Oncology/Hematology

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

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PyTMLE: A Flexible Python Library for Targeted Estimation of Survival and Competing Risks using Causal Machine Learning

Targeted estimation offers a robust and unbiased approach for causal inference of the average treatment effect (ATE) from observational data, even with confounding, dependent censoring, and competing risks. Its advantages include double robustness, statistical rigor, and flexible data-adaptive modeling, potentially leveraging machine/deep learning. However, existing implementations lack model sele...

Cascaded Multimodal Deep Learning in the Differential Diagnosis, Progression Prediction, and Staging of Alzheimer’s and Frontotemporal Dementia

Dementia is a complex condition whose multifaceted nature poses significant challenges in the diagnosis, prognosis, and treatment of patients. Despite the availability of large open-source data fueling a wealth of promising research, effective translation of preclinical findings to clinical practice remains difficult. This barrier is largely due to the complexity of unstructured and disparate prec...

Evaluation and Application of a Probability of Success (POS) Framework in Oncology Trials

Oncology drug development encounters considerable challenges due to positive Phase I trials rarely leading to regulatory approvals, extremely competit...

Multi-Omics and AI-/ML-Driven Integration of Nutrition and Metabolism in Cancer: A Systematic Review, Meta-Analysis, and Translational Algorithm

Cancer is increasingly recognized as a metabolic disease with strong nutritional determinants. Recent advances in multi-omics technologies and artific...

The Golgi Apparatus as an Arbiter of Oncofetal Reprogramming: A Systematic Review and Meta-Analysis Linking Embryonic Germ Layer Origin to the Post-Translational Modification Landscape of Cancer

Post-translational modifications (PTMs) represent a fourth dimension of the genetic code, orchestrated by the Golgi apparatus and central to the biolo...

Integrating Bioinformatics and Machine Learning to Identify Mitochondria-Related Biomarkers and Their Association with Immune Infiltration in BK polyomavirus-associated nephropathy

BK polyomavirus-associated nephropathy (BKPyVAN) is a serious complication of kidney transplantation. Numerous kidney diseases such as BKPyVAN have be...

AI-Powered Segmentation and Prognosis with Missing MRI in Pediatric Brain Tumors

Brain MRI is the main imaging modality for pediatric brain tumors (PBTs); however, incomplete MRI exams are common in pediatric neuro-oncology setting...

Deep learning based treatment remission prediction to transcranial direct current stimulation in bipolar depression using EEG power spectral density

Bipolar disorder is characterized by marked changes in mood and activity levels and is a leading cause of disability worldwide. We sought to investiga...

Evaluation of Large Language Model-Generated Patient Information for Communicating Radiation Risk

Large language models are increasingly used to generate patient information in healthcare. However, their ability to communicate complex topics, such ...

Large Language Model-Based Entity Extraction Reliably Classifies Pancreatic Cysts and Reveals Predictors of Malignancy: A Cross-Sectional and Retrospective Cohort Study

Pancreatic cystic lesions (PCLs) are often discovered incidentally on imaging and may progress to pancreatic ductal adenocarcinoma (PDAC). PCLs have a...

DeepSpot: Leveraging Spatial Context for Enhanced Spatial Transcriptomics Prediction from H&E Images

Spatial transcriptomics technology remains resource-intensive and unlikely to be routinely adopted for patient care soon. This hinders the development...

Implementing a context-augmented large language model to guide precision cancer medicine

The rapid expansion of molecularly informed therapies in oncology, coupled with evolving regulatory FDA approvals, poses a challenge for oncologists s...

Comprehensive characterization of granular fibrotic and cellular features in liver tissue enabled by deep learning models

Histologic staging of metabolic dysfunction-associated steatohepatitis (MASH) requires semiquantitative assessment of hepatocellular ballooning, steat...

Prediction of OncotypeDX recurrence score using H&E stained WSI images

The OncotypeDX 21-gene assay is a widely adopted tool for estimating recurrence risk and informing chemotherapy decisions in early-stage, hormone rece...

Deep Learning-Based Oral Cancer Screening via Smartphone Imagery and Real-Time Web Interface

Oral cancer is a significant public-health issue and the existing methods of its detection are not as simple or fast as to be applicable by a wide pop...

Beyond Accuracy: Multidimensional Evaluation of Large Language Models in Hepatocellular Carcinoma Management Emphasizing Prompting

Hepatocellular carcinoma is the most common type of primary liver cancer and remains a major global health challenge. In resource-limited settings, pa...

AI-based Hepatic Steatosis Detection and Integrated Hepatic Assessment from Cardiac CT Attenuation Scans Enhances All-cause Mortality Risk Stratification: A Multi-center Study

Hepatic steatosis (HS) is a common cardiometabolic risk factor frequently present but under-diagnosed in patients with suspected or known coronary art...

Network-Based Stratification Refines Stratification of Intermediate-Risk Acute Myeloid Leukemia Samples

The European LeukemiaNet (ELN) risk stratification of acute myeloid leukemia (AML) uses genetic and molecular markers to categorize patients. However,...

“A Population-Specific Breast Cancer Risk Prediction Model for Indian Women (A Pilot Study): Advancing Beyond Traditional Assessment Tools”

Breast cancer is the most prevalent cancer among women in India, characterized by late-stage diagnoses and high mortality rates. Existing breast cance...

Leveraging proteomics and transfer learning for head and neck cancer detection in saliva

Early detection of Head and neck cancer (HNC) has the potential to substantially improve patient survival, yet no biomarker tests for early detection ...

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