Acute lymphoblastic leukemia (ALL) treatment is frequently complicated by infections, emergency visits, and therapy interruptions, yet early prediction of short-term clinical deterioration remains challenging. Traditional prognostic markers rely on static laboratory values, whereas dynamic hematologic fluctuations may provide earlier warning signals. This study presents and internally validates a clinically applicable prediction model based on dynamic hematologic parameters and clinician-documented symptoms for predicting short-term (7-day) clinical events in children with ALL.

Included in this retrospective study were 44 pediatric ALL patients treated with Berlin-Frankfurt-Münster-based protocols between January 2023 and June 2025. Weekly observation units were created by aggregating complete blood count values and clinician-documented symptoms. Dynamic hematologic indices included mean absolute neutrophil count (ANC), coefficient of variation (ANC-CV), and time in target range (ANC-TTR). The composite outcome was defined as any of the following occurring within 7 days: unplanned emergency visit, ≥48-h chemotherapy interruption, or infection requiring systemic antibiotics. Mixed-effects logistic regression was used to account for within-patient clustering. Model performance was assessed using discrimination, calibration, decision curve analysis, and bootstrap internal validation.

A total of 1136 weekly observations were analyzed. Composite clinical events occurred in 32.3% of weeks. Event weeks demonstrated lower ANC, higher ANC-CV, reduced ANC-TTR, lower hemoglobin levels, and higher symptom burden (all p <0.01). In the hematology-only model, ANC, ANC-CV, ANC-TTR, hemoglobin levels, and platelet counts were independent predictors (AUROC = 0.77). Adding the symptom score improved discrimination (AUROC = 0.83) and calibration. Decision curve analysis demonstrated greater net clinical benefit for the combined model across threshold probabilities of 10-40%.

Dynamic hematologic trajectories and clinician-documented symptoms enable accurate early prediction of short-term clinical events in pediatric ALL. This low-cost, accessible prediction model may support individualized risk stratification and proactive supportive care.

Tumor budding (TB) and poorly differentiated clusters (PDCs) are histopathological parameters that have been associated with poor prognosis in various malignancies, particularly colorectal carcinoma. Although numerous studies have demonstrated an association between the microcystic, elongated, and fragmented (MELF) pattern in endometrial carcinomas and lymphovascular invasion as well as lymph node metastasis, the literature regarding TB and PDCs in this context remains limited.This study aimed to investigate the potential associations of the MELF pattern, TB, and PDCs with overall survival, progression-free survival, and clinicopathological parameters in cases of endometrioid endometrial carcinoma (EEC).

A total of 190 cases diagnosed with EEC through hysterectomy specimens between 2010 and 2023, with complete hospital records, were selected from the archives of the Department of Pathology at Manisa Celal Bayar University Faculty of Medicine.

The presence of TB and PDCs was significantly associated with high histological grade (p < 0.001), deep myometrial invasion (p < 0.001), lymphovascular invasion (p < 0.001), lymph node metastasis (p < 0.001), cervical stromal involvement (p < 0.001), serosal involvement (p < 0.001), advanced stage (p < 0.001), and larger tumor size (p = 0.042 and p = 0.027, respectively). The presence of TB and PDCs was found to be associated with reduced overall survival (p = 0.001 and p < 0.001, respectively) and reduced progression-free survival (p = 0.043 and p = 0.004, respectively). The MELF pattern was not significantly associated with overall survival (p = 0.772).

These findings suggest that the presence of TB and PDCs may be valuable in stratifying prognostic risk in EEC and support the inclusion of these parameters in routine pathology reporting.

Skin adnexal tumours are a morphologically heterogeneous group of neoplasms arising from cutaneous appendages. Their clinical similarity to common benign lesions renders preoperative diagnosis challenging, and systematic concordance data from Indian tertiary institutions remain limited.

The objectives of this study are to characterise the histopathological spectrum and clinicopathological associations of skin adnexal tumours and to quantify clinical-histopathological concordance.

A cross-sectional, descriptive-analytical study was conducted at a tertiary care teaching hospital over six years using consecutive sampling. Data were analysed using descriptive statistics, Fisher's exact test, Chi-square test, Mann-Whitney U test, and Cohen's Kappa coefficient.

