Metabolic syndrome (MeS) is a major risk factor for cardiovascular disease and is characterized by chronic low-grade inflammation, immune dysregulation, and metabolic abnormalities. However, the molecular mechanisms linking MeS to diabetic coronary artery disease (DMCAD) remain incompletely understood. Publicly available peripheral blood mononuclear cell (PBMC) transcriptomic datasets of MeS and DMCAD were analyzed using an integrative bioinformatics approach. Differentially expressed genes (DEGs) were identified using the limma package, followed by functional enrichment, protein-protein interaction (PPI) network construction, weighted gene co-expression network analysis (WGCNA), gene set enrichment analysis (GSEA), and miRNA regulatory network analysis. Candidate genes were further evaluated using an independent type 2 diabetes mellitus (T2DM) dataset for external transcriptomic validation. Integrated analyses identified immune-inflammatory and immuno-metabolic pathways as central features of both MeS and DMCAD. Enrichment analyses highlighted cytokine signaling, leukocyte activation, chemotaxis, complement activation, oxidative stress, and vascular inflammatory responses. Network analyses identified CD86, CD33, CCR1, C5AR1, FPR1, CXCL16, and LILRA5 as key hub genes associated with immune regulation and cardiometabolic dysfunction. External transcriptomic validation supported the relevance of CD33, CD86, and LILRA5. miRNA network analysis identified members of the miR-17/92 family and miR-146a-5p as potential upstream regulators. TAM 2.0 enrichment analysis further linked these miRNAs to metabolic syndrome, diabetes mellitus, atherosclerosis, coronary heart disease, immune response, inflammation, and angiogenesis. Our findings suggest that coordinated immune-inflammatory and metabolic signaling networks contribute to the progression from MeS to DMCAD. The identified hub genes and miRNAs may serve as potential biomarkers and therapeutic targets for inflammation-driven cardiometabolic disease.

Childhood cancer is a leading cause of disease-related mortality, with increasing incidence in many high-income countries. Socioeconomic disparities in childhood cancer survival are well documented, but less is known about incidence.

To examine associations between area-based deprivation and childhood cancer incidence in a universal health care system.

This population-based cross-sectional study linked data from the population-based Pediatric Oncology Group of Ontario Networked Information System cancer registry with Statistics Canada's postal code and Ontario Marginalization Index (ON-Marg) databases to identify pediatric cancer cases diagnosed in Ontario from January 1, 1999, to December 31, 2023, among approximately 2.3 million children aged 0 to 14 years living in Ontario, Canada. Data analysis was performed from October 2025 to February 2026.

ON-Marg dimensions: material resources (income and educational attainment), households and dwellings (residential instability), age and labor force (dependents and adults not in the labor force), and racialized and newcomer populations.

Crude and age-standardized incidence rates for each ON-Marg dimension; incidence rate ratios were estimated using multivariable Poisson regression models adjusted for age, sex, and diagnosis period for the overall population and stratified by cancer subtype (ie, leukemia and lymphoma, central nervous system tumors, and other non-central nervous system solid tumors).

Of the 9063 pediatric cancer cases (4981 [55.0%] male; median [IQR] age at diagnosis, 5 [2-10] years), the age-standardized incidence rate was 162.7 cases per million (95% CI, 159.3-166.1). Most cancer cases were found in areas with the greatest concentration of racialized and newcomer populations; however, after accounting for population size, there was no difference in cancer incidence across quintiles. A key finding of this study was that the least marginalized quintiles of material resources (incidence rate ratio, 1.15; 95% CI, 1.07-1.22) and households and dwellings (incidence rate ratio, 1.08; 95% CI, 1.01-1.16) had significantly higher incidence rates compared with the most marginalized quintiles. These findings were driven primarily by hematologic and other solid cancer types.

This large population-based cross-sectional study with virtually complete ascertainment of pediatric cancer cases in a Canadian province with a universal health care system found higher cancer incidence among children who lived in affluent and residentially stable neighborhoods. These findings underscore the importance of considering sociodemographic factors when examining pediatric cancer epidemiology; more research is needed to understand the mechanisms driving these differences, their impact on outcomes, and how the findings may inform targeted prevention and intervention strategies.

Gamma Knife radiosurgery (GKRS) is an established adjunct for residual or recurrent craniopharyngioma, yet prescription dose selection remains experience-driven and long-term failure risk is difficult to individualize. Machine learning may support more consistent planning and risk-informed follow-up.

To develop and internally validate two complementary AI models for craniopharyngioma GKRS: (1) prescription dose prediction and (2) treated-lesion progression risk prediction.

In this retrospective single-center cohort, we trained a random forest regressor to predict delivered single-fraction margin dose (Gy) from baseline clinical and tumor features and a random forest classifier to estimate the probability of treated-lesion progression using baseline features and delivered dose. Internal validation used cross-validation with discrimination and calibration metrics.

