P</em> = 0.012) or baseline ALC, cranial LN-PTV limit (OR = 1.019, <em>P</em> &lt; 0.001) and EQD2 to LN-PTV (OR = 1.095, <em>P</em> = 0.002), when replacing ID with the cranial LN-PTV limit.</div></div><div><h3>Conclusions</h3><div>Severe AL after PNI for PCa is largely modulated by baseline ALC, with an independent role of the LN-PTV cranial limit or, alternatively, of ID, with the risk increasing by 5%-10% per 10² Gy∗l.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"11 ","pages":"Article 100680"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-world comparison of avelumab maintenance versus pembrolizumab at progression after first-line platinum-based chemotherapy in metastatic urothelial cancer","authors":"J. Bosveld ,&nbsp;B.B.M. Suelmann ,&nbsp;M.J. Bijlsma ,&nbsp;M.D. Franken ,&nbsp;K.K.H. Aben ,&nbsp;ProBCI Study Group ,&nbsp;R.P. Meijer ,&nbsp;A. Richters","doi":"10.1016/j.esmorw.2026.100685","DOIUrl":"10.1016/j.esmorw.2026.100685","url":null,"abstract":"<div><h3>Background</h3><div>For patients with metastatic urothelial cancer of the bladder (mUCB), the standard of care after first-line (1L) platinum-based chemotherapy has shifted from immune checkpoint inhibitor (ICI) treatment upon progression to maintenance ICI, without a direct comparison between both treatment strategies. We aimed to estimate the effect of the introduction of maintenance avelumab on overall survival (OS) compared with pembrolizumab at disease progression.</div></div><div><h3>Materials and methods</h3><div>This nationwide cohort study included patients with synchronous mUCB between November 2017 and December 2023 without disease progression after four or more cycles of 1L platinum-based chemotherapy from the Netherlands Cancer Registry. Patients were grouped into the pembrolizumab era or avelumab era representing the prevailing standards of care based on the date of completing chemotherapy. Kaplan–Meier methods and multivariable flexible parametric survival models were used to compare OS, summarized as restricted mean survival time (RMST).</div></div><div><h3>Results</h3><div>We identified 308 mUCB patients, 153 in the pembrolizumab era and 155 in the avelumab era. Baseline characteristics were mostly comparable between the two groups. ICI use was 56.2% in the pembrolizumab era and 72.3% in the avelumab era (<em>P</em> = 0.05). Adjusted RMST difference (avelumab era – pembrolizumab era) up to 24 months was 0.7 months (95% confidence interval –1.1 to 2.3).</div></div><div><h3>Conclusion</h3><div>No improved survival was observed following the change in standard of care from pembrolizumab at progression to avelumab maintenance, though more patients received ICI. These findings suggest that a treatment-free interval after chemotherapy, followed by ICI at progression, may be as effective as immediate maintenance ICI therapy, while minimizing toxicity, treatment burden, and healthcare resource use.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"11 ","pages":"Article 100685"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147421110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI assistance in tumor multidisciplinary teams","authors":"R.J. Geukes Foppen ,&nbsp;M. Morkūnas ,&nbsp;A. Traverso ,&nbsp;R. Dienstmann","doi":"10.1016/j.esmorw.2026.100684","DOIUrl":"10.1016/j.esmorw.2026.100684","url":null,"abstract":"<div><div>Multidisciplinary teams (MDTs), the cornerstone of modern cancer care, are facing significant operational inefficiencies. These challenges include the laborious, manual synthesis of unstructured, multimodal patient data for case preparation, which is time-consuming and prone to information overload. Furthermore, heterogeneous operational processes across MDTs themselves compound these issues. Additionally, clinical decisions are often archived in static documents, preventing the systematic collection of decision rationales essential for continuous learning and research. We propose that artificial intelligence (AI), particularly natural language processing (NLP) and large language models (LLMs), can act as integrated partners to solve these problems. The capacity of AI to seamlessly integrate diverse datasets—including imaging, histopathology, genomics, and