Oncology/Hematology

Prediction of hepatocellular carcinoma response to radiation segmentectomy using an MRI-based machine learning approach.

PURPOSE: To evaluate the value of pre-treatment MRI-based radiomics in patients with hepatocellular carcinoma (HCC) for the prediction of response to Yttrium 90 radiation segmentectomy.

A Recognition System for Diagnosing Salivary Gland Neoplasms Based on Vision Transformer.

Salivary gland neoplasms (SGNs) represent a group of human neoplasms characterized by a remarkable cytomorphologic diversity, which frequently poses diagnostic challenges. Accurate histologic categorization of salivary gland tumors is crucial to make precise diagnoses and guide decisions regarding patient management. Within the scope of this study, a computer-aided diagnosis model using Vision Tra...

Utilizing Pseudo Color Image to Improve the Performance of Deep Transfer Learning-Based Computer-Aided Diagnosis Schemes in Breast Mass Classification.

The purpose of this study is to investigate the impact of using morphological information in classifying suspicious breast lesions. The widespread use...

Deep learning analysis of histopathological images predicts immunotherapy prognosis and reveals tumour microenvironment features in non-small cell lung cancer.

BACKGROUND: Non-small cell lung cancer (NSCLC) is one of the leading causes of cancer mortality worldwide. Immune checkpoint inhibitors (ICIs) have em...

Screening of BindingDB database ligands against EGFR, HER2, Estrogen, Progesterone and NF-κB receptors based on machine learning and molecular docking.

Breast cancer, the second most prevalent cancer among women worldwide, necessitates the exploration of novel therapeutic approaches. To target the fou...

Image quality of virtual monochromatic and material density iodine images for evaluation of head and neck neoplasms using deep learning-based CT image reconstruction - A retrospective observational study.

PURPOSE: To compare the quality of deep learning image reconstructed (DLIR) virtual monochromatic images (VMI) and material density (MD) iodine images...

Machine learning-based new classification for immune infiltration of gliomas.

BACKGROUND: Glioma is a highly heterogeneous and poorly immunogenic malignant tumor, with limited efficacy of immunotherapy. The characteristics of th...

Patient-Specific Deep Learning Tracking Framework for Real-Time 2D Target Localization in Magnetic Resonance Imaging-Guided Radiation Therapy.

PURPOSE: We propose a tumor tracking framework for 2D cine magnetic resonance imaging (MRI) based on a pair of deep learning (DL) models relying on pa...

An optimized siamese neural network with deep linear graph attention model for gynaecological abdominal pelvic masses classification.

An adnexal mass, also known as a pelvic mass, is a growth that develops in or near the uterus, ovaries, fallopian tubes, and supporting tissues. For w...

Aggressiveness classification of clear cell renal cell carcinoma using registration-independent radiology-pathology correlation learning.

BACKGROUND: Renal cell carcinoma (RCC) is a common cancer that varies in clinical behavior. Clear cell RCC (ccRCC) is the most common RCC subtype, wit...

Key genes and pathways in the molecular landscape of pancreatic ductal adenocarcinoma: A bioinformatics and machine learning study.

Pancreatic ductal adenocarcinoma (PDAC) is recognized for its aggressive nature, dismal prognosis, and a notably low five-year survival rate, undersco...

Robust brain MRI image classification with SIBOW-SVM.

Primary Central Nervous System tumors in the brain are among the most aggressive diseases affecting humans. Early detection and classification of brai...

Predicting Breast Cancer Relapse from Histopathological Images with Ensemble Machine Learning Models.

Relapse and metastasis occur in 30-40% of breast cancer patients, even after targeted treatments like trastuzumab for HER2-positive breast cancer. Acc...

Harnessing explainable artificial intelligence for patient-to-clinical-trial matching: A proof-of-concept pilot study using phase I oncology trials.

This study aims to develop explainable AI methods for matching patients with phase 1 oncology clinical trials using Natural Language Processing (NLP) ...

Deep-AutoMO: Deep automated multiobjective neural network for trustworthy lesion malignancy diagnosis in the early stage via digital breast tomosynthesis.

Breast cancer is the most prevalent cancer in women, and early diagnosis of malignant lesions is crucial for developing treatment plans. Digital breas...

Lightweight skin cancer detection IP hardware implementation using cycle expansion and optimal computation arrays methods.

Skin cancer is recognized as one of the most perilous diseases globally. In the field of medical image classification, precise identification of early...

The SINFONIA project repository for AI-based algorithms and health data.

The SINFONIA project's main objective is to develop novel methodologies and tools that will provide a comprehensive risk appraisal for detrimental eff...

Addressing Challenges in Skin Cancer Diagnosis: A Convolutional Swin Transformer Approach.

Skin cancer is one of the top three hazardous cancer types, and it is caused by the abnormal proliferation of tumor cells. Diagnosing skin cancer accu...

Multiparametric MRI-Based Deep Learning Models for Preoperative Prediction of Tumor Deposits in Rectal Cancer and Prognostic Outcome.

RATIONALE AND OBJECTIVES: To investigate the predictive value of a deep learning model based on multiparametric MRI (mpMRI) for tumor deposit (TD) in ...

Neural Network-Enabled Multiparametric Impedance Signal Templating for High throughput Single-Cell Deformability Cytometry Under Viscoelastic Extensional Flows.

Cellular biophysical metrics exhibit systematic alterations during processes, such as metastasis and immune cell activation, which can be used to iden...