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.

Patients with advanced melanoma are treated with immune checkpoint inhibitors (ICIs), yet less than 50% of patients achieve a durable response while all patients are exposed to the risk of severe side effects. Tumor-infiltrating lymphocytes (TILs) in pathology images are associated with ICI outcomes, but manual assessment is subjective. In addition, the predictive value of other immune cell subsets, including plasma cells, neutrophils, histiocytes, and melanophages, remains unclear. We organized the Panoptic segmentation of nUclei and tissue in advanced MelanomA (PUMA) challenge to evaluate whether the spatial localization of TILs and other immune cell subsets on melanoma H&E slides collected before start of treatment was associated with treatment outcomes. Algorithm performance was evaluated on a hidden test set, after which top-ranked algorithms were applied to pre-treatment metastatic whole-slide images from a large, multicenter cohort of patients with advanced melanoma treated with first-line ICIs (n=1102). Automatically quantified tissue features and immune cell subsets were then associated with clinical outcomes. Top-performing algorithms improved detection of immune cell subsets, although accuracy for rare classes remained limited. Across challenge participants, TIL density showed the most consistent association with treatment response and survival. Associations for stromal TILs were weaker, while plasma cells, histiocytes, melanophages, neutrophils, necrosis and blood vessels did not show independent associations with outcomes. Overall, the results from the PUMA challenge improved the state of the art of immune cell detection in melanoma histopathology and show that intra-tumoral lymphocytes are the immune cell subset most consistently associated with treatment response and survival. HighlightsO_LIWe organized the first melanoma-specific tissue and nuclei segmentation competition C_LIO_LIWinning algorithms were applied to 1102 whole-slide images for biomarker analysis C_LIO_LIIntra-tumoral TILs were associated with response to immune checkpoint inhibitors C_LIO_LIOther immune cell subsets showed no independent association with treatment outcomes C_LIO_LITissue segmentation on WSIs was limited by low heterogeneity in training data. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/26347935v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@13838e4org.highwire.dtl.DTLVardef@1f34a6org.highwire.dtl.DTLVardef@b9a65borg.highwire.dtl.DTLVardef@58d300_HPS_FORMAT_FIGEXP M_FIG C_FIG

The phase 1b TICIMEL clinical trial evaluated the safety, tolerability, and anti-tumor activity of combining the immune checkpoint inhibitors (ICI), ipilimumab and nivolumab, with tumor necrosis factor (TNF) blockers, certolizumab or infliximab, to treat advanced melanoma patients. A higher proportion of responses was observed in patients receiving ICI and certolizumab, while patients treated with ICI and infliximab demonstrated superior tolerability. Moreover, CITE-Seq analyses of circulating CD8 T cells showed that ICI plus certolizumab promoted an IFN signature, whereas ICI plus infliximab reduced the induction of genes associated with T cell activation. In preclinical models, ICI and TNF blockade with certolizumab increased IFN-{gamma}+ CD8 T cells and reduced regulatory T cells in tumors. The IgG1 Fc fragment of infliximab was identified as counteracting the benefits of TNF blockade. These findings underscore the importance of selecting the optimal TNF blocker to combine with ICI to enhance therapy efficacy in melanoma patients. ClinicalTrials.gov identifiers: NCT03293784; NCT05867004.

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.

BackgroundImmune checkpoint inhibitors (ICI) have improved outcomes in Merkel cell carcinoma (MCC). Population analyses suggest improved survival following the 2017 approval of ICI, but registry data lack treatment-level information including type of systemic therapy and initiation timepoint to directly estimate the benefit attributable to immunotherapy. This study compared Merkel Cell Carcinoma-specific survival between patients treated with first-line ICI versus cytotoxic chemotherapy. MethodsPatients were identified from the Seattle Merkel Cell Carcinoma Registry. Among 1,517 patients with MCC, 463 received first-line systemic therapy with either ICI or chemotherapy. Propensity scores were estimated using logistic regression including AJCC 8th stage, age, sex, MCPyV status, and immunosuppression. One-to-one nearest-neighbor matching produced balanced cohorts of 133 ICI-treated and 133 chemotherapy-treated patients. Merkel Cell Carcinoma-specific survival from therapy initiation was analyzed using Kaplan-Meier and Cox proportional hazards models with follow-up administratively censored at five years. ResultsBaseline clinical characteristics were comparable between matched cohorts. ICI therapy was associated with significantly improved Merkel Cell Carcinoma-specific survival compared with chemotherapy (log-rank p<0.0001). Five-year Merkel Cell Carcinoma-specific survival was 56.8% (95% CI 46.8-65.6) for ICI versus 23.9% (95% CI 16.9-31.6) for chemotherapy. In multivariable stage-stratified Cox analysis, ICI remained independently associated with improved Merkel Cell Carcinoma-specific survival (HR 0.32, 95% CI 0.21-0.50; p<0.0001), while immunosuppression was associated with worse Merkel Cell Carcinoma-specific survival (HR 2.03, 95% CI 1.10-3.74; p=0.0228). ConclusionsICI therapy was associated with substantially improved MCC-specific survival compared with chemotherapy.