clinical data—may be instrumental in enhancing diagnostic accuracy, refining personalized treatment plans within a complex cancer management journey. This integration can be achieved through a tiered approach, utilizing models from small NLP for targeted information extraction to foundational generative NLP for complex evidence synthesis, while addressing key challenges in validation, ethical governance, and regulatory oversight. International initiatives are actively developing validated frameworks to facilitate the widespread and standardized adoption of these AI solutions, while taking into account heterogeneous operational processes. By improving data management, streamlining decision making, and establishing crucial feedback loops, AI integration promises to enhance patient outcomes and optimize resource utilization within cancer care.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"11 ","pages":"Article 100684"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “Prevalence of functional iron deficiency in patients with malignancies and its impact on fatigue-specific quality of life”","authors":"S. Zimmermann ,&nbsp;S. Aebi ,&nbsp;O. Gautschi ,&nbsp;M. Gregor ,&nbsp;B. Müller ,&nbsp;H. Nilius ,&nbsp;E. Pardo ,&nbsp;D. Weiler ,&nbsp;S.S. Zeerleder ,&nbsp;A. Rüfer","doi":"10.1016/j.esmorw.2025.100174","DOIUrl":"10.1016/j.esmorw.2025.100174","url":null,"abstract":"","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"11 ","pages":"Article 100174"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147538503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How to bring generative AI to oncology practice","authors":"D. Truhn ,&nbsp;J.N. Kather","doi":"10.1016/j.esmorw.2025.100679","DOIUrl":"10.1016/j.esmorw.2025.100679","url":null,"abstract":"<div><div>Generative artificial intelligence (AI) is entering oncology. Large language models are the near-term workhorse because oncology runs on narrative text and structured tables. We review current adoption and outline a practical path from stand-alone chat models to retrieval-augmented systems and, ultimately, agentic assistants that plan tasks, call domain tools, and integrate multimodal data within the electronic health record. Concrete uses include molecular tumor board synthesis with transparent evidence, grading along guidelines, synoptic radiology and pathology drafting, and computable trial matching. We also map the constraints: fragmented hospital information technology, privacy and provenance requirements, domain shift across sites, and persistent hallucinations. We envision that evaluation must move beyond leaderboards toward multicenter, prospective designs with endpoints that reflect clinical utility, such as faithfulness to cited sources, extraction accuracy, time to task completion, plan correctness, recovery after tool failure, and silent clinical studies before exposure. Finally, we sketch an adoption trajectory. Institutions will replace <em>ad hoc</em> use of public tools with sanctioned drafting assistants, then embed retrieval and calculators inside the record, and only later enable event-driven agents that propose context-aware actions. The destination is augmentation, not automation: a learning assistant that shows its work, improves routine care, and leaves clinical judgment with clinicians.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"11 ","pages":"Article 100679"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prompting large language models and evaluating inter- and intra-rater agreement for cancer progression assessment from radiology reports","authors":"T.Ó. Kristjánsson,&nbsp;A.F. Henriksen,&nbsp;M.L. Hansen,&nbsp;D.G. Kovacs,&nbsp;A. Bjerrum","doi":"10.1016/j.esmorw.2026.100689","DOIUrl":"10.1016/j.esmorw.2026.100689","url":null,"abstract":"<div><h3>Background</h3><div>Manual annotation of free-text radiology reports is time-consuming and costly, delaying real-world evidence (RWE) studies in oncology. This study aimed to evaluate the performance of large language models (LLMs) in annotating cancer progression from Danish free-text radiology reports. The objectives were to determine whether human-to-LLM inter-rater agreement was non-inferior to human-to-human agreement, establish human intra-rater agreement, and develop a framework for tuning LLM performance to RWE needs.