Seventy-two treated tumors were analyzed. Prescription dose prediction showed clinically tight error, with MAE 1.30 Gy and RMSE 1.65 Gy (R² 0.21), indicating the model approximated physician dosing patterns within ~1-2 Gy for most cases. For outcome modeling, risk prediction achieved ROC-AUC 0.75 and PR-AUC 0.582, with reasonable calibration (Brier score 0.112; recalibration slope 0.86, intercept 0.096). Together, the two models enabled simultaneous estimation of an expected prescription dose and an individualized probability of long-term treated-lesion failure, supporting risk stratification beyond dose alone.

A dual-model AI framework for craniopharyngioma GKRS is feasible and provides both dose estimates and individualized long-horizon failure risk predictions, with potential to standardize prescriptions and tailor surveillance intensity.

Not applicable.

Breast cancer remains a leading cause of cancer-related mortality in women worldwide, making early and accurate detection a global clinical priority. Conventional screening modalities, including mammography, ultrasonography, and Magnetic Resonance Imaging (MRI), each have well-established strengths and limitations; in particular, mammographic sensitivity is reduced in women with heterogeneously or extremely dense breast tissue, where it may fall to approximately 50%. Liquid Crystal Thermography (LCT) is a contact-based, non-invasive, radiation-free technique that uses thermochromic liquid crystal (TLC) foils to map surface thermal patterns on the breast and has been investigated as a potential adjunct to standard breast screening. This narrative review summarises the physical and physiological basis of LCT, describes the Braster-based contact thermography workflow, evaluates published diagnostic performance data, and provides a structured analysis of current limitations, confounding factors, and evidence gaps in comparison with established screening modalities. Available studies suggest that LCT may have a role as an investigational adjunctive or triage tool, particularly in settings with limited access to standard imaging; however, the evidence base is heterogeneous, predominantly small in sample size, and methodologically variable, and is insufficient at this stage to establish LCT as a standalone substitute for mammography. The U.S. Food and Drug Administration (FDA) has explicitly stated that thermography should not be used in place of mammography for breast cancer screening or diagnosis. Any potential advantage of LCT in dense breast tissue remains unconfirmed, as no published comparative study has yet provided head-to-head false-negative and false-positive counts across modalities within this subgroup. Preliminary observational data from an ongoing Malaysian case series are included only to illustrate the clinical workflow; these findings are exploratory, unpublished, and not peer-reviewed, and should not be interpreted as confirmatory evidence of diagnostic performance. Future research priorities include large-scale prospective validation, standardised acquisition and interpretation protocols, and rigorous comparative reporting of sensitivity, specificity, and misclassification rates.

Parathyroid carcinoma (PC) is an extremely rare endocrine malignancy with limited treatment options for unresectable or recurrent cases. With the increasing use of comprehensive genomic profiling (CGP), treatment based on genomic findings is becoming more common. However, the frequency and clinical significance of elevated tumor mutational burden (TMB) in PC remain unclear because previous studies have been limited by small sample sizes.

We retrospectively analyzed genomic and clinical data of patients with PC registered in the Center for Cancer Genomics and Advanced Therapeutics database in Japan between June 2019 and March 2025. TMB values were obtained as reported by each CGP assay. TMB-H was defined as TMB ≥ 10 mut/Mb for descriptive analyses. We also assessed genomic alterations, microsatellite instability (MSI) status, and clinicogenomic characteristics.

Twenty-five patients with PC were included. The median assay-reported TMB was 4.0 mut/Mb (range, 0-35). Seven tumors (28.0%) had assay-reported TMB values of ≥ 10 mut/Mb, including three (12.0%) with TMB ≥ 20 mut/Mb. The most frequently altered genes were CDC73 (40%), TP53 (32%), and MEN1 (24%). No co-alterations were observed between CDC73 and MEN1 or between CDC73 and TP53. One tumor was MSI-high and was included in the TMB-H group. POLE alterations were detected in three cases, including two tumors in the TMB-H group.

This nationwide, real-world study demonstrated that a subset of PCs showed elevated assay-reported TMB values and genomic features potentially related to abnormalities in DNA replication or repair pathways. These findings support the clinical relevance of comprehensive genomic profiling in identifying the molecular heterogeneity and potential therapeutic opportunities for this rare malignancy.

Accurate delineation of target volumes and organs at risk (OAR) is essential in radiotherapy planning for cervical cancer. Deep learning (DL)-based auto-segmentation has the potential to improve contouring efficiency and workflow. This study reports the prospective development and internal validation of a DL-based auto-segmentation model- Deep contour (DC) for cervical cancer radiotherapy.

