Oncology/Hematology

Deep Learning-Based Synthetic Computed Tomography for Low-Field Brain Magnetic Resonance-Guided Radiation Therapy.

PURPOSE: Magnetic resonance (MR)-guided radiation therapy enables online adaptation to address intra- and interfractional changes. To address the need of high-fidelity synthetic computed tomography (synCT) required for dose calculation, we developed a conditional generative adversarial network for synCT generation from low-field MR imaging in the brain.

AutoLNMNet: Automated Network for Estimating Lymph-Node Metastasis in EGC Using a Pyramid Vision Transformer and Data Derived From Multiphoton Microscopy.

Lymph-node status is important in decision-making during early gastric cancer (EGC) treatment. Currently, endoscopic submucosal dissection is the mainstream treatment for EGC. However, it is challenging for even experienced endoscopists to accurately diagnose and treat EGC. Multiphoton microscopy can extract the morphological features of collagen fibers from tissues. The characteristics of collage...

Multi-instance learning for identifying high-risk subregions associated with synchronous distant metastasis in clear cell renal cell carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is one of the most common histological subtypes of renal tumors.

Development of learning-based predictive models for radiation-induced atrial fibrillation in non-small cell lung cancer patients by integrating patient-specific clinical, dosimetry, and diagnostic information.

BACKGROUND AND PURPOSE: Radiotherapy (RT) in non-small cell lung cancer (NSCLC) can induce cardiac adverse events, including atrial fibrillation (AF),...

Machine learning models based on CT radiomics features for distinguishing benign and malignant vertebral compression fractures in patients with malignant tumors.

BACKGROUND: Radiomics has become an important tool for distinguishing benign and malignant vertebral compression fractures (VCFs). It is more clinical...

Preoperative markers for identifying CT ≤2 cm solid nodules of lung adenocarcinoma based on image deep learning.

BACKGROUND: The solid pattern is a highly malignant subtype of lung adenocarcinoma. In the current era of transitioning from lobectomy to sublobar res...

A machine learning approach in a monocentric cohort for predicting primary refractory disease in Diffuse Large B-cell lymphoma patients.

INTRODUCTION: Primary refractory disease affects 30-40% of patients diagnosed with DLBCL and is a significant challenge in disease management due to i...

Deep Learning Approaches for Brain Tumor Detection and Classification Using MRI Images (2020 to 2024): A Systematic Review.

Brain tumor is a type of disease caused by uncontrolled cell proliferation in the brain leading to serious health issues such as memory loss and motor...

A Survey of Perspectives and Educational Needs of Canadian Oncology Residents on Artificial Intelligence.

This study evaluated the perspectives and educational needs of Canadian oncology residents with regard to artificial intelligence (AI) in medicine, ex...

scCrab: A Reference-Guided Cancer Cell Identification Method based on Bayesian Neural Networks.

Cancer is a significant global public health concern, where early detection can greatly enhance curative outcomes. Therefore, the identification of ca...

Augmented histopathology: Enhancing colon cancer detection through deep learning and ensemble techniques.

Colon cancer poses a significant threat to human life with a high global mortality rate. Early and accurate detection is crucial for improving treatme...

Prediction of Lesion-Based Treatment Response after Two Cycles of Lu-177 Prostate Specific Membrane Antigen Treatment in Metastatic Castration-Resistant Prostate Cancer Using Machine Learning.

INTRODUCTION: Lutetium-177 (Lu-177) prostate-specific membrane antigen (PSMA) therapy is a radionuclide treatment that prolongs overall survival in me...

PViT-AIR: Puzzling vision transformer-based affine image registration for multi histopathology and faxitron images of breast tissue.

Breast cancer is a significant global public health concern, with various treatment options available based on tumor characteristics. Pathological exa...

Retrospective Analysis of Radiofrequency Ablation in Patients with Small Solitary Hepatocellular Carcinoma: Survival Outcomes and Development of a Machine Learning Prognostic Model.

BACKGROUND AND OBJECTIVE: The effectiveness of radiofrequency ablation (RFA) in improving long-term survival outcomes for patients with a solitary hep...

Machine learning and biological validation identify sphingolipids as potential mediators of paclitaxel-induced neuropathy in cancer patients.

BACKGROUND: Chemotherapy-induced peripheral neuropathy (CIPN) is a serious therapy-limiting side effect of commonly used anticancer drugs. Previous st...

Machine learning optimized DriverDetect software for high precision prediction of deleterious mutations in human cancers.

The detection of cancer-driving mutations is important for understanding cancer pathology and therapeutics development. Prediction tools have been cre...

Precision HER2: a comprehensive AI system for accurate and consistent evaluation of HER2 expression in invasive breast Cancer.

BACKGROUND: With the development of novel anti-HER2 targeted drugs, such as ADCs, it has become increasingly important to accurately interpret HER2 ex...

Personalized prediction of immunotherapy response in lung cancer patients using advanced radiomics and deep learning.

BACKGROUND: Lung cancer (LC) is a leading cause of cancer-related mortality, and immunotherapy (IO) has shown promise in treating advanced-stage LC. H...

Exploring the potential of structure-based deep learning approaches for T cell receptor design.

Deep learning methods, trained on the increasing set of available protein 3D structures and sequences, have substantially impacted the protein modelin...

Integrating machine learning to advance epitope mapping.

Identifying epitopes, or the segments of a protein that bind to antibodies, is critical for the development of a variety of immunotherapeutics and dia...