Oncology/Hematology

A novel Convolutional Shuffle Attention Xtreme Gradient Boost Network for improved lung cancer detection using computed tomography images.

Lung cancer is a severe and life-threatening type of cancer that originates in the lung tissues. Computed Tomography (CT) image emerges as the primary diagnostic tool for identifying lung cancer. However, manual interpretation of CT images necessitates the development of automated techniques. Hence, this article proposes a hybrid model, Convolutional Shuffle Attention Xtreme Gradient Boost Network...

Photon-counting detector computed tomography in thoracic oncology: revolutionizing tumor imaging through precision and detail.

Photon-counting detector computed tomography (PCD-CT) is an emerging imaging technology that promises to overcome the limitations of conventional energy-integrating detector (EID)-CT, particularly in thoracic oncology. This narrative review summarizes technical advances and clinical applications of PCD-CT in the thorax with emphasis on spatial resolution, dose-image-quality balance, and intrinsic ...

Radiomics-based artificial intelligence (AI) models in colorectal cancer (CRC) diagnosis, metastasis detection, prognosis, and treatment response prediction.

Colorectal cancer (CRC) is the third most common cause of cancer-related morbidity and mortality in the world. Radiomics and radiogenomics are utilize...

Refining the Classroom: The Self-Supervised Professor Model for Improved Segmentation of Locally Advanced Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of cancer-related deaths, with accurate staging being critical for treatment planning. Auto...

Performance of a Novel Frameless and Maskless Robotic Head Motion Compensation System for Stereotactic Radiosurgery in a Realistic Clinical Environment with Healthy Volunteers.

PURPOSE: Stereotactic radiosurgery (SRS) is a nonsurgical method for treating brain abnormalities and small tumors. Traditional high-accuracy SRS requ...

HAMIL: Hierarchical Attention Multi-Instance Learning for Label-Free Colorectal Cancer Typing.

Colorectal cancer (CRC) is one of the leading gastrointestinal malignancies, underscoring the need for an in-depth analysis of the cellular within the...

Bias or Best Fit? A Comparative Analysis of the SEER and NCDB Data Sets in Single-model Machine Learning for Predicting Osteosarcoma Survival Outcomes.

BACKGROUND: Machine-learning models are increasingly used in orthopaedic oncology to predict survival outcomes for patients with osteosarcoma. Typical...

Multitask radioclinical decision stratification in non-metastatic colon cancer: integrating MMR status, pT staging, and high-risk pathological factors.

OBJECTIVES: Constructing a multi-task global decision support system based on preoperative enhanced CT features to predict the mismatch repair (MMR) s...

Long-Term Outcomes and Cost-Effectiveness of Artificial Intelligence for Breast Cancer Screening: A Modeling Study.

OBJECTIVES: Artificial intelligence (AI)-assisted breast cancer screening may improve diagnostic accuracy; however, the long-term health outcomes and ...

Prognosis models for nasopharyngeal carcinoma recurrences by using tabu search algorithm.

Cancer is a significant public health issue that has a global impact. Significant mortality rates have already been observed due to this disease, and ...

Lightweight Transfer Learning Models for Multi-Class Brain Tumor Classification: Glioma, Meningioma, Pituitary Tumors, and No Tumor MRI Screening.

Glioma, pituitary tumors, and meningiomas constitute the major types of primary brain tumors. The challenge in achieving a definitive diagnosis stem f...

Exosomal biomarkers in cancer: Insights from Multi-OMIC approaches.

Extracellular vesicles, particularly exosomes, are emerging as powerful tools in cancer research due to their role in intercellular communication and ...

Dose Reduction in 4-Dimensional Computed Tomography Imaging: Breathing Signal-Guided Deep Learning-Driven Data Acquisition.

PURPOSE: Four-dimensional computed tomography (4D CT) imaging is essential for radiation therapy planning in thoracic tumors. However, current protoco...

Current State-of-the-Art 3D MRI Sequences for Assessing Bone Morphology with Emphasis on Cranial and Spinal Imaging: A Narrative Review.

Traditionally, CT has been the go-to method for visualizing bone structures, while MRI has been preferred for assessing soft tissues, because structur...

Artificial intelligence-assisted PET imaging for predicting neoadjuvant chemotherapy response in breast cancer: a systematic review and meta-analysis.

PURPOSE: This study aims to evaluate the performance of artificial intelligence (AI)-assisted PET imaging in predicting neoadjuvant chemotherapy (NAC)...

Habitat-aware radiomics and adaptive 2.5D deep learning predict treatment response and long-term survival in ESCC patients undergoing neoadjuvant chemoimmunotherapy.

PURPOSE: Current radiomic approaches inadequately resolve spatial intratumoral heterogeneity (ITH) in esophageal squamous cell carcinoma (ESCC), limit...

Nivolumab plus ipilimumab for potentially resectable hepatocellular carcinoma: Long-term efficacy and biomarker exploration.

BACKGROUND & AIMS: Immune checkpoint inhibitor-based combination therapy has demonstrated high objective response rates in patients with hepatocellula...

A Metabolism-Driven Prognostic Model and PSMD14-SP1-GYS1 Axis Reveal Therapeutic Vulnerabilities in Melanoma.

Melanoma is a highly aggressive cutaneous malignancy characterized by a strong propensity for metastasis and therapy resistance, with its progression ...

ToBaFu: Topology-based fusion model for classification of two-dimensional cancer images.

Medical images play a pivotal role in disease diagnosis. Numerous studies on cancer image analysis focus on end-to-end deep neural networks, neglectin...

Prospective clinical validation of a novel artificial intelligence system for real-time detection of solid pancreatic masses during endoscopic ultrasonography.

Endoscopic ultrasonography (EUS) is the most sensitive modality for accurately establishing a tissue diagnosis in patients with solid pancreatic masse...