Whether robot-assisted thoracic surgery (RATS) improves nodal staging over video-assisted thoracoscopic surgery (VATS) early in adoption is unknown. We compared occult nodal upstaging and lymph node (LN) retrieval between early RATS and an established VATS practice by one surgeon, and asked whether total LN count reflects the number of stations sampled. Retrospective single-surgeon cohort of consecutive lung cancer resections with systematic LN dissection (2023-2026). Primary outcome: occult nodal upstaging (pathologic N + in clinically N0 patients). Secondary outcomes: LN yield, distinct stations sampled, and yield per sampled station, and perioperative outcomes. Eighty-seven patients qualified (RATS, n = 49; VATS, n = 38; 92.1% of VATS operations after RATS introduction). Occult upstaging was 10.6% vs. 10.5% (95% CI for the difference, - 14.8 to + 13.7% points; not significant). Total LN yield was higher with VATS (13.8 vs. 17.7; p = 0.018), whereas stations sampled were similar (4.7 vs. 4.7; equivalent within post-hoc margins of ± 1 and ± 0.75 but not ± 0.5 stations); yield per sampled station was lower with RATS (2.5 vs. 3.3; p = 0.001), mostly in mediastinal stations. This did not attenuate across the 49 RATS cases and persisted after multivariable adjustment (rate ratio 1.29; 95% CI 1.04-1.60). Operative time was longer with RATS; complications and R0 resection did not differ. No significant difference in occult upstaging was observed, but the study was underpowered and this result is inconclusive. Higher VATS node counts reflected yield per sampled station rather than more stations, for reasons that could not be determined. Station-level yield should be reported alongside total node count.

Effective disease-modifying therapies remain unavailable for neurodegenerative diseases (NDDs). Tyrosine kinase inhibitors (TKIs) indicated for chronic myeloid leukemia (CML) have been hypothesized to modulate the pathological mechanisms underlying neurodegeneration. Therefore, we aimed to explore the association between initiation of six different TKIs and the subsequent risk of developing NDDs in Swedish nationwide registries (2005-2024).

CML patients ≥50 years at diagnosis initiating TKI therapy were identified, alongside five comparators without CML matched on sex, age, and residence at diagnosis. CML cases and comparators were followed from the initiation date of the respective TKI and could contribute follow-up time to multiple TKI cohorts. A composite measure of NDD diagnosis or death due to NDD was the outcome. Hazard ratios (HRs) were estimated using Cox proportional hazards models, with adjustments for calendar year at follow-up initiation, and relevant comorbidities.

A total of 1322 CML cases were included in the study. 1188 initiated imatinib (of which 41 experienced an NDD event), 541 dasatinib (15 events), 390 nilotinib (five events), 174 bosutinib, 71 ponatinib, and 41 asciminib. Adjusted hazard ratios comparing each TKI group to their matched comparator populations were 0.98 (95% confidence interval [CI], 0.71 to 1.36) for imatinib users, 1.18 (95% CI, 0.68 to 2.03) for dasatinib users, and 0.37 (95% CI, 0.15 to 0.90) for nilotinib users. Bosutinib, ponatinib, and asciminib users were too few to conduct comparative analyses.

Initiation of nilotinib, but not imatinib or dasatinib, was associated with a significantly lower NDD incidence relative to general population comparators.

Synthetic lethality (SL) provides a powerful foundation for targeted oncology by identifying gene pairs whose simultaneous disruption is fatal to cancer cells while sparing healthy tissue. However, mapping these interactions experimentally across the human genome is restricted by massive combinatorial scale and high costs. To address this, computational approaches specifically graph neural networks (GNNs) have become the standard for modeling these complex biological relationships. Hence, by integrating heterogeneous knowledge graph (KG) and multi-omics data, GNNs can effectively capture the topological and molecular context required for accurate SL prediction. This review systematically evaluates the computational SL landscape across three core dimensions. First, we examine how diverse GNN architectures integrate multi-layered biological data. Second, we address model interpretability, contrasting intrinsically transparent architectures with post-hoc explanation methods, as a requirement for clinical trust. Finally, we explore the emerging role of Generative AI and large language models to leverage their zero-shot reasoning and broad biomedical knowledge.

