In our Special Issue titled “Molecular Mechanisms of Liver Metastases,” we aimed to attract articles that connect metastasis mechanisms and biomarkers with clinical disease characteristics and patient outcomes [...]

mRNA and dendritic cell vaccines are emerging immunotherapies for solid tumors. However, their relative immunogenicity and clinical efficacy have not been directly compared. We aimed to evaluate the comparative immune response, tumor response, safety, and survival outcomes of mRNA versus dendritic cell vaccines in patients with solid tumors. We performed a systematic review and network meta-analysis of clinical trials assessing mRNA and dendritic cell cancer vaccines in patients with solid tumors. We included 60 unique studies (67 trials) with a total of 1777 patients. Outcomes evaluated included immunogenicity (immune response), tumor response (objective response rate and disease control rate), safety (incidence of mild and severe adverse events), and survival (overall and progression-free survival). The review was prospectively registered in PROSPERO (No. CRD420251012772; registered 17 March, 2025). mRNA vaccines elicited significantly stronger immune responses than dendritic cell vaccines. mRNA vaccine recipients also experienced a higher incidence of adverse events, including mild and severe events. By contrast, dendritic cell vaccines achieved significantly higher objective response and disease control rates. No significant differences in overall survival or progression-free survival were observed between the two vaccine groups. Despite moderate between-trial heterogeneity, the findings were consistent and robust across analyses. This comprehensive network meta-analysis provides the first comparative evaluation of mRNA versus dendritic cell vaccines in solid tumors. It indicates that while mRNA vaccines induce more potent immunogenicity, dendritic cell vaccines confer better tumor control, with no observed differences in survival outcomes. These findings fill a critical evidence gap and provide an exploratory synthesis highlighting potential strengths and limitations of each vaccine type. Given that most included studies were early-phase, small-sample trials, these findings should be considered hypothesis-generating and interpreted with caution.

Background: Randomized controlled trials (RCTs) are costly, time-consuming, and often infeasible, while treatment-effect estimation from observational data is limited by unobserved confounding. Methods: We developed a three-step framework to address unobserved confounding in observational survival data. First, we infer a latent prognostic factor (U) from restricted mean survival time (RMST) discrepancies between patients with similar observed factors, the same treatment, and divergent outcomes, leveraging the idea that the aggregate effect of unmeasured factors can be inferred even if individual factors cannot. Second, we balance U with observed baseline covariates using prognostic matching, entropy balancing, or inverse probability of treatment weighting. Third, we apply multivariable survival analysis to estimate hazard ratios (HRs). We evaluated the framework in three observational cohorts with RCT benchmarks, two RCT cohorts, and six multicenter observational cohorts. Results: In three observational cohorts (nine comparisons), balancing U improved agreement with trial HRs in all cases; in the strongest settings, it reduced absolute log-HR error by approximately ten-fold versus using observed covariates alone (mean reduction 0.344; p=0.001). In two RCT cohorts, U was balanced across arms (most SMDs <0.1) and adjustment had minimal impact on log-HRs (mean absolute change 0.08). Across six multicenter cohorts, balancing U within centers reduced cross-center dispersion in chemotherapy log-HR estimates (mean reduction 0.147; p=0.016); when populations were directly balanced across centers to account for case-mix differences, cross-center survival differences were narrowed in 75 Conclusions: Inferring and balancing a latent prognostic signal may reduce unobserved confounding and improve treatment-effect estimation from real-world data.