BackgroundA retrospective study in Nature recently reported improved overall survival (OS) when COVID-19 mRNA vaccination was administered around initiation of immune checkpoint inhibitor (ICI) therapy. Whether this association depends on vaccination timing relative to ICI treatment remains unclear. MethodsWe conducted a single-centre retrospective cohort study of patients receiving palliative-intent ICI therapy at a UK tertiary cancer centre (October 2014-December 2025). Two vaccination exposure definitions were evaluated: (1) vaccination within 100 days of the first ICI cycle (initial window); and (2) vaccination from 100 days before the first ICI cycle to 100 days after the final ICI cycle (extended window). OS was analysed using Kaplan-Meier methods and Cox models relative to unvaccinated patients. ResultsAmong 2109 patients, 515 (24.4%) received [&ge;]1 COVID-19 vaccine dose. Under the initial window, mRNA vaccination was associated with a longer OS in the all-tumours cohort only (HR 0.76; 95% CI 0.58-0.99; p=0.04). Under the extended window, mRNA vaccination was associated with longer OS in the all-tumours cohort (HR 0.58; 95% CI 0.46-0.75; p<0.0001), including melanoma (HR 0.35; 95% CI 0.18-0.69; p=0.002) and kidney cancer (HR 0.47; 95% CI 0.28-0.79; p=0.004), but not NSCLC. In an era-restricted analysis limited to patients receiving ICI therapy from 2020 onwards, the all-tumours association persisted (HR 0.76; 95% CI 0.59-0.98; p=0.04) with no significant tumour-specific associations. ConclusionsCOVID-19 mRNA vaccination was associated with improved OS, with magnitude and tumour specificity dependent on vaccination exposure definition. Prospective studies are required to assess causality and tumour-specific effects.

Selection of systemic therapy for breast cancer remains largely empirical, particularly for chemotherapy, due to the lack of robust biomarkers that predict treatment response at the individual patient level. We developed Oncology CoPilot, a real-world, transcriptomics-guided machine learning (ML) decision-support model designed to integrate heterogeneous tumor gene expression data and treatment response annotations to support treatment response stratification across therapeutic classes. Oncology CoPilot was trained on a pan-cancer cohort comprising 11,414 patients across 15 cancer types and 150 systemic drug regimens derived from publicly available and published datasets. Retrospective external validation was performed using five independent breast cancer cohorts comprising 503 patients, spanning multiple molecular subtypes, transcriptomic platforms, and six commonly used treatment settings, including chemotherapy, endocrine therapy, and targeted therapy. Across the external validation cohort, the model demonstrated an overall accuracy of 72.8%, with balanced sensitivity (71.5%) and specificity (73.4%), and an ROC-AUC of 0.783. Regimen-specific analyses demonstrated stable performance for chemotherapy-based regimens (accuracy 70.1%-79.1%), highlighting the potential of transcriptomics-guided modeling to inform treatment response stratification in clinical settings where therapy selection is often empirical. For endocrine therapy, the model achieved 95.0% accuracy for tamoxifen, suggesting that transcriptomic features may capture biologically relevant estrogen-responsive and resistance-associated programs beyond receptor status alone, although this result is exploratory and based on a small sample size. In contrast, HER2-targeted therapies showed lower and more variable predictive performance, with accuracies of 66.0% for trastuzumab monotherapy and 61.9% for anthracycline-taxane chemotherapy combined with trastuzumab, likely reflecting smaller cohort sizes and the biological heterogeneity characteristic of HER2-positive disease. Overall, these findings demonstrate the feasibility of leveraging real-world transcriptomic data and machine learning to achieve generalizable treatment response stratification across diverse cohorts, platforms, and therapeutic classes, supporting the potential role of transcriptomics-guided models as complementary decision-support tools in oncology.