Lung cancer is one of the leading causes of cancer-related mortality worldwide, emphasizing the need for accurate, automated, and clinically interpretable diagnostic systems. Although existing deep learning (DL) models have achieved promising performance in classifying lung nodules as benign or malignant, most approaches are limited to binary classification and provide insufficient support for detailed tumor characterization and TNM staging. Furthermore, conventional convolutional neural networks (CNNs) are effective in extracting local morphological features but often fail to capture long-range spatial dependencies. In contrast, transformer-based models can learn global contextual information but may compromise fine-grained local feature representation. To address these limitations, this study proposes a hybrid ResNet-50-Vision Transformer with Radiomic Feature Fusion (ReViT-RF) framework for lung tumor classification and component-wise TNM stage prediction using thoracic computed tomography (CT) images from the publicly available Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset. The primary clinical objectives of the proposed framework are to: (i) classify pulmonary nodules as benign or malignant, and (ii) predict individual TNM components, including primary tumor extent (T), lymph node involvement (N), and metastatic status (M) for malignant cases. To improve tumor visibility and image quality, CT images are initially preprocessed using Contrast-Limited Adaptive Histogram Equalization (CLAHE) and intensity normalization. The proposed ReViT-RF architecture integrates the complementary advantages of ResNet-50 and Vision Transformer (ViT) for comprehensive feature learning. The CNN branch extracts local morphological, textural, and structural characteristics, whereas the transformer branch captures long-range spatial relationships and global contextual dependencies. In addition, handcrafted radiomic features, including shape, size, texture, and spatial descriptors, are fused with deep features to provide clinically meaningful information for accurate TNM component prediction. The dataset is divided into training, validation, and testing subsets using an 80:10:10 ratio, with data augmentation applied only to the training set to prevent information leakage. Experimental results demonstrate that the proposed framework achieves an accuracy of 97.62% for benign-malignant classification, along with high precision, recall, and F1-score values. Moreover, independent prediction of TNM components provides detailed stage-related information rather than relying on a single aggregated stage label, enabling more precise tumor assessment and clinical decision support. Furthermore, explainability analysis using Gradient-weighted Class Activation Mapping (Grad-CAM) identifies the critical tumor regions contributing to model predictions, improving interpretability and enhancing clinician confidence. Overall, by combining hybrid deep feature extraction, radiomic feature fusion, component-wise TNM stage prediction, and explainable AI analysis, the proposed ReViT-RF framework provides a reliable, comprehensive, and clinically interpretable solution for automated lung tumor analysis.

Over the past 5 years, the number of antibody-drug conjugates (ADCs) entering the oncology market has surged. By contrast, the clinical use of immunoliposomes-nanoparticles that also employ antibodies as targeting agents (i.e., antibody-nanoconjugates, ANCs)-has yet to receive approval. One possible reason is the limited understanding of the relationship between ANC exposure and efficacy. To address this gap, we developed a pharmacokinetic/pharmacodynamic (PK/PD) model for a docetaxel-trastuzumab ANC. PK and PD studies were conducted in MDA-MB-231 tumor-bearing mice, divided into three treatment groups: (i) control, (ii) free docetaxel + free trastuzumab, and (iii) docetaxel-trastuzumab ANCs. PK data were obtained by fluorescence imaging and captured systemic and tumor distribution of ANCs, while PD data were based on tumor growth measurements. The PK model was built using data from the ANC group, and the PD model was developed across all three treatment groups, with a leave-one-out cross validation strategy for predictive evaluation. Treatment schedule simulations were then performed. Using a population nonlinear mixed-effects (NLME) approach, the final PK model included a central and a tumor compartment, each with independent elimination rates. Tumor growth dynamics were best described by a reduced Gompertz model coupled with a Simeoni resistance model. This model demonstrated strong predictive performance. Using only data from the first two treatment cycles (4 time points) and Bayesian estimation in the test set, it accurately predicted systemic and tumor ANC concentrations across the subsequent four treatment cycles, with a relative error (RE) below 15%. Prediction precision was further improved (RE < 5%) when data from one additional treatment cycle were incorporated. Similar performance was achieved for PD data, with tumor volume predictions exhibiting RE < 15% and RE < 7.5% when implementing data from two and three treatment cycles, respectively. The model was then applied to simulate alternative treatment schedules. Interestingly, while systemic exposure remained comparable across regimens, every-5-day administration yielded the greatest tumor exposure compared to daily, weekly, and every-35-day dosing (mean ± SD tumor AUC: 55.4 ± 10.9 mg/kg day vs. 47.6 ± 9.38 mg/kg day, 51.4 ± 11.5 mg/kg day, and 44.6 ± 10.0 mg/kg day, respectively, one-way ANOVA, p < 0.001), highlighting the importance of dosing frequency. Overall, these results illustrate how PK/PD modeling can refine intra-tumor PK understanding and support preclinical development of innovative formulations such as ANCs.