</div></div><div><h3>Materials and methods</h3><div>We identified 376 radiology reports from 184 patients with metastatic breast cancer from Danish electronic health records. Six human annotators, including two experts, classified radiology reports as progressive disease (PD) or non-PD. A ‘reverse questioning’ strategy was used to evaluate five LLM model series (Mistral, Gemma, Gemma 2, Llama 3, and Llama 3.1). Bootstrapping estimated confidence intervals (CIs) and assessed non-inferiority of the best-performing LLM ensemble compared with human agreement, using a non-inferiority margin of 0.1.</div></div><div><h3>Results</h3><div>The LLM framework was non-inferior to human annotators with a mean Cohen’s kappa of 0.82 (95% CI 0.74-0.89) for human-to-LLM versus 0.79 (95% CI 0.71-0.86) for human-to-human agreement (<em>P</em> &lt; 0.001). The best-performing ensemble model, Llama 3.1:70B, achieved 100% sensitivity, a specificity of 90%, and an F1 score of 84% on the test set. The mean human intra-rater variability was 0.87.</div></div><div><h3>Conclusions</h3><div>The proposed LLM framework was non-inferior to human annotators in classifying cancer progression from free-text radiology reports. This offers significant potential for using LLMs as a tool for identifying tumor progression events in clinical assessment and research.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"11 ","pages":"Article 100689"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147421109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UK real-world evidence on pembrolizumab and neoadjuvant chemotherapy for early-stage triple-negative breast cancer","authors":"J. McKeon ,&nbsp;E. Daniels ,&nbsp;A. Halstead ,&nbsp;C. Machado ,&nbsp;S. Potter ,&nbsp;T. Strawson-Smith ,&nbsp;A. Okines ,&nbsp;S. Mcintosh ,&nbsp;H. Tovey ,&nbsp;T. Robinson","doi":"10.1016/j.esmorw.2025.100664","DOIUrl":"10.1016/j.esmorw.2025.100664","url":null,"abstract":"<div><h3>Background</h3><div>The KEYNOTE-522 trial showed that use of pembrolizumab-chemotherapy for stage 2-3 triple-negative breast cancer improved pathological complete response (pCR) rates and event-free and overall survival compared with chemotherapy alone. This retrospective multicentre cohort study collated real-world-evidence (RWE) of the efficacy and safety of neoadjuvant pembrolizumab-chemotherapy.</div></div><div><h3>Patients and methods</h3><div>Patients treated with pembrolizumab-chemotherapy who completed pembrolizumab treatment before 1 July 2024 were included in the study. Data were collected on safety and efficacy of treatment. pCR rates were compared between subgroups using chi-square and tests for trend (where appropriate), no adjustment was made for multiplicity.</div></div><div><h3>Results</h3><div>Five hundred and forty-five patients were recruited from 34 UK centres (median age 50 years, range 21-79 years). Neoadjuvant treatment delays and discontinuations were reported by 59% [95% confidence interval (CI) 55% to 63%] and 47% (95% CI 42% to 51%) respectively. Immune-related adverse events (irAEs) were experienced by 65% (95% CI 61% to 69%); 20% of patients (95% CI 16% to 23%) experienced grade ≥3 irAEs. A total of 83% (95% CI 80% to 86%) and 45% (95% CI 41% to 50%) of patients had unplanned medical contacts and admissions. pCR rate was 56% (95% CI 52% to 60%). IrAEs were associated with increased pCR rates, but steroid use and pembrolizumab-chemotherapy discontinuation did not adversely affect rates of pCR.</div></div><div><h3>Conclusions</h3><div>This large, national RWE study demonstrates pCR rate was lower compared with that observed in KEYNOTE-522. High rates of dose reductions, discontinuations and unplanned medical contacts (with significant health care resource use) suggests that patient selection is important for this regimen. Collection of long-term outcomes will be important to better evaluate the benefit/risks of pembrolizumab, especially in certain subgroups.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"11 ","pages":"Article 100664"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}