Oncology/Hematology

Tailored self-supervised pretraining improves brain MRI diagnostic models.

Self-supervised learning has shown potential in enhancing deep learning methods, yet its application in brain magnetic resonance imaging (MRI) analysis remains underexplored. This study seeks to leverage large-scale, unlabeled public brain MRI datasets to improve the performance of deep learning models in various downstream tasks for the development of clinical decision support systems. To enhance...

Surface-Enhanced Raman Scattering (SERS) combined with machine learning enables accurate diagnosis of cervical cancer: From molecule to cell to tissue level.

The rising number of cervical cancer cases is placing a heavy economic strain on the country and its people. Improving survival rates hinges on early detection, precise diagnosis, and thorough treatment. Common screening and diagnostic methods like Pap smears, HPV testing, colposcopy, and histopathological exams are used in clinical practice, but they are often costly, time-consuming, invasive, su...

Predicting lung cancer bone metastasis using CT and pathological imaging with a Swin Transformer model.

Bone metastasis is a common and serious complication in lung cancer patients, leading to severe pain, pathological fractures, and reduced quality of l...

Joint resting state and structural networks characterize pediatric bipolar patients compared to healthy controls: a multimodal fusion approach.

Pediatric bipolar disorder (PBD) is a highly debilitating condition, characterized by alternating episodes of mania and depression, with intervening p...

Feasibility of U-Net model for cerebral arteries segmentation with low-dose computed tomography angiographic images with pre-processing methods.

Subtraction computed tomography angiography (sCTA) can effectively separate enhanced cerebral arteries from similar signal intensity and proximity (i....

Machine learning-based characterization of stemness features and construction of a stemness subtype classifier for bladder cancer.

BACKGROUND: Bladder cancer (BLCA) is a highly heterogeneous disease that presents challenges in predicting prognosis and treatment response. Cancer st...

Enhancing Physician-Patient Communication in Oncology Using GPT-4 Through Simplified Radiology Reports: Multicenter Quantitative Study.

BACKGROUND: Effective physician-patient communication is essential in clinical practice, especially in oncology, where radiology reports play a crucia...

Conversion of Mixed-Language Free-Text CT Reports of Pancreatic Cancer to National Comprehensive Cancer Network Structured Reporting Templates by Using GPT-4.

OBJECTIVE: To evaluate the feasibility of generative pre-trained transformer-4 (GPT-4) in generating structured reports (SRs) from mixed-language (Eng...

Learning Chemotherapy Drug Action via Universal Physics-Informed Neural Networks.

OBJECTIVE: Quantitative systems pharmacology (QSP) is widely used to assess drug effects and toxicity before the drug goes to clinical trial. However,...

An Ultrasound-based Machine Learning Model for Predicting Tumor-Infiltrating Lymphocytes in Breast Cancer.

IntroductionTumor-infiltrating lymphocytes (TILs) are key indicators of immune response and prognosis in breast cancer (BC). Accurate prediction of TI...

Enhancing Specificity in Predicting Axillary Lymph Node Metastasis in Breast Cancer through an Interpretable Machine Learning Model with CEM and Ultrasound Integration.

IntroductionThe study aims to evaluate the performance of an interpretable machine learning model in predicting preoperative axillary lymph node metas...

Machine Learning-Based Prediction of Unplanned Readmission Due to Major Adverse Cardiac Events Among Hospitalized Patients with Blood Cancers.

BackgroundHospitalized patients with blood cancer face an elevated risk for cardiovascular diseases caused by cardiotoxic cancer therapies, which can ...

The role and machine learning analysis of mitochondrial autophagy-related gene expression in lung adenocarcinoma.

OBJECTIVE: Lung adenocarcinoma (LUAD) continues to be a primary cause of cancer-related mortality globally, highlighting the urgent need for novel ins...

Comparison of CNNs and Transformer Models in Diagnosing Bone Metastases in Bone Scans Using Grad-CAM.

PURPOSE: Convolutional neural networks (CNNs) have been studied for detecting bone metastases on bone scans; however, the application of ConvNeXt and ...

Histological tumor necrosis predicts decreased survival after neoadjuvant chemotherapy in head and neck squamous cell carcinoma.

OBJECTIVE: Despite growing interest in neoadjuvant therapies, there are no methods to predict radio- (RT) or chemoradiotherapy (CRT) response in head ...

Genome evolution of Kaposi sarcoma-associated herpesvirus (KSHV).

UNLABELLED: Kaposi sarcoma (KS) is the most common cancer in people living with HIV (PLWH), particularly in sub-Saharan Africa (SSA), where Kaposi sar...

Enhancing security in electromagnetic radiation therapy using fuzzy graph theory.

This research investigates the application of fuzzy graph theory to address critical security challenges in electromagnetic radiation therapy systems....

AI-assisted SERS imaging method for label-free and rapid discrimination of clinical lymphoma.

BACKGROUND: Lymphoma is a malignant tumor of the immune system and its incidence is increasing year after year, causing a major threat to people's hea...

Optimizing Immunotherapy: The Synergy of Immune Checkpoint Inhibitors with Artificial Intelligence in Melanoma Treatment.

Immune checkpoint inhibitors (ICIs) have transformed melanoma treatment; however, predicting patient responses remains a significant challenge. This s...

Artificial intelligence-assisted RNA-binding protein signature for prognostic stratification and therapeutic guidance in breast cancer.

BACKGROUND: Breast cancer is the most common malignancy in women globally, with significant heterogeneity affecting prognosis and treatment. RNA-bindi...