Other Cancers

Assessing the completeness of reporting in imaging studies using artificial neural network models for cancer diagnosis: Adherence to the TRIPOD-AI guideline.

BACKGROUND: Developments in artificial neural networks (ANNs) offer significant promise for cancer screening and risk prediction, with the potential to improving patient outcomes. Ensuring complete and transparent reporting of study methodologies is important for ensuring model reproducibility. This review aims to evaluate the completeness of reporting of imaging studies that utilise ANN models fo...

Targeting signaling pathways in lymphoma: From molecular mechanisms to clinical breakthroughs.

The treatment paradigm for lymphoma, a highly heterogeneous group of hematologic malignancies, has been revolutionized by the development of therapies targeting oncogenic signaling pathways. This shift from conventional chemotherapy to precision medicine is driven by a deep molecular understanding of the pathways that govern lymphoma cell survival and proliferation. This review comprehensively sur...

Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy.

Cancer immunotherapy, which leverages the immune system to combat tumor cells, has made significant advancements in oncology treatment in recent years...

Multi-Scale Mapping of Gene Expression from Whole-slide Images for Identifying Phenotype-Associated Subpopulations.

Discovery of phenotype-associated subpopulations is critical for targeted therapies and prognostic biomarker discovery, which requires multi-scale gen...

Comprehensive assessment of postoperative metachronous liver metastasis risk in colon cancer based on inflammatory indicators: a multicenter prospective study.

OBJECTIVE: Insidiousness is a hallmark of metachronous liver metastasis. Owing to the absence of a comprehensive machine-learning model integrating sy...

Comment on "Artificial Intelligence for predicting post-excision recurrence and malignant progression in oral potentially malignant disorders: a retrospective cohort study".

An artificial intelligence-assisted comprehensive model for predicting lymph node metastasis in superficial esophageal squamous cell carcinoma: a diagnostic study.

BACKGROUND: Superficial esophageal squamous cell carcinoma (ESCC) is increasingly detected due to enhanced endoscopic surveillance, yet accurately pre...

Research trends of tumor-associated macrophages in colorectal cancer: a bibliometric analysis.

BACKGROUND: Colorectal cancer (CRC) represents the third most prevalent malignancy worldwide and accounts for the second-highest cancer-related mortal...

Phosphoenolpyruvate carboxykinase 2 as a prognostic biomarker: expression and clinical significance in Group 3 and Group 4 medulloblastoma.

PURPOSE: Despite Phosphoenolpyruvate carboxykinase 2 (PCK2) has attracted growing attention as a potential biomarker in cancer research, its role in m...

Combination Immunotherapy as a Promising Strategy to Overcome Immunotherapy Resistance: From Emergence to Next-Generation Approaches.

Advances in immunotherapy, particularly with immune checkpoint inhibitors, have improved clinical outcomes in various cancer subtypes; however, resist...

Automated Delineation of Putative Non-Contrast-Enhancing Tumor in Glioblastoma: Prognostic Insights.

BACKGROUND: Precise delineation of non-contrast-enhancing tumor (nCET) in glioblastoma (GB) is critical for maximal safe resection, yet routine imagin...

INHBA: a mitochondrial-related pan-cell death gene associated with the prognosis and immunity of OSCC.

Oral squamous cell carcinoma (OSCC) is a malignant tumor characterized by both clinical and molecular heterogeneity. Its rising global incidence and m...

Leveraging infarct topography for early warning: a robust model for predicting malignant cerebral edema after endovascular treatment in acute ischemic stroke.

BACKGROUND: The early prediction of malignant cerebral edema (MCE) following endovascular therapy for acute ischemic stroke is of paramount importance...

Development and interpretability of an XGBoost model to predict high grade cervical intraepithelial neoplasia.

TRKB-based signature identifies high-risk squamous cell carcinoma cases and TRKB blockade reprograms tumor and stromal cells toward suppressive phenotypes.

BACKGROUND: Cutaneous squamous cell carcinoma (cSCC) is a common age-related cancer, with a subset prone to recurrence and metastasis. Currently, no u...

Ultrasound-Responsive Serotonylation and Hallmark Pathway Gene Signatures Reveal Tumor Microenvironment Vulnerabilities and Prognostic Subtypes in Colorectal Cancer.

BACKGROUND: Focused ultrasound, low-intensity focused ultrasound, and microbubble-enhanced sonoporation are examples of ultrasound-based cancer therap...

A machine learning model integrating circulating Temra cell transcriptional profiles to predict immunotherapy efficacy.

BACKGROUND: While immunotherapy has transformed treatment paradigms for cancer, durable response rates remain below 30%. Currently available biomarker...

[Utilizing machine learning and SHAP analysis to develop a prognostic survival prediction model for gastric cancer patients following radical gastrectomy].

Objective: To construct a prediction model and website for the overall survival (OS) of gastric cancer patients after radical gastrectomy based on SHa...

Nomogram based on the pre-treatment systemic oxidative stress score to predict the prognosis of surgically treated patients with oral squamous cell carcinoma: competing risk model analysis.

The aim of this study was to explore the prognostic predictive importance of the pre-treatment systemic oxidative stress score (SOS) in surgically tre...

Advanced computational analysis in metagenomic studies to support precision medicine.

BACKGROUND: The human microbiome has been linked to host health and is suggested to play a direct role in the onset of certain human diseases, as well...