European Journal of Cancer

BackgroundCutaneous melanoma (CM) is an aggressive skin cancer with rising incidence, representing a growing public health concern. Despite the remarkable success of immune-checkpoint inhibitors (ICIs) in the management of advanced disease, mortality remains high due to therapy resistance. Identifying reliable prognostic and predictive biomarkers is therefore essential to improve patient stratification, optimize treatment selection, and minimize unnecessary toxicity. MethodsWe comprehensively profiled the circulating immune landscape of 54 treatment-naive CM patients by integrating flow cytometry immunophenotyping with clinicopathological data, and performed tumor gene expression analysis in a subset of 26 patients. ResultsElevated HLA-DR and CD69 expression on circulating CD4+ T cells, together with reduced circulating CD8+ T cell frequency, emerged as candidate prognostic biomarkers associated with improved survival. Prognostic models combining these immune variables with clinical covariates accurately stratified patients by overall survival (89.5% sensitivity, 72.7% specificity; AUC = 0.872, p < 0.0001) and progression/recurrence risk (75% sensitivity and 71.4% specificity; AUC = 0.763, p = 0.001). In a subset of 43 patients subsequently treated with ICIs, elevated baseline HLA-DR and CD69 expression on circulating CD4+ T cells was also associated with therapeutic benefit. A predictive model integrating these markers with clinical covariates achieved good discriminatory performance (65.2% sensitivity, 88.9% specificity; AUC = 0.775, p = 0.0027). Tumor gene expression profiling supported the role of IFN-{gamma}-related signatures, previously linked to ICI response, as complementary prognostic and predictive tools. ConclusionThese findings highlight systemic CD4+ T cell activation status as a promising, easily measurable biomarker in CM, laying the foundation for future strategies to refine patient stratification and guiding immunotherapy decisions.

Immune checkpoint inhibitors such as anti-PD1 antibodies are essential in cancer therapy. Emerging data suggest that lower doses may be effective and more economical, though further evidence is needed. We conducted a retrospective study at Centre Leon Berard to assess the efficacy and safety of low-dose nivolumab (20 mg every three weeks) in patients with advanced cancer, mainly squamous cell carcinomas (SCC). Between 2023 and 2024, 53 patients were treated, with a median age of 74 years; 39.6% were over 80. Most were male (64%) and had ECOG >1 (69.9%). Primary tumor sites included cutaneous SCC (34%), head and neck SCC (32%), and soft tissue sarcoma (15%). After a median follow-up of 8.3 months, median overall survival was 7.5 months. The objective response rate (ORR) was 20.8% overall, rising to 35.3% in cutaneous SCC and 23.5% in head and neck SCC-comparable to standard-dose nivolumab. Toxicity was manageable: 18.7% experienced immune-related adverse events, with only 3.7% grade 3. Low-dose nivolumab demonstrates encouraging efficacy and tolerability in a frail population, supporting its potential role in resource-limited settings. Prospective trials are warranted to confirm these findings in broader populations.

Background: Although thin, T1 melanomas have an excellent cure rate with surgery alone, >25% of melanoma deaths originate from thin melanomas (TMs). There is, therefore, an urgent need to improve the identification and management of patients with TMs at high risk of recurrence. Methods: Patients with T1 melanoma and recurrence [&le;] 2 years of diagnosis (T1 rapid group) were compared to patients with T1 melanoma and recurrence [&ge;]10 years after diagnosis (T1 late group). Results: 442 patients from 14 sites were included: 310 and 132 patients in the T1 rapid and late groups, respectively. Median age at primary melanoma diagnosis was 51 years [15-85], 272 (62%) male, 254 (58%) superficial spreading and 101 (23%) head/neck primary. The majority (73%) of recurrences in the T1 rapid group were locoregional. Using univariable logistic regression analysis, age >65 years (p<0.0001), lentigo maligna (LM) melanoma subtype (p=0.025), head/neck primary site (p=0.0065), mitoses [&ge;]1/mm2 (p=0.0181) and ulceration (p=0.0087) were significantly associated with T1 rapid recurrence compared to T1 late recurrence. Using multivariable analysis, age >65 years (p=0.0010), mitoses [&ge;]1/mm2 (p=0.049) and ulceration (p=0.037) remained significant. Conclusions: Rapid recurrence of TM is associated with age >65 years, LM subtype, head/neck primary site, mitoses [&ge;]1/mm2 and ulceration.

