Oncology/Hematology

A highly generalized federated learning algorithm for brain tumor segmentation.

Brain image segmentation plays a pivotal role in modern healthcare by enabling precise diagnosis and treatment planning. Federated Learning (FL) enables collaborative model training across institutions while safeguarding sensitive patient data. The integration of these technologies holds significant potential for advancing artificial intelligence (AI) in healthcare. However, medical institutions f...

Predicting cisplatin response in cholangiocarcinoma patients using chromosome pattern and related gene expression.

Cholangiocarcinoma (CCA) is a prevalent bile duct cancer with limited treatment options. Cisplatin-based chemotherapy is a common approach, but response rates vary. Recently, chromosome aberrations have emerged as predictors of chemotherapy response in various malignancies. This study aimed to identify chromosomal aberrations associated with cisplatin response in CCA. A histoculture drug response ...

Prediction of axillary lymph node metastasis in triple negative breast cancer using MRI radiomics and clinical features.

To develop and validate a machine learning-based prediction model to predict axillary lymph node (ALN) metastasis in triple negative breast cancer (TN...

Automated sleep staging model for older adults based on CWT and deep learning.

Sleep staging plays a crucial role in the diagnosis and treatment of sleep disorders. Traditional sleep staging requires manual classification by prof...

Lung cancer screening with low-dose CT: definition of positive, indeterminate, and negative screen results. A nodule management recommendation from the European Society of Thoracic Imaging.

Early detection of lung cancer through low-dose CT lung cancer screening in a high-risk population has proven to reduce lung cancer-specific mortality...

Impact of tertiary lymphoid structure-associated biomarkers on pancreatic cancer via a dual-disease analysis of psoriasis and pancreatic cancer.

Pancreatic cancer (PC), often referred to as the "king of cancers", has demonstrated limited success with immunotherapies, many of which are still und...

A genotype-to-drug diffusion model for generation of tailored anti-cancer small molecules.

Despite advances in precision oncology, developing effective cancer therapeutics remains a significant challenge due to tumor heterogeneity and the li...

Deep learning assessment of metastatic relapse risk from digitized breast cancer histological slides.

Accurate risk stratification is critical for guiding treatment decisions in early breast cancer. We present an artificial intelligence (AI)-based tool...

Deep learning model for grading carcinoma with Gini-based feature selection and linear production-inspired feature fusion.

The most common types of kidneys and liver cancer are renal cell carcinoma (RCC) and hepatic cell carcinoma (HCC), respectively. Accurate grading of t...

A novel approach to overcome black box of AI for optical diagnosis in colonoscopy.

Accurate real-time optical diagnosis that distinguishes neoplastic from non-neoplastic colorectal lesions during colonoscopy can lower the costs of pa...

Identification and verification of immune and oxidative stress-related diagnostic indicators for malignant lung nodules through WGCNA and machine learning.

Early detection of lung nodules (LNs) is critical for prevention and treatment of lung cancer. However, current noninvasive diagnostic methods face si...

Artificial intelligence-driven label-free detection of chronic myeloid leukemia cells using ghost cytometry.

Early diagnosis and treatment initiation of chronic myeloid leukemia (CML) are considered to increase the rate of deep molecular response. However, th...

Automated classification of chondroid tumor using 3D U-Net and radiomics with deep features.

Classifying chondroid tumors is an essential step for effective treatment planning. Recently, with the advances in computer-aided diagnosis and the in...

Machine learning developed LKB1-AMPK signaling related signature for prognosis and drug sensitivity in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common tumors worldwide, posing a significant threat to the life and health of people globally. LKB1...

Modeling the prediction of spontaneous rupture and bleeding in hepatocellular carcinoma via machine learning algorithms.

This study aimed to identify the risk factors associated with spontaneous rupture and bleeding in hepatocellular carcinoma, establish a prediction mod...

An enhanced deep learning model for accurate classification of ovarian cancer from histopathological images.

Ovarian Cancer is a malignancy that develops from ovarian cells and is frequently characterized by aberrant cell proliferation that leads to the creat...

Fabrication of serum-based SERS-tailored 3D structures for thyroid cancer diagnosis.

Early detection of thyroid cancer improves patient survival rate from 51.9% to 99.9%. Fine needle aspiration cytology is the primary method for diagno...

Clinical characteristics and predictive models of HER2-low breast cancer patients who only received adjuvant chemotherapy: a real-world retrospective multicenter study.

HER2-low positivity is reported to play substantial role in neoadjuvant chemotherapy for breast cancer, but its role in adjuvant chemotherapy remains ...

Deep learning-driven drug response prediction and mechanistic insights in cancer genomics.

In the field of cancer therapy, the diversity and heterogeneity of cancer genomes in clinical patients complicate and challenge the effective use of n...

Integrated analysis of shared gene expression signatures and immune microenvironment heterogeneity in type 2 diabetes mellitus and colorectal cancer.

Emerging evidence suggests a bidirectional relationship between colorectal cancer (CRC) and type 2 diabetes mellitus (T2DM), yet the shared molecular ...