Oncology/Hematology

Developing an innovative lung cancer detection model for accurate diagnosis in AI healthcare systems.

Accurate Lung cancer (LC) identification is a big medical problem in the AI-based healthcare systems...

Artificial Intelligence-Powered Insights into Polyclonality and Tumor Evolution.

Recent studies have revealed that polyclonality-where multiple distinct subclones cooperate during e...

Benford's Law in histology.

Digital pathology is an emerging field that is gaining popularity due to its numerous advantages ove...

Photoacoustic-Integrated Multimodal Approach for Colorectal Cancer Diagnosis.

Colorectal cancer remains a major global health challenge, emphasizing the need for advanced diagnos...

Artificial Intelligence in Palliative Care: A Scoping Review of Current Applications, Challenges, and Future Directions.

BackgroundArtificial Intelligence (AI) is increasingly integrated into healthcare systems, presentin...

An MOF-Enhanced Anti-Fouling Immunoprobe Platform for Efficient Direct Screening of Pancreatic Cancer.

Monitoring biomarkers offers insights for early disease (e.g., cancer, chronic diseases) screening, ...

Burnout protective patterns among oncology nurses: a cross-sectional study using machine learning analysis.

BACKGROUND: Oncology nurses face unique and intense demands due to the nature of their work, caring ...

An interpretable machine learning model for predicting early liver metastasis after pancreatic cancer surgery.

BACKGROUND: Liver metastasis is the most frequent site of distant metastasis in pancreatic ductal ad...

Machine learning algorithms predict breast cancer incidence risk: a data-driven retrospective study based on biochemical biomarkers.

BACKGROUND: Current breast cancer prediction models typically rely on personal information and medic...

Risk factors and prediction of distant metastasis (DM) of colon adenocarcinoma: a logistic regression and machine learning study based on surveillance, epidemiology, and end results (SEER) database.

Deep learning radiomics and mediastinal adipose tissue-based nomogram for preoperative prediction of postoperative‌ brain metastasis risk in non-small cell lung cancer.

Deep learning for automated segmentation of radiation-induced changes in cerebral arteriovenous malformations following radiosurgery.

BACKGROUND: Despite the widespread use of stereotactic radiosurgery (SRS) to treat cerebral arteriov...

Development and validation of a small-sample machine learning model to predict 5-year overall survival in patients with hepatocellular carcinoma.

BACKGROUND: Early-onset hepatocellular carcinoma (HCC) is insidious, with characteristics of easy me...

MRI radiomics model for predicting tumor immune microenvironment types and efficacy of anti-PD-1/PD-L1 therapy in hepatocellular carcinoma.

BACKGROUND: To improve the prediction of immune checkpoint inhibitors (ICIs) efficacy in hepatocellu...

Perilesional dominance: radiomics of multiparametric MRI enhances differentiation of IgG4-Related ophthalmic disease and orbital MALT lymphoma.

BACKGROUND: To develop and validate a diagnostic framework integrating intralesional (ILN) and peril...

Federated learning-based CT liver tumor detection using a teacher‒student SANet with semisupervised learning.

BACKGROUND: Detecting liver tumors via computed tomography (CT) scans is a critical but labor-intens...

2.5D deep learning radiomics and clinical data for predicting occult lymph node metastasis in lung adenocarcinoma.

BACKGROUND: Occult lymph node metastasis (OLNM) refers to lymph node involvement that remains undete...

Evaluation of a fusion model combining deep learning models based on enhanced CT images with radiological and clinical features in distinguishing lipid-poor adrenal adenoma from metastatic lesions.

OBJECTIVE: To evaluate the diagnostic performance of a machine learning model combining deep learnin...

Development and clinical validation of deep learning-based immunohistochemistry prediction models for subtyping and staging of gastrointestinal cancers.

BACKGROUND: Immunohistochemistry (IHC) is a critical tool for tumor diagnosis and treatment, but it ...

Mapping the EORTC QLQ-C30 and QLQ-LC13 to the SF-6D utility index in patients with lung cancer using machine learning and traditional regression methods.

BACKGROUND: Preference-based measures of health-related quality of life (HRQoL), such as the Short F...

Can mutation abundance assess the biological behavior of BRAF-positive papillary thyroid carcinoma?

BACKGROUND: BRAF mutation is the most common genetic change in papillary thyroid carcinoma (PTC). Ne...