Oncology/Hematology

Deep learning-based multimodal spatial transcriptomics analysis for cancer.

The advent of deep learning (DL) and multimodal spatial transcriptomics (ST) has revolutionized cancer research, offering unprecedented insights into tumor biology. This book chapter explores the integration of DL with ST to advance cancer diagnostics, treatment planning, and precision medicine. DL, a subset of artificial intelligence, employs neural networks to model complex patterns in vast data...

Multiparametric MRI-Based Deep Learning Radiomics Model for Assessing 5-Year Recurrence Risk in Non-Muscle Invasive Bladder Cancer.

BACKGROUND: Accurately assessing 5-year recurrence rates is crucial for managing non-muscle-invasive bladder carcinoma (NMIBC). However, the European Organization for Research and Treatment of Cancer (EORTC) model exhibits poor performance.

Unraveling druggable cancer-driving proteins and targeted drugs using artificial intelligence and multi-omics analyses.

The druggable proteome refers to proteins that can bind to small molecules with appropriate chemical affinity, inducing a favorable clinical response....

Real-time estimation of the optimal coil placement in transcranial magnetic stimulation using multi-task deep learning.

Transcranial magnetic stimulation (TMS) has emerged as a promising neuromodulation technique with both therapeutic and diagnostic applications. As acc...

Exploring the role of artificial intelligence, large language models: Comparing patient-focused information and clinical decision support capabilities to the gynecologic oncology guidelines.

Gynecologic cancer requires personalized care to improve outcomes. Large language models (LLMs) hold the potential to provide intelligent question-ans...

Perovskite Probe-Based Machine Learning Imaging Model for Rapid Pathologic Diagnosis of Cancers.

Accurately distinguishing tumor cells from normal cells is a key issue in tumor diagnosis, evaluation, and treatment. Fluorescence-based immunohistoch...

An end-to-end deep learning method for mass spectrometry data analysis to reveal disease-specific metabolic profiles.

Untargeted metabolomic analysis using mass spectrometry provides comprehensive metabolic profiling, but its medical application faces challenges of co...

Application of artificial intelligence in chronic myeloid leukemia (CML) disease prediction and management: a scoping review.

BACKGROUND: Navigating the complexity of chronic myeloid leukemia (CML) diagnosis and management poses significant challenges, including the need for ...

Machine learning to predict completion of treatment for pancreatic cancer.

BACKGROUND: Chemotherapy enhances survival rates for pancreatic cancer (PC) patients postsurgery, yet less than 60% complete adjuvant therapy, with a ...

Deep reinforcement learning in radiation therapy planning optimization: A comprehensive review.

PURPOSE: The formulation and optimization of radiation therapy plans are complex and time-consuming processes that heavily rely on the expertise of me...

Integration of Bioinformatics and Machine Learning to Identify CD8+ T Cell-Related Prognostic Signature to Predict Clinical Outcomes and Treatment Response in Breast Cancer Patients.

UNLABELLED: The incidence of breast cancer (BC) continues to rise steadily, posing a significant burden on the public health systems of various countr...

Prediction of CD8+T lymphocyte infiltration levels in gastric cancer from contrast-enhanced CT and clinical factors using machine learning.

BACKGROUND: CD8+ T lymphocyte infiltration is closely associated with the prognosis and immunotherapy response of gastric cancer (GC). For now, the ex...

An efficient colorectal cancer detection network using atrous convolution with coordinate attention transformer and histopathological images.

The second most common type of malignant tumor worldwide is colorectal cancer. Histopathology image analysis offers crucial data for the clinical diag...

Chemical analogue based drug design for cancer treatment targeting PI3K: integrating machine learning and molecular modeling.

Cancer is a generic term for a group of disorders defined by uncontrolled cell growth and the potential to invade or spread to other parts of the body...

Harnessing Deep Learning for Accurate Pathological Assessment of Brain Tumor Cell Types.

Primary diffuse central nervous system large B-cell lymphoma (CNS-pDLBCL) and high-grade glioma (HGG) often present similarly, clinically and on imagi...

Predicting tissue distribution and tumor delivery of nanoparticles in mice using machine learning models.

Nanoparticles (NPs) can be designed for targeted delivery in cancer nanomedicine, but the challenge is a low delivery efficiency (DE) to the tumor sit...

Machine learning-based identification of biomarkers and drugs in immunologically cold and hot pancreatic adenocarcinomas.

BACKGROUND: Pancreatic adenocarcinomas (PAADs) often exhibit a "cold" or immunosuppressive tumor milieu, which is associated with resistance to immune...

Deep Learning-Based Prediction of Post-treatment Survival in Hepatocellular Carcinoma Patients Using Pre-treatment CT Images and Clinical Data.

The objective of this study was to develop and evaluate a model for predicting post-treatment survival in hepatocellular carcinoma (HCC) patients usin...

Predictive Study of Machine Learning-Based Multiparametric MRI Radiomics Nomogram for Perineural Invasion in Rectal Cancer: A Pilot Study.

This study aimed to establish and validate the efficacy of a nomogram model, synthesized through the integration of multi-parametric magnetic resonanc...

Deep Convolutional Neural Network for Automated Staging of Periodontal Bone Loss Severity on Bite-wing Radiographs: An Eigen-CAM Explainability Mapping Approach.

Periodontal disease is a significant global oral health problem. Radiographic staging is critical in determining periodontitis severity and treatment ...