Forty-seven cases were studied (M:F = 1.47:1; mean age 42.32 ± 15.37 years). Eccrine differentiation predominated (66.0%), followed by follicular (23.4%) and sebaceous (10.6%) lineages. The consolidated Hidradenoma group was the most frequent individual diagnosis (17.0%), followed by pilomatricoma (10.6%). Benign tumours constituted 83.0%, whereas malignant tumours constituted 17%; the head and neck was the predominant site (44.7%). No clinicopathological variable was significantly associated with biological behaviour. The overall concordance rate was 21.3% (Cohen's κ = -0.575; 95% CI: -0.792 to -0.357; poor agreement). Differentiation type (χ² = 11.655, p = 0.003) and anatomical site (χ² = 7.881, p = 0.049) were the only significant concordance predictors; sebaceous tumours (80.0%; OR = 28.0) and head and neck location (38.1%) showed the highest rates, while extremity tumours were uniformly discordant (0%).

A 78.7% discordance rate confirms the indispensability of histopathological examination for all excised adnexal lesions. Sebaceous differentiation and head-and-neck location predict better clinical recognition; extremity lesions require heightened diagnostic vigilance.

High-Grade Serous Ovarian Carcinoma (HGSOC) and Low-Grade Serous Ovarian Carcinoma (LGSOC) are distinct subtypes of epithelial ovarian cancer with significant differences in pathogenesis and prognosis, posing challenges for precise diagnosis. Identifying reliable biomarkers is crucial for improving differential diagnosis and clinical management.

Transcriptome RNA-seq data of HGSOC and LGSOC were obtained from the GEO database (GSE27651, GSE126132). Differentially expressed immune-related genes (DIRGs) were identified. Functional enrichment analysis and protein-protein interaction (PPI) network construction were performed. The Least Absolute Shrinkage and Selection Operator (LASSO) regression and multiple Support Vector Machine Recursive Feature Elimination (mSVM-RFE) algorithms were used to select predictive genes. Diagnostic performance was evaluated using receiver operating characteristic (ROC) curves, and a nomogram was developed. Findings were validated in an independent dataset and via immunohistochemistry (IHC). The CIBERSORT algorithm assessed correlations between key DIRGs and tumor-infiltrating immune cells, with false discovery rate (FDR) correction applied for multiple testing.

Seventy-one DIRGs were identified in HGSOC versus LGSOC, predominantly enriched in cytokine-mediated signaling, cytokine-cytokine receptor interaction, and JAK-STAT pathways. STAT1 and IL-7 were selected as diagnostic biomarkers, with area under the curve (AUC) values of 0.908 and 0.842 in the train group. Respectively, validation in an independent merged cohort (GSE14001, GSE73168, GSE146965; 55 HGSOC, 13 LGSOC) yielded AUCs of 0.703 (95% CI: 0.517-0.889) for STAT1 and 0.706 (95% CI: 0.501-0.912) for IL-7. IHC confirmed significantly higher STAT1 and lower IL-7 protein expression in HGSOC tissues (P < 0.05). Immune microenvironment analysis revealed that HGSOC exhibited significantly higher fractions of naïve B cells, M2 macrophages, and neutrophils, and lower fractions of resting memory CD4+ T cells and eosinophils after FDR correction (all q < 0.05). STAT1 expression was strongly positively correlated with M1 macrophages (ρ = 0.688, q = 9.9×10^{- 8}), and showed correlation trends with other immune cell types that did not remain significant after FDR correction. IL-7 expression exhibited a negative correlation trend with neutrophils (ρ = -0.372, raw P = 0.0048, q = 0.100).

STAT1 and IL-7 are consistently differentially expressed between HGSOC and LGSOC and may serve as ancillary diagnostic biomarkers in histologically ambiguous cases. However, their clinical utility-particularly in multi-gene combinations-requires prospective validation.

In recent years, neoadjuvant chemoimmunotherapy (NICT) has become a research focus in the treatment of esophageal cancer. This study aims to evaluate the predictive value of tumor volume changes before and after NICT for prognosis in patients with operable esophageal squamous cell carcinoma (ESCC).