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).

Patient selection and enrolment into phase III randomized clinical trials (RCTs) of adjuvant cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitor therapies depend on accurate risk definition. However, standard clinicopathologic criteria incompletely capture recurrence risk, limiting their efficacy in treatment selection. To assess whether artificial intelligence (AI)-enhanced prognostication may enrich the clinical risk groups utilized in the adjuvant NATALEE trial, we evaluated Ataraxis Breast RISK (ATX), a multimodal AI test that integrates clinical data with morphological features from H&E-stained slides. ATX risk scores were generated for 2,228 patients with HR+/HER2- early breast cancer, of which 918 (41%) were classified as clinical high-risk and 1,310 (59%) were clinical low-risk. ATX was significantly associated with recurrence-free interval in both clinical risk groups and identified high-risk patients not captured by current clinical criteria, as well as individuals with limited benefit despite clinical high-risk classification. Consequently, integration of AI-enhanced risk assessment may improve selection of patients likely to benefit from adjuvant CDK4/6 inhibitors relative to current criteria.

Consensus molecular subtyping (CMS) defines the transcriptomic taxonomy of colorectal cancer (CRC) and guides precision therapy. Although current approaches can predict CMS from histopathology, they rely on surgical specimens, limiting their preoperative applicability. In this study, we developed a deep learning model to infer CMS directly from preoperative computed tomography (CT) scans, enabling noninvasive molecular stratification of CRC. A multi-institutional cohort of 2,444 CRC patients was collected from the Sixth Affiliated Hospital of Sun Yat-sen University and Liaoning Cancer Hospital, comprising a discovery cohort (n = 416), an internal validation cohort (n = 1,671), and an external validation cohort (n = 357). To achieve robust feature extraction, a self-supervised 3D representation learning network was first pretrained on large-scale public CT datasets to capture generalizable imaging features. These representations were subsequently integrated into a multi-instance learning (MIL) classifier for CMS prediction, with attention mechanisms to enhance interpretability. Model performance was evaluated by cross-validation on the discovery cohort and verified on the two validation cohorts. CT4CMS demonstrated strong performance in predicting CMS subtypes directly from CT scans, achieving a cross-validation AUC of 0.867. In both validation cohorts, patients predicted as CMS4 exhibited significantly poorer disease-free survival yet derived substantial benefit from adjuvant chemotherapy, consistent with transcriptome-defined subtyping trends observed in the discovery cohort. Interpretability analysis revealed distinct subtype-specific radiomic features, suggesting that CT-derived imaging features capture underlying molecular characteristics and enable CMS classification. Overall, this study establishes a noninvasive and interpretable deep learning framework for CMS prediction in CRC, paving the way for imaging-based molecular stratification and personalized therapeutic decision-making.

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.

BackgroundThe management of residual axillary disease after neoadjuvant therapy (NAT) remains controversial, as current recommendations often treat ypN1 breast cancer as a homogeneous entity despite potential prognostic heterogeneity. Evidence supporting uniform axillary surgical strategies across different levels of residual nodal burden is limited. We investigated whether survival associations related to axillary surgical evaluation differ according to residual nodal burden in ypN1 disease, using an adjuvant cohort to validate a SEER-based proxy for surgical extent. MethodsPatients with 1-3 positive lymph nodes were identified in the SEER database (2000-2022) and stratified into neoadjuvant (NAT; n=30,560) and adjuvant (AT; n=197,586) cohorts. Axillary surgical evaluation was categorized as limited (2-3 examined nodes) or extensive ([&ge;]10 examined nodes). Survival was analyzed using Kaplan-Meier methods and log-logistic accelerated failure-time models, adjusted with inverse probability of treatment weighting. ResultsIn the ypN1 cohort, limited axillary evaluation was not associated with inferior overall survival among patients with a single residual positive node (IPTW-adjusted HR: 1.15, p=0.134; time ratio [TR]: 0.86, p=0.184). In contrast, limited evaluation was associated with worse survival in patients with two positive nodes (HR: 1.70, 95%CI 1.54-1.87; TR: 0.58, 95%CI 0.53-0.64). The findings were similar when using breast cancer-specific survival as the endpoint. ConclusionsSurvival associations related to axillary surgical evaluation after NAT vary according to residual nodal burden. Axillary de-escalation appears feasible in patients with a single residual positive node but cannot be extrapolated to those with multiple residual nodes, underscoring heterogeneity within ypN1 disease.