In this prospective single-institution study, a 2-dimensional convolutional neural network based on the LinkNet architecture, DC, was trained on 190 computed tomography (CT) datasets for abdominal and pelvic OARs and 90 cervical cancer datasets for target volumes. Independent validation was performed on 20 CT datasets. Model performance was evaluated using dice similarity coefficient (DSC), Jaccard Index (JI), 95th percentile Hausdorff distance (HD95), average symmetric surface distance (ASSD), and surface dice coefficient (NSD). Expert internal and external radiation oncologists qualitatively explored clinical acceptability using a Likert scale, and segmentation time was compared with manual contouring.

The DC demonstrated the greatest geometric performance for the femur (DSC 0.92 ± 0.03; NSD 0.94 ± 0.04), bowel bag (DSC 0.89 ± 0.03; NSD 0.77 ± 0.12), and bladder (DSC 0.88 ± 0.14; NSD 0.87 ± 0.12). Among the target volumes, the inguinal nodal clinical target volume (CTVn_inguinal) achieved the greatest agreement (DSC 0.77 ± 0.04; NSD 0.76 ± 0.07). Moderate performance was observed for the rectum (DSC 0.75 ± 0.16), liver (DSC 0.73 ± 0.21), and pelvic nodal clinical target volume (CTVn_pelvis) (DSC 0.60 ± 0.10), whereas lower performance was observed for anatomically complex structures such as the duodenum, anal canal, common bile duct, pancreas, and pelvic vessels. Clinical evaluation of two cases revealed a Likert score of III-IV for key pelvic organs, such as the bladder, femur, pelvic bowel bag, rectum, and sigmoid. Auto-segmentation significantly reduced the segmentation time from 77 min to 5 s per dataset (p < 0.001).

This prospective validation demonstrates that DC auto-segmentation model can achieve acceptable geometric performance congruent across multiple abdominal and pelvic OARs and reasonable geometric performance for the elective inguinal CTV volume. Further validation on larger datasets and evaluation of clinical workflow integration are warranted.

CTRI, TRN: CTRI/2024/02/063055, Registration date: February 22, 2024.

Posaconazole prophylaxis is indicated in high-risk patients with haematological malignancies to prevent invasive fungal diseases (IFDs) with guidelines advising steady-state posaconazole plasma concentrations (PPCs) above 0.5-0.7 mg/L. Therapeutic drug monitoring (TDM), however, is not routinely recommended for posaconazole delayed-release tablet (DRT) prophylaxis.

To describe PPCs in hospitalized high-risk patients with haematological malignancies treated for AML or undergoing allogeneic haematopoietic cell transplantation receiving posaconazole prophylaxis with posaconazole DRT and factors influencing exposure.

This prospective, two-centre cohort study measured serial PPCs at Days 7, 14 and 21, and during diarrhoea episodes in adult high-risk patients receiving prophylaxis with posaconazole DRT between August 2019 and May 2023. Patients were followed during hospital admission and for 7 days after the last dose of posaconazole or hospital discharge.

Ninety-two patients contributed 223 PPCs. Subtherapeutic PPCs (<0.7 mg/L) occurred in 77 (34.5%) samples and 49 (53.3%) patients recorded ≥1 subtherapeutic PPC. The median Day 7 PPC was 0.84 (IQR: 0.47-1.16) mg/L, with no significant changes over time. In patients with diarrhoea compared with no diarrhoea, the median PPC was significantly lower [0.74 (IQR: 0.48-1.00) mg/L versus 0.92 (IQR: 0.64-1.40) mg/L, P = 0.007]. Multivariate analysis identified older age (>60 years) was protective against subtherapeutic PPCs. Six IFDs developed in five patients (5.4%) during follow-up and posaconazole-attributed hepatotoxicity resulted in cessation for one patient (1.1%).

Subtherapeutic PPCs are common during posaconazole DRT prophylaxis, suggesting the need for routine TDM to optimize dosing in those receiving this posaconazole formulation.

Surgical decision making in patients with brain metastasis is complex, particularly for patients with melanoma brain metastasis (MBM). Few studies specifically address neurosurgical outcomes based on histology. This study aims to identify clinical factors associated with early mortality and overall survival (OS) after tumor resection in patients with MBM.

Patients diagnosed with MBM from 2009 to 2018 at our institution who underwent surgical resection as their first-line therapy were included in the study. The primary outcomes were postoperative OS, 90-day mortality, and leptomeningeal disease (LMD) incidence. Associations between OS and postoperative 90-day mortality with demographic/clinical factors were assessed using Cox proportional hazards regression models and logistic regression models, respectively. The cumulative incidence of LMD was determined using competing risks, and associations with demographic/clinical factors were assessed using proportional subdistribution hazards regression models.