PURPOSEWe explore whether survival model performance in underrepresented high- and low-risk subgroups-regions of the prognostic spectrum where clinical decisions are most consequential-can be improved through targeted restructuring of the training data set. Rather than modifying model architecture, we propose a novel risk-stratified sampling method that addresses imbalances in prognostic subgroup density to support more reliable learning in underrepresented tail strata.METHODSWe introduce a novel methodology that partitions patients by baseline prognostic risk and applies matching within each stratum to equalize representation across the risk distribution. We implement this framework on a cohort of 1,799 patients with resected colorectal liver metastases (CRLM), including 1,197 who received adjuvant chemotherapy and 602 who did not. All models used in this study are Cox proportional hazards models trained on the same set of selected variables. Model performance is assessed via Harrell's C index and Integrated Calibration Index, with internal validation using Efron's bias-corrected bootstrapping. External validation is conducted on two independent CRLM data sets.RESULTSCox models trained on risk-balanced cohorts showed consistent improvements in internal validation compared with models trained on the full data set. The proposed approach preserved overall model calibration while noticeably improving stratified C index values in underrepresented high- and low-risk strata of the external cohorts.CONCLUSIONOur findings suggest that survival model performance in observational oncology cohorts can be meaningfully improved through targeted rebalancing of the training data across prognostic risk strata. This approach offers a practical and model-agnostic complement to existing methods, especially in applications where predictive reliability across the full risk continuum is critical to downstream clinical decisions.

BACKGROUND:The European Organisation for Research and Treatment of Cancer's STRASS trial, the only completed randomised study of preoperative radiotherapy in retroperitoneal sarcoma, showed no overall benefit. Its subgroup analysis has been interpreted as supporting radiotherapy for all patients with liposarcoma, whereas the STREXIT extension has been interpreted as supporting radiotherapy for well differentiated liposarcoma and low-grade or intermediate-grade dedifferentiated liposarcoma. We aimed to identify subsets of patients who might benefit from preoperative radiotherapy and to quantify this benefit in terms of abdominal recurrence. METHODS:In this artificial intelligence (AI)-based reanalysis of the STRASS dataset, we trained a random survival forest model on all 266 randomly assigned patients (radiotherapy plus surgery vs surgery alone) to predict 5-year abdominal recurrence-free survival under both treatment options, based on pretreatment variables. These predictions were used to fit an optimal policy tree (OPT) that partitions patients into nodes by predicted abdominal recurrence-free survival benefit from radiotherapy. We then compared outcomes in OPT-defined subgroups, STRASS subgroups, and STREXIT subgroups through Kaplan-Meier curves, and Fine-Gray competing-risks models within STRASS. FINDINGS:The OPT partitioned the cohort into seven subgroups; three subgroups (152 of 266 patients) were predicted to benefit from radiotherapy, and for two of these subgroups the benefit was statistically significant: patients with well differentiated liposarcoma aged 60 years or younger and patients with dedifferentiated liposarcoma who underwent curative-intent surgery. In these two subgroups combined, 3-year abdominal recurrence-free survival was 79% (95% CI 70-89) with radiotherapy versus 58% (48-72) without (hazard ratio [HR] 0·40 [95% CI 0·22-0·71], p=0·0016), and the cumulative incidence of abdominal recurrence was significantly lower with radiotherapy (17% [95% CI 9-27] vs 33% [22-45] without radiotherapy; HR 0·40, p=0·0090). Inverse probability of censoring weight-adjusted 5-year abdominal recurrence-free survival estimates showed similar absolute gains (25·6 percentage points). A Cox model found a significant radiotherapy-age interaction (p=0·012). By contrast, STRASS-defined and STREXIT-defined radiotherapy subgroups did not show significant abdominal recurrence-free survival improvement when re-evaluated within STRASS. INTERPRETATION:The AI-guided partition of STRASS identified younger patients with well differentiated liposarcoma and patients with dedifferentiated liposarcoma and curative-intent surgery as subgroups in which radiotherapy appears to meaningfully improve abdominal recurrence-free survival, whereas radiotherapy strategies for all well differentiated liposarcoma and low-grade or intermediate-grade dedifferentiated liposarcoma were not supported by the randomised controlled trial data. These findings argue for a more selective use of preoperative radiotherapy in retroperitoneal sarcoma and provide a concrete basis for focused future trials, which, if successful, could substantiate these findings before these strategies become standards of care. FUNDING:National Cancer Institute and Memorial Sloan Kettering Cancer Center.