BackgroundEarly and accurate prediction of pathological complete response (pCR) is essential for personalizing neoadjuvant chemotherapy (NACT) in invasive breast cancer. However, most high-performing predictive models rely on costly, multi-modal data that are not routinely available in standard clinical practice. PurposeTo develop and validate Breast Cancer Biological Multi-modal Information Transfer for Response Prediction Model (BC-BioMIXER), a biologically informed predictive model that transfers multi-omics-derived knowledge to routine clinical data, enabling accurate prediction of pathological complete response prior to neoadjuvant chemotherapy initiation. Material and MethodsBC-BioMIXER was developed in a multi-modality cohort of 648 patients with invasive breast cancer (T2-4, any N, M0) incorporating transcriptomic, proteomic, MRI, and clinical data. The model was externally validated in three independent cohorts (total N = 830), including one multi-modality cohort, one clinical trial cohort, and one contemporary real-world cohort. All patients received NACT followed by surgery. The framework employs a teacher-student knowledge-transfer paradigm in which a multi-omics teacher model learns biologically integrated representations that are subsequently transferred to a student model using only routine clinical data. Predictive performance for pCR was benchmarked against a multi-modality reference model and evaluated across cohorts, receptor-defined subgroups (HER2 and hormone receptor [HR]), and treatment groups (NACT with or without immune checkpoint inhibitors [ICI]). Prognostic value was assessed using distant recurrence-free survival (DRFS). The potential to inform immunotherapy decision-making was explored by comparing DRFS between NACT + ICI and NACT-alone groups within model-predicted pCR and non-pCR subgroups. ResultsBC-BioMIXER achieved pCR prediction performance comparable to the multi-modality benchmark (AUC 0.82 vs. 0.85; p = 0.271) and demonstrated consistent discrimination across all validation cohorts (AUCs 0.82, 0.81, and 0.80; all p < 0.001). Patients predicted to achieve pCR experienced significantly improved 3-year DRFS (HR = 0.36; 95% CI, 0.20-0.67; p < 0.001). In patients treated with NACT + ICI, BC-BioMIXER showed numerically superior pCR prediction compared with PD-L1 expression alone (AUC 0.84 vs. 0.72; p = 0.08). Notably, within the model-predicted non-pCR subgroup, patients receiving NACT + ICI had significantly inferior DRFS compared with those receiving NACT alone (HR = 2.70; p = 0.032), whereas no significant difference was observed in the predicted pCR subgroup. ConclusionBC-BioMIXER translates multi-omics-derived biological knowledge into a robust, routine-data-based predictive tool for breast cancer NACT. Its consistent validation across evolving clinical settings and its potential to inform personalized immunotherapy strategies highlight a step toward scalable and accessible precision oncology. HighlightsO_LIBrings multi-omics power to routine clinical practice: Through cross-modality knowledge transfer, BC-BioMIXER leverages transcriptomic and proteomic data during training to enable highly accurate pCR prediction using only standard MRI and clinical variables (AUC 0.82 vs. 0.85 for full multi-modality benchmark, p=0.271). C_LIO_LIConsistently strong and generalizable performance: Validated in three independent cohorts (total N=830), the model maintained robust pCR discrimination (AUC 0.80-0.82, all p<0.001) across receptor subtypes (HR/HER2) and treatment regimens, including with or without immune checkpoint inhibitors. C_LIO_LIGuides personalized immunotherapy de-escalation: In HER2-negative patients predicted as non-pCR, adding ICI to neoadjuvant chemotherapy was associated with significantly worse distant recurrence-free survival (HR 2.70, p=0.032) compared to chemotherapy alone. This effect was not seen in the predicted pCR group, suggesting the model may help identify patients unlikely to benefit from additional immunotherapy. C_LI