Prehabilitation (PRH) is a preoperative process aimed at optimizing patients functional capacity to improve surgical outcomes and overall well-being. While its physical and cognitive benefits are increasingly documented, its emotional impact, particularly in neuro-oncology patients, remains less explored. This study assessed the psychological effects of a PRH program on 29 brain tumor patients. The primary outcome, emotional well-being, was measured using quality of life and emotional distress metrices. Secondary outcomes included perceived stress levels and control attitudes. Additionally, qualitative data from structured interviews provided further insights into the psychological effects of the intervention. The results indicated significant improvements in quality of life and reductions in emotional distress, particularly among women. While perceived stress levels remained stable, control attitudes showed an increase. Qualitative analysis further highlighted the positive changes in the control sense and identified additional factors, such as the importance of social support sources during the PRH process. Overall, these findings suggest that PRH interventions play a significant role in enhancing emotional well-being among neuro-oncological patients in the preoperative phase. These results underscore the importance of implementing comprehensive and personalized PRH approaches to optimize clinical status both before and after surgery, thereby promoting sustained psychological benefits in this population. This study is based on data collected at Institut Guttmann in Barcelona in the context of the Prehabilita project (ClinicalTrials.gov identifier: NCT05844605; registration date: 06/05/2023).

BackgroundTreatment optimization in HPV-associated oropharyngeal cancer (OPSCC) remains challenging, as recent de-escalation trials have shown limited success. Current patient selection strategies based on smoking history and TNM classification are insufficient, highlighting the need for robust, standardized prognostic biomarkers. We report the first validation of the Immunoscore (IS) for prognostic stratification in HPV-associated OPSCC. Patients and methodsWe analyzed 191 HPV-associated (p16+ and HPV DNA/RNA+) OPSCC patients from an international multicenter cohort (2015-2024), comprising a French monocentric retrospective training cohort (N = 48) and three validation cohorts: French monocentric retrospective (N = 48), French multicenter prospective (N = 50), and US multicenter retrospective (N = 45). IS is a standardized digital pathology assay quantifying CD3lJ and CD8lJ densities in tumor cores and invasive margins, with cut-offs defined in the training cohort and validated across cohorts. Associations with disease-free survival (DFS), time to recurrence (TTR) and overall survival (OS) were assessed, alongside 3RNA-seq and sequential immunofluorescence profiling of immune composition. ResultsMedian age 65; 80% male; 74% smokers; 66% T1-2; 82% N0-1 (AJCC8th). IS-High patients demonstrated superior 3-year DFS in the training and validation cohorts 1-3 (all log-rank P < 0.05). Multivariable analysis identified IS-Low as the strongest independent risk factor for DFS (HR 9.03; 95% CI: 4.02-20.31; P < 0.001). The model combining IS with clinical factors showed higher predictive accuracy for DFS (C-index 0.82) than clinical variables alone (0.7; P < 0.0001). Similar findings were observed for TTR and OS. IS-High tumors showed markedly higher enrichment of lymphoid and myeloid immune cell populations, contrasting with immune-poor signatures in IS-Low tumors. ConclusionsIS is a robust biomarker that outperforms standard clinical variables in both prognostic and predictive accuracy. The enriched cytotoxic immune infiltrate in IS-High tumors explains favorable outcomes and supports their suitability for treatment de-escalation. Prospective validation is warranted.

Background: Predicting whether a treatment will demonstrate meaningful clinical benefit before committing to a large-scale trial remains a major unmet need in oncology. Patient-derived organoids (PDOs) recapitulate individual tumor drug sensitivity, but have not been used to forecast population-level trial outcomes. We developed SCOPE (Screening-to-Clinical Outcome Prediction Engine), a platform that integrates PDO drug screening with clinical prognostic modeling to predict arm-level median progression-free survival (mPFS) and objective response rate (ORR) without access to any trial outcome data. Patients and methods: SCOPE was trained on 54 treatment lines from patients with metastatic colorectal cancer (mCRC, n=15) and metastatic pancreatic ductal adenocarcinoma (mPDAC, n=39) with matched clinical data and PDO drug screening across 9 compounds. A Clinical Score module captures baseline prognosis; a Drug Screen Score module quantifies treatment-specific organoid sensitivity. To predict trial outcomes, synthetic patient profiles are generated from published eligibility criteria and matched to a biobank of 81 PDO lines. Predictions were externally validated against 32 arms from 23 published trials, treatment ranking was assessed across 8 head-to-head comparisons, and prospective applicability was tested for daraxonrasib (RMC-6236), a novel pan-RAS inhibitor in mPDAC. Results: Predicted mPFS strongly agreed with published outcomes (R2=0.85, MAE=0.82 months; Pearson r=0.92, P<0.001), approaching the empirical concordance between two independently measured clinical endpoints (ORR vs. mPFS, R2=0.87). ORR prediction was similarly robust (R2=0.71, MAE=7.3 percentage points). Integrating organoid and clinical data significantly outperformed either alone (P=0.001). SCOPE correctly identified the superior arm in 7 of 8 head-to-head comparisons (88%, P<0.05). Applied to daraxonrasib prior to phase 3 data availability, the platform predicted superiority over standard chemotherapy in KRAS-mutant mPDAC, consistent with emerging clinical data. Conclusion: By combining functional organoid drug screening with clinical modeling, SCOPE generates calibrated efficacy predictions for both established regimens and novel agents without prior clinical data. This approach could support clinical trial design, treatment arm selection, and go/no-go decisions, offering a new tool to improve the efficiency of gastrointestinal cancer drug development.