This retrospective study included 163 patients with histologically confirmed ESCC from two medical centers between October 1, 2020, and October 1, 2022. All patients received NICT before undergoing radical esophagectomy. Based on pre- and post-treatment CT images, we delineated and calculated the volume of the esophageal tumor. The rate of tumor volume change was then analyzed for its association with patient prognosis. The study endpoints were overall survival (OS), disease-free survival (DFS) and progression-free survival (PFS).

Multivariate regression analysis indicated that tumor location, number of NICT cycles, and tumor volume change rate were independent influencing factors for both DFS and PFS. The interval between NICT completion and surgery, along with tumor volume change rate, were independent factors for OS. Significant tumor volume reduction served as a predictor of favorable prognosis, demonstrating certain predictive value for long-term survival in patients. Surgery performed within 6 weeks of neoadjuvant therapy was significantly associated with longer OS, while DFS and PFS also showed a trend toward improvement.

The change in tumor volume before and after neoadjuvant therapy is an independent prognostic factor for esophageal cancer patients receiving NICT. Additionally, performing radical surgery as soon as possible after completing neoadjuvant therapy is associated with improved survival outcomes.

Gastric cancer (GC) is plagued by profound intratumoral heterogeneity and a complex tumor microenvironment (TME), which are the core obstacles to precise diagnosis and treatment. Conventional bulk multi-omics technologies average molecular signals across tissues, thus masking cellular heterogeneity; single-cell multi-omics resolves cellular diversity but dissociates cells from their native spatial context, leading to the loss of critical information on intercellular crosstalk and molecular spatial distribution. These limitations result in an incomplete understanding of GC pathogenesis and TME regulatory networks. Spatial multi-omics technologies, integrating genomics, transcriptomics, proteomics, and metabolomics with high-resolution spatial localization, address these key scientific problems by preserving the native tissue architecture and elucidating the spatiotemporal dynamics of molecular and cellular events in GC. This review systematically synthesizes the latest advances in the application of four major spatial multi-omics modalities in GC research over the past 15 years, with a critical evaluation of the technical performance, methodological shortcomings, and clinical translation potential of existing studies. Unlike previous reviews that only summarize research findings, this work uniquely integrates technical principles, mechanistic discoveries, and clinical translation of spatial multi-omics in GC, deeply analyzes the practical barriers to clinical application, and systematically elaborates the integration of spatial multi-omics with artificial intelligence (AI). We also identify unresolved challenges in the field and propose future development directions, providing a comprehensive and in-depth reference for the advancement of GC precision medicine based on spatial multi-omics.

The outlook for individuals dealing with metastatic or recurrent cervical cancer is still unfavorable, and existing biomarkers that assess the effectiveness of immune checkpoint inhibitors (ICIs) have certain drawbacks. Our objective was to assess the predictive importance of the combined neutrophil-to-lymphocyte ratio (Combined.NLR), incorporating both pre-treatment and post-treatment measurements in this patient population.

This retrospective cohort analysis, performed at one center, encompassed 148 individuals diagnosed with metastatic or recurrent cervical cancer who underwent treatment with ICIs. Patients were categorized into the Combined.NLR groups according to the median neutrophil-to-lymphocyte ratio (NLR) values obtained before and after treatment. The relationship between the Combined.NLR and progression-free survival (PFS) was examined through Cox regression analysis. A prognostic nomogram that includes various factors was created.

The Combined.NLR was identified as an independent prognostic factor for PFS. Individuals classified in the poor group exhibited a notably increased risk of disease progression in comparison to those in the good group [hazard ratio (HR) = 1.355, 95% confidence interval (CI): 1.006-1.824, p = 0.046]. The nomogram, which integrated the Combined.NLR, histological type, PD-L1 expression, the count of previous treatment lines, and the presence of multi-organ metastasis demonstrated a concordance index of 0.70. Calibration curves showed a strong correlation between the predicted results and the actual outcomes.

The Combined.NLR is a dynamic and readily accessible prognostic marker. The developed nomogram provides individualized risk prediction for individuals with metastatic or recurrent cervical cancer, potentially aiding clinical decision-making.