Chronic wounds affect over 1.2 million Canadians and incur healthcare costs exceeding $13 billion annually, with global expenditures approaching $149 billion. Current clinical practice relies on manual measurements and subjective visual evaluations, which overestimate wound area by up to 40% and demonstrate poor-to-moderate inter-rater reliability. This variability complicates longitudinal monitoring and evidence-based treatment selection. We developed and evaluated an integrated mobile platform combining deep learning-based wound assessment with clinical decision support. A curated dataset of 1,648 de-identified clinical wound photographs was assembled from wound care clinics, representing diverse aetiologies (arterial, venous, diabetic foot ulcers, pressure injuries) and skin tones (32% Monk Skin Tone 7-10). Three convolutional neural networks were trained: (1) an EfficientNet-B7-based classifier for wound etiology, (2) a gated pressure injury staging network, and (3) a DeepLabv3 encoder-decoder architecture with ResNet backbone for multi-class tissue segmentation (epithelialization, granulation, slough, eschar). Fiducial marker-based calibration enabled automated wound size quantification. A rule-based recommendation engine mapped assessment outputs to evidence-based dressing selections. The system was deployed as a cross-platform mobile application with cloud-native backend infrastructure. The wound classification model achieved 91.75% mean accuracy across four wound categories. Pressure injury staging accuracy ranged from 67% (Stage III) to 92% (Stage I). Tissue segmentation yielded a mean Dice similarity coefficient of 0.64 {+/-} 0.06 and pixel-level accuracy of 98%. Automated size estimation demonstrated strong correlation with manual measurements (r = 0.73, n=53), with mean absolute error of 3.7 {+/-} 2.1 mm; 84.2% of measurements fell within the {+/-}5 mm clinical equivalence margin. Fiducial marker detection succeeded in 93% of test images. Performance remained stable across skin tone categories and imaging conditions. This integrated platform demonstrates technical feasibility for standardized, objective wound assessment addressing documented limitations of manual practices. The system provides interpretable segmentation overlays and actionable treatment recommendations while maintaining clinician oversight. These findings support progression to prospective validation studies evaluating real-world clinical utility and patient outcomes.

Structured AbstractO_ST_ABSPurpose/ObjectivesC_ST_ABSThe role of postmastectomy radiotherapy (PMRT) in patients with pathologic N1 (pN1) breast cancer, including triple-negative breast cancer (TNBC), remains controversial in the era of modern systemic therapy. We evaluated the association between PMRT and recurrence-free survival (RFS) and overall survival (OS) and identified prognostic factors in a contemporary single-institution pN1 cohort. Materials/MethodsWe retrospectively reviewed female patients with pT1-2N1M0 breast cancer treated with mastectomy between 2016 and 2022. RFS and OS were estimated using Kaplan-Meier methods and compared by PMRT status with log-rank testing. Univariable Cox proportional hazards models assessed associations between clinical factors--including tumor laterality, receptor subtype (TNBC vs non-TNBC), nodal burden, and adjuvant therapies--and survival outcomes, with subgroup analyses by PMRT status and receptor subtype. ResultsFifty-seven patients were included; 22 (38.6%) received PMRT. With a median follow-up of 85 months, PMRT was not associated with improved RFS (median 133 vs 120 months; p=0.256) or OS (not reached vs 195 months; p=0.154). Hormone therapy was significantly associated with improved RFS (HR 0.43; p=0.026) and OS (HR 0.13; p=0.003), while having 2-3 positive lymph nodes predicted worse RFS (HR 2.86; p=0.007). No significant differential benefit from PMRT was observed in patients with TNBC or non-TNBC disease. ConclusionsPMRT was not associated with a survival benefit in this pN1 cohort, including patients with TNBC. Interpretation is limited by modest sample size and statistical power. Outcomes appeared driven by tumor biology, nodal burden, and systemic therapy, supporting individualized PMRT decision-making.