External validation is widely regarded as the gold standard for prognostic model evaluation. In this study, we challenge the assumption that successful external calibration guarantees model generalizability and propose two complementary strategies to improve transportability of prognostic models across cohorts. Using six real-world surgical cohorts from tertiary academic centers, we tested whether successful external calibration depends largely on similarity in covariates and outcomes between training and validation cohorts, quantified using Kullback-Leibler (KL) divergence, with calibration assessed by the Integrated Calibration Index (ICI). From the model-developer's perspective, we trained the "best-on-average" prognostic model by tuning toward a meta-analysis-derived covariate and outcome distribution as an approximation of the broader target population. From the end-user perspective, we proposed a simple measure for cohort outcome similarity to identify, among published models, the one most suitable for a given target cohort in terms of both calibration and clinical utility. External calibration worsened as distributional mismatch increased. Higher KL divergence was associated with higher ICI in both surgery-alone (Spearman ρ=0.614, p=0.004) and surgery + adjuvant chemotherapy cohorts (Spearman ρ=0.738, p<0.001). Meta-analysis-informed weighting improved calibration in most settings without materially affecting discrimination, with the clearest benefit when evaluated on the aggregated external population (p=0.037). Models developed in more similar cohorts achieved lower ICI in surgery-alone (Spearman ρ=0.803, p<0.001) and surgery + adjuvant chemotherapy cohorts (Spearman ρ=0.737, p<0.001), and provided greater clinical utility on DCA.

To report on cases of primary pancreatic signet ring cell carcinoma (PSRCC) from our institution, review the published literature, and evaluate the clinicopathological characteristics. We present a recent case of PSRCC and report 11 additional cases from China, utilizing the China National Knowledge Infrastructure (CNKI) database. The literature search encompassed publications from January 1994 to December 2023. We also performed a literature search using PubMed and Web of Science to identify cases of PSRCC outside of China and identified a total of 13 additional cases. A total of 12 cases from China were identified, with a mean age of 64 years. Among these, 11 patients (91.7

BACKGROUND AND OBJECTIVE:Liver transplantation (LT) for unresectable colorectal liver metastases (uCRLM) initially showed no clear survival advantage in early attempts, leading to waning enthusiasm. Interest was revived in 2013 following the prospective, non-randomized Norwegian Secondary Cancer (SECA) I study, which reported a 5-year overall survival (OS) of 60%-far surpassing outcomes with systemic therapy alone. More recently, the TransMet randomized controlled trial demonstrated a 5-year OS of 73% in the LT-plus-chemotherapy arm vs. 9% with chemotherapy alone, a result comparable to outcomes for established LT indications. This review aims to summarize recent advances and discuss key considerations for implementing LT for uCRLM in clinical practice-particularly patient selection and standardization of protocols. METHODS:In this narrative review of currently available reports on the outcomes of LT for uCRLM, we identified eight studies [2017-2025] from European and North American centers. KEY CONTENT AND FINDINGS:Four were prospective (including one randomized trial) and three were multicenter. Their protocols varied considerably, especially regarding donor sources (living vs. deceased) and inclusion criteria for factors such as primary tumor laterality, kirsten rat sarcoma viral oncogene homolog (KRAS) mutation status, and metabolic tumor volume. Overall, 3-year OS ranged from 62% to 100%. Recurrence-free survival (RFS) also showed wide variability, with 3-year RFS between 38% and 68.6%. Centers that employed consistent selection protocols typically reported better survival outcomes, underscoring the importance of standardization. Donor availability emerged as a key factor, with living donor LT offering an alternative in regions where deceased donor access is limited-such as North America and parts of Asia. Extended observation periods and stratification by KRAS status or tumor location (right- vs. left-sided) might help refine patient selection. CONCLUSIONS:Although LT for uCRLM is no longer considered purely exploratory, questions remain about the best use of adjuvant chemotherapy. Moving forward, multicenter collaborations, standardized protocols, incorporation of tumor biology insights from resectable CRLM literature, and decision-support strategies (including artificial intelligence) may help optimize patient selection and improve outcomes in this advancing field.