Systemic inflammation, immunity, and nutritional status are integral to tumor biology, shaping the microenvironment and influencing esophageal cancer (EC) outcomes. Yet, their integration into pragmatic prognostic tools-and potential implications for immunotherapy stratification-remain limited. This retrospective study assessed the prognostic value of the inflammation-immunity-nutrition score (IINS) and red cell distribution width-to-lymphocyte ratio (RLR), indicators reflecting host immunity, systemic inflammation, and nutritional reserve, in EC patients.

Clinical data from 660 EC patients who underwent radical surgery (2012-2018) were retrospectively analyzed and randomly assigned to training (n = 459) and validation (n = 201) cohorts. Candidate predictors were screened using LASSO and entered into multivariable Cox models. A nomogram incorporating IINS, RLR, and clinical covariates was constructed and validated with the C-index, calibration, and time-dependent AUC; clinical utility was evaluated with decision curve analysis (DCA), Integrated Discrimination Improvement (IDI), and Net Reclassification Index (NRI).

IINS, RLR, and eight additional factors were independent prognostic variables. The nomogram showed good calibration and superior discrimination versus AJCC staging, with a higher C-index and AUC in both cohorts. DCA, IDI, and NRI confirmed greater net benefit and improved risk reclassification.

This study proposes and internally validates a nomogram linking immune-nutritional surrogates with survival in EC. By reflecting systemic inflammation and host immunity, the model supports individualized risk stratification, perioperative optimization, and may inform patient selection for immunotherapy. External multicenter validation is warranted.

Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of non-small cell lung cancer (NSCLC); however, their efficacy is confined to a subset of patients. The urgent development of biomarkers capable of predicting therapeutic efficacy prior to treatment is essential, as this could mitigate unnecessary adverse effects and reduce healthcare costs. In this study, we conducted an integrated lipidomic (phospholipid) and proteomic (whole serum and extracellular vesicle) analysis of pre-treatment serum samples obtained from patients with NSCLC who received nivolumab. The pre-treatment serum phospholipid profiles revealed significant differences between responder and non-responder groups. Notably, the lysophosphatidylcholine (LPC) class-particularly LPC(20:0)-emerged as a predictive biomarker, exhibiting elevated levels in responders (AUC = 0.782; LOOCV AUC = 0.720; Bootstrap AUC = 0.781). Proteomic analysis further indicated increased expression of complement components and acute-phase proteins in the non-responder group. Moreover, integration of serum, extracellular vesicle proteome, and phospholipid datasets using Weighted Gene Co-expression Network Analysis and Multi-Omics Factor Analysis suggested that biological processes potentially associated with LPC involve neutrophil and platelet activation pathways. Pre-treatment serum LPC levels are a promising biomarker for predicting the response to nivolumab therapy in NSCLC. This LPC signature reflects a systemic immunometabolic state involving platelet and neutrophil activity, suggesting a novel biological mechanism underlying ICI treatment efficacy.

Hepatocellular carcinoma (HCC) complicated by portal vein tumor thrombosis (PVTT) is associated with limited therapeutic options and poor survival. Immune checkpoint inhibitors, especially when combined with tyrosine kinase inhibitors (TKIs), locoregional therapies, or radiotherapy, are reshaping the management of advanced HCC; however, the optimal way to integrate these modalities, particularly in patients with vascular invasion and low programmed death-ligand 1 (PD-L1) expression, remains uncertain. Herein, we describe a 52-year-old man with advanced HCC complicated by PVTT who initially received transarterial chemoembolization (TACE), hepatic resection, and adjuvant lenvatinib but later developed postoperative recurrent disease, including a right perirenal lesion and a synchronous right subpleural metastatic lesion. He then achieved a durable complete response and long-term survival with an immunotherapy-centered regimen combining radiotherapy, lenvatinib, and tislelizumab. This case suggests that an immunotherapy-based multimodal regimen integrating TACE, surgery, TKI therapy, radiotherapy, and programmed cell death protein 1 (PD-1) blockade can achieve deep and durable remission in selected patients with advanced HCC and low PD-L1 expression.