BackgroundTamoxifen is a cornerstone of endocrine treatment for hormone receptor-positive breast cancer, reducing recurrence and breast cancer-specific mortality. However, its use is associated with a small, yet clinically relevant, increase in uterine cancer. As diagnosis of this cancer remains symptom-triggered, it is essential for patients to be aware of this risk and report symptoms promptly for optimal outcomes. We therefore assessed risk awareness among breast cancer survivors while exploring their attitudes towards potential future endometrial surveillance strategies. MethodsOver a 10-month period, a web-based survey was conducted among breast cancer survivors with/without tamoxifen treatment. The mixed-format questionnaire included closed-ended questions and optional free-text comments. Quantitative data were summarized descriptively and analyzed statistically; qualitative responses were reviewed thematically to contextualize survey findings. ResultsOf 163 respondents, 154 breast cancer survivors were included in the analysis, 128 of whom had received tamoxifen. Among tamoxifen-associated participants, 60% reported insufficient awareness of the associated uterine cancer risk, and half expressed uncertainty about the adequacy of the current symptom-triggered endometrial evaluation. Despite this, acceptance of tamoxifen therapy was high; only one patient declined treatment over concerns about side effects. Almost all participants (96%) were willing to adopt endometrial surveillance methods, if developed and validated. ConclusionAs evaluation of tamoxifen-associated uterine pathology is symptom-triggered, our data highlight the need for improved and standardized risk communication to promote timely symptom recognition, reporting, and diagnostic evaluation. Moreover, our findings support incorporating patient-reported preferences into the development of future endometrial detection strategies to improve survivorship care.

BackgroundGastrointestinal stromal tumor (GIST) is the most common gastrointestinal mesenchymal tumor, driven by tyrosine-protein kinase KIT and platelet-derived growth factor receptor A (PDGFRA) mutations. Specific variants, such as KIT exon 11 deletions, carry prognostic and therapeutic implications, whereas wild-type (WT) variants derive limited benefit from tyrosine kinase inhibitors (TKIs). Given the limited reproducibility of established clinicopathological risk models, deep learning (DL) applied to whole-slide images (WSIs) emerged as a promising tool for molecular classification and prognostic assessment. Patients and methodsWe analyzed 8398 GIST cases from 21 centers in 7 countries, including 7238 with molecular data and 2638 with clinical follow-up. DL models were trained on WSIs to predict mutations, treatment sensitivity, and recurrence-free survival (RFS). ResultsDL predicted mutational status in GIST from WSIs, with area under the curve (AUC) of 0.87 for KIT, 0.96 for PDGFRA. High performance was observed for subtypes, including KIT exon 11 delinss 557-558 (0.67) and PDGFRA exon 18 D842V (0.93). For therapeutic categories, performance reached 0.84 for avapritinib sensitivity, 0.81 for imatinib sensitivity. DL models predicted RFS, with hazard-ratios (HR) of 8.44 (95%CI 6.14-11.61) in the overall cohort and 4.74 (95%CI 3.34-6.74) in patients receiving adjuvant therapy. Prognostic performance was comparable to pathology-based scores, with highest discrimination in the overall cohort and in patients without adjuvant therapy (9.44, 95%CI (5.87-15.20)). ConclusionDL applied to WSIs enables prediction of molecular alterations, treatment sensitivity, and RFS in GIST, performing comparably to established risk scores across international cohorts, providing a baseline for future multimodal predictors. HighlightsO_LIDeep learning on histology predicts KIT and PDGFRA mutations in a large international cohort of GISTs from multiple centers C_LIO_LIWhole-slide image models stratify recurrence-free survival comparable to pathology-based risk scores C_LIO_LIPrognostic value of deep learning is preserved in adjuvant therapy subgroups, supporting treatment duration decisions C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=117 SRC="FIGDIR/small/26345350v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@652548org.highwire.dtl.DTLVardef@729a2borg.highwire.dtl.DTLVardef@1e7b6b9org.highwire.dtl.DTLVardef@18d6721_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstract.C_FLOATNO Overview of study design and dataset characteristics. (A) Multinational collection of WSIs from seven countries (Spain, France, Italy, Germany, the Netherlands, Poland, and Japan), followed by standard image preprocessing with the STAMP pipeline and clinical data preprocessing/standardization via the Grammar Data Curation framework. The workflow was divided into two main branches: (i) molecular mutation and treatment sensitivity prediction, and (ii) RFS prediction. Model performance was evaluated using AUROC and F1 score for classification tasks, and Kaplan-Meier survival curves with hazard ratios for RFS. Model explainability was assessed through heatmaps of WSIs and identification of top predictive tiles. (B) Summary of clinical dataset composition: proportion of cases receiving adjuvant therapy, tumor location distribution, mutation distribution at the exon level, and mutation distribution at the codon level. C_FIG