Background: Manual chart abstraction is a major bottleneck in clinical research. In oncology, important outcomes such as disease recurrence and the treatment history are often only documented in clinical notes, limiting the scale and quality of observational and epidemiologic studies. We developed an open-source pipeline that, in a HIPAA-compliant setting, can use any commercially available large language model (LLM) to determine whether variables from complex longitudinal oncology records can be abstracted with performance similar to that of expert medical oncologists. Methods: We randomly selected 100 patients from an institutional breast cancer cohort enriched for complex care. We abstracted a range of key variables from unstructured data, including dates of diagnosis and recurrence, clinical stage, biomarker subtype, genetic testing results, and prescribed systemic therapies, including treatment timing, intent, and reason for discontinuation. The inputs to the LLM were unnormalized, unlabeled, and unedited clinical notes, pathology reports, med admin records, and demographics. Breast oncologists abstracted the same variables to create the reference standard. For systemic therapy extraction, a second oncologist and research coordinators served as comparators. In addition to variable-level performance, we examined whether survival and hazard-ratio estimates were similar for fully LLM-derived datasets compared with expert-derived datasets. Results: Among 100 patients, the median chart had more than 3,100 pages of text; patients received a median of 7 lines of therapy over 6.5 years of follow-up. The best-performing LLM achieved 99% concordance with the expert for recurrence status, 100% for germline BRCA1/2 pathogenic variant detection, 99% for hormone receptor status, 96% for HER2 status, 91% for clinical stage, 91% for PIK3CA mutation status, and 90% for ESR1 mutation status. For anti-cancer drug extraction, the best-performing LLM approached inter-oncologist variability. For exact therapy-line reconstruction, mean patient-level performance remained 9 percentage points lower than the second oncologist, although inter-LLM disagreement was similar to inter-oncologist disagreement. All four LLMs tested outperformed the research coordinators on systemic therapy abstraction. Recurrence-free survival, overall survival, and hazard ratio estimates were similar between expert-derived and LLM-derived datasets. In an external cohort of 97 young patients with early-stage breast cancer, the unmodified pipeline showed similar performance for recurrence detection and adjuvant endocrine therapy use. Conclusions: Off-the-shelf LLMs in a fixed retrieval pipeline were able to abstract a range of variables from complex longitudinal oncology records with performance approaching inter-oncologist variability for key tasks, without any fine-tuning or institution-specific retraining. This approach offers a practical path to scaling the creation of research-grade retrospective datasets from narrative medical records.

Background: Population-based cancer registers (PBCR) are important for monitoring trends in cancer epidemiology, facilitating the implementation of effective cancer services. Adolescents and Young Adult (AYA) with cancer are a patient group with a unique set of needs. The utility of PBCR in AYA is limited by the lack of AYA-specific data items. STRONG AYA, an international multidisciplinary consortium is addressing this through federated learning (FL) methodology and novel data visualisation concepts. A Core Outcome Set (COS) has been developed to measure outcomes of importance through clinical data and Patient Reported Outcomes (PROs). We describe how data from the Yorkshire Specialist Register of Cancer in Children and Young People (YSRCCYP), a PBCR in the UK is being used within STRONG AYA and how the subsequent analyses can guide patient consultations. Methods: Data from the YSRCCYP were imported into a Vantage 6 node, from which FL analyses are performed along with data provided by other consortium members. The results are extracted into the PROMPT software and integrated into patient electronic healthcare records. Results: Healthcare professionals can view the results of individual PROs at various time points and in comparison, to summary analyses carried out within the STRONG AYA infrastructure. Results can be filtered by age, disease, country and stage. Conclusion: We have demonstrated how a regional PBCR can contribute to a pan-European infrastructure and analyses viewed to enhance patient consultations. Such analyses have the potential to be used for research and policy-making, improving outcomes for AYA.

