Oncology/Hematology

Auto-Segmentation via deep-learning approaches for the assessment of flap volume after reconstructive surgery or radiotherapy in head and neck cancer.

Reconstructive flap surgery aims to restore the substance and function losses associated with tumor resection. Automatic flap segmentation could allow quantification of flap volume and correlations with functional outcomes after surgery or post-operative RT (poRT). Flaps being ectopic tissues of various components (fat, skin, fascia, muscle, bone) of various volume, shape and texture, the anatomic...

Potential role of TNFRSF12A in linking glioblastoma and alzheimer's disease via shared tumour suppressor pathways.

Tumor suppressor genes (TSGs) are critical regulators of cellular homeostasis and are extensively studied in cancer biology. However, their roles in neurodegenerative diseases, particularly Alzheimer's disease (AD), remain poorly understood. Recent evidence of an inverse association between cancer and AD suggests the existence of shared molecular mechanisms. We conducted an integrative analysis to...

Synergizing advanced algorithm of explainable artificial intelligence with hybrid model for enhanced brain tumor detection in healthcare.

Brain tumor causes life-threatening consequences due to which its timely detection and accurate classification are critical for determining appropriat...

A superpixel based self-attention network for uterine fibroid segmentation in high intensity focused ultrasound guidance images.

Ultrasound guidance images are widely used for high intensity focused ultrasound (HIFU) therapy; however, the speckles, acoustic shadows, and signal a...

Identification of CXCR4 as a potential preventive gene in clear cell renal cell carcinoma from machine learning and immune analysis.

Clear cell renal cell carcinoma (ccRCC) represents a prevalent malignant kidney tumor characterized by high metastatic potential and recurrence rates....

Decoding the diagnostic biomarkers of mitochondrial dysfunction related gene variants in pediatric T cell acute lymphoblastic leukemia.

Mitochondrial dysfunction is crucial in the pathogenesis and drug resistance of pediatric T-cell acute lymphoblastic leukemia (T-ALL), a malignant hem...

Hybrid transfer learning and self-attention framework for robust MRI-based brain tumor classification.

Brain tumors are a significant contributor to cancer-related deaths worldwide. Accurate and prompt detection is crucial to reduce mortality rates and ...

Consensus artificial intelligence-driven prognostic signature for predicting the prognosis of hepatocellular carcinoma: a multi-center and large-scale study.

Hepatocellular carcinoma (HCC), a leading cause of global cancer mortality, requires molecular stratification to advance precision oncology. This stud...

Radiomics analysis based on dynamic contrast-enhanced MRI for predicting early recurrence after hepatectomy in hepatocellular carcinoma patients.

This study aimed to develop a machine learning model based on Magnetic Resonance Imaging (MRI) radiomics for predicting early recurrence after curativ...

A novel UHPLC-HRMS method for simultaneous determination of 20 amino metabolites and proteins in lymphoma patients' cells and serum.

Highly sensitive and selective monitoring of amino metabolites such as glutamine, arginine, tryptophan and related proteins played significant roles i...

Predictive value of machine learning for radiation pneumonitis and checkpoint inhibitor pneumonitis in lung cancer patients: a systematic review and meta-analysis.

Some studies have developed machine learning (ML) models for the prediction of pneumonitis following immunotherapy and radiotherapy for patients with ...

Evaluation of MRI-based synthetic CT for lumbar degenerative disease: a comparison with CT.

Patients with lumbar degenerative disease typically undergo preoperative MRI combined with CT scans, but this approach introduces additional ionizing ...

Comparison of AI chatbot predicted and realworld survival outcomes in hepatocellular carcinoma.

This study compares survival predictions made by an artificial intelligence (AI) based chatbot with real-world data in hepatocellular carcinoma (HCC) ...

Activated cancer-associated fibroblasts correlate with poor survival and decreased lymphocyte infiltration in infiltrative type distal cholangiocarcinoma.

Cancer-associated fibroblasts promote tumor progression through growth facilitation, invasion, and immune evasion. This study investigated the impact ...

A hybrid compound scaling hypergraph neural network for robust cervical cancer subtype classification using whole slide cytology images.

Cervical cancer is a major cause of mortality among women, particularly in low-income countries with insufficient screening programs. Manual cytologic...

Hybrid model integration with explainable AI for brain tumor diagnosis: a unified approach to MRI analysis and prediction.

Effective treatment for brain tumors relies on accurate detection because this is a crucial health condition. Medical imaging plays a pivotal role in ...

Regulatory T cells and matrix-producing cancer associated fibroblasts contribute on the immune resistance and progression of prognosis related tumor subtypes in ccRCC.

Clear cell renal cell carcinoma (ccRCC) is a prevalent malignant tumor in the field of urology. The effect of cell heterogeneity on the prognosis and ...

Determination of the oral carcinoma and sarcoma in contrast enhanced CT images using deep convolutional neural networks.

Oral cancer is a hazardous disease and a major cause of morbidity and mortality worldwide. The purpose of this study was to develop the deep convoluti...

Leveraging an ensemble of EfficientNetV1 and EfficientNetV2 models for classification and interpretation of breast cancer histopathology images.

Breast cancer is the second leading cause of cancer-related deaths among women, following lung cancer, as of 2024. Conventional cancer diagnosis relie...

Validation of an AI-enabled exome/transcriptome liquid biopsy platform for early detection, MRD, disease monitoring, and therapy selection for solid tumors.

Effective clinical management of patients with cancer requires highly accurate diagnosis, precise therapy selection, and highly sensitive monitoring o...