Oncology/Hematology

Machine learning-based radiomics analysis of PET imaging for early prediction of concurrent chemoradiotherapy response in stage II-III cervical squamous cell carcinoma.

BACKGROUND: Cervical squamous cell carcinoma is a major global health burden, with many patients presenting with locally advanced disease requiring concurrent chemoradiotherapy (CCRT). Early assessment of treatment response (TR) is critical for optimizing therapeutic strategies and improving clinical outcomes; however, conventional imaging parameters provide limited predictive value. This study ai...

PET-CT radiomics and artificial intelligence for predicting lymph node metastasis in cervical cancer: a systematic review and meta-analysis.

PURPOSE: Preoperative identification of lymph node metastasis (LNM) in cervical cancer is crucial for guiding therapeutic strategies but remains clinically challenging. This study represents the first systematic meta-analysis to quantitatively evaluate the diagnostic efficacy of PET/CT-based radiomics and artificial intelligence (AI) models in predicting LNM, providing a robust synthesis of curren...

Empathic AI for Patient-Centered Cancer Care: A Scoping Review of Patient Navigation, Support, and Clinical Practice.

BACKGROUND: Artificial intelligence (AI) is rapidly reshaping oncology, offering advancements in clinical care and patient support. A growing area of ...

Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application.

The tumor microenvironment (TME), composed of tumor cells together with stromal cells, immune cells, vascular networks, and other components, constitu...

Improved tumor-only variant calling and mutation burden estimation with VarNet-T.

Somatic variant calling algorithms typically detect mutations in cancer genomes by comparing sequence data from a tumor sample against a matched norma...

Non-invasive endometriosis staging prediction using integrated radiomics and spatiotemporal transformer model based on dynamic contrast-enhanced MRI.

Precise staging of endometriosis remains a clinical challenge, as current diagnosis depends almost entirely on laparoscopic visualization-an invasive ...

FedLiverNet: a federated learning framework for privacy-preserving and efficient liver cancer detection.

Liver cancer continues to be a significant health issue on the global front, and proper segmentation of the liver and the tumor formed from the comput...

Federated CT foundation models for multi-center detection of lymph node metastasis in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, with prognosis strongly influenced by the presence of lymph node ...

Preoperative MRI-Derived Navigation Reduces Radiation Exposure in Pediatric Spinal Fusion.

BACKGROUND: Minimizing radiation exposure during pediatric spinal deformity correction is critical due to the cumulative lifetime effects of ionizing ...

Development and evaluation of a multimodal feature-based predictive model for radiotherapy-induced oral mucositis in nasopharyngeal carcinoma.

BACKGROUND: Accurate prediction of radiation-induced oral mucositis is crucial for personalized treatment in head and neck cancer. However, developing...

Artificial intelligence in clinical oncology: Multimodal integration and translational development.

Artificial intelligence (AI) is rapidly reshaping clinical oncology, as cancer care increasingly relies on integrating heterogeneous data streams span...

Targeting cholesterol metabolism: a core regulator of tumor-associated macrophage plasticity and immunotherapy response.

Tumor-associated macrophages (TAMs) are one of the main immunosuppressive components in the tumor microenvironment (TME). Growing evidence indicates t...

Artificial intelligence (AI) for early identification of radiotherapy related toxicities from the electronic health records of patients with head and neck cancer.

We evaluated artificial intelligence (AI) for detecting osteoradionecrosis, fibrosis, trismus, and dysphagia in 207 head and neck cancer patient elect...

Artificial intelligence-enhanced three-dimensional radiological analysis for predicting spread through air spaces in early-stage lung adenocarcinoma.

OBJECTIVES: Spread through air space (STAS) is a pathological feature that correlates with poor prognosis, especially in patients with lung adenocarci...

Machine learning-based programmed cell death gene signature for prognosis and drug sensitivity in breast cancer.

Breast cancer (BRCA) heterogeneity necessitates robust prognostic biomarkers. Programmed cell death (PCD) serves a key role in tumor progression and t...

Targeting immunosenescence in lung diseases: mechanistic insights and clinical interventions.

Immunosenescence, the age-related decline in immune function, plays a crucial role in the pathogenesis and progression of lung diseases, including chr...

Digital Enabled Diagnosis System for Mammogram Images Classification Using Machine Learning.

Breast tumor is the most commonly detected tumors and remain one of the leading causes of cancer-related mortality among women worldwide. Although mam...

Computerized diagnosis of brain tumor using graph based CNN classification.

Magnetic resonance imaging (MRI) is essential in the accurate diagnosis of brain tumors so that sound treatment planning can be done, however, clinica...

Machine learning-based clustering differentiates bilateral hepatocellular carcinoma characteristics and prognosis.

BACKGROUND: Bilateral hepatocellular carcinoma represents a biologically heterogeneous disease with uncertain optimal surgical selection criteria. Alt...

Artificial Intelligence in Personalized Breast Cancer Drug Safety: From Preclinical Toxicology to Clinical Risk Management.

PURPOSE: The artificial intelligence (AI) implementation in personalized medicine has transformed drug safety, especially in breast cancer treatment. ...