Background To advance clinical research and innovation in bladder cancer care, the Dutch Prospective Bladder Cancer Infrastructure (ProBCI) was established as a nationwide platform and has now been operational for 5 years. Materials and methods ProBCI is a population-based cohort including all patients diagnosed since 2020 with high-risk non-muscle-invasive bladder cancer (HR-NMIBC), muscle-invasive bladder cancer (MIBC), or metastatic bladder cancer (mBC) in the Netherlands, comprising a Full and an Active Cohort. The full cohort includes all eligible patients, from whom core (HR-NMIBC and MIBC) or extended (mBC) clinical data and follow-up are collected within the Netherlands Cancer Registry (NCR). The active cohort is a subset of the full cohort and consists of patients who provided informed consent for additional collection of patient-reported outcomes and biomaterials alongside detailed clinical data and follow-up until death. Results As of 1 January 2026, the ProBCI full cohort comprised 20 737 patients. A growing number of participating hospitals (now 24) have been prospectively recruiting patients in the active cohort, with 1519 patients included to date. Among these, 73% completed baseline questionnaires (optional component) and 91% provided blood samples. Data from both cohorts are used by various studies including population characterizations, biomarker validation, and as concurrent control groups for single-arm trials. Conclusions ProBCI has evolved into a uniquely positioned research infrastructure, with data collection embedded in the NCR, providing high-quality, high-granularity data and biomaterials to address diverse research questions. ProBCI demonstrates that nationwide collaboration can generate high-quality resources that support bladder cancer research and contribute to future improvements in patient outcomes.
Background Portugal’s population-based cancer registries have evolved from fragmented municipal initiatives, starting in 1976, to full national coverage by 2007, culminating in the legislative creation of the National Cancer Registry [Registo Oncológico Nacional (RON)] in 2017. Patients and methods This short communication describes RON’s structural evolution, data quality, research utility, and readiness for international reporting. Results Although regional registries captured 20 core epidemiological variables, RON introduced >30 new clinical variables aligned with European Network of Cancer Registries recommendations, refining tumor characterization and treatment details. Analysis of 2022 data reveals that core incidence variables are 100% complete, with microscopic verification (97.8%), death certificate only (<0.1%), and unknown basis of diagnosis (<0.1%) meeting international benchmarks. However, challenges persist: stage is missing in 40.4% of cases, ill-defined morphology or topography affects 3.5%, unrelated surgical codes affect 14.9%, and poor stage standardization is evidenced by 779 recorded options. Despite partial automations, manual data entry remains substantial, contributing to a 3-year publication lag. Nevertheless, RON has supported 56 national and international studies since 2018, with data now sufficient for consideration in international reports. Conclusions Portugal has achieved remarkable progress in establishing a comprehensive national cancer registry with robust core data quality and growing international recognition. Strategic investments in automated data integration, real-time validation, targeted training, and phased data release, building on existing European collaborations, will further strengthen RON as a timely, high-impact resource for real-world oncology research and policy.
Background Concern exists that disruptions to health services in combination with changed health-seeking behaviour as a consequence of the coronavirus disease (COVID-19) pandemic would negatively impact cancer outcomes. The purpose of this study was to assess how the COVID-19 pandemic affected cancer incidence, stage at diagnosis, mortality, and 1-year net survival in Ireland. Materials and methods Age-standardised incidence and mortality rates were generated for three periods (2018-2019, 2020-2021, and 2022). Quarterly age-standardised incidence rates, incidence rate ratios (IRRs), and percentage change (PC) by cancer site were generated for Q1 2020 to Q4 2021. Annual incidence rates, IRRs and PC by stage at diagnosis were estimated. Age- and stage-adjusted 1-year net survival was estimated using the Pohar–Perme method. Results The age-standardised quarterly incidence rate of all invasive cancers (excluding nonmelanoma skin cancer) decreased significantly in Q2 2020, by 27% (95% CI −42% to −7%, P = 0.01), compared with 2018-2019. Decreases in the incidence of early-stage cancers (stages I or II) were seen in 2020, particularly for cancers in which organised or opportunistic screening occurs. There was little evidence of consistent increases in the incidence rate of stage III or IV cancers in 2021 or 2022. Age- and stage-adjusted 1-year net survival for all cancer sites in 2020-2021 remained in line with previous years. Conclusions The COVID-19 pandemic significantly disrupted cancer detection and diagnostic activity in Ireland during 2020. The early overall picture suggests that the resilience and adaptability of the Irish health care system helped to safeguard cancer outcomes during an unprecedented public health crisis.