A total of 103 patients with MBM were included. Ninety-day mortality occurred in 18% (n = 19). Elevated lactate dehydrogenase at MBM diagnosis (odds ratio [OR] [95% CI]: OR = 7.17 [1.50-34.25]; P = .013) was associated increased odds of early mortality in multivariable analysis. Postoperative Karnofsky Performance Scale ≥80 (OR = 0.13 [0.03-0.62]; P = .010) and MBM at stage 4 diagnosis (OR = 0.11 [0.02-0.67]; P = .016) were associated with reduced odds of early mortality. Factors associated with better postoperative OS (hazard ratio [HR] [95% CI]) included synchronous diagnosis of MBM and stage 4 disease (HR = 0.59 [0.36-0.95]; P = .032), preoperative Karnofsky Performance Scale ≥80 (HR = 0.46 [0.27-0.80]; P = .006), adjuvant stereotactic radiosurgery (HR = 0.55 [0.32-0.93]; P = .026), and surgical reduction of volumetric intracranial tumor burden ≥95.6% (HR = 0.47 [0.28-0.80]; P = .005). No factors were significantly associated with cumulative incidence of LMD.

This is the largest analysis of patients with MBM who underwent surgery as first-line therapy. We identified clinical factors associated with early postoperative mortality and survival including surgical reduction of intracranial tumor burden.

The aim of this study was to identify the places of death of patients with solid cancer under follow-up by a Palliative Care team and to recognize factors that influence the place of death.

Retrospective study of cancer patients whose deaths occurred between January 1, 2022, and January 1, 2024, followed up by a Palliative Care team in a Portuguese public hospital. Demographic and clinical data were reviewed through clinical file consultation and were analyzed by Statistical Package for the Social Sciences® v.29, considering a p<0.05 as statistically significant.

The sample included 413 cancer patients; 60.5% were male, and 72.9% were Eastern Cooperative Oncology Group 3-4. The median age was 75 years, and the median follow-up duration in Palliative Care was 40 days. Death in a hospital setting occurred in 64.4% of the patients. The most common types of cancer were gastrointestinal/hepato-bilio-pancreatic (41.6%) and lung cancers (18.6%). Death due to cancer progression was the main cause of death (88.4%). In a multivariate logistic regression, factors associated with higher odds of hospital death were age <75 years (OR 1.88, 95%CI 1.22-2.90) and follow-up duration in Palliative Care <30 days (OR 2.59, 95%CI 1.66-4.04). Death due to cancer progression was associated with lower odds of hospital death (OR 0.03, 95%CI 0.004-0.21).

Death in a hospital setting was predominant in this sample, which is in line with outcomes from international studies. These findings highlight the need for early palliative care referral and advance care planning to support patients' preferences for place of death.

The Hemoglobin-Albumin-Lymphocyte-Platelet score has shown prognostic value across various cancers; however, its utility in predicting cytoreductive outcomes in advanced epithelial ovarian cancer remains unclear. The aim of this study was to determine whether the preoperative Hemoglobin-Albumin-Lymphocyte-Platelet score predicts maximal cytoreduction at primary cytoreductive surgery.

In this single-center study, we analyzed data from 140 patients with epithelial ovarian cancer who underwent primary cytoreductive surgery at our clinic. Patients were categorized by residual disease: maximal cytoreduction (no macroscopic residual disease, n=104), optimal cytoreduction (n=19), and suboptimal cytoreduction (n=17). Preoperative Hemoglobin-Albumin-Lymphocyte-Platelet, CA-125, and clinical variables were compared across groups. Discrimination for maximal cytoreduction was assessed using receiver operating characteristic analysis.

The maximal cytoreduction group had higher preoperative hemoglobin and lymphocyte counts, lower platelet counts, and lower CA-125 levels (p<0.005). Median Hemoglobin-Albumin-Lymphocyte-Platelet was significantly higher in maximal cytoreduction vs. optimal cytoreduction vs. suboptimal cytoreduction (35.37 vs. 21.10 vs. 20.19; all p<0.001). For predicting maximal cytoreduction, the Hemoglobin-Albumin-Lymphocyte-Platelet cutoff of 24.96 yielded a sensitivity of 71.4% and a specificity of 71.4% (area under the curve: 0.792; p<0.001). A CA-125 cutoff of 260.50 IU/mL provided a sensitivity of 71.4% and a specificity of 65.7% (area under the curve: 0.758; p<0.001).

The preoperative Hemoglobin-Albumin-Lymphocyte-Platelet score demonstrates good discrimination for predicting maximal cytoreduction in advanced epithelial ovarian cancer and appears to perform at least comparably to CA-125. Given that patients selected for neoadjuvant chemotherapy were excluded, these performance estimates apply to a surgically selected population. Given its low cost and universal availability, Hemoglobin-Albumin-Lymphocyte-Platelet may support preoperative selection and counseling before cytoreductive surgery.