Oncology/Hematology

Enhancing cell instance segmentation in scanning electron microscopy images via a deep contour closing operator.

Accurately segmenting and individualizing cells in scanning electron microscopy (SEM) images is a highly promising technique for elucidating tissue architecture in oncology. While current artificial intelligence (AI)-based methods are effective, errors persist, necessitating time-consuming manual corrections, particularly in areas where the quality of cell contours in the image is poor and require...

Clinically applicable semi-supervised learning framework for multiple organs at risk and tumor delineation in lung cancer brachytherapy.

PURPOSE: The generalization ability of deep learning-based automatic segmentation techniques for lung cancer in practical clinical applications remains under-validated. We reported an investigation that validated a robust semi-supervised conditional nnU-Net (SSC-nnUNet) model in multiple organs at risk (OARs) and tumor segmentation in lung cancer brachytherapy, also explored its potential in robot...

Deep Learning-driven Microfluidic-SERS to Characterize the Heterogeneity in Exosomes for Classifying Non-Small Cell Lung Cancer Subtypes.

Lung cancer exhibits strong heterogeneity, and its early diagnosis and precise subtyping are of great importance, as they can increase the ability to ...

Artificial intelligence predicts multiclass molecular signatures and subtypes directly from breast cancer histology: a multicenter retrospective study.

Detection of biomarkers of breast cancer incurs additional costs and tissue burden. We propose a deep learning-based algorithm (BBMIL) to predict clas...

Improved gated recurrent unit-based osteosarcoma prediction on histology images: a meta-heuristic-oriented optimization concept.

The major prevalent primary bone cancer is osteosarcoma. Preoperative chemotherapy is accompanied by resection as part of the normal course of treatme...

A hybrid parallel convolutional spiking neural network for enhanced skin cancer detection.

The most widespread kind of cancer, affecting millions of lives is skin cancer. When the condition of illness worsens, the chance of survival is reduc...

Integrating single-cell RNA sequencing, WGCNA, and machine learning to identify key biomarkers in hepatocellular carcinoma.

The microarray and single-cell RNA-sequencing (scRNA-seq) datasets of hepatocellular carcinoma (HCC) were downloaded from the Gene Expression Omnibus ...

Identifying chemotherapy beneficiaries in nasal and paranasal sinus cancers: epidemiological trends and machine learning insights.

BACKGROUND: Studies on the epidemiological characteristics, treatment strategies and prognosis of nasal and paranasal sinus cancer are still relativel...

AI-driven patient support: Evaluating the effectiveness of ChatGPT-4 in addressing queries about ovarian cancer compared with healthcare professionals in gynecologic oncology.

PURPOSE: Artificial intelligence (AI) chatbots, such as ChatGPT-4, allow a user to ask questions on an interactive level. This study evaluated the cor...

Feasibility and Impact of a Radiation Therapy Technologist-Driven Workflow for Cone Beam Computed Tomography Guided Partial Breast Adaptive Radiation Therapy.

PURPOSE: Cone beam computed tomography-based online adaptive radiation therapy (ART) allows significantly smaller planning target volume margins for p...

Taking the 3Rs to a higher level: replacement and reduction of animal testing in life sciences in space research.

Human settlements on the Moon, crewed missions to Mars and space tourism will become a reality in the next few decades. Human presence in space, espec...

AI-Based Identification Method for Cervical Transformation Zone Within Digital Colposcopy: Development and Multicenter Validation Study.

BACKGROUND: In low- and middle-income countries, cervical cancer remains a leading cause of death and morbidity for women. Early detection and treatme...

Clinical implications of deep learning based image analysis of whole radical prostatectomy specimens.

Prostate cancer (PCa) diagnosis faces significant challenges due to its complex pathological characteristics and insufficient pathologist resources. W...

Gd-EOB-DTPA-enhanced MRI radiomics and deep learning models to predict microvascular invasion in hepatocellular carcinoma: a multicenter study.

BACKGROUND: Microvascular invasion (MVI) is an important risk factor for early postoperative recurrence of hepatocellular carcinoma (HCC). Based on ga...

Anticancer drug response prediction integrating multi-omics pathway-based difference features and multiple deep learning techniques.

Individualized prediction of cancer drug sensitivity is of vital importance in precision medicine. While numerous predictive methodologies for cancer ...

Impact of Tumor Location on Predicting Early-Stage Breast Cancer Patient Survivability Using Explainable Machine Learning Models.

PURPOSE: This study aims to investigate the impact of tumor quadrant location on the 5-year early-stage breast cancer survivability prediction using e...

An Early Thyroid Screening Model Based on Transformer and Secondary Transfer Learning for Chest and Thyroid CT Images.

IntroductionThyroid cancer is a common malignant tumor, and early diagnosis and timely treatment are crucial to improve patient prognosis. With the in...

A weakly supervised deep learning framework for automated PD-L1 expression analysis in lung cancer.

The growing application of immune checkpoint inhibitors (ICIs) in cancer immunotherapy has underscored the critical need for reliable methods to ident...

Automated detection of metastatic lymph nodes in head and neck malignant tumors on high - resolution MRI images using an improved convolutional neural network.

PURPOSE: To develop an AI-based diagnostic model for assessing cervical lymph nodes in head and neck malignant tumors using MRI, enabling non-invasive...

Prognostic assessment of osteolytic lesions and mechanical properties of bones bearing breast cancer using neural network and finite element analysis.

The management of skeletal-related events (SREs), particularly the prevention of pathological fractures, is crucial for cancer patients. Current clini...