Background Hormone receptor-positive (HR-positive) and human epidermal growth factor receptor 2-negative (HER2-negative) tumors are the most prevalent subtypes of early breast cancer (eBC). Despite the generally favorable prognosis, a relevant proportion experiences invasive disease events following surgery. We aimed to describe characteristics, treatment modalities, and survival outcomes of surgically treated patients with HR-positive/HER2-negative eBC in Germany. Materials and methods This study used data from eight population-based clinical cancer registries in Germany. Patients diagnosed between 2017 and 2021 with HR-positive/HER2-negative eBC who underwent surgery within 4 months of diagnosis were included. Patient and tumor characteristics and treatment received before recurrence were described. Kaplan–Meier statistics and multivariable Cox regression were used to assess invasive disease-free survival (iDFS). Results A total of 43 214 patients with a median follow-up time of 47 months were included. Among these, 5635 experienced an iDFS event, of which 38.0% were recurrences as the first observed event. Recurrence more often occurred among men, premenopausal women, and patients with larger tumors, greater nodal involvement, and higher tumor grade. The 5-year iDFS was 82.9% (95% confidence interval 82.4-83.3). In multivariable analysis, higher age, tumor size, nodal involvement, histologic grade, and mastectomy compared with breast-conserving surgery were associated with a lower iDFS. Conclusions These real-world data emphasize that 83% of patients with HR-positive/HER2-negative eBC do not experience recurrence, second primary malignancy, and death within 5 years. The prognostic relevance of established tumor-related factors for iDFS was confirmed in a large unselected patient population in the German routine care setting.
Background Oncologists face increasing difficulty staying current with rapidly evolving clinical data, guidelines, and regulatory updates. Building and maintaining an annotated clinical trial evidence library is time- and labor-intensive. To address this challenge, we developed and validated a living oncology evidence platform (Living-OEP) for breast cancer (BC), powered by an agentic artificial intelligence (AI) system that supports daily, human-conducted, AI-augmented systematic literature review (SLR). Materials and methods The agentic AI system, incorporating GPT-4.1 and o3 (OpenAI) and Claude [Anthropic, PBC, San Francisco, CA] Sonnet-4 (Anthropic), was designed to emulate expert-led, Cochrane-compliant SLR workflows. Guided by a human-developed annotation manual, the system decomposes tasks, self-debugs, and validates outputs. Training data included 29 236 clinical trial abstracts across BC, lung, and prostate cancer, each annotated with four review and 32 extraction variables. Structured data were integrated with guideline-based treatment pathways, forming a real-time, evidence-linked OEP. Accuracy was benchmarked against 1997 human annotations. Living-OEP evidence quality was compared with AI chatbots (ChatGPT [OpenAI, San Francisco, CA], Perplexity [Perplexity AI, Inc., San Francisco, CA], Consensus [Consensus, Boston, MA], and OpenEvidence [OpenEvidence Inc., Miami, FL]) using six criteria across eight BC treatment scenarios. Results The agentic AI review accuracy ranged from 95.1% to 97.2%. Extraction accuracy ranged from 51.1% to 99.4%, with three variables still undergoing iterative refinement. Compared with other AI tools, the Living-OEP provided more comprehensive and accurate evidence and linked all data to original publications and Food and Drug Administration labels. Conclusions A human-conducted, AI-augmented living SLR integrated with guidelines and regulatory data can provide real-time evidence support. Future studies are required to evaluate its impact on physician workflows, clinical decision-making, and implementation in oncology practice.