BackgroundIn immune checkpoint inhibitor (ICI) trials, overall survival (OS) benefits are well established, yet improvements in quality of life (QoL) are often inconsistent or absent in conventional analyses. This apparent discordance raises important questions: are QoL outcomes truly unrelated to survival, and how can QoL results be better utilized and interpreted? MethodsA model-based meta-analysis (MBMA) of longitudinal EORTC QLQ-C30 global health status/quality of life data from randomized ICI trials was conducted. Longitudinal QoL trajectories were analyzed using a nonlinear mixed-effects model to estimate treatment-related toxicity and long-term QoL improvement. Associations between QoL trajectory parameters and OS were assessed using spearman rank correlation tests and Cox proportional hazards models. ResultsTwenty-seven studies (8,149 ICI and 5,593 control patients) contributed longitudinal QoL data, and 18 studies provided matched OS data. Raw QoL trajectories showed overlap between treatment arms, while OS consistently favored ICIs. MBMA revealed that ICIs had similar toxicity but significantly faster QoL improvement than control therapies (p < 0.0001). Baseline QoL, toxicity, and QoL improvement rate were all significantly associated with OS (p < 0.001). MBMA-based QoL comparisons were more sensitive in detecting associations with survival than raw QoL data, with the strongest association observed at Week 24 (R = -0.37, p = 0.067). ConclusionsConventional analyses comparing QoL at a single time point may obscure meaningful patient-reported benefits. By capturing longitudinal QoL trajectories across trials, MBMA reveals how patient experience evolves alongside survival outcomes and supports improved interpretation and utilization of QoL data in treatment evaluation.

IntroductionDespite advancements in non-small cell lung cancer (NSCLC) management through the use of molecular biomarkers, the recently introduced 9th edition of the TNM staging system remains based exclusively on anatomic descriptors, with no consistently demonstrated improvement in risk stratification for early-stage disease. This study explores the integration of a molecular prognostic classifier into the conventional TNM staging system. MethodsWe analyzed 502 patients with stage I-III lung adenocarcinoma (LUAD) who underwent surgical resection with tumor-based gene expression profiling at the Quebec Heart and Lung Institute. A molecular prognostic classifier was developed and integrated into the 9th edition TNM staging system to generate a novel model (TNMEx). Prognostic performance was compared with the 8th and 9th TNM editions using prognostic discrimination and reclassification metrics. External validation of the molecular classifier was performed in 271 LUAD cases from The Cancer Genome Atlas (TCGA). An independent cohort of 606 resected LUAD patients from the National Cancer Center Hospital (Tokyo) was used to externally compare the prognostic performance of the 8th and 9th TNM staging systems in the absence of molecular data. ResultsThe molecular prognostic classifier was developed based on the expression levels of 26 prognosis-associated genes, weighted by their corresponding coefficients. The classifier was subsequently integrated into the 9th edition TNM staging to generate the TNMEx model. The TNMEx system demonstrated superior prognostic performance, achieving a higher concordance index (C-index = 0.72) compared to the 9th edition TNM (C-index = 0.65, p=0.006). Moreover, TNMEx significantly improved patient risk reclassification compared to both the 8th (net reclassification improvement [NRI] = 0.27, integrated discrimination improvement [IDI] = 0.04) and 9th editions (NRI = 0.40, IDI = 0.05), underscoring its superior ability to stratify outcomes. The 8th and 9th editions showed only limited improvement in overall prognostic accuracy and risk stratification, as reflected by their relatively modest C-index values (0.62 and 0.65, respectively) and minimal reclassification gains (NRI = -0.06, IDI = 0.003). ConclusionsIncorporating a molecular-based prognostic model significantly enhanced the ability to recognize patients at high risk and to predict their survival outcomes more accurately than traditional TNM staging systems.