Postoperative gastrointestinal dysfunction (POGD) and Postoperative gastrointestinal intolerance (POGI) remain a critical challenge in abdominal surgery, particularly for colorectal cancer patients undergoing laparoscopic procedures. Current evidence suggests anesthetic agents may influence gastrointestinal recovery, yet the impact of esketamine remains underexplored. This trial aimed to investigate whether intraoperative esketamine administration accelerates gastrointestinal function recovery.

In this single-center, randomized, outcome-assessor -blind trial, 104 patients (aged 18-75, ASA I-III) scheduled for elective laparoscopic colorectal cancer surgery were allocated to receive either intravenous esketamine (0.2 mg/kg) or saline (control) 30 minutes before surgery completion. Primary outcome was time to first flatus; secondary outcomes included I-FEED scores, defecation time, oral intake tolerance, and postoperative complications assessed within seven days after surgery.

The esketamine group demonstrated significantly shorter time to first flatus (52 [50.25-53.75] vs 60 [58.59-61.41] hours; P < 0.001) and first defecation (87 [81.96-92.04] vs 95 [92.65-97.35] hours; P = 0.04) compared to controls. I-FEED scores were markedly lower (median 1 [0.25-2] vs 2.5 [1-3]; P < 0.001), with reduced nausea (P = 0.001) and abdominal distension duration (P < 0.001). Esketamine also improved postoperative sleep quality, but did not affect hospitalization costs or length of stay.

Intraoperative esketamine accelerates gastrointestinal function recovery after laparoscopic colorectal surgery, especially reducing postoperative gastrointestinal intolerance symptoms of nausea and distension while improving early sleep quality. However, no evident benefit was observed in reducing hospital length of stay or patient costs.

Solid pseudopapillary neoplasm (SPN) and pancreatic acinar cell carcinoma (PACC) represent two uncommon pancreatic tumors with distinct biological behaviors and management strategies. Both entities can present with overlapping appearances on contrast-enhanced computed tomography (CECT), posing a diagnostic challenge preoperatively. Therefore, reliable imaging differentiation between these tumors is clinically important. This study aimed to evaluate computed tomography (CT) imaging features for differentiating SPN from PACC and to develop a combined model to improve preoperative diagnostic performance.

We retrospectively reviewed 68 patients (51 SPN, 17 PACC) who underwent preoperative CECT. Clinical and CT imaging features were compared between groups. Variables that were significant in univariate analysis were entered into a forward stepwise multivariable logistic regression model to identify independent predictors. Diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis, and differences between ROC curves were compared using the DeLong test.

