Oncology/Hematology

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

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Artificial intelligence for small molecule anticancer drug discovery.

INTRODUCTION: The transition from conventional cytotoxic chemotherapy to targeted cancer therapy wit...

Small sized centroblasts as poor prognostic factor in follicular lymphoma - Based on artificial intelligence analysis.

Histological assessment of centroblasts is an important evaluation in the diagnosis of follicular ly...

Noninvasive prediction of lymph node metastasis in pancreatic cancer using an ultrasound-based clinicoradiomics machine learning model.

OBJECTIVES: This study was designed to explore and validate the value of different machine learning ...

Survival trend and outcome prediction for pediatric Hodgkin and non-Hodgkin lymphomas based on machine learning.

Pediatric Hodgkin and non-Hodgkin lymphomas differ from adult cases in biology and management, yet t...

An extensive analysis of artificial intelligence and segmentation methods transforming cancer recognition in medical imaging.

Recent advancements in computational intelligence, deep learning, and computer-aided detection have ...

Exploratory mapping of tumor associated macrophage nanoparticle article abstracts using an eLDA topic modeling machine learning approach.

The role of macrophages in regulating the tumor microenvironment has spurned the exponential generat...

Improving platelet-RNA-based diagnostics: a comparative analysis of machine learning models for cancer detection and multiclass classification.

Liquid biopsy demonstrates excellent potential in patient management by providing a minimally invasi...

A Multiscale Connected UNet for the Segmentation of Lung Cancer Cells in Pathology Sections Stained Using Rapid On-Site Cytopathological Evaluation.

Lung cancer is an increasingly serious health problem worldwide, and early detection and diagnosis a...

Performance Evaluation of a Novel Artificial Intelligence-Assisted Digital Microscopy System for the Routine Analysis of Bone Marrow Aspirates.

Bone marrow aspiration (BMA) smear analysis is essential for diagnosis, treatment, and monitoring of...

Super-resolution deep-learning reconstruction for cardiac CT: impact of radiation dose and focal spot size on task-based image quality.

This study aimed to evaluate the impact of radiation dose and focal spot size on the image quality o...

Robust brain tumor classification by fusion of deep learning and channel-wise attention mode approach.

Diagnosing brain tumors is a complex and time-consuming process that relies heavily on radiologists'...

A machine learning model based on clinical features and ultrasound radiomics features for pancreatic tumor classification.

OBJECTIVE: This study aimed to construct a machine learning model using clinical variables and ultra...

Diagnostic accuracy of CT-based radiomics and deep learning for predicting lymph node metastasis in esophageal cancer.

BACKGROUND: Esophageal cancer remains a global challenge due to late diagnoses and limited treatment...

Multimodal data integration using machine learning to predict the risk of clear cell renal cancer metastasis: a retrospective multicentre study.

PURPOSE: To develop and validate a predictive combined model for metastasis in patients with clear c...

Systematic review of approaches to detection and classification of skin cancer using artificial intelligence: Development and prospects.

In recent years, there has been a significant improvement in the accuracy of the classification of p...

Deep learning-based pathological prediction of lymph node metastasis for patient with renal cell carcinoma from primary whole slide images.

BACKGROUND: Metastasis renal cell carcinoma (RCC) patients have extremely high mortality rate. A pre...

Ultrasensitive plasma-based monitoring of tumor burden using machine-learning-guided signal enrichment.

In solid tumor oncology, circulating tumor DNA (ctDNA) is poised to transform care through accurate ...

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