Oncology/Hematology

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

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Deep learning-based diagnosis of histopathological patterns for invasive non-mucinous lung adenocarcinoma using semantic segmentation.

OBJECTIVES: The application of artificial intelligence (AI) to the field of pathology has facilitate...

Deep learning-based multiomics integration model for predicting axillary lymph node metastasis in breast cancer.

To develop a deep learning-based multiomics integration model. Five types of omics data (mRNA, DNA...

A performance evaluation of drug response prediction models for individual drugs.

Drug response prediction is important to establish personalized medicine for cancer therapy. Model c...

A deep-learning assisted bioluminescence tomography method to enable radiation targeting in rat glioblastoma.

. A novel solution is required for accurate 3D bioluminescence tomography (BLT) based glioblastoma (...

Follow-up of liver metastases: a comparison of deep learning and RECIST 1.1.

OBJECTIVES: To compare liver metastases changes in CT assessed by radiologists using RECIST 1.1 and ...

Non-endoscopic Applications of Machine Learning in Gastric Cancer: A Systematic Review.

PURPOSE: Gastric cancer is an important health burden characterized by high prevalence and mortality...

A CT-based Deep Learning Radiomics Nomogram for the Prediction of EGFR Mutation Status in Head and Neck Squamous Cell Carcinoma.

RATIONALE AND OBJECTIVES: Accurately assessing epidermal growth factor receptor (EGFR) mutation stat...

Development of a deep learning-based model to diagnose mixed-type gastric cancer accurately.

OBJECTIVE: The accurate diagnosis of mixed-type gastric cancer from pathology images presents a form...

Fovea-UNet: detection and segmentation of lymph node metastases in colorectal cancer with deep learning.

BACKGROUND: Colorectal cancer is one of the most serious malignant tumors, and lymph node metastasis...

Prediction of lymphoma response to CAR T cells by deep learning-based image analysis.

Clinical prognostic scoring systems have limited utility for predicting treatment outcomes in lympho...

Does tumor rupture during robot-assisted partial nephrectomy have an impact on mid-term tumor recurrences?

BACKGROUND: Intraoperative kidney tumor rupture (TR) can occur during robot-assisted partial nephrec...

Biochemical recurrence after chemohormonal therapy followed by robot-assisted radical prostatectomy in very-high-risk prostate cancer patients.

Robot-assisted radical prostatectomy (RARP) has become one of the standard radical treatments for pr...

Deep learning model for predicting the presence of stromal invasion of breast cancer on digital breast tomosynthesis.

To develop a deep learning (DL)-based algorithm to predict the presence of stromal invasion in breas...

Effectiveness of deep learning classifiers in histopathological diagnosis of oral squamous cell carcinoma by pathologists.

The study aims to identify histological classifiers from histopathological images of oral squamous c...

Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives.

Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that primarily involve...

A review of deep learning and radiomics approaches for pancreatic cancer diagnosis from medical imaging.

PURPOSE OF REVIEW: Early and accurate diagnosis of pancreatic cancer is crucial for improving patien...

Deep learning exploration of single-cell and spatially resolved cancer transcriptomics to unravel tumour heterogeneity.

Tumour heterogeneity is one of the critical confounding aspects in decoding tumour growth. Malignant...

A Review of the Systemic Manifestations of Hepatitis B Virus Infection, Hepatitis D Virus, Hepatocellular Carcinoma, and Emerging Therapies.

Chronic hepatitis B virus (HBV) infection affects about 262 million people worldwide, leading to ove...

Identifying key factors in cell fate decisions by machine learning interpretable strategies.

Cell fate decisions and transitions are common in almost all developmental processes. Therefore, it ...

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