Other Cancers

ASO Special Article: Proceedings of the Inaugural Joint US-India Cancer Dialogue: Accelerating Collaboration to Advance Cancer Prevention, Early Detection, Treatment, and Care.

BACKGROUND: Cancer is a leading cause of death in both the USA and India. During Prime Minister Narendra Modi's State Visit to Washington, DC in June 2023, the two governments announced several cooperation commitments. One such commitment was the establishment of the US-India Cancer Dialogue, initiating a structured bilateral effort to address shared cancer challenges. The aim of this article is t...

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 stratification to identify patients who can safely omit adjuvant therapy without compromising survival. We evaluated whether machine-learning (ML) survival models improve postoperative risk grouping and guide adjuvant treatment selection. METHODS: Using the N...

Maximizing pancreatic carcinoma classification performance using parrot optimized vision transformer.

Pancreatic cancer is a rare kind of cancer that is detected during the final stages. This is because the symptoms are very common and also do not show...

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 ...

A multi-level attention CNN-transformer based framework for the detection of brain tumor using regional dual-score explainability.

Deep learning models for brain tumor diagnosis often lack interpretability beyond qualitative visual heatmaps. Clinicians require not only tumor local...

Immune Checkpoint Inhibitor-Associated Cardiovascular Toxicity: Mechanisms, Clinical Manifestations, and Clinical Impact.

Immune checkpoint inhibitors (ICIs) represent a class of novel anticancer agents that enhance T cell-mediated recognition and elimination of tumor cel...

Artificial intelligence in kidney cancer: a review of clinical applications across the disease spectrum.

PURPOSE OF REVIEW: This review examines recent advances (2024-2025) in the application of artificial intelligence (AI) to kidney cancer diagnosis, pro...

An integrated AI pipeline for automated cytogenetic analysis of bone marrow karyograms in hematological malignancies: A Pix2Pix enhancement and deep learning detection approach.

BACKGROUND: Conventional cytogenetic analysis remains central to the diagnosis and risk stratification of hematological malignancies but is constraine...

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...

Performance of a self-attention-based model in the task of differentiating clear cell renal cell carcinoma from other renal tumors: variable Vision Transformer (vViT).

OBJECTIVES: To examine the performance of the variable Vision Transformer (vViT) in comparison with that of convolutional neural networks (CNNs) in th...

Prognostic Significance of Baseline 18F-FDG PET/CT Parameters in Combination with an Artificial Intelligence-Based Pleural Effusion Segmentation Model for Malignant Pleural Effusion.

OBJECTIVES: We aimed to use an artificial intelligence (AI)-based pleural effusion segmentation model on baseline 18F-FDG positron emission tomography...

Single-cell analysis reveals that NRG1/3-ERBB4 signaling affects metabolic reprogramming and immune escape in Wilms tumor.

BACKGROUND: Wilms tumor presents a heterogeneous tumor microenvironment. This study aimed to characterize the tumor microenvironment and identify prog...

Precise Staging of Diabetic Retinopathy Through Machine Learning Analysis of Leakage Source Characteristics: A Radiomics-Driven Approach Integrating OCT-A and FFA.

INTRODUCTION: Evaluating retinal fundus image for diabetic retinopathy (DR) assessment is used to reduce the risk of blindness among diabetic patients...

Artificial intelligence-based vascular pattern profiling predicts prognosis and therapeutic response in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is characterized by active angiogenesis and heterogeneous vascular patterns. However, vascular pattern prof...

Development and Validation of a Deep Learning-enabled Single Breath-hold Abbreviated MRI Protocol for Hepatocellular Carcinoma Diagnosis.

Purpose To develop a deep learning-enabled single breath-hold abbreviated MRI (DL-SBH-aMRI) protocol for hepatocellular carcinoma (HCC) diagnosis. Mat...

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...

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...

PFAS is associated with perineural invasion in triple-negative breast cancer with a potential role for Cathepsin D dysregulation: a multi-omics and experimental study.

Per- and polyfluoroalkyl substances (PFAS) are persistent pollutants linked to breast cancer (BC), but their role in perineural invasion (PNI) of trip...

Targeting tumor-associated G-protein coupled receptors: beyond single-axis inhibition toward multidimensional regulation.

G protein-coupled receptors (GPCRs) serve as central hubs in tumor signal transduction and microenvironment regulation. However, their therapeutic exp...

Evaluation of 2D and 3D nnU-Net models with two-label and three-label strategies for automatic segmentation and total metabolic tumor volume estimation of metastatic differentiated thyroid carcinoma on FDG-PET/CT.

PURPOSE: To evaluate the segmentation performance and total metabolic tumor volume (TMTV) prediction accuracy of 2D and 3D nnU-Net models under two-la...