Application of machine learning in the management of lymphoma

This comprehensive review of the general workflow of ML highlights recent advances in ML techniques in the diagnosis, treatment, and prognosis of lymphoma.

ML can promote accurate lymphoma diagnosis and provide patients with prognostic information and more individualized treatment options.

Application of machine learning in the management of lymphoma

In the realm of oncology, ML has shown potential in identifying patterns and anomalies indicative of diseases such as lymphoma. By utilizing extensive datasets ...

Paper: A New Machine Learning-Based Prognostic Prediction ...

A multitude of clinical, biological and PET data are available and could help to precise R/R DLBCL probability. In this study, we propose a new strategy based ...

Artificial Intelligence Applications in Lymphoma Diagnosis and ...

Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has emerged as a promising tool to enhance the accuracy ...

Machine Learning–Based Survival Prediction Models for ...

The new prognostic model for aHL on the basis of ML techniques demonstrated a substantial improvement compared with the IPS models, but yielded ...

Artificial intelligence performance in detecting lymphoma from ...

Accurate diagnosis and early treatment are essential in the fight against lymphatic cancer. The application of artificial intelligence (AI) ...

Application of machine learning in the management of lymphoma

... The application of ML in lymphoma management has been shown to be promising concerning clinical studies, demonstrating great potential in different aspects ...

Machine Learning Models for the Diagnosis and Prognosis ...

Classification models were established to predict the diagnosis and prognosis of HGBL by machine learning algorithms in this study. The Extreme ...

Machine learning for evolutive lymphoma and residual masses ...

We present in this work, a machine learning-based computer-aided diagnosis (CAD) applied to whole-body diffusion-weighted magnetic resonance images. Methods: ...

Machine Learning-Based Prognostic Model for Hodgkin Lymphoma ...

Researchers developed a machine learning (ML)-based prognostic model for advanced-stage Hodgkin lymphoma (aHL) that, in a recent study, ...

Artificial Intelligence for the Diagnosis and Treatment of Lymphoma

Machine learning and deep learning aim to use AI to extract knowledge from large datasets in order to accurately classify patients or cancers ...

An artificial intelligence method using FDG PET to predict treatment ...

Convolutional neural networks (CNNs) may improve response prediction in diffuse large B-cell lymphoma (DLBCL). The aim of this study was to ...

Machine Learning to Predict Interim Response in Pediatric Classical ...

The envisaged solution is the development of a practical scoring system or a mobile smartphone application. Such tools would allow for the input ...

A Predictive Model Based on Machine Learning for Early ...

Introduction:Management of follicular lymphoma (FL) improved dramatically in the last decade, and progression/relapse of disease within 24 ...

Integrative Prognostic Machine Learning Models in Mantle Cell ...

Mantle cell lymphoma (MCL) is a rare, incurable B-cell malignancy with heterogeneous clinical outcomes and molecular pathogeneses. Most patients ...

Machine learning in haematological malignancies - The Lancet

These applications include the management of haematological malignancy, in which machine learning has created inroads in pathology, radiology, genomics, and the ...

Applications of Multimodal Artificial Intelligence in Non-Hodgkin ...

Advanced AI systems such as machine learning (ML) and deep learning (DL) can effectively integrate and process these diverse data types to uncover complex ...

The application of machine learning to imaging in hematological ...

A second major application of ML techniques is to improve the detection and monitoring of hematological malignancies for accurate diagnosis, treatment, and ...

Harnessing Machine Learning for Mantle Cell Lymphoma Risk ...

Through these data-driven approaches, we ended up developing of a research team, which is fully focused on artificial intelligence (AI) for ...