Peer review and editorial triage together are the cornerstones of scientific credibility yet their combined cracks are increasingly visible. As real-world data and digital health technologies have become increasingly important sources of evidence generation, many promising submissions from under-resourced settings receive terse desk rejections or nonspecialist reviews that obscure remediable methodological and reporting issues, limiting learning and career progression. We propose a compact, author-initiated, equity-centred remediation pathway—the mentorship escrow—that integrates upstream supports, a core remediation window, and a downstream pathway, converting borderline submissions into time-bounded and capacity-building interventions. We outline practical implementation steps, pilot design considerations, and measurable equity and quality metrics; discuss trade-offs across components; and address variable constraints including mentor availability, funding, potential selection bias, and risks of dependency, proposing mitigation strategies to ensure scalable, sustainable capacity building that preserves editorial standards while broadening the circulation of locally relevant evidence.
Background Structured administrative fields in oncology electronic health records (EHRs) are used in studies but their interpretation may depend on when they are completed. We assessed whether dated primary care physician (PCP) documentation was associated with survival after accounting for documentation timing. Materials and methods We conducted a retrospective cohort study using routinely collected oncology EHR data from a single French comprehensive cancer centre (ConSoRe platform), including patients treated for cancer between 2015 and 2017. Survival was analysed from cancer care initiation to death from any cause or administrative end of follow-up; no patients were censored before 12 months. Conventional analyses compared patients with or without an exploitable dated PCP declaration using inverse probability of treatment weighting (IPTW) targeting the average treatment effect based on age, sex, metastatic status, and tumour location. Temporally aligned analyses included baseline-available exposure definitions, a time-dependent Cox model, and a 6-month landmark analysis. Results Among the 15 216 patients, 10 418 had a dated PCP declaration (68.5%), 4757 had a declared PCP but no exploitable date (31.2%), and 41 had no PCP declared (0.3%). During the median follow-up of 102.1 months, 7175 deaths occurred. In conventional IPTW analysis, dated PCP documentation was associated with improved survival [hazard ratio (HR) 0.86, 95% confidence interval (CI) 0.82-0.90, P < 0.001]. Patients with declarations recorded after 6 months had the most favourable survival, consistent with immortal-time bias. In the time-dependent Cox model, dated documentation was not protective (HR 1.15, 95% CI 1.10-1.21, P < 0.001). In the 6-month landmark analysis, unadjusted postlandmark survival did not differ significantly (log-rank P = 0.140), whereas the adjusted landmark Cox model showed a small association in the opposite direction to the conventional analysis (HR 0.94, 95% CI 0.89-0.99, P = 0.011). Conclusions The apparent survival benefit associated with dated PCP documentation was explained by documentation timing rather than a robust prognostic effect. Time-dependent EHR fields require temporally aligned analyses in real-world oncology research.
Background: Biliary tract cancer (BTC) is a rare disease with limited therapies and bad prognosis. Patients and methods: A real-world cohort of patients with BTC from the Spanish RETUD Registry, diagnosed from 2017 to 2025, was analyzed for demographic/clinical characteristics, tumor molecular profile, therapeutic procedures, prognostic factors, and outcomes. Populations were defined as per the first therapy received: surgery/neoadjuvant therapy for the resectable disease (RD) population and first-line therapy for the advanced disease population. Results: A total of 1756 patients were included in the study, 695 were RD, of whom 55.9% subsequently experienced tumor recurrence, and 1061 were metastatic at diagnosis. Median age was 68.6 years, and 55.7% were male. The most frequent tumor location was intrahepatic cholangiocarcinoma (53%). Biomarker information was provided for 591 patients, and 42.6% had targetable alterations. Median follow-up time was 14.7 months. In the RD population, surgery was carried out in 95.7% of patients; 5% and 68.9% received neoadjuvant and adjuvant therapies, respectively. Median overall survival (OS) was 33.0 months [95% confidence interval (CI) 30.2-38.9], and median time to relapse was 19.4 months (95% CI 17.3-22.2). In the advanced disease population, immunotherapy was administered to 96.4% and 42.6% of patients in the first- and second-line settings, respectively, whereas targeted therapies were administered to 10.7% and 6.3% of patients, respectively. Median OS was 10.4 months (95% CI 9.7-11.2). Median progression-free survival in patients treated with approved targeted therapies in Spain was 7.1 (95% CI 4.4-NA) versus 4.0 months (95% CI 3.1-5.0), hazard ratio 0.54 (95% CI 0.30-0.99), P = 0.047. Conclusions: This study provides valuable data on the clinical characteristics, therapeutic procedures, and evolution of patients with BTC treated in clinical practice in Spain.