Background In clinical trials for alopecia areata (AA) the treatment effect (percentage of hair loss) is estimated using the Severity of Alopecia Tool (SALT) score. Trials in patients with severe AA (>=50% hair loss) employed a local rating of the SALT score performed at trial sites by different investigators. However, in mild-to-moderate AA (<= 50% hair loss) where SALT scores are lower, potential inter rater variability and margin of error may compromise the results. Objectives To compare Centralised and Local measurement of hair loss in mild moderate AA. Methods In a Phase 2 clinical trial a centralised measurement of hair loss was performed from photographic images taken using a standardised protocol and professional camera equipment. Local scoring was also undertaken at screening/baseline for eligibility. We assessed: the repeatability of the central system (screening vs baseline values), the reproducibility of the central versus the local rating system and the potential impact of each method on the endpoints using a Monte-Carlo simulation method. Results There was good agreement and consistency of scoring with Central rating. This provided much smaller margins of error, 50% lower than Local rating. The simulations demonstrated that substituting Local rating for Central rating would result in a reduction of the likelihood of a statistically significant outcome by at least 50% depending on the SALT score defined clinical response endpoint. Conclusions Central rating is most appropriate in the Phase 2 learning stage of clinical development and provides an accurate representation of the quantity of hair loss, minimising error and ensuring consistency in measurements.

Background: Early breast cancer detection remains central to improving clinical outcomes, yet conventional screening pathways, particularly mammography, have recognized limitations in sensitivity, specificity, and performance in dense breast tissue. Circulating microRNAs (miRNAs) have emerged as promising minimally invasive biomarkers, while artificial intelligence and machine learning (AI/ML) offer powerful tools for identifying diagnostically relevant multi-marker patterns within complex biomarker datasets. This systematic review and meta-analysis evaluated the diagnostic performance of AI/ML-based circulating miRNA signatures for early breast cancer detection. Methods: A systematic search of PubMed/MEDLINE, Scopus, and Web of Science Core Collection was conducted from database inception to 31 December 2025. Studies were eligible if they were original human investigations evaluating circulating miRNAs using an AI/ML-based diagnostic model for breast cancer detection and reporting extractable diagnostic performance metrics. Study selection followed PRISMA 2020 and PRISMA-DTA guidance. Methodological quality was assessed using QUADAS 2. Pooled sensitivity and specificity were synthesized using a bivariate random-effects model, and overall diagnostic performance was summarized using a hierarchical summary receiver operating characteristic framework. Results: Seven studies met the inclusion criteria for qualitative synthesis, with eligible studies contributing to the quantitative analysis depending on data availability. Across the pooled analysis, AI/ML-based circulating miRNA models demonstrated good overall diagnostic performance, with a pooled AUC of 0.905 (95% CI: 0.890 to 0.921), pooled sensitivity of 81.3% (95% CI: 76.8% to 85.2%), and pooled specificity of 87.0% (95% CI: 82.4% to 90.7%). Heterogeneity was moderate for AUC (I2 = 42.3%) and sensitivity (I2 = 38.7%) and low for specificity (I2 = 28.4%). Risk-of-bias assessment showed overall low-to-moderate methodological concern, with patient selection representing the most variable domain. Deeks funnel plot asymmetry test showed no significant evidence of publication bias (p = 0.34). Conclusions: AI/ML based circulating miRNA signatures show promising diagnostic accuracy for early breast cancer detection and may have value as non invasive adjunctive tools within imaging supported diagnostic pathways. However, the evidence base remains limited by methodological heterogeneity, variable validation rigor, and the predominance of retrospective case control designs. Prospective, standardized, and externally validated studies are needed before routine clinical implementation can be justified.

