Other Cancers

Prediction of Sleep Stages Via Deep Learning Using Smartphone Audio Recordings in Home Environments: Model Development and Validation.

BACKGROUND: The growing public interest and awareness regarding the significance of sleep is driving...

Deep learning-based radiomics: pacing immunotherapy in lung cancer.

Fully Automated Tumor Bud Assessment in Hematoxylin and Eosin-Stained Whole Slide Images of Colorectal Cancer.

Tumor budding (TB), the presence of single cells or small clusters of up to 4 tumor cells at the inv...

Deep learning model accurately classifies metastatic tumors from primary tumors based on mutational signatures.

Metastatic propagation is the leading cause of death for most cancers. Prediction and elucidation of...

Differential diagnosis of benign and malignant vertebral compression fractures: Comparison and correlation of radiomics and deep learning frameworks based on spinal CT and clinical characteristics.

PURPOSE: Differentiating benign from malignant vertebral compression fractures (VCFs) is a diagnosti...

Deep learning for segmentation of the cervical cancer gross tumor volume on magnetic resonance imaging for brachytherapy.

BACKGROUND: Segmentation of the Gross Tumor Volume (GTV) is a crucial step in the brachytherapy (BT)...

Three-Port Transoral Robotic Thyroidectomy in Papillary Thyroid Carcinoma.

BACKGROUND: Although transoral thyroidectomy has become popular in thyroid surgery, transoral roboti...

Prediction of MYCN Gene Amplification in Pediatric Neuroblastomas: Development of a Deep Learning-Based Tool for Automatic Tumor Segmentation and Comparative Analysis of Computed Tomography-Based Radiomics Features Harmonization.

OBJECTIVE: MYCN oncogene amplification is closely linked to high-grade neuroblastoma with poor progn...

Use of systems with deep learning and machine learning for the detection and classification of malignant vs. benign spinal fractures with MRI: can deep/machine learning help us further for detection and characterization?

Non-invasive grading of brain tumors using online support vector machine with dynamic fuzzy rule-based parameters optimization.

Non-invasive grading of brain tumors provides a valuable understanding of tumor growth that helps ch...

Domain-specific transfer learning in the automated scoring of tumor-stroma ratio from histopathological images of colorectal cancer.

Tumor-stroma ratio (TSR) is a prognostic factor for many types of solid tumors. In this study, we pr...

Multiscale deep learning framework captures systemic immune features in lymph nodes predictive of triple negative breast cancer outcome in large-scale studies.

The suggestion that the systemic immune response in lymph nodes (LNs) conveys prognostic value for t...

Biologically Interpretable Deep Learning To Predict Response to Immunotherapy In Advanced Melanoma Using Mutations and Copy Number Variations.

Only 30-40% of advanced melanoma patients respond effectively to immunotherapy in clinical practice,...

Deep Learning-Based Computer-Aided Diagnosis for Breast Lesion Classification on Ultrasound: A Prospective Multicenter Study of Radiologists Without Breast Ultrasound Expertise.

Computer-aided diagnosis (CAD) systems for breast ultrasound interpretation have been primarily eva...

Deep convolutional neural network algorithm for the automatic segmentation of oral potentially malignant disorders and oral cancers.

This study aimed to develop an algorithm to automatically segment the oral potentially malignant dis...

The artificial intelligence and machine learning in lung cancer immunotherapy.

Since the past decades, more lung cancer patients have been experiencing lasting benefits from immun...

Reconstructive Allograft Preparation for Long Bone Intercalary Segments After Tumor Resections: Toronto Sarcoma Protocol.

BACKGROUND: The Reconstructive Allograft Preparation by Toronto Sarcoma (RAPTORS) protocol is reliab...

Deep learning-based markerless lung tumor tracking in stereotactic radiotherapy using Siamese networks.

BACKGROUND: Radiotherapy (RT) is involved in about 50% of all cancer patients, making it a very impo...

AI-assisted compressed sensing and parallel imaging sequences for MRI of patients with nasopharyngeal carcinoma: comparison of their capabilities in terms of examination time and image quality.

OBJECTIVE: To compare examination time and image quality between artificial intelligence (AI)-assist...

Prediction of Bone Marrow Biopsy Results From MRI in Multiple Myeloma Patients Using Deep Learning and Radiomics.

OBJECTIVES: In multiple myeloma and its precursor stages, plasma cell infiltration (PCI) and cytogen...

A Scalable Radiomics- and Natural Language Processing-Based Machine Learning Pipeline to Distinguish Between Painful and Painless Thoracic Spinal Bone Metastases: Retrospective Algorithm Development and Validation Study.

BACKGROUND: The identification of objective pain biomarkers can contribute to an improved understand...