Background: Biliary tract cancer (BTC) comprises a heterogeneous group of malignancies, often diagnosed at advanced stages. Phase III trials have shown that adding immune checkpoint inhibitors (ICIs) to gemcitabine and cisplatin improves survival, supporting their use as first-line therapy; however, real-world evidence remains limited. Materials and methods: We conducted a multicenter observational study including patients with advanced BTC treated with ICI-based regimens plus chemotherapy. Clinical data were retrospectively collected. Overall survival (OS) was estimated using the Kaplan–Meier method, and prognostic factors were evaluated with multivariate Cox regression. A historical control cohort without ICI exposure was analyzed using propensity score matching. Results: A total of 152 patients received ICI-based therapy (median age 66.5 years, 57.9% men, and 68.4% metastatic). The most common primary sites were intrahepatic cholangiocarcinoma (52.6%) and gallbladder carcinoma (23.7%). Durvalumab plus gemcitabine/cisplatin was used in 88.1% of cases. After a median follow-up of 8.3 months, the median OS was 15.5 months (95% confidence interval 12.3-21.5), and the response rate was 38.8%. Maintenance therapy was given to 37.5%, mainly as ICI monotherapy. Independent adverse prognostic factors included Eastern Cooperative Oncology Group performance status of >0 [hazard ratio (HR) 3.91], neutrophil-to-lymphocyte ratio of ≥3 (HR 1.98), and vascular invasion (HR 1.83). Ten (6.6%) grade ≥2 immune-related adverse events were reported. A propensity score-matched analysis showed improved survival with ICI versus the historical cohort without ICI (HR 0.61, P = 0.025). Conclusion: In this real-world cohort, ICI plus chemotherapy achieved outcomes comparable with pivotal trials, supporting its effectiveness in broader populations and highlighting the need for improved access.
Background: Proton pump inhibitors (PPIs) are commonly coprescribed in patients with advanced or metastatic non-small-cell lung cancer (NSCLC), including those treated with tyrosine kinase inhibitors (TKIs) or immune checkpoint inhibitors (ICIs), often without a well-defined indication. PPIs may reduce TKI bioavailability by increasing gastric pH and compromising ICI efficacy through alteration of the gut microbiota. Observational studies have reported an association between concomitant PPI use and poorer survival in these patients. However, causal inference remains limited by confounding and time-related biases, and conducting a dedicated randomized trial is unlikely to be feasible. Target trial emulation provides a structured methodological framework to estimate causal effects using real-world data (RWD). Materials and methods: Two target trials will be emulated using RWD from the nationwide UNICANCER Epidemiological Strategy and Medical Economics (ESME)-Lung Cancer database, linked to the French National Health Data System. Advanced or metastatic NSCLC patients initiating first-line treatment with a TKI or an ICI between 2015 and 2024 could be included. Two strategies will be compared: initiation of a TKI or an ICI with concomitant PPI dispensation within a 12-week grace period versus without PPIs during this period. Time zero is defined as TKI or ICI initiation. The primary endpoint is overall survival; secondary endpoints include real-world progression-free survival and real-world time to next treatment. Causal effects will be estimated using a cloning, censoring, and weighting approach with inverse probability of censoring weights to address confounding and immortal time bias. Sensitivity analyses will include G-computation, quantitative bias analysis, and E values.
