Oncology/Hematology

Unsupervised non-small cell lung cancer tumor segmentation using cycled generative adversarial network with similarity-based discriminator.

BACKGROUND: Tumor segmentation is crucial for lung disease diagnosis and treatment. Most existing deep learning-based automatic segmentation methods rely on manually annotated data for network training.

Bald eagle-optimized transformer networks with temporal-spatial mid-level features for pancreatic tumor classification.

The classification and diagnosis of pancreatic tumors present significant challenges due to their inherent complexity and variability. Traditional methods often struggle to capture the dynamic nature of these tumors, highlighting the need for advanced techniques that improve precision and robustness. This study introduces a novel approach that combines temporal-spatial mid-level features (CTSF) wi...

Machine learning technique-based four-autoantibody test for early detection of esophageal squamous cell carcinoma: a multicenter, retrospective study with a nested case-control study.

BACKGROUND: Autoantibodies represent promising diagnostic blood-based biomarkers that may be generated prior to the first clinically detectable signs ...

Incorporation of explainable artificial intelligence in ensemble machine learning-driven pancreatic cancer diagnosis.

Despite the strides made in medical science, pancreatic cancer continues to be a threat, highlighting the urgent need for creative strategies to addre...

Emerging artificial intelligence-driven precision therapies in tumor drug resistance: recent advances, opportunities, and challenges.

Drug resistance is one of the main reasons for cancer treatment failure, leading to a rapid recurrence/disease progression of the cancer. Recently, ar...

The predictive role of identifying frailty in assessing the need for palliative care in the elderly: the application of machine learning algorithm.

BACKGROUND: Palliative care is a key component of integrated care to improve care quality and reduce hospitalization costs for patients with chronic o...

Constructing a neural network model based on tumor-infiltrating lymphocytes (TILs) to predict the survival of hepatocellular carcinoma patients.

BACKGROUND: Hepatocellular carcinoma (HCC) is the most common primary liver cancer worldwide, and early pathological diagnosis is crucial for formulat...

Clinical performance of a machine learning-based model for detecting lymph node metastasis in papillary thyroid carcinoma: A multicenter study.

Papillary thyroid carcinoma (PTC) is a common endocrine malignancy with a generally favorable prognosis, but lymph node metastasis (LNM) complicates t...

Bridging surgical oncology and personalized medicine: the role of artificial intelligence and machine learning in thoracic surgery.

Lung cancer remains the leading cause of cancer-related deaths globally, often detected in advanced stages with poor prognosis. While surgical resecti...

Reinforcement learning-driven automated head and neck simultaneous integrated boost (SIB) radiation therapy: flexible treatment planning aligned with clinical preferences.

Head-and-neck simultaneous integrated boost (SIB) treatment planning using intensity modulated radiation therapy is particularly challenging due to th...

A comparative analysis of three graph neural network models for predicting axillary lymph node metastasis in early-stage breast cancer.

The presence of axillary lymph node metastasis (ALNM) in breast cancer patients is an important factor in deciding whether to have axillary surgery or...

Preoperative lymph node metastasis risk assessment in invasive micropapillary carcinoma of the breast: development of a machine learning-based predictive model with a web-based calculator.

BACKGROUND: Invasive micropapillary carcinoma (IMPC) is a rare subtype of breast cancer characterized by a high risk of lymph node metastasis (LNM). T...

Data-driven survival modeling for breast cancer prognostics: A comparative study with machine learning and traditional survival modeling methods.

Background This investigation delves into the potential application of data-driven survival modeling approaches for prognostic assessments of breast c...

Advancing personalized, predictive, and preventive medicine in bladder cancer: a multi-omics and machine learning approach for novel prognostic modeling, immune profiling, and therapeutic target discovery.

OBJECTIVE: This study aimed to identify and analyze immunogenic cell death (ICD)-related multi-omics features in bladder cancer (BLCA) using single-ce...

Towards real-time conformal palliative treatment of spine metastases: A deep learning approach for Hounsfield Unit recovery of cone beam CT images.

BACKGROUND: The extension of onboard cone-beam CT (CBCT) imaging for real-time treatment planning is constrained by limitations in image quality. Synt...

Machine Learning-Enhanced Cerebrospinal Fluid N-Glycome for the Diagnosis and Prognosis of Primary Central Nervous System Lymphoma.

The diagnosis and prognosis of Primary Central Nervous System Lymphoma (PCNSL) present significant challenges. In this study, the potential use of mac...

Combining Ultrasound Imaging and Molecular Testing in a Multimodal Deep Learning Model for Risk Stratification of Indeterminate Thyroid Nodules.

Indeterminate cytology (Bethesda III and IV) represents 15-30% of biopsied thyroid nodules and require additional diagnostic testing. Molecular testi...

Machine learning-based identification of cuproptosis-related lncRNA biomarkers in diffuse large B-cell lymphoma.

Multiple machine learning techniques were employed to identify key long non-coding RNA (lncRNA) biomarkers associated with cuproptosis in Diffuse Larg...

Bioinformatics Approach to Identifying Molecular Targets of Isoliquiritigenin Affecting Chronic Obstructive Pulmonary Disease: A Machine Learning Pharmacology Study.

To identify the molecular targets and possible mechanisms of isoliquiritigenin (ISO) in affecting chronic obstructive pulmonary disease (COPD) by regu...

Use of ChatGPT for patient education involving HPV-associated oropharyngeal cancer.

OBJECTIVE: This study aims to investigate the ability of ChatGPT to generate reliably accurate responses to patient-based queries specifically regardi...