Background: Current deep learning models in computational pathology, radiology, and digital pathology produce opaque predictions that lack the explainable artificial intelligence (xAI) capabilities required for clinical adoption. Despite achieving radiologist-level performance in tasks from whole-slide image (WSI) classification to mammographic screening, these models function as black boxes: clinicians cannot trace predictions to specific biological features, verify outputs against established morphological criteria, or integrate AI reasoning into precision oncology workflows and tumor board decision-making. Methods: We present Virtual Spectral Decomposition (VSD), a modality-agnostic, interpretable-by-design framework that decomposes medical images into six biologically interpretable tissue composition channels using sigmoid threshold functions - the same mathematical structure as CT windowing. Unlike post-hoc xAI methods (Grad-CAM, SHAP, LIME) applied to black-box deep learning models, VSD channels have pre-defined biological meanings derived from tissue physics, providing inherent explainability without sacrificing quantitative rigor. For whole-slide image (WSI) analysis in digital pathology, we introduce the dendritic tile selection algorithm, a biologically-inspired hierarchical architecture achieving 70-80% computational reduction while preferentially sampling the tumor immune microenvironment. VSD is validated across three cancer types and imaging modalities: pancreatic ductal adenocarcinoma (PDAC) on CT imaging, lung adenocarcinoma (LUAD) on H&E-stained pathology slides using TCGA data, and breast cancer on screening mammography. Composition entropy of the six-channel vector is computed as a visual Biological Entropy Index (vBEI) - an imaging biomarker quantifying the diversity of active biological defense systems. Results: In pancreatic cancer, the fat-to-stroma ratio (a novel CT-derived radiomics biomarker) declines from >5.0 (normal) to <0.5 (advanced PDAC), enabling early detection of desmoplastic invasion before mass formation on standard imaging. In lung cancer, composition entropy from H&E whole-slide images correlates with tumor immune microenvironment markers from RNA-seq (CD3: rho=+0.57, p=0.009; CD8: rho=+0.54, p=0.015; PD-1: rho=+0.54, p=0.013) and predicts overall survival (low entropy immune-desert phenotype: 71% mortality vs 29%, p=0.032; n=20 TCGA-LUAD), providing immune phenotyping for checkpoint immunotherapy patient selection from a $5 H&E slide without molecular assays. In breast cancer, each lesion type produces a characteristic six-channel fingerprint functioning as an interpretable computer-aided diagnosis (CAD) system for quantitative BI-RADS assessment and subtype classification (IDC vs ILC vs DCIS vs IBC). A five-level xAI audit trail provides complete traceability from clinical decision support output to specific biological structures visible on the original images. Conclusion: VSD establishes a unified, interpretable-by-design mathematical framework for explainable tissue composition analysis across imaging modalities and cancer types. Unlike black-box deep learning and post-hoc xAI approaches, VSD provides inherently interpretable, clinically verifiable cancer detection and immune phenotyping from standard clinical imaging at existing costs - without requiring foundation model infrastructure, specialized hardware, or molecular assays. The open-source pipeline (Google Colab, Supplementary Material) enables immediate reproducibility and extension to additional cancer types across the pan-cancer TCGA atlas.

Background: Accurate preoperative prediction of lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) remains challenging, particularly in clinically node-negative (cN0) patients, leading to potential overtreatment. We aimed to develop and validate a Transformer-based 2.5D deep learning model (ThyLNT) using preoperative computed tomography (CT) images for robust prediction of LNM and to explore its underlying biological basis through multi-omics analyses. Methods: A total of 1,560 PTC patients from six hospitals were retrospectively included. The Tongji Hospital cohort (n=1,010) was divided into training (70%) and internal validation (30%) sets, while five independent institutions served as external test cohorts. For each lesion, seven 2.5D slices were extracted and modeled using a DenseNet201 backbone. Slice-level features were integrated using a Transformer-based feature-level fusion strategy and compared with ensemble learning, multi-instance learning (MIL), and traditional radiomics approaches. Model performance was assessed using area under the receiver operating characteristic curve (AUC), calibration analysis, decision curve analysis (DCA), and precision-recall curves. Multi-omics analyses, including bulk RNA-seq, single-cell RNA-seq, spatial transcriptomics, and spatial metabolomics, were performed to investigate biological correlates. Results: The Transformer-based model consistently outperformed comparator models across cohorts. In the training and validation cohorts, ThyLNT achieved AUCs of 0.882 and 0.787, respectively, with external AUCs ranging from 0.772 to 0.827. Compared with ultrasound (US) and CT, ThyLNT showed superior predictive performance (all P < 0.001 in the validation cohort). Simulation analysis in cN0 patients suggested that ThyLNT could reduce unnecessary lymph node dissection (LND) from 52.16% to 4.88%. Transcriptomic analysis combined with WGCNA and correlation analysis identified VEGFA as the gene most strongly associated with ThyLNT prediction scores. Single-cell and spatial transcriptomic analyses suggested metastasis-related tumor microenvironment remodeling, while enrichment analysis of genes affected by virtual knockout of VEGFA indicated involvement of angiogenesis- and epithelial-mesenchymal transition (EMT)-related pathways. Spatial metabolomics further revealed coordinated lipid metabolic reprogramming in metastatic tissues. These findings suggest that ThyLNT provides robust predictive performance while capturing biologically relevant features associated with metastatic progression.

