Oncology/Hematology

A prospective multimethod investigation of cancer-related pain integrating clinical data and machine learning: results from the RUGGI Study.

BACKGROUND: Cancer-related pain is a multidimensional phenomenon, and key determinants of pain intensity remain unclear. The aim of this study was to identify the clinical determinants of cancer-related pain intensity using a convergent, multimethod analytical framework. METHODS: Prospective observational study of patients with cancer-related pain. Pain was assessed using a 0-10 numeric rating sca...

Early ctDNA stratifies survival in locally advanced and oligometastatic lung cancer treated with radiotherapy.

PURPOSE: Circulating tumor fraction estimate (ctFE) is a machine learning-derived composite metric of circulating tumor DNA (ctDNA) burden. We hypothesized that pre- and early on-treatment ctFE could robustly risk-stratify patients with locally advanced and oligometastatic non-small cell lung cancer (NSCLC) treated with radiotherapy. EXPERIMENTAL DESIGN: In a prospective phase II clinical trial (N...

An integrative machine learning and structure-driven drug repositioning strategy for human IRAK4-targeted cancer therapy.

The upregulation of interleukin-1 receptor-associated kinase 4 (IRAK4) drives pro-tumorigenic signaling across various malignancies. Currently availab...

Predicting early response to ablative radiotherapy in oligometastatic disease: a scoping review of radiomics-based machine learning and deep learning models.

OBJECTIVES: Oligometastatic disease represents an intermediate stage of cancer, often treated with surgery or ablative radiotherapy (ART). This scopin...

Identification of tolerogenic dendritic cells-related prognostic biomarkers in Wilms tumor via machine learning integration.

Wilms tumor (WT) is the most common pediatric kidney cancer. Tolerogenic dendritic cells (TolDCs) promote tumor immune evasion in the tumor microenvir...

Integrating single-cell RNA and bulk RNA sequencing data to identify prognostic genes associated with pyrimidine metabolism in triple-negative breast cancer by machine learning algorithm combinations.

BACKGROUND: Pyrimidine metabolism plays a crucial role in DNA synthesis and cell proliferation and is associated with the development of various cance...

Improving Long-Term Adherence to Endocrine Therapy Among Breast Cancer Survivors: Development of a Multiscale Modeling and Intervention System.

BACKGROUND: Breast cancer is a significant public health burden. Despite its critical role in preventing the recurrence of breast cancer, rates of lon...

Time-Dynamic AI Models to Predict Quality of Life in Patients With Breast Cancer: Development and Validation Study Using the EORTC BALANCE Cohort.

BACKGROUND: Patients with breast cancer often experience health-related quality of life (HRQoL) impairments that remain difficult to predict on an ind...

Localized heat induces ERK activation and signal propagation in solid tumors.

Localized plasmonic heating by metallic nanoparticles offers a promising strategy to destroy cancer cells through controlled thermal stress. However, ...

Machine learning outperforms large language models for survival prediction in advanced hepatocellular carcinoma: a multicenter study.

Accurate prognostic prediction remains a critical unmet need in advanced hepatocellular carcinoma (HCC). While machine learning (ML) models have demon...

A quantitative analysis of global AI medical studies: gaps in randomized controlled trials.

Medical artificial intelligence (AI) has advanced rapidly, yet a comprehensive quantitative overview of its clinical evaluation landscape remains lack...

Peripheral blood mononuclear cell DNA methylation signatures guide surgical decision-making in indeterminate pulmonary nodules.

BACKGROUND: Distinguishing malignant from benign pulmonary nodules remained a significant clinical challenge. Given the involvement of DNA methylation...

Imaging of ewing sarcoma: an updated analysis including presenting features, prognostic imaging biomarkers, and treatment response assessment.

Ewing sarcoma is a highly aggressive small round cell sarcoma primarily affecting children and adolescents. Imaging plays a central role from diagnosi...

Integration of 117 machine learning algorithms and single-cell transcriptomics identifies macrophage polarization and ER stress signatures for cancer prognosis and precision therapy.

BACKGROUND: Macrophage polarization and endoplasmic reticulum (ER) stress play critical yet incompletely understood roles in cancer progression and th...

Integration of Single-Cell RNA Sequencing and Machine Learning to Identify and Validate Prognostic Genes With Lymph Node Metastasis and Immune Cell Signatures in Lung Adenocarcinoma.

The poor prognosis of lung adenocarcinoma (LUAD) remains unimproved. This study aimed to identify lymph node metastasis (LNM)-related and cellular imm...

Multi-Omics and Machine Learning-Uncovered FLT1-Mediated Epithelial-Endothelial Crosstalk in Cellular Senescence Driving Clear Cell Renal Cell Carcinoma Malignancy.

Clear cell renal cell carcinoma (ccRCC) is distinguished by the absence of definitive diagnostic markers and efficacious treatment modalities, factors...

The Role of Artificial Intelligence Algorithms in Challenging Diagnostic Cases - Between Potential and Real Clinical Support.

INTRODUCTION: Extranodal natural killer/T-cell lymphoma, nasal type (ENKTL), is a rare EBV-associated malignancy characterized by destructive tumors i...

[Physiological age estimation model on pediatric panoramic radiographs based on machine learning and deep learning].

Objective: To automatically estimate children's physiological age from pediatric panoramic radiographs, employing a two-stage approach which involves ...

Predicting survival of Hodgkin lymphoma using machine learning-an analysis based on the SEER database.

OBJECTIVES: This study aimed to develop an effective model for predicting Hodgkin lymphoma (HL) prognosis as to assist clinicians in making optimal cl...

Multi omics and mechanistic investigation reveals CBFB as a prognostic biomarker in gastric cancer.

BACKGROUND: Gastric cancer is an aggressive malignancy with poor prognosis due to complex pathogenesis, underscoring the need for biomarkers and targe...