Background: Randomized controlled trials (RCTs) remain the gold standard for evidence on treatment efficacy but face limitations: restrictive eligibility criteria exclude real-world populations, head-to-head comparisons between approved regimens are rare, and absolute effectiveness in unselected cohorts often differs from trial results. High-quality cancer registries address these gaps by documenting treatment reality and quantifying effectiveness outside trial settings. We describe how the breadth and depth of prospective, longitudinal oncology registry data enable advanced research complementing RCTs. Materials and methods: The iOMEDICO oncologist and hematologist network currently operates nine prospective, multicenter registry platforms across major cancer types in Germany. The registries employ regulatory-grade electronic data capture with audit trails, continuous data management, and consecutive patient enrollment. Beyond demographics and clinical variables, platforms systematically collect comorbidities, comprehensive biomarker data, complete treatment pathways, and patient-reported outcomes (PROs). Results: More than 55 000 patients have been documented so far across >400 sites representing diverse care settings. Our registry data enable patient-centered research in diverse applications: (i) characterizations of patient populations, treatment patterns, and outcomes to evaluate the current standards of care, identify unmet needs, and follow developments over time; (ii) predictive and prognostic model developments; (iii) comparative effectiveness research such as target trial emulation for head-to-head treatment comparisons; (iv) translational research into biomarker prevalence; and (v) evaluation of PROs. Conclusion: Well-designed prospective, longitudinal registries collecting broad and deep real-world data complement RCTs, inform Health Technology Assessments, and fill knowledge gaps by creating treatment transparency, addressing evidence gaps, and providing realistic outcome expectations for the heterogeneous populations in routine care.
Background Defining, measuring, and reporting exposure in real-world data are important for the valid generation and interpretation of real-world evidence (RWE). This is especially challenging for oncology medicines, due to complicated treatment regimens and adjustments in dose and/or schedule. This scoping review aims to assess the reporting of drug exposure definitions for oncology medicines in RWE studies, using colorectal cancer (CRC) as a case study. Methods We searched PubMed and Embase for RWE studies assessing clinical outcomes of cyclically administered oncology medicines in patients with CRC, published in 2023-2024. We extracted publication characteristics, study characteristics, and exposure variables. We calculated a drug exposure reporting score (0-7), composed of active substance, dose, dosing schedule, number of cycles, treatment episode length, and drug exposure start/end. We exploratively compared characteristics of studies with a high and low score. Results Of the 61 included studies, 59% used hospital data, and 36.1% used drug administration data. Most studies (83.6%) reported the start of drug exposure, and 34.4% reported the end of drug exposure. Median drug exposure reporting score was 3.0 (interquartile range 2.0-4.0). Studies with a high score were 4.6 times more often published in an oncology journal and reported use of guidelines for observational studies 1.8 times more often than studies with a low score. Conclusion Definitions of drug exposure are often underreported in RWE studies on oncology medicines. Lack of clarity concerning oncological drug exposure complicates the assessment of the methodological quality and reproducibility and may affect the interpretation of the association between oncology medicines and clinical outcomes.
Background Chemotherapy Electronic Prescribing and Administration Systems (CEPAS) in Scotland support safe prescription and administration of systemic anti-cancer therapy (SACT), with instances set up according to local processes and governance criteria in each of the five cancer centres. We sought to create a national SACT dataset (nSACTd) standardising this information, allowing population-level analysis of activity, prescribing trends, patient safety and outcomes. Materials and methods SACT data from five cancer centres, covering all hospital-prescribed SACT for adult patients in National Health Service Scotland, were brought into Public Health Scotland through a weekly data connection, resulting in separate records for every dose administered, linking to other national datasets and the latest reference information at the time of appointment using a unique personal identifier. Data feeds were adapted for regional differences in recording using national mapping tables and derivations to standardise and prepare data for analysis. Results The nSACTd contains standardised population-level information on >2.2 million SACT appointments, detailing ∼300 000 treatments in 144 000 patients, covering both solid tumours and haematological malignancies since 2014. It provides valuable, timely information being utilised for monitoring, service planning and research purposes. Initial uses highlighted the ever-increasing demand for SACT and the impact of coronavirus disease 2019 on regimen routes. Conclusions The Scottish nSACTd is a globally unique, comprehensive population-based data collection of SACT information. National mapping tables and algorithms provide a robust method of bringing together separately managed CEPAS into one data platform. Many applications of the nSACTd have been undertaken, and development continues to explore full utilisation of these valuable data.
