Oncology/Hematology

DeepHLApan: A Deep Learning Approach for the Prediction of Peptide-HLA Binding and Immunogenicity.

Neoantigens are crucial in distinguishing cancer cells from normal ones and play a significant role in cancer immunotherapy. The field of bioinformatics prediction for tumor neoantigens has rapidly developed, focusing on the prediction of peptide-HLA binding affinity. In this chapter, we introduce a user-friendly tool named DeepHLApan, which utilizes deep learning techniques to predict neoantigens...

[Advances in artificial intelligence-assisted MRI radiomics in the diagnosis and treatment of prostate cancer].

Prostate cancer (PCa) is the second most common cancer worldwide and the fifth leading cause of cancer deaths in men. Magnetic resonance imaging (MRI), with its high sensitivity and specificity in detecting PCa, is currently the most widely used imaging technique for tumor localization and staging. MRI plays a significant role in risk stratification of patients with neoplasm, surveillance of low-r...

An XGBoost Machine Learning Based Model for Predicting Ki-67 Value ≥ 15% in TNM Stage Primary Breast Cancer Receiving Neoadjuvant Chemotherapy Using Clinical Data and Delta-Radiomic Features on Ultrasound Images and Overall Survival Analysis: A 5-Year Postoperative Follow-Up Study.

To establish a model based on clinical and delta-radiomic features within ultrasound images using XGBoost machine learning to predict proliferation-a...

[Diagnosis of benign laryngeal tumors using neural network].

The article describes our experience in developing and training an artificial neural network based on artificial intelligence algorithms for recognizi...

Highly accurate brain tumor detection with high sensitivity using transform-based functions and machine learning algorithms.

BACKGROUND: Brain tumor is an extremely dangerous disease with a very high mortality rate worldwide. Detecting brain tumors accurately is crucial due ...

Insights into a Machine Learning-Based Palmitoylation-Related Gene Model for Predicting the Prognosis and Treatment Response of Breast Cancer Patients.

BACKGROUND: Breast cancer is a prevalent public health concern affecting numerous women globally and is associated with palmitoylation, a post-transla...

PD-1 Targeted Antibody Discovery Using AI Protein Diffusion.

The programmed cell death protein 1 (PD-1, CD279) is an important therapeutic target in many oncological diseases. This checkpoint protein inhibits T ...

Multimodal Deep Learning Network for Differentiating Between Benign and Malignant Pulmonary Ground Glass Nodules.

OBJECTIVE: This study aimed to establish a multimodal deep-learning network model to enhance the diagnosis of benign and malignant pulmonary ground gl...

Molecular characterization, immunocorrelation analysis, WGCNA analysis and machine learning modeling of genes associated with copper death subtypes of laryngeal cancer.

BACKGROUND: Laryngeal cancer is a malignant tumor that originates from the mucous membrane of the larynx. Currently, the specific involvement mechanis...

Multimodal Data-Driven Segmentation of Bone Metastasis Lesions in SPECT Bone Scans Using Deep Learning.

BACKGROUND: Patients with malignant tumors often develop bone metastases. SPECT bone scintigraphy is an effective tool for surveying bone metastases d...

Parametric optimization and comparative study of machine learning and deep learning algorithms for breast cancer diagnosis.

Breast Cancer is the leading form of cancer found in women and a major cause of increased mortality rates among them. However, manual diagnosis of the...

Artificial Intelligence Application for Anti-tumor Drug Synergy Prediction.

Currently, the main therapeutic methods for cancer include surgery, radiation therapy, and chemotherapy. However, chemotherapy still plays an importan...

Predicting Bone Marrow Metastasis in Neuroblastoma: An Explainable Machine Learning Approach Using Contrast-Enhanced Computed Tomography Radiomics Features.

PURPOSE: To predict bone marrow metastasis in neuroblastoma using contrast-enhanced computed tomography (CECT) radiomics features and explainable mach...

The Application of Artificial Intelligence in Lung Cancer Research.

The advent of artificial intelligence in healthcare is transforming medical research and clinical practice, with significant advancements in the areas...

The effectiveness of deep learning model in differentiating benign and malignant pulmonary nodules on spiral CT.

BACKGROUND: Pulmonary nodule, one of the most common clinical phenomena, is an irregular circular lesion with a diameter of ⩽ 3 cm in the lungs, which...

Meta-transfer Learning for Brain Tumor Segmentation: Within and Beyond Glioma.

In recent years, numerous algorithms have emerged for the segmentation of brain tumors, propelled by both the advancements of deep learning techniques...

Machine Learning and Radiomics in Gliomas.

The integration of machine learning (ML) and radiomics is emerging as a pivotal advancement in glioma research, offering novel insights into the diagn...

Machine and Deep Learning in Hyperspectral Fluorescence-Guided Brain Tumor Surgery.

Malignant glioma resection is often the first line of treatment in neuro-oncology. During glioma surgery, the discrimination of tumor's edges can be c...

Computational Modeling and AI in Radiation Neuro-Oncology and Radiosurgery.

The chapter explores the extensive integration of artificial intelligence (AI) in healthcare systems, with a specific focus on its application in ster...

Constructing a Classification Model for Cervical Cancer Tumor Tissue and Normal Tissue Based on CT Radiomics.

This study aimed to develop an automated classification framework for distinguishing between cervical cancer tumor and normal uterine tissue, leveragi...