Oncology/Hematology

A novel radiomics approach for predicting TACE outcomes in hepatocellular carcinoma patients using deep learning for multi-organ segmentation.

Transarterial chemoembolization (TACE) represent the standard of therapy for non-operative hepatocellular carcinoma (HCC), while prediction of long term treatment outcomes is a complex and multifactorial task. In this study, we present a novel machine learning approach utilizing radiomics features from multiple organ volumes of interest (VOIs) to predict TACE outcomes for 252 HCC patients. Unlike ...

Immune Cell-Based Microrobots for Remote Magnetic Actuation, Antitumor Activity, and Medical Imaging.

Translating medical microrobots into clinics requires tracking, localization, and performing assigned medical tasks at target locations, which can only happen when appropriate design, actuation mechanisms, and medical imaging systems are integrated into a single microrobot. Despite this, these parameters are not fully considered when designing macrophage-based microrobots. This study presents livi...

A non-invasive artificial intelligence model for identifying axillary pathological complete response to neoadjuvant chemotherapy in breast cancer: a secondary analysis to multicenter clinical trial.

BACKGROUND: This study aims to develop a stacking model for accurately predicting axillary lymph node (ALN) response to neoadjuvant chemotherapy (NAC)...

Skin-CAD: Explainable deep learning classification of skin cancer from dermoscopic images by feature selection of dual high-level CNNs features and transfer learning.

Skin cancer (SC) significantly impacts many individuals' health all over the globe. Hence, it is imperative to promptly identify and diagnose such con...

Machine learning identifies activation of RUNX/AP-1 as drivers of mesenchymal and fibrotic regulatory programs in gastric cancer.

Gastric cancer (GC) is the fifth most common cancer worldwide and is a heterogeneous disease. Among GC subtypes, the mesenchymal phenotype (Mes-like) ...

Machine Learning-Enhanced Extraction of Biomarkers for High-Grade Serous Ovarian Cancer from Proteomics Data.

Comprehensive biomedical proteomic datasets are accumulating exponentially, warranting robust analytics to deconvolute them for identifying novel biol...

Identification of metastasis-related genes for predicting prostate cancer diagnosis, metastasis and immunotherapy drug candidates using machine learning approaches.

BACKGROUND: Prostate cancer (PCa) is the second leading cause of tumor-related mortality in men. Metastasis from advanced tumors is the primary cause ...

Artificial intelligence in radiotherapy: Current applications and future trends.

Radiation therapy has dramatically changed with the advent of computed tomography and intensity modulation. This added complexity to the workflow but ...

Integrative machine learning and neural networks for identifying PANoptosis-related lncRNA molecular subtypes and constructing a predictive model for head and neck squamous cell carcinoma.

PURPOSE: PANoptosis is considered a novel type of cell death that plays important roles in tumor progression. In this study, we applied machine learni...

Development and Validation Study of the Prognostic Impact of Deep Learning-Determined Myxoid Stroma After Neoadjuvant Chemotherapy in Patients with Esophageal Squamous Cell Carcinoma.

PURPOSE: This study was designed to investigate the prognostic significance of artificial intelligence (AI)-based quantification of myxoid stroma in p...

Prediction of hepatic metastasis in esophageal cancer based on machine learning.

This study aimed to establish a machine learning (ML) model for predicting hepatic metastasis in esophageal cancer. We retrospectively analyzed patien...

Deep learning models for predicting the survival of patients with medulloblastoma based on a surveillance, epidemiology, and end results analysis.

Medulloblastoma is a malignant neuroepithelial tumor of the central nervous system. Accurate prediction of prognosis is essential for therapeutic deci...

Learning and depicting lobe-based radiomics feature for COPD Severity staging in low-dose CT images.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a prevalent and debilitating respiratory condition that imposes a significant healthcare b...

scHolography: a computational method for single-cell spatial neighborhood reconstruction and analysis.

Spatial transcriptomics has transformed our ability to study tissue complexity. However, it remains challenging to accurately dissect tissue organizat...

Development of a risk prediction model for radiation dermatitis following proton radiotherapy in head and neck cancer using ensemble machine learning.

PURPOSE: This study aims to develop an ensemble machine learning-based (EML-based) risk prediction model for radiation dermatitis (RD) in patients wit...

Integrated analysis of single-cell sequencing and machine learning identifies a signature based on monocyte/macrophage hub genes to analyze the intracranial aneurysm associated immune microenvironment.

Monocytes are pivotal immune cells in eliciting specific immune responses and can exert a significant impact on the progression, prognosis, and immuno...

Artificial intelligence in hepatocellular carcinoma diagnosis: a comprehensive review of current literature.

BACKGROUND AND AIM: Hepatocellular carcinoma (HCC) diagnosis mainly relies on its pathognomonic radiological profile, obviating the need for biopsy. T...

Artificial Intelligence in Head and Neck Surgery.

This article explores artificial intelligence's (AI's) role in otolaryngology for head and neck cancer diagnosis and management. It highlights AI's po...

Machine learning assisted microfluidics dual fluorescence flow cytometry for detecting bladder tumor cells based on morphological characteristic parameters.

BACKGROUND: Bladder cancer (BC) is the most common malignant tumor and has become a major public health problem, leading the causes of death worldwide...

Improved prediction of anti-angiogenic peptides based on machine learning models and comprehensive features from peptide sequences.

Angiogenesis is a key process for the proliferation and metastatic spread of cancer cells. Anti-angiogenic peptides (AAPs), with the capability of inh...