Chemotherapy

Explainable machine learning-guided integrated multiomics analysis reveals macrophage-driven immune suppression in breast cancer.

Despite thorough characterizations of cellular compositions within the breast tumor microenvironment (TME), their implications for disease progression and patient prognosis remain poorly understood. Unraveling these effects is vital for identifying potential targets to improve treatment outcomes. In this study, we devise an explainable machine learning (XML) pipeline to scrutinize the associations...

Tobacco rattle virus triggers local defense and systemically coordinates taxoid biosynthesis in Taxus baccata revealed by integrated metabolic analysis.

The clinical demand for paclitaxel necessitates novel strategies to enhance its production. While recent breakthroughs have elucidated the complete biosynthetic pathway in heterologous hosts, optimizing production within the native Taxus plant remains crucial. Here, we investigated the unexplored potential of using Tobacco rattle virus (TRV) as a controlled biotic elicitor in Taxus baccata seedlin...

Predicting response to neoadjuvant chemotherapy combined with immunotherapy in gastric cancer based on habitat imaging and peritumoral radiomics: a two-center study.

BACKGROUND: Predicting pathological response to neoadjuvant chemotherapy combined with immunotherapy (NACI) in locally advanced gastric cancer (LAGC) ...

Liquid Biopsy in Modern Oncology: Advances, Challenges, and Future Perspectives in Early Cancer Detection, Minimal Residual Disease, and Dynamic Patient Monitoring.

PURPOSE OF REVIEW: Liquid biopsy has emerged as a minimally invasive approach to overcome the limitations of tissue biopsy in oncology. This review ai...

Molecular and spatial differences in the tumor microenvironment of high-grade serous ovarian cancers with short versus long-term survival.

OBJECTIVE: Survival of patients diagnosed with advanced-stage high-grade serous ovarian cancer (HGSOC) varies widely. Understanding the biological and...

Estimating temporal treatment-effect patterns of radiotherapy and chemotherapy in lower-grade gliomas using causal machine learning.

Treatment decisions for lower-grade gliomas (WHO grades 2-3) rest on trial averages, which lack temporal resolution. We applied Causal Analysis of Sur...

Treatment effect heterogeneity of radiotherapy in localized Ewing sarcoma: A secondary analysis of the EURO-E.W.I.N.G. 99 and Ewing 2008 trial.

INTRODUCTION: Standard Ewing sarcoma treatment includes chemotherapy and local therapy. Previous studies found no average treatment effect of combined...

Empowering researchers and healthcare professionals in academic publishing: key insights and practical lessons from the British Society of Antimicrobial Chemotherapy (BSAC) workshop 2025.

The British Society of Antimicrobial Chemotherapy (BSAC) Academic Publishing Workshop, held at the Birmingham City Event Centre in the UK from 19 to 2...

Radiological staging clinical decision support model for rectal cancer lymph node metastasis detection on MRI.

BACKGROUND: Accurate staging of lymph node metastasis (LNM) is crucial for personalising rectal cancer treatment. Lymph nodes (LNs) are the most commo...

Automated deep learning model for predicting pathological complete response in rectal cancer: A tool to organ-preserving strategies.

BACKGROUND: Accurate identification of pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal canc...

Radiomics analysis of restaging MRI for detection of pathological complete response in locally advanced rectal cancer.

PURPOSE: magnetic resonance imaging (MRI)-based radiomics has emerged as a promising approach for non-invasive prediction of treatment response in rec...

Guideline-Aligned Machine Learning for Predicting Ondansetron Administration at the End of Anaesthesia: Explainable Decision Support for PONV Prophylaxis.

Artificial Intelligence (AI) and Clinical Practice Guidelines (CPGs) both aim to support clinical decision-making but may provide conflicting suggesti...

"Bioprinting of Hair: How Far We Proceeded and What Needs to be Done".

Alopecia is a common dermatologic disorder with psychological and quality-of-life impact. Current options such as PRP, topical agents, microneedling o...

Optimizing adjuvant therapy selection with machine learning after transoral surgery in HPV-related oropharyngeal cancer.

BACKGROUND: De-escalation after transoral surgery (TOS) for HPV-related oropharyngeal squamous cell carcinoma (OPSCC) requires accurate risk stratific...

Pan-cancer analysis of single-cell profiles with polygenic signals reveals genetic influences on tumor immunity across cancers.

Inherited genetic variation can weaken the ability of the immune system to detect and eliminate malignant cells, limiting the effectiveness of cancer ...

Bioinformatic and experimental analysis reveals ITGA5 as a driver of ovarian clear cell carcinoma progression and platinum resistance.

UNLABELLED: Ovarian clear cell carcinoma (OCCC) is a clinically aggressive subtype of epithelial ovarian cancer with limited therapeutic options. Here...

Tumor-preserving Deformable Registration of Longitudinal Breast DCE MRI during Neoadjuvant Chemotherapy.

Purpose To develop a deep learning-based deformable registration method for breast dynamic contrast-enhanced (DCE) MRI that preserves tumor regions wh...

Antibiotic therapy in febrile neutropenia in haematological patients: current considerations and future challenges.

The management of febrile neutropenia (FN) in oncohematological patients is undergoing a paradigm shift driven by a deeper understanding of patients' ...

Cytokine-induced killer (CIK) Cells-associated transcriptome signature reveals the potential immunomodulatory role of TNFSF14 in clear cell renal cell carcinoma.

Among adoptive immune cell therapies, cytokine-induced killer cell (CIK) therapy has demonstrated clear therapeutic relevance in multiple cancers, par...

Interpretable machine learning models for bladder cancer overall survival prediction development and external validation via SEER database and Chinese cohort analysis.

OBJECTIVE: We developed interpretable machine learning(ML) models to predict overall survival in bladder cancer patients. This approach aims to improv...