Breast Cancer

Clinical value of ultra-low-dose chest CT with deep learning image reconstruction in the detection and characterization of pulmonary nodules.

OBJECTIVE: To evaluate the clinical value of ultra-low-dose CT (ULDCT) with deep learning image reconstruction (DLIR) in the diagnosis of pulmonary nodules. MATERIALS AND METHODS: This was a prospective study that included 115 patients with suspected pulmonary nodules who provided informed consent to participate. All patients were examined by both standard-dose CT (SDCT) and ULDCT. The SDCT images...

Gene driven analytical learning model for accurate breast cancer diagnosis.

Patients diagnosed with breast cancer exhibit a diverse range of prognostic outcomes due to the varied nature of the disease across different patient groups. To address this complexity and enhance prognostic predictions based on gene expression data from breast cancer samples, this study has developed an integrated deep learning method that combines Convolutional Neural Networks (CNN) with Bidirec...

Artificial Intelligence Integration in Radiation Therapy Education: A Multi-Modal Approach.

INTRODUCTION: Critical workforce shortages in radiation oncology have led tertiary institutions to rapidly expand their radiation therapy (RT) student...

Minimally invasive surgery for advanced-stage ovarian cancer: a review of evidence and emerging paradigms.

Minimally invasive surgery has emerged as a promising approach to the management of advanced-stage epithelial ovarian cancer, particularly in the sett...

Cluster-Based MR Radiomics Model for Predicting Induction Chemotherapy Response in Nasopharyngeal Carcinoma.

RATIONALE AND OBJECTIVES: To develop a cluster-specific magnetic resonance (MR) radiomics model for predicting induction chemotherapy (ICT) response i...

Prediction of Neoadjuvant Chemotherapy Efficacy for Locally Advanced Nasopharyngeal Carcinoma Using MRI-Based Deep Learning Features Combined with Vision Transformer.

RATIONALE AND OBJECTIVES: Predicting neoadjuvant chemotherapy (NACT) efficacy is vital for advanced nasopharyngeal carcinoma (LA-NPC) management. Exis...

Subtype-specific enhancer RNAs define transcriptional regulators and prognosis in breast cancers.

Gene expression is tightly controlled by DNA elements called enhancers by associating with lineage-specific transcription factors. These enhancers tra...

Thermal analysis of flat plate solar air heater system with radiation reflectors and W-shaped roughness: artificial neural network & machine learning approach.

The lower thermal behavior of solar-based thermal systems limits the contribution of solar systems to meet current energy demand of industries. The Fl...

Emerging Trends and Innovations in Radiologic Diagnosis of Thoracic Diseases.

Over the past decade, Investigative Radiology has published numerous studies that have fundamentally advanced the field of thoracic imaging. This revi...

Diagnostic and Technological Advances in Magnetic Resonance (Focusing on Imaging Technique and the Gadolinium-Based Contrast Media), Computed Tomography (Focusing on Photon Counting CT), and Ultrasound-State of the Art.

Magnetic resonance continues to evolve and advance as a critical imaging modality for disease diagnosis and monitoring. Hardware and software advances...

Integrated Single-Cell and Bulk Transcriptome Analysis Reveals the Prognostic Significance and Immune Regulatory Mechanisms of Sialylation in Hepatocellular Carcinoma.

Liver Hepatocellular Carcinoma (LIHC) is a high-mortality primary liver cancer. Its treatment and prognosis are highly dependent on disease stage and ...

When AI joins the table: evaluating large language model performance in soft tissue sarcoma tumor board decisions.

OBJECTIVES: Multidisciplinary tumor boards (MDTs) are critical for the personalized management of soft tissue sarcomas (STS), but they are limited by ...

Development and validation of an interpretable machine learning model for postoperative radiotherapy decision-making in ypN0 breast cancer after neoadjuvant chemotherapy: a real-world study.

OBJECTIVE: The subset of breast cancer patients with pathologic node-negative status (ypN0) after neoadjuvant chemotherapy (NAC) who benefit from post...

Integrated machine learning risk model for predicting radiation pneumonitis in lung cancer patients with interstitial lung disease.

BACKGROUND: Radiation pneumonitis (RP) is a serious complication in lung cancer patients with pre-existing interstitial lung disease (ILD) undergoing ...

Thermal signatures in breast cancer: Deciphering latent biomarkers through deep learning and explainable AI.

Breast cancer, characterized by its aggressive pro-gression and high mortality rates, continues to be among the most common types of cancer. While ear...

Harnessing Hitchhikers: Outer Membrane Vesicles Leading the Way in Advanced Cancer Therapeutics and Diagnostic- A Review.

Cancer therapy is increasingly shaped by delivery platforms designed to overcome the limitations of conventional chemotherapy and radiotherapy. Among ...

Deep Learning-Enhanced Biomarker Interpretation on Cytology Cell Blocks - Foundations and Emerging Opportunities in Spatial Pathobiology.

Cytology cell block specimens are essential diagnostic materials in patients with advanced-stage malignancy and often represent the only available sub...

Diagnostic Imaging Pathways in Peripheral Artery Disease: A Comparative Review of Duplex Ultrasound Through Computed Tomography Angiography.

Peripheral artery disease (PAD) is a major global health challenge, affecting more than 200 million people worldwide and an estimated 8 to 12 million ...

Artificial intelligence model for cardiovascular disease risk prediction in breast cancer patients using electronic health records and computed tomography scans.

BACKGROUND AND PURPOSE: Cardiovascular disease (CVD) is the leading cause of death globally [1] as well as the leading cause of death among cancer sur...

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...