Oncology/Hematology

Rapid and precise detection of cancers via label-free SERS and deep learning.

Early, express, and reliable detection of cancer can provide a favorable prognosis and decrease mortality. Tumor biomarkers have been proven to be closely related to tumor occurrence and development. Conventional tumor biomarker detection based on genomic, proteomic, and metabolomic methods is time and equipment-consuming and always needs a specific target marker. Surface-enhanced Raman scattering...

AI-Driven sleep staging from actigraphy and heart rate.

Sleep is an important indicator of a person's health, and its accurate and cost-effective quantification is of great value in healthcare. The gold standard for sleep assessment and the clinical diagnosis of sleep disorders is polysomnography (PSG). However, PSG requires an overnight clinic visit and trained technicians to score the obtained multimodality data. Wrist-worn consumer devices, such as ...

Self-Propelled Janus Nanocatalytic Robots Guided by Magnetic Resonance Imaging for Enhanced Tumor Penetration and Therapy.

Biomedical micro/nanorobots as active delivery systems with the features of self-propulsion and controllable navigation have made tremendous progress ...

Neurosurgical skills analysis by machine learning models: systematic review.

Machine learning (ML) models are being actively used in modern medicine, including neurosurgery. This study aimed to summarize the current application...

Coronary Computed Tomography Angiography with Deep Learning Image Reconstruction: A Preliminary Study to Evaluate Radiation Exposure Reduction.

Coronary computed tomography angiography (CCTA) is a medical imaging technique that produces detailed images of the coronary arteries. Our work focuse...

One-Step Robot-Assisted Complete Urinary Tract Extirpation in Man with End-Stage Renal Disease on Dialysis: The First Case Report.

Urothelial carcinoma (UC) could be observed in urinary bladder (UBUC) and upper urinary tracts (UTUC). In the National Comprehensive Cancer Network gu...

A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data.

BACKGROUND: There is an increasing interest in the use of Deep Learning (DL) based methods as a supporting analytical framework in oncology. However, ...

Artificial intelligence-based prediction of cervical lymph node metastasis in papillary thyroid cancer with CT.

OBJECTIVES: To develop an artificial intelligence (AI) system for predicting cervical lymph node metastasis (CLNM) preoperatively in patients with pap...

Emerging uses of artificial intelligence in breast and axillary ultrasound.

Breast ultrasound is a valuable adjunctive tool to mammography in detecting breast cancer, especially in women with dense breasts. Ultrasound also pla...

Serum Concentration of MMP-9 as a Predictive Biomarker for the Progression of Oral Cancer.

BACKGROUND: Oral cancer is the most prominent cancer subtype among all head and neck cancers, the incidence and prevalence of which has been consisten...

Deep learning-based classification of organs at risk and delineation guideline in pelvic cancer radiation therapy.

Deep learning (DL) models for radiation therapy (RT) image segmentation require accurately annotated training data. Multiple organ delineation guideli...

Deep learning-based protoacoustic signal denoising for proton range verification.

Proton therapy is a type of radiation therapy that can provide better dose distribution compared to photon therapy by delivering most of the energy at...

A Novel Approach for Brain Tumor Classification Using an Ensemble of Deep and Hand-Crafted Features.

One of the most severe types of cancer caused by the uncontrollable proliferation of brain cells inside the skull is brain tumors. Hence, a fast and a...

Clinical approaches for integrating machine learning for patients with lymphoma: Current strategies and future perspectives.

Machine learning (ML) approaches have been applied in the diagnosis and prediction of haematological malignancies. The consideration of ML algorithms ...

Predicting the survival of patients with pancreatic neuroendocrine neoplasms using deep learning: A study based on Surveillance, Epidemiology, and End Results database.

BACKGROUND: The study aims to evaluate the performance of three advanced machine learning algorithms and a traditional Cox proportional hazard (CoxPH)...

A deep learning approach for automatic delineation of clinical target volume in stereotactic partial breast irradiation (S-PBI).

Accurate and efficient delineation of the clinical target volume (CTV) is of utmost significance in post-operative breast cancer radiotherapy. However...

Letter to the Editor - Update from Ukraine: Development of the Cloud-based Platform for Patient-centered Telerehabilitation of Oncology Patients with Mathematical-related Modeling.

This Letter to the Editor provides an update on the research from the Glushkov Institute of Cybernetics of the National Academy of Sciences of Ukraine...

Comparison of surgical completeness in patients operated on conventional open total thyroidectomy (OT) or trans-axillary robot-assisted total thyroidectomy (RATT) by a single axillary approach.

Trans-axillary robot-assisted total thyroidectomy (RATT) is nowadays worldwide accepted but the completeness obtained by RATT is still debated. The Ai...

Benign vs malignant vertebral compression fractures with MRI: a comparison between automatic deep learning network and radiologist's assessment.

OBJECTIVE: To test the diagnostic performance of a deep-learning Two-Stream Compare and Contrast Network (TSCCN) model for differentiating benign and ...

Robot-assisted Radical Cystectomy Versus Open Radical Cystectomy: A Systematic Review and Meta-analysis of Perioperative, Oncological, and Quality of Life Outcomes Using Randomized Controlled Trials.

CONTEXT: Differences in recovery, oncological, and quality of life (QoL) outcomes between open radical cystectomy (ORC) and robot-assisted radical cys...