Oncology/Hematology

Editorial: Spotlighting the interaction network of hub genes, molecules, and cells in the tumor immune microenvironment (TIME) and their contribution to malignant progression.

Rhabdomyosarcoma Classification: Refining Our Understanding.

Rhabdomyosarcoma (RMS) is the most common soft tissue sarcoma in children, representing approximatel...

Digital Pathology-based Artificial Intelligence Biomarker Validation in Metastatic Prostate Cancer.

BACKGROUND AND OBJECTIVE: Owing to the expansion of treatment options for metastatic hormone-sensiti...

Machine Learning Reveals Demographic Disparities in Palliative Care Timing Among Patients With Traumatic Brain Injury Receiving Neurosurgical Consultation.

BACKGROUND: Timely palliative care (PC) consultations offer demonstrable benefits for patients with ...

Establishment of multiple machine learning prognostic model for gene differences between primary tumors and lymph nodes in luminal breast cancer.

BACKGROUND: This study aimed to explore the correlation between primary tumors (PT) and paired metas...

Non-invasive Prediction of Lymph Node Metastasis in NSCLC Using Clinical, Radiomics, and Deep Learning Features From F-FDG PET/CT Based on Interpretable Machine Learning.

PURPOSE: This study aimed to develop and evaluate a machine learning model combining clinical, radio...

Let it shine: Autofluorescence of Papanicolaou-stain improves AI-based cytological oral cancer detection.

BACKGROUND AND OBJECTIVES: Oral cancer is a global health challenge. The disease can be successfully...

MultiSCCHisto-Net-KD: A deep network for multi-organ explainable squamous cell carcinoma diagnosis with knowledge distillation.

Squamous cell carcinoma is a prevalent cancer type that affects various organs in the human body. Ma...

Development and validation of a deep learning model for morphological assessment of myeloproliferative neoplasms using clinical data and digital pathology.

The subjectivity of morphological assessment and the overlapping pathological features of different ...

Classification of acute myeloid leukemia by pre-trained deep neural networks: A comparison with different activation functions.

Acute Myeloid Leukemia(AML) is a rapidly progressing cancer affecting blood and bone marrow, marked ...

Integrated Proteomics and Machine Learning Approach Reveals PYCR1 as a Novel Biomarker to Predict Prognosis of Sinonasal Squamous Cell Carcinoma.

Sinonasal squamous cell carcinoma (SNSCC) is a rare tumor with a high 5-year mortality rate. However...

Integrating machine learning, bioinformatics and experimental verification to identify a novel prognostic marker associated with tumor immune microenvironment in head and neck squamous carcinoma.

Head and neck squamous carcinoma (HNSC), characterized by a high degree of malignancy, develops in c...

Revolutionary drug repositioning: the preventive and therapeutic potential of metformin and other antidiabetic drugs in age-related macular degeneration.

Age-related macular degeneration (AMD) is a leading cause of blindness among the elderly worldwide. ...

Advancing brain tumor classification: A robust framework using EfficientNetV2 transfer learning and statistical analysis.

Brain tumors are a significant health risk threatening humanity, and they seem to be unique challeng...

Enhancing brain tumor classification by integrating radiomics and deep learning features: A comprehensive study utilizing ensemble methods on MRI scans.

BACKGROUND AND OBJECTIVE: This study aims to assess the effectiveness of combining radiomics feature...

Integrating necroptosis into pan-cancer immunotherapy: a new era of personalized treatment.

INTRODUCTION: Necroptosis has emerged as a promising biomarker for predicting immunotherapy response...

AI potential in PET/CT cancer imaging.

Positron emission tomography/computed tomography (PET/CT) is a hybrid medical imaging technique that...

iDCNNPred: an interpretable deep learning model for virtual screening and identification of PI3Ka inhibitors against triple-negative breast cancer.

Triple-negative breast cancer (TNBC) lacks estrogen, progesterone, and HER2 expression, accounting f...

The Value of Machine Learning-based Radiomics Model Characterized by PET Imaging with Ga-FAPI in Assessing Microvascular Invasion of Hepatocellular Carcinoma.

RATIONALE AND OBJECTIVES: This study aimed to develop a radiomics model characterized by Ga-fibrobla...

VaxOptiML: leveraging machine learning for accurate prediction of MHC-I and II epitopes for optimized cancer immunotherapy.

Cancer immunotherapy hinges on accurate epitope prediction for advancing vaccine development. VaxOpt...

Federated learning for enhanced dose-volume parameter prediction with decentralized data.

BACKGROUND: The widespread adoption of knowledge-based planning in radiation oncology clinics is hin...