ObjectiveTo quantify the gap between pregnancy desire and pregnancy attempts among young women with and without a history of breast cancer (BC), and to identify factors associated with this gap. DesignCross-sectional cohort study. SettingThe FEERIC study, conducted in France. PopulationWomen aged 18-43 years without or with prior BC filling inclusion forms of a collaborative study. MethodsPregnancy desire was assessed by self-report ("Do you currently desire a pregnancy?"). Attempt was defined as engaging in unprotected intercourse with the intention to conceive. The pregnancy desire-attempt gap was defined as expressing a desire for pregnancy without actively trying to conceive. Logistic regression was used to evaluate associated demographic, clinical, and treatment-related factors. Main outcome measuresPrevalence of the pregnancy desire-attempt gap and predictors of this gap among BC survivors. ResultsOf 4,351 participants (517 with BC and 3,834 controls), 735 (16.9%) reported a pregnancy desire with 54% attempting conception and 46% who did not. The desire-attempt gap was significantly more frequent in women with a history of BC (OR=1.62, 95%CI[1.15-2.30]). Among BC survivors, younger age (<30years), nulliparity, being single, and ongoing endocrine therapy were independently associated with the gap, whereas prior chemotherapy or trastuzumab were not. ConclusionsNearly half of women declaring desiring pregnancy do not initiate pregnancy attempts, with a larger gap among BC survivors. These findings highlight the need to explore both medical barriers and psychosocial determinants underlying this gap and underscore the importance of refining the language used in reproductive research. FundingThis study was supported by "SHS INCa" grant no.2016-124 and is part of a research project on young women funded by Monoprix*.

BackgroundThe tumour microenvironment (TME) influences breast cancer progression and treatment response. We investigated whether TME composition predicts tamoxifen benefit in postmenopausal women with oestrogen receptor-positive, HER2-negative (ER+HER2-) breast cancer. MethodsThis study included 513 patients from the Stockholm Tamoxifen (STO-3) trial, which randomised postmenopausal, lymph node-negative women to tamoxifen or no endocrine therapy. Bulk tumour transcriptomes were deconvoluted with the ConsensusTME algorithm to estimate the relative abundance of 18 immune and stromal cell types. A summary score of combined immune cells was created on a per patient basis and evaluated alongside fibroblast and endothelial stromal compartments. Patients were categorised into immune and stromal tertiles on the basis of these scores. Associations between TME composition and tumour characteristics were evaluated using Spearman correlations and Fishers exact test. Tamoxifen benefit was analysed by univariable Kaplan-Meier (log-rank) and multivariable Cox proportional hazards adjusting for age, tumour size, grade, progesterone receptor, Ki-67, and radiotherapy. Differential expression was assessed with limma and pathway enrichment with fgsea using Hallmark gene sets from MSigDB. ResultsLow immune abundance was significantly associated with higher ER expression (Fishers exact test p < 0.001). Among tamoxifen-treated patients, those with low immune scores showed improved distant recurrence-free interval (DRFI) relative to untreated patients (log-rank p < 0.001). Similarly, intermediate endothelial (p < 0.001) and low/intermediate fibroblast abundances (p = 0.042, p = 0.009) were associated with favourable DRFI. In multivariable models, low immune (aHR = 0.17, 95% CI 0.08-0.40), intermediate endothelial (aHR = 0.21, 95% CI 0.09-0.51), and low/intermediate fibroblast tertiles (aHR = 0.50, 95% CI 0.27-0.93; aHR = 0.36, 95% CI 0.17-0.77) retained significance. Transcriptomic analysis revealed enrichment of oestrogen-response, MYC-target, and oxidative-phosphorylation pathways in low-immune and low-fibroblast tumours, while interferon-{gamma} response and allograft rejection pathways were downregulated. ConclusionsTME composition modulates tamoxifen benefit in postmenopausal ER+HER2-breast cancer. Low immune, intermediate endothelial, and low/intermediate fibroblast abundances are associated with improved benefit from tamoxifen, suggesting that both immune and stromal compartments influence endocrine treatment efficacy.

Oncology research and clinical practice in North America increasingly rely on complex endpoints, heterogeneous study designs, and high-dimensional molecular data. In this landscape, data visualization serves as a critical analytic instrument for study design communication, model diagnostics, safety reporting, and real-time clinical decision support. Despite its importance, the oncology visualization ecosystem remains fragmented across commercial platforms and bespoke scripts, lacking a unified, code-first reference that emphasizes reproducibility and auditability in the R programming environment. This paper addresses this gap by presenting a North American collaborative atlas of 62 oncology visualization templates: 24 for clinical trials, 12 for real-world evidence (RWE), and 26 common to both settings. A core innovation of this atlas is its simulation-driven approach; each plot is illustrated using transparent, reproducible data-generating mechanisms. This allows users to deterministically recreate figures and easily adapt templates to alternative endpoints, censoring patterns, and subgroup structures. The paper provides foundational notation for oncology endpoints, an operational taxonomy based on data geometry, and a consolidated review of relevant R software. We further synthesize the practical utility of these methods through four representative case studies and provide a comparative analysis of the strengths, limitations, and future challenges of oncology data visualization. A detailed tutorial on fishplot is included to demonstrate a publication-ready workflow for clonal evolution.

