Oncology/Hematology

Latest AI and machine learning research in oncology/hematology for healthcare professionals.

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POCALI: Prediction and Insight on CAncer LncRNAs by Integrating Multi-Omics Data with Machine Learning.

Long non-coding RNAs (lncRNAs) are receiving increasing attention as biomarkers for cancer diagnosis...

Mitigating bias in prostate cancer diagnosis using synthetic data for improved AI driven Gleason grading.

Prostate cancer (PCa) is a leading cause of cancer-related mortality in men, with Gleason grading cr...

Immune-related gene risk model establishment and role of key gene FUCA1 in malignant pleural mesothelioma.

BACKGROUND: Malignant pleural mesothelioma (MPM) is a rare type of tumor closely associated with asb...

Innate immune cell barrier-related genes inform precision prognosis in pancreatic cancer.

INTRODUCTION: Pancreatic cancer (PC) remains a lethal malignancy with limited treatment options. The...

Construction of a predictive model for relapse of primary autoimmune hemolytic anemia: a retrospective cohort study.

OBJECTIVES: To develop a machine learning-based model to predict the relapse risk of Primary Autoimm...

Deep-Learning-Based Prediction of t(11;14) in Multiple Myeloma H&E-Stained Samples.

BACKGROUND: Translocation of chromosomes 11 and 14 [t(11;14)(q13;32)] is the most common primary tra...

Explainable AI Model Reveals Informative Mutational Signatures for Cancer-Type Classification.

: The prediction of cancer types is primarily reliant on driver genes and their specific mutations. ...

AttentionAML: An Attention-based Deep Learning Framework for Accurate Molecular Categorization of Acute Myeloid Leukemia.

Acute myeloid leukemia (AML) is an aggressive hematopoietic malignancy defined by aberrant clonal ex...

Deep-learning-enabled multi-omics analyses for prediction of future metastasis in cancer.

Metastasis remains the leading cause of cancer-related mortality, yet predicting future metastasis i...

Random field image representations speed up binary discrimination of brain scans and estimate a phenotype glioblastoma cancer cell model.

MRI brain scans alone are not a definitive measure of dementia. Deep-learning algorithms (DLA) and p...

Deep Learning-Based Multimodal Feature Interaction-Guided Fusion: Enhancing the Evaluation of EGFR in Advanced Lung Adenocarcinoma.

RATIONALE AND OBJECTIVES: The aim of this study is to develop a deep learning-based multimodal featu...

Machine learning for cardio-oncology: predicting global longitudinal strain from conventional echocardiographic measurements in cancer patients.

INTRODUCTION: Global longitudinal strain (GLS) is an important prognostic indicator for predicting h...

Interpretable prediction of drug synergy for breast cancer by random forest with features from Boolean modeling of signaling pathways.

Breast cancer is a complex and challenging disease to treat, and despite progress in combating it, d...

Imputing single-cell protein abundance in multiplex tissue imaging.

Multiplex tissue imaging enables single-cell spatial proteomics and transcriptomics but remains limi...

Revealing 3D microanatomical structures of unlabeled thick cancer tissues using holotomography and virtual H&E staining.

In histopathology, acquiring subcellular-level three-dimensional (3D) tissue structures efficiently ...

Artificial intelligence in neuro-oncology: methodological bases, practical applications and ethical and regulatory issues.

Artificial Intelligence (AI) is transforming neuro-oncology by enhancing diagnosis, treatment planni...

ESR Essentials: a step-by-step guide of segmentation for radiologists-practice recommendations by the European Society of Medical Imaging Informatics.

High-quality segmentation is important for AI-driven radiological research and clinical practice, wi...

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