Oncology/Hematology

Integrating multimodality CT-MRI deep learning with clinical-radiological features for preoperative hepatocellular carcinoma grading: a multicenter study.

OBJECTIVE: To develop and externally validate an integrated model that combines multimodality CT-MRI deep learning with clinical and radiological features for noninvasive preoperative hepatocellular carcinoma (HCC) histologic grading, and to evaluate whether this integration outperforms single-modality imaging, the multimodality imaging model alone, and a clinical-radiological model. METHODS: In t...

Multi-omics integration and machine learning define an iron-sulfur cluster/zinc-binding protein prognostic signature in esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) is characterized by substantial intratumoral heterogeneity and poor clinical prognosis. Although metalloproteins are well-documented to drive ESCC malignant progression, incomplete functional annotation of this protein family significantly impedes the clinical translation of related research outcomes. This study reports the development and validation of a ...

NADPH oxidases in immunometabolism and disease pathology: mechanistic networks, pollutant triggers, and therapeutic frontiers.

NADPH oxidases (NOXs) have emerged as central hubs that link environmental, metabolic, and immune cues through spatially organized redox signaling. Ho...

Weighted Gene Co-Expression Network Analysis and Machine Learning Reveal that USP1 Drives Lipid Metabolism and Macrophage Polarization in Cervical Cancer Cells.

Cervical cancer is a leading preventable cause of cancer morbidity and mortality globally. Previous studies have indicated that dysregulation of the u...

Multidimensional 5-hydroxymethylcytosine features in cell-free DNA enable the detection, staging and subtyping of pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignant cancer with limited biomarkers for early detection and disease stratification. He...

Combining Statistical Modeling and Machine Learning for Prognostic Feature Selection in Cervical Cancer: A Retrospective Study Based on SEER 2004 to 2015 Data.

OBJECTIVES: Cervical cancer is a leading female malignancy with high global morbidity/mortality, and remains high recurrence risk after standard treat...

Beyond robotics: a systematic review of technology integration in direct anterior approach total hip arthroplasty.

Technology-assisted implant positioning has emerged as a strategy to improve component placement accuracy in total hip arthroplasty (THA). However, th...

CDKN3, PKP2, ADM, MS4A1, FAM83A, and DKK1 Define a Posttranslational Modification (PTM)-Associated Prognostic Model for Lung Adenocarcinoma.

INTRODUCTION: Posttranslational modification (PTM) plays an important role in protein regulation and may influence tumor initiation and progression. H...

MRI-Based Deep Learning Guides Multi-Omics Discovery of NBPF4 as a Therapeutic Target for Breast Cancer Lymph Node Metastasis.

Deep learning models are increasingly used to analyze medical images, but their "black box" nature makes it hard to understand the underlying biology ...

Microbiome-Driven Risk Stratification and Biotherapeutics in Radiation Enteritis.

Radiation enteritis (RE) is a severe, dose-limiting complication of cancer radiotherapy that affects the therapeutic outcomes of patients and their qu...

AI for tumour proportion scoring of programmed death-ligand 1 immunohistochemistry in non-small cell lung cancer: a review of commercial and non-commercial tools.

Programmed death-ligand 1 (PD-L1) expression, commonly quantified as tumour proportion score (TPS), is a key biomarker guiding immunotherapy in non-sm...

EffiSegNet: Advances in Automated Segmentation of Actinic Cheilitis and Lip Squamous Cell Carcinoma in Clinical Photographs.

BACKGROUND: Actinic cheilitis (AC) is a potentially malignant oral disorder linked to lip squamous cell carcinoma (LSCC). Clinical diagnosis is hinder...

The Lymph Node Ratio as a Predictive Biomarker for Individualized Benefit from Adjuvant Chemotherapy in Gastric Cancer: A Retrospective Cohort and Causal Machine Learning Study.

BACKGROUND: Optimizing adjuvant chemotherapy (AC) for gastric cancer (GC) remains challenging due to patient heterogeneity. While the lymph node ratio...

Neoantigen in cancer immunotherapy: mechanism, therapeutic strategies and future perspectives.

Neoantigens are tumor-specific antigens resulting from genetic, transcriptomic, and proteomic changes, making them a promising avenue for personalized...

Precision Pancreatic Cancer Care: From Early Detection to Adaptive Precision Oncology.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) remains highly lethal, and genome-centric precision oncology shows limited impact due to low actio...

Integrative single-cell and machine-learning analysis identifies LGALS1 as a macrophage-associated diagnostic and prognostic biomarker in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is characterized by marked heterogeneity and an immunosuppressive microenvironment in which tumor-associate...

Integrative single-cell sequencing and machine learning analyses identify chronic inflammation and cancer-associated fibroblast subsets as drivers of adverse outcomes in ovarian cancer.

BACKGROUND: Ovarian cancer remains the deadliest gynecological malignancy, with neoadjuvant chemotherapy (NACT) often leaving residual fibroblast-enri...

Organ-at-risk contouring education in the era of AI-assisted practice: Insights from an Australian undergraduate radiation therapy program.

INTRODUCTION: The integration of artificial intelligence (AI) tools into radiation therapy workflows offers significant opportunities to improve effic...

Machine Learning-Guided Personalized Immunochemotherapy Strategies in Intrahepatic Cholangiocarcinoma.

BACKGROUND & AIMS: Immunochemotherapy (IO-chemo) has become standard care for patients with unresectable intrahepatic cholangiocarcinoma (iCCA), but b...

Super-resolution MRI and 2.5D deep learning for intratumoral-peritumoral radiomics in preoperative prediction of rectal cancer perineural invasion.

OBJECTIVES: Preoperative prediction of perineural invasion (PNI) in rectal cancer (RC) is challenging due to limited MRI resolution and the neglect of...