Oncology/Hematology

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

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Exploring the potential of structure-based deep learning approaches for T cell receptor design.

Deep learning methods, trained on the increasing set of available protein 3D structures and sequence...

Artificial intelligence in the radiological diagnosis of cancer.

Artificial intelligence (AI) is being used to diagnose deadly diseases such as cancer. The possible ...

Integrating machine learning to advance epitope mapping.

Identifying epitopes, or the segments of a protein that bind to antibodies, is critical for the deve...

Salidroside sensitizes Triple-negative breast cancer to ferroptosis by SCD1-mediated lipogenesis and NCOA4-mediated ferritinophagy.

INTRODUCTION: Triple-negative breast cancer (TNBC) is the primary cause of breast cancer-induced dea...

Aliado - A design concept of AI for decision support in oncological liver surgery.

BACKGROUND: The interest in artificial intelligence (AI) is increasing. Systematic reviews suggest t...

Wee1 inhibitor optimization through deep-learning-driven decision making.

Deep learning has gained increasing attention in recent years, yielding promising results in hit scr...

Navigating the gray zone: Machine learning can differentiate malignancy in PI-RADS 3 lesions.

INTRODUCTION: The objective of this study is to predict the probability of prostate cancer in PI-RAD...

A vision transformer-based deep transfer learning nomogram for predicting lymph node metastasis in lung adenocarcinoma.

BACKGROUND: Lymph node metastasis (LNM) plays a crucial role in the management of lung cancer; howev...

Artificial intelligence-based pathological application to predict regional lymph node metastasis in Papillary Thyroid Cancer.

In this study, a model for predicting lymph node metastasis in papillary thyroid cancer was trained ...

Integrating HRMAS-NMR Data and Machine Learning-Assisted Profiling of Metabolite Fluxes to Classify Low- and High-Grade Gliomas.

Diagnosing and classifying central nervous system tumors such as gliomas or glioblastomas pose a sig...

A deep learning-informed interpretation of why and when dose metrics outside the PTV can affect the risk of distant metastasis in SBRT NSCLC patients.

PURPOSE: Recent papers suggested a correlation between the risk of distant metastasis (DM) and dose ...

Predictive model of prognosis index for invasive micropapillary carcinoma of the breast based on machine learning: a SEER population-based study.

BACKGROUND: Invasive micropapillary carcinoma (IMPC) is a rare subtype of breast cancer. Its epidemi...

Development and validation of machine learning models for diagnosis and prognosis of lung adenocarcinoma, and immune infiltration analysis.

The aim of our study was to develop robust diagnostic and prognostic models for lung adenocarcinoma ...

Explainable machine learning model for predicting paratracheal lymph node metastasis in cN0 papillary thyroid cancer.

Prophylactic dissection of paratracheal lymph nodes in clinically lymph node-negative (cN0) papillar...

Length-scale study in deep learning prediction for non-small cell lung cancer brain metastasis.

Deep learning-assisted digital pathology has demonstrated the potential to profoundly impact clinica...

Machine learning predicts cuproptosis-related lncRNAs and survival in glioma patients.

Gliomas are the most common tumor in the central nervous system in adults, with glioblastoma (GBM) r...

High-resolution AI image dataset for diagnosing oral submucous fibrosis and squamous cell carcinoma.

Oral cancer is a global health challenge with a difficult histopathological diagnosis. The accurate ...

Deep learning-based segmentation for high-dose-rate brachytherapy in cervical cancer using 3D Prompt-ResUNet.

To develop and evaluate a 3D Prompt-ResUNet module that utilized the prompt-based model combined wit...

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