Introduction Agreement between radiologists regarding treatment response in Pleural Mesothelioma (PM) is acknowledged to be poor, but downstream effects in clinical trials have not been quantified. Methods We performed a mixed methods study, composed of a multicentre, retrospective cohort study and in silico modelling. CT images and data were retrieved from 4 UK centres regarding chemotherapy-treated patients. Expert radiologists classified response using modified Response Evaluation Criteria In Solid Tumours criteria (mRECIST) v1.1, generating discordance rate (%) and agreement. In silico modelling simulated two-arm trials of an active therapy with intended 80% power and confidence intervals for four endpoints (objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS), overall survival (OS)) covering 95% of the true effect. Actual power and endpoint coverage were modelled against mRECIST misclassification rate (a single reporter equivalent of discordance rate). Consecutive simulations varied misclassification rate from 0-100% in 1% increments, each repeated 10,000 times. Results 172 cases were included. Discordance rate was 35% (60/172), kappa=0.456. In silico modelling demonstrated reduced power and endpoint precision with increasing misclassification. At 17% misclassification, corresponding to the observed 35% discordance, power dropped from 80% to 55% for ORR, 53% for DCR, 65% for PFS and 66% for OS, with endpoint coverage reduced to 88%, 89%, 92% and 92%, respectively. 50/60 (83%) discordances reflected interpretation or measurement differences intrinsic to mRECIST. Discordance was not associated with tumour volume. Conclusions Inconsistent response classification is common in PM and substantially reduces statistical power and endpoint precision in clinical trials.

The high heterogeneity of pediatric cancers presents significant diagnostic challenges, underscoring the need for accurate classification. Although molecular profiling supports first-line diagnostics and guides treatment, it can delay final diagnosis. While Nanopore-based methylation analysis has enabled rapid CNS tumor diagnosis, its application to pediatric solid tumors and lymphomas has remained largely unexplored. We developed Tucan, a deep-learning classifier trained on 3,818 methylation array profiles representing 84 subtypes, designed to classify tumors from sparse Nanopore methylation data. In retrospective validation (n=514), Tucan generated confident predictions (CFT[&ge;] 0.7) within 30 minutes of sequencing in 385 cases, achieving 372 correct diagnoses (F1-score: 0.98). In prospective testing (n=74; 63 classifiable), 52 samples reached the confidence threshold with 96% accuracy, confirming the original diagnosis in 47 cases and correctly refining or revising it in three. Together, Tucan enables rapid, high-confidence molecular classification of pediatric solid tumors and lymphomas.

Purpose In resource-limited settings, locally advanced rectal cancer (LARC) often presents at advanced stages. Long-course chemoradiotherapy (LCCRT) remains a cornerstone of neoadjuvant therapy, yet outcome data from such settings remain limited. This study assessed tumor resectability, pathologic response, and factors associated with resectability following neoadjuvant LCCRT at Ethiopias largest tertiary oncology center. Methods A retrospective cohort study was conducted among patients with stage II-III rectal adenocarcinoma (cT3-4 and/or cN+) who completed neoadjuvant LCCRT at Tikur Anbessa Specialized Hospital between 2018 and 2023. Tumor resectability was determined by multidisciplinary team (MDT) assessment. Multivariable logistic regression was used to identify factors associated with post-LCCRT resectability, adjusting for initial T stage, circumferential resection margin (CRM) status, histologic subtype, radiotherapy technique, and neoadjuvant regimen. Results Among 58 eligible patients (median age 45 years; 62% male), 62% had cT4 tumors, 53% had cN2 disease, and 84.5% had involved CRM. The median diagnosis-to-LCCRT interval was 64 weeks (interquartile range [IQR], 37-82). After LCCRT, 27 patients (46.6%) were deemed resectable by MDT assessment; 19 patients (32.8%) ultimately underwent curative-intent surgery (median interval from LCCRT to surgery, 10 weeks; IQR, 7-15). Initial cT3 stage was associated with higher odds of resectability (adjusted odds ratio [AOR], 6.2; 95% CI, 1.06-36.37), whereas receipt of total neoadjuvant therapy was associated with lower odds (AOR, 0.10; 95% CI, 0.02-0.49). No pathologic complete responses were observed. Conclusion In this cohort characterized by advanced disease at presentation and treatment delays, neoadjuvant LCCRT resulted in low resectability and limited pathologic response. To enhance curative potential, concerted efforts are needed to expedite the timely initiation of radiotherapy, optimize multidisciplinary team assessment, and increase surgical capacity.