Background:Anaemia complicates recovery in surgical patients. Intravenous (IV) iron supplementation shows promise in improving outcomes, but optimal timing remains uncertain. In this review, we compare the efficacy, safety, tolerability, and outcomes between preoperative and postoperative IV iron supplementation. Methods:In this systematic review and network meta-analysis, we searched PubMed, EMBASE, Cochrane Library, and Web of Science from inception to May 1, 2025, for randomised controlled trials (RCT) investigating IV iron supplementation in surgical patients either 7-30 days before surgery (preoperative) or 0-30 days after surgery (postoperative). Studies were excluded if they included patients with critical illness or prior transfusion or if iron was given outside the defined time frames or with other agents. Two reviewers independently appraised the data and extracted summary estimates from published reports. The primary outcomes were: (1) proportion of patients who received blood transfusion; (2) change between the baseline haemoglobin level and the haemoglobin level on postoperative day (POD) 7 and POD30. Data processing was conducted based on frequentist network meta-analysis. The risk of bias was assessed using the Cochrane Risk of Bias tool. The protocol is registered with PROSPERO, CRD42024533265. Findings:Among 129 identified studies, 22 RCTs with 3026 patients were included. All included studies had a low (n = 6) or moderate (n = 16) risk of bias. Compared to controls, postoperative IV iron supplementation reduced transfusion rates (RR 0.80, 95% CI 0.68-0.94; I2 = 0.0%). Postoperative IV iron supplementation did not affect haemoglobin levels (MD -4.51, 95% CI -9.75 to 0.72; I2 = 90.3%) at POD7 but increased haemoglobin levels (MD 5.45, 95% CI 2.70-8.20; I2 = 45.5%) at POD30. In comparison, preoperative IV iron supplementation resulted in higher haemoglobin levels than postoperative supplementation at POD30 (MD 6.67, 95% CI 1.61-11.72) but did not influence transfusion rates (RR 0.91, 95% CI 0.72-1.15; I2 = 0.0%). Interpretation:Our results suggest that postoperative IV iron supplementation reduces transfusion rates, while preoperative supplementation improves haemoglobin recovery. Clinicians may choose either strategy in an individualised, patient-centered manner. These conclusions should be interpreted with caution due to heterogeneity among included studies, limited data for subgroup analyses, and the absence of direct comparisons between preoperative and postoperative approaches. Funding:National Key Research and Development Program of China, National Natural Science Foundation of China, Beijing Natural Science Foundation, Capital's Funds for Health Improvement and Research, National High Level Hospital Clinical Research Funding, and Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences.

Observational studies provide the only evidence on the effectiveness of interventions when randomized controlled trials (RCTs) - apart from the initial RCT that establishes the efficacy of a treatment compared to a placebo - are impractical due to cost, ethical concerns, or time constraints. While many methodologies aim to draw causal inferences from observational data, there is a growing trend to model observational study designs after hypothetical or existing RCTs, a strategy known as "target trial emulation." Despite its potential, causal inference through target trial emulation is challenging because it cannot fully address the confounding bias inherent in real-world data due to the lack of randomization. In this work, we present a novel framework for target trial emulation that aims to overcome several key limitations, including confounding bias. The framework proceeds as follows: First, we apply the eligibility criteria of a specific trial to an observational cohort derived from real-world data. We then "correct" this cohort by extracting a subset that, through optimization techniques, matches both the distribution of covariates and baseline prognoses (i.e., the prognosis in the trial's control group) of the target RCT. Next, we address unmeasured confounding by adjusting the prognosis estimates of the treated group to align with those observed in the trial, using cost-sensitive counterfactual models. Following trial emulation, we go a step further by leveraging the emulated cohort to train optimal decision trees, developed by our team, to identify subgroups of patients exhibiting heterogeneity in treatment effects (HTE). The absence of confounding is verified using two external models, and the validity of the treatment effects estimated by our framework is independently confirmed by the team responsible for the original trial we emulate. To our knowledge, this is the first framework to successfully address both observed and unobserved confounding, a challenge that has historically limited the use of randomized trial emulation and causal inference in general since the 1950s. Additionally, our framework holds promise in advancing precision or personalized medicine by identifying patient subgroups that benefit most from specific treatments.