Background:Population-based cancer registries aim for timely reporting of cancer incidence and rapid identification of unexpected patterns to support cancer control. To facilitate large-scale, systematic, first evaluation of incidence data, a semi-automated tool was built that flags statistically significant deviations from historical trends, which may merit further investigation. Materials and methods:The tool was developed in the open-source programming language R and uses a CSV file to specify input parameters. For each row, observed or age-standardised incidence rates are calculated, and corresponding graphical outputs, with various stratifications and substratifications, are produced. A flexible regression model is fitted to all but the most recent incidence year, with an automated algorithm determining optimal knotpoint placement. The model is then extrapolated to estimate the expected incidence for the final year, which is statistically compared with the observed value. The model is visually represented, including confidence bands, in the graphical output. Results are exported as PNG files organised in hierarchical folders, along with a structured Quarto HTML report containing all figures. Results:The modelling approach captured complex temporal patterns without overfitting and reliably identified deviations from historical trends. This high-throughput tool is highly customisable and can be extended to analyse stage-specific incidence trends or stratifications by other categorical variables. Conclusions:This semi-automated tool enables efficient first-line visual evaluation of newly available registry data to highlight emerging trends potentially warranting further investigation from either a data quality or public health perspective. The code is available from the authors upon request. Output is highly customisable via CSV input and can be stratified on multiple levels.
Background:The AI-HOPE Lung Cancer study is a multicenter initiative designed to integrate artificial intelligence (AI) and real-world data to improve outcome prediction in patients with metastatic non-small-cell lung cancer treated with first-line immunotherapy-based regimens. AI-HOPE aims to leverage machine learning (ML) models to generate individualized predictions of progression-free survival (PFS), overall survival (OS), and treatment-related toxicity in a broad, unselected population. Materials and methods:Clinical and imaging data are harmonized and stored within a privacy-compliant infrastructure (San Raffaele Ai CEnter [S-RACE] platform), promoting FAIR (Findable, Accessible, Interoperable and Reusable) data principles and minimizing manual workload. The primary objective is the development of time-to-event models for PFS and OS. Complementary binary classification models will explore early progression, long-term survival, and clinically relevant toxicities. Results:The study includes retrospective (from 2017) and prospective (until 2027) phases across 21 European centers. So far, 920 patients have been recruited for the study, of whom 621 have baseline imaging scans available for centralized analysis. In the AI-HOPE study, a flexible methodological approach integrates multiple ML models tailored to specific clinical questions, complemented by explainable AI tools. Multimodal models combining clinical variables with computed tomography and [18F]2-fluoro-2-deoxy-d-glucose-positron emission tomography imaging features (when available) are supported through the S-RACE platform, which provides a partially automated imaging analysis workflow. Conclusions:By combining structured clinical variables and multimodal imaging data, the AI-HOPE Lung Cancer study aims to support refined risk stratification and treatment personalization, ultimately facilitating the responsible integration of AI into routine thoracic oncology practice.
Background:Early discontinuation (ED) in clinical trials (CTs) is frequent and deleterious for the patients, the care team, and the study duration. ED comprises screening failure or discontinuation during the first month of the treatment phase, and is often difficult to predict by clinicians. We aim at predicting ED by automatic analysis of patient's clinical record using language models (LMs). Materials and methods:We fine-tuned a French LM on the oncology clinical reports of a French cancer center, and obtained a pretrained LM named OncoBERT. We then selected consultation reports of patients included in oncology CTs for any tumor type that we used to fine-tune OncoBERT and obtained a new model for ED prediction. We carried out a retrospective and prospective evaluation and used eXplainable Artificial Intelligence (XAI) methods to interpret the predictions. Results:On the retrospective test cohort of 1007 reports, the model achieved a precision of 0.77, recall of 0.95, and could have decreased the ED rate from 25.3% to 21.3%. On the prospective test cohort it reached a precision of 0.75, recall of 0.75, and could have decreased the ED rate from 33.7% to 27%. Using XAI showed that the words used by the model to predict ED reflect deterioration of general condition, a well-known factor of ED. Conclusion:We have developed a LM that is portable, explainable, with near-human performances for ED prediction in oncology CTs. We anticipate that democratization of automatic trial matching tools should be complemented by ED prediction tools to fully optimize access to CTs and patient recruitment.