Patients with PACC were significantly older and more likely to be male than those with SPN (both P<0.001). The tumor-to-pancreas enhancement ratio in the pancreatic parenchymal phase (PPP) was significantly higher in PACC (P=0.004). The "floating cloud" sign was observed more frequently in SPN (P=0.010), whereas pancreatic duct dilation occurred more frequently in PACC (P=0.004). Multivariate analysis identified the tumor-to-pancreas enhancement ratio in the PPP [odds ratio (OR) =1.10; P=0.002] and pancreatic duct dilation (OR =4.65; P=0.036) as independent predictors of PACC, whereas the "floating cloud" sign was independently associated with SPN (OR =0.12;P=0.010). The combined model achieved the highest area under the curve (AUC) among all evaluated diagnostic models in this cohort (AUC =0.865).

A combined assessment incorporating the tumor-to-pancreas enhancement ratio in the PPP, the "floating cloud" sign, and pancreatic duct dilation may improve preoperative differentiation between PACC and SPN.

Body image distress is a major concern for cancer survivors and includes views on physical appearance and body functions. As body image is largely developed in adolescence and young adulthood with implications for self-identity and social relationships, it may impact adolescent and young adult (AYA) cancer survivors in distinct ways.

Explore the manifestations of body image distress in adolescent and young adult cancer survivors who participated in a body image-focused expressive writing intervention.

Semi-structured interviews focusing on the body image experience were conducted and reviewed with a reflexive thematic analysis approach embedded within an interpretative phenomenological framework.

Qualitative analysis of 24 interviews revealed the following themes: Feasibility and Acceptability; Reconciling Intellectual and Emotional Understanding of Anticipated Body Image Changes; What We Gained and What We Lost; Body Image Distress as a Source of Guilt and Shame; We are a Community, but We Lack a Community.

Discussion of body image distress represented an example of trauma-informed care that allowed participants to build a small community within the study and regain a sense of self identity lost in the cancer experience. This approach informs suggestions for cancer survivorship care and adopting a trauma-informed care approach during clinical or research environment. Future studies will endeavor to facilitate the adaptation of expressive writing to the needs of a given cancer survivor while building a community around those who seek to further their intellectual and emotional understanding of the ramifications of body image distress with peers.

Hepatocellular carcinoma (HCC) lacks effective therapies. Compound Muji Granules (MJ), a traditional Chinese medicine, shows potential anti-HCC activity, but its material basis and synergistic mechanism with exosomal delivery remain unclear. Using UPLC-Q-TOF-MS/MS, 98 compounds were identified in MJ, including 25 blood-absorbed and 23 tissue-distributed components, with gallic acid (GA) as the core active constituent. hUC-MSC-derived exosomes (hUC-MSCs-Exos) were prepared and combined with MJ or GA. In HepG2 cells and rat HCC organoids, exosome-combined treatment significantly reduced IC50, increased apoptosis and necrosis, and inhibited ATP production. Transcriptomic analysis and qPCR revealed regulation of the IL-17/MAPK/p53 axis, with downregulation of MEK1/2, CREB1, and IL17A, and activation of TP53. These findings indicate that MJ exerts anti-HCC efficacy through multi-component synergy, with GA as the main material basis, and hUC-MSCs-Exos enhance these effects by modulating IL-17/MAPK/p53 signaling, inhibiting tumor proliferation and energy metabolism, and promoting apoptosis.

Alcohol-related hepatocellular carcinoma is an increasingly prevalent consequence of alcohol use and the often-silent progression of alcohol-related liver disease. A substantial proportion of patients with alcohol-related hepatocellular carcinoma are diagnosed at an advanced stage, frequently because underlying liver disease has not been previously identified and surveillance has not been implemented. This review summarizes recent epidemiological trends and clinical features of alcohol-related hepatocellular carcinoma, with a particular focus on the high proportion of undiagnosed cirrhosis and the challenges posed by alcohol use disorder, comorbidities and socioeconomic vulnerability. Strengthening collaboration between hepatology and addiction medicine, alongside population-level strategies to reduce alcohol use, will be essential to enhance surveillance uptake and ultimately improve outcomes in alcohol-related hepatocellular carcinoma.