Background: Atopic dermatitis (AD) is a prevalent chronic inflammatory skin disease associated with clinical, psychosocial, and economic burden. Accurate severity assessment is essential for guiding treatment escalation and monitoring disease activity, yet clinician-based scoring systems such as the Eczema Area and Severity Index (EASI) are limited by subjectivity and considerable inter- and intra-rater variability. Erythema, a key driver of AD severity grading, is particularly prone to inconsistent evaluation due to differences in ambient lighting, device quality, skin tone, and rater experience, underscoring the need for objective, reproducible assessment tools. Objective: To develop and validate an artificial intelligence (AI) pipeline for grading erythema, excoriation, and lichenification severity in AD from clinical photographs. The study evaluated the level of agreement between AI severity ratings in each category against dermatologists, non-specialists, and a consensus reference standard, with erythema as the primary outcome of interest. Methods: A two-stage AI pipeline was developed using EfficientNet B7 convolutional neural networks (CNNs). The first CNN was trained as a binary AD classifier on 451 AD and 601 non-AD images for lesion detection and segmentation. The second CNN was trained on 173 dermatologist-annotated AD images which were scored on a 0-3 ordinal scale for erythema, excoriation, and lichenification. This CNN had a downstream feature extraction algorithms such red channel contrast for erythema, Law's E5L5 for excoriation, and S5L5 texture maps for lichenification. In a cross-sectional validation study, 41 independent test images were scored by two blinded dermatologists and two blinded physicians. AI predictions were compared to individual rater groups and mode-derived consensus scores using weighted Cohen's kappa, classification accuracy, confusion matrices, and error direction analyses. Results: On internal validation, the severity CNN achieved 84% overall accuracy (averaged across all three attributes), 86% sensitivity, 87% specificity, and a macro-averaged area under the receiver operating characteristic curve (AUC) of 0.90. In the external comparison with blinded human raters, erythema agreement between the AI and dermatologist consensus was substantial (accuracy 80.7%; kappa = 0.68), with no large (>2-point) misclassifications. Physician consensus agreement was lower (accuracy 54.8%; kappa = 0.34), reflecting greater variability among primary care physicians (non-specialists). For excoriation, AI-dermatologist agreement was moderate (accuracy 72.4%; kappa = 0.62); for lichenification, agreement was similar (accuracy 71.4%; kappa = 0.59). Across all features, disagreements were predominantly between adjacent severity categories. The AI was able to generate erythema severity grades for images of darker skin tones that dermatologists typically would not rate and were marked as "unable to assess". Limitations: The validation set was small (41 images), severe cases (score 3) were underrepresented, one rater participated in both training annotation and validation scoring, and sample size was insufficient for robust stratification by skin tone or body site. Conclusion: The AI pipeline demonstrated dermatologist-level accuracy for erythema scoring, consistent moderate agreement for excoriation and lichenification, and a potential advantage in assessing erythema on darker skin tones. These findings support its potential as a standardized, objective tool for AD severity assessment. Prospective validation in larger, more diverse cohorts is warranted.

Background: Patient-reported outcome measures provide essential data on treatment quality across diverse populations. The FACE-Q Skin Cancer Module was developed to assess outcomes specific to facial skin cancer patients. Longitudinal data characterizing outcome trajectories from surgery through early recovery remain limited. Objective: We tracked how patient outcomes change from preoperatively through three months after surgery using the FACE-Q Skin Cancer Module in a prospective cohort of 288 patients undergoing facial skin cancer surgery. Methods: Participants completed the module preoperatively and at 1 week and 3 months postoperatively. Five scales were evaluated: Appearance, Psychosocial Distress, Cancer Worry, Scars, and Adverse Effects. Friedman tests assessed overall change across timepoints; paired t-tests and Wilcoxon signed-rank tests evaluated pairwise comparisons. Results: Of 288 enrolled patients (mean age 68.6+/-11.9 years, 46.5% female), 252 (87.5%) and 220 (76.4%) completed 1-week and 3-month follow-up, respectively. Facial appearance declined at 1 week (55.6 to 52.0, p=0.005) and returned to baseline by 3 months (57.0, p=0.274). Psychosocial distress increased acutely (14.5 to 19.0, p<0.001) with partial recovery at 3 months (17.1, p=0.012). Cancer worry decreased substantially (delta=-7.8, SRM=-0.54, p<0.001), and scar satisfaction improved from 1 week to 3 months (delta=+9.4, SRM=0.54, p<0.001). Adverse effects showed the largest improvement (delta=-12.8, SRM=-0.88, p<0.001). Women showed less improvement in facial appearance than men (delta=-2.2 vs +4.9, p=0.022). Clinical meaningfulness was assessed using minimally important difference thresholds: 36.9% of patients achieved meaningful improvement in appearance, 39.6% remained stable, and 23.4% experienced meaningful deterioration. Conclusions: Short-term outcomes follow a predictable pattern, with acute perioperative worsening followed by recovery by 3 months for most patients.