Spending on cancer medicines has increased rapidly worldwide, driven by the introduction of immunotherapy and targeted therapy. Australia exemplifies this trend, with cancer medicines representing one of the largest and fastest-growing areas of expenditure for Australia's public medicines funder, the Pharmaceutical Benefits Scheme. While this growth reflects therapeutic innovation and expanded clinical use, it has intensified concerns around the real-world safety, effectiveness, and value of novel therapies once adopted into routine care. Randomised clinical trials remain essential for regulatory approval but often provide limited insight into outcomes in broader, more heterogeneous populations, particularly as many therapies enter practice via accelerated pathways based on surrogate endpoints. Population-based cancer registries offer an important resource for postmarket surveillance when linked with national administrative datasets such as dispensing and hospitalisations records. However, limitations in registries' timeliness, disease stage ascertainment, biomarker and genomic data capture, and information on recurrence and progression constrain their current utility. This perspective examines the Australian population-based cancer registry landscape, highlighting its strengths, untapped potential, and critical gaps. We outline priority enhancements required to realise a robust, whole-of-population cancer medicine surveillance system that can inform clinical practice, policy, and sustainable health care decision making.
Background:Radical cystectomy with neoadjuvant and/or adjuvant therapy is the standard treatment of muscle-invasive urothelial cancer (MIUC). Although perioperative immunotherapy use has expanded, real-world data remain limited. The MINOTAURO study evaluated perioperative treatment patterns and outcomes in Spain. Patients and methods:This retrospective, multicenter study included adults with MIUC treated at 17 hospitals in Northern and Eastern Spain (April 2022-June 2024). Clinical data were extracted from electronic health records. Outcomes included perioperative treatment patterns, pathological response, safety, recurrence, and survival. Overall survival and disease-free survival were estimated using Kaplan-Meier methods. Results:A total of 629 patients were analyzed (median age 71 years; 79.5% male). Most tumors originated in the bladder/urethra (95.0%) and were urothelial (92.8%); 70.4% were clinical stage II. Cisplatin-based neoadjuvant chemotherapy (NAC) was administered to 40% of patients, primarily cisplatin-gemcitabine. The main reasons for not receiving NAC were creatinine clearance <60 mL/min (40.8%), Eastern Cooperative Oncology Group performance status ≥2 (18.4%), and cardiovascular disease (18%). Among NAC-treated patients, 41.3% achieved a pathologically documented complete response, and 50% achieved downstaging. Surgery was carried out in 77% of patients, mainly radical cystectomy. Adjuvant chemotherapy was given to 10.7%, and 46.2% of eligible patients received adjuvant immunotherapy, predominantly nivolumab. Recurrence occurred in 28.9% of patients. At a median follow-up of 18.5 months, 75% were alive and 69.7% were recurrence-free. Conclusions:MINOTAURO demonstrates evolving real-world perioperative management of MIUC, with notable use of perioperative immunotherapy in routine clinical practice and outcomes that appear favorable in comparison with historical data, although no formal comparative analysis was carried out.
Artificial intelligence (AI) is rapidly reshaping oncology, from diagnosis to treatment planning and clinical research. This perspective defines the oncologist in the era of AI as a clinician able to critically interpret, supervise, and communicate AI outputs, while understanding the principles, limitations, and ethical implications of these tools. We ground this discussion in a survey of 475 UK-based participants, including cancer patients and survivors, members of the public and healthcare staff. Acceptance of AI was substantial but conditional: it increased sharply with self-reported understanding and depended on assurances of clinician involvement, transparency and data security. These findings motivate the three concerns around which we structure the perspective: ‘AI will replace the clinician’, ‘AI may be biased and unfair’, and ‘AI does not safeguard data’. Using the example of an AI system for cancer treatment recommendation, we illustrate how these concerns can be addressed through practical, technically grounded approaches, including concept-based modelling, uncertainty quantification, and federated learning. Finally, we argue that AI skills development must become an integral part of oncology education, enabling oncologists not only to use AI safely, but also to explain, contextualise, and critically shape its integration into patient-centred cancer care.