Introduction The role of visceral fat in disease development, particularly in Crohn´s disease (CD), is significant. However, its preoperative prognostic value for postoperative complications and CD relapse after ileocecal resection (ICR) remains unknown. This study aims to assess the predictive potential of preoperatively measured visceral and subcutaneous fat in postoperative complications and CD recurrence using magnetic resonance imaging (MRI). The primary endpoint was postoperative anastomotic leakage of the ileocolonic anastomosis, with secondary endpoints evaluating postoperative complications according to the Clavien Dindo classification and CD recurrence at the anastomosis. Methods We conducted a retrospective analysis of 347 CD patients who underwent ICR at our tertiary referral center between 2010 and 2020. We included 223 patients with high-quality preoperative MRI scans, recording demographics, postoperative outcomes, and CD recurrence rates at the anastomosis. To assess adipose tissue distribution, we measured total fat area (TFA), visceral fat area (VFA), subcutaneous fat area (SFA), and abdominal circumference (AC) at the lumbar 3 (L3) level using MRI cross-sectional images. Ratios of these values were calculated. Results None of the radiological variables showed an association with anastomotic leakage (TFA p = 0.932, VFA p = 0.982, SFA p = 0.951, SFA/TFA p = 0.422, VFA/TFA p = 0.422), postoperative complications, or CD recurrence (TFA p = 0.264, VFA p = 0.916, SFA p = 0.103, SFA/TFA p = 0.059, VFA/TFA p = 0.059). Conclusions Radiological visceral obesity variables were associated with postoperative outcomes or clinical recurrence in CD patients undergoing ICR. Preoperative measurement of visceral fat measurement is not specific for predicting postoperative complications or CD relapse.

BACKGROUND:Recent studies have suggested that certain combinations of KRAS or BRAF biomarkers with clinical factors are associated with poor outcomes and may indicate that surgery could be "biologically" futile in otherwise technically resectable colorectal liver metastasis (CRLM). However, these combinations have yet to be validated through external studies. PATIENTS AND METHODS:We conducted a systematic search to identify these studies. The overall survival (OS) of patients with these combinations was evaluated in a cohort of patients treated at 11 tertiary centers. Additionally, the study investigated whether using high-risk KRAS point mutations in these combinations could be associated with particularly poor outcomes. RESULTS:The recommendations of four studies were validated in 1661 patients. The first three studies utilized KRAS, and their validation showed the following median and 5-year OS: (1) 30 months and 16.9%, (2) 24.3 months and 21.6%, and (3) 46.8 months and 44.4%, respectively. When analyzing only patients with high-risk KRAS mutations, median and 5-year OS decreased to: (1) 26.2 months and 0%, (2) 22.3 months and 15.1%, and (3) not reached and 44.9%, respectively. The fourth study utilized BRAF, and its validation showed a median OS of 10.4 months, with no survivors beyond 21 months. CONCLUSION:The combinations of biomarkers and clinical factors proposed to render surgery for CRLM futile, as presented in studies 1 (KRAS high-risk mutations) and 4, appear justified. In these studies, there were no long-term survivors, and survival was similar to that of historic cohorts with similar mutational profiles that received systemic therapies alone for unresectable disease.

We propose a prognostic stratum matching framework that addresses the deficiencies of Randomized trial data subgroup analysis and transforms ObservAtional Data to be used as if they were randomized, thus paving the road for precision medicine. Our approach counters the effects of unobserved confounding in observational data by correcting the estimated probabilities of the outcome under a treatment through a novel two-step process. These probabilities are then used to train Optimal Policy Trees (OPTs), which are decision trees that optimally assign treatments to subgroups of patients based on their characteristics. This facilitates the creation of clinically intuitive treatment recommendations. We applied our framework to observational data of patients with gastrointestinal stromal tumors (GIST) and validated the OPTs in an external cohort using the sensitivity and specificity metrics. We show that these recommendations outperformed those of experts in GIST. We further applied the same framework to randomized clinical trial (RCT) data of patients with extremity sarcomas. Remarkably, despite the initial trial results suggesting that all patients should receive treatment, our framework, after addressing imbalances in patient distribution due to the trial's small sample size, identified through the OPTs a subset of patients with unique characteristics who may not require treatment. Again, we successfully validated our recommendations in an external cohort.

BACKGROUND:Current guidelines recommend use of adjuvant imatinib therapy for many patients with gastrointestinal stromal tumours (GISTs); however, its optimal treatment duration is unknown and some patient groups do not benefit from the therapy. We aimed to apply state-of-the-art, interpretable artificial intelligence (ie, predictions or prescription logic that can be easily understood) methods on real-world data to establish which groups of patients with GISTs should receive adjuvant imatinib, its optimal treatment duration, and the benefits conferred by this therapy. METHODS:In this observational cohort study, we considered for inclusion all patients who underwent resection of primary, non-metastatic GISTs at the Memorial Sloan Kettering Cancer Center (MSKCC; New York, NY, USA) between Oct 1, 1982, and Dec 31, 2017, and who were classified as intermediate or high risk according to the Armed Forces Institute of Pathology Miettinen criteria and had complete follow-up data with no missing entries. A counterfactual random forest model, which used predictors of recurrence (mitotic count, tumour size, and tumour site) and imatinib duration to infer the probability of recurrence at 7 years for a given patient under each duration of imatinib treatment, was trained in the MSKCC cohort. Optimal policy trees (OPTs), a state-of-the-art interpretable AI-based method, were used to read the counterfactual random forest model by training a decision tree with the counterfactual predictions. The OPT recommendations were externally validated in two cohorts of patients from Poland (the Polish Clinical GIST Registry), who underwent GIST resection between Dec 1, 1981, and Dec 31, 2011, and from Spain (the Spanish Group for Research in Sarcomas), who underwent resection between Oct 1, 1987, and Jan 30, 2011. FINDINGS:Among 1007 patients who underwent GIST surgery in MSKCC, 117 were included in the internal cohort; for the external cohorts, the Polish cohort comprised 363 patients and the Spanish cohort comprised 239 patients. The OPT did not recommend imatinib for patients with GISTs of gastric origin measuring less than 15·9 cm with a mitotic count of less than 11·5 mitoses per 5 mm2 or for those with small GISTs (<5·4 cm) of any site with a count of less than 11·5 mitoses per 5 mm2. In this cohort, the OPT cutoffs had a sensitivity of 92·7% (95% CI 82·4-98·0) and a specificity of 33·9% (22·3-47·0). The application of these cutoffs in the two external cohorts would have spared 38 (29%) of 131 patients in the Spanish cohort and 44 (35%) of 126 patients in the Polish cohort from unnecessary treatment with imatinib. Meanwhile, the risk of undertreating patients in these cohorts was minimal (sensitivity 95·4% [95% CI 89·5-98·5] in the Spanish cohort and 92·4% [88·3-95·4] in the Polish cohort). The OPT tested 33 different durations of imatinib treatment (<5 years) and found that 5 years of treatment conferred the most benefit. INTERPRETATION:If the identified patient subgroups were applied in clinical practice, as many as a third of the current cohort of candidates who do not benefit from adjuvant imatinib would be encouraged to not receive imatinib, subsequently avoiding unnecessary toxicity on patients and financial strain on health-care systems. Our finding that 5 years is the optimal duration of imatinib treatment could be the best source of evidence to inform clinical practice until 2028, when a randomised controlled trial with the same aims is expected to report its findings. FUNDING:National Cancer Institute.

Colorectal cancer is the second most common cause of cancer death in the United States, and up to half of patients develop colorectal liver metastases (CRLMs). Notably, somatic genetic mutations, such as mutations in RAS, BRAF, mismatch repair (MMR) genes, TP53, and SMAD4, have been shown to play a prognostic role in patients with CRLM. This review summarizes and appraises the current literature regarding the most relevant somatic mutations in surgically treated CRLM by not only reviewing representative studies, but also providing recommendations for areas of future research. In addition, advancements in genetic testing and an increasing emphasis on precision medicine have led to a more nuanced understanding of these mutations; thus, more granular data for each mutation are reviewed when available. Importantly, such knowledge can pave the way for precision medicine with the ultimate goal of improving patient outcomes.