Article

Sensitivity of an AI method