Oncology/Hematology

CellOMaps: A compact representation for robust classification of lung adenocarcinoma growth patterns.

Lung adenocarcinoma (LUAD) is a morphologically heterogeneous disease, characterized by five primary histological growth patterns. The classification of such patterns is crucial due to their direct relation to prognosis but the high subjectivity and observer variability pose a major challenge. Related studies in the literature focus on machine learning methods for growth pattern classification, of...

Cancer type and survival prediction based on transcriptomic feature map.

This study achieved cancer type and survival time prediction by transforming transcriptomic features into feature maps and employing deep learning models. Using transcriptomic data from 27 cancer types and survival data from 10 types in the TCGA database, a pan-cancer transcriptomic feature map was constructed through data cleaning, feature extraction, and visualization. Using Inception network an...

Oral cavity carcinoma detection using BAT algorithm-optimized machine learning models with transfer learning and random sampling.

BACKGROUND: Oral cavity carcinoma remains a major public health concern, where early and accurate detection is vital for improving patient outcomes an...

Digital Pathology and Artificial Intelligence in Pediatric Pathology.

Applications of artificial intelligence (AI) and machine learning (ML) are rapidly developing to support the diagnosis and classification of pathology...

Four Different Artificial Intelligence Models Logistic Regression to Enhance the Diagnostic Accuracy of Fecal Immunochemical Test in the Detection of Colorectal Carcinoma in a Screening Setting.

BACKGROUND/AIM: This study aimed to evaluate the diagnostic accuracy (DA) of four artificial intelligence (AI) models compared to logistic regression ...

Machine Learning Model to Guide Empirical Antimicrobial Therapy in Febrile Neutropenic Patients With Hematologic Malignancies.

BACKGROUND/AIM: Optimal antimicrobial selection for patients with febrile neutropenia (FN) may differ depending on the underlying mechanisms. We aimed...

3-D contour-aware U-Net for efficient rectal tumor segmentation in magnetic resonance imaging.

Magnetic resonance imaging (MRI), as a non-invasive detection method, is crucial for the clinical diagnosis and treatment plan of rectal cancer. Howev...

Machine Learning-Assisted Analysis of the Oral Cancer Immune Microenvironment: From Single-Cell Level to Prognostic Model Construction.

Oral cancer is among the most prevalent malignant tumours worldwide; prognosis can be affected by several factors, including molecular subtypes, immun...

Adaptive Breast MRI Scanning Using AI.

Background MRI protocols typically involve many imaging sequences and often require too much time. Purpose To simulate artificial intelligence (AI)-di...

Scientific hypothesis generation by large language models: laboratory validation in breast cancer treatment.

Large language models (LLMs) have transformed artificial intelligence (AI) and achieved breakthrough performance on a wide range of tasks. In science,...

Dimensional Synthesis of 6-DOF Parallel Robot for Intra-Operative Radiation Therapy.

BACKGROUND: In order to meet the kinematic requirements of large range of motion, payload, and stiffness of Intra-Operative Radiation Therapy robots, ...

Artificial Intelligence and Machine Learning Innovations to Improve Design and Representativeness in Oncology Clinical Trials.

The integration of artificial intelligence (AI) and machine learning (ML) in oncology clinical trials is rapidly evolving alongside the broader field....

Multi-class brain malignant tumor diagnosis in magnetic resonance imaging using convolutional neural networks.

Glioblastoma (GBM), primary central nervous system lymphoma (PCNSL), and brain metastases (BM) are common malignant brain tumors with similar radiolog...

Explainable transformer-based deep survival analysis in childhood acute lymphoblastic leukemia.

BACKGROUND: Acute lymphoblastic leukemia (ALL) is the most common type of leukemia among children and adolescents and can be life-threatening. The inc...

Mitosis detection and classification for breast cancer diagnosis: What we know and what is next.

Breast cancer is the second most deadly malignancy in women, behind lung cancer. Despite significant improvements in medical research, breast cancer i...

multiPI-TransBTS: A multi-path learning framework for brain tumor image segmentation based on multi-physical information.

Brain Tumor Segmentation (BraTS) plays a critical role in clinical diagnosis, treatment planning, and monitoring the progression of brain tumors. Howe...

Optimized multiple instance learning for brain tumor classification using weakly supervised contrastive learning.

Brain tumors have a great impact on patients' quality of life and accurate histopathological classification of brain tumors is crucial for patients' p...

Advancing the frontier of artificial intelligence on emerging technologies to redefine cancer diagnosis and care.

BACKGROUND: Artificial Intelligence (AI) is capable of revolutionizing cancer therapy and advancing precision oncology via integrating genomics data a...

Machine learning predictions of tumor progression: How reliable are we?

BACKGROUND: Cancer continues to pose significant challenges in healthcare due to the complex nature of tumor progression. In this digital era, artific...

Prediction methodology of air absorbed dose rates for Chinese cities with deep learning models.

Air absorbed dose rate is a key indicator of environmental radiation exposure. In China, automated environmental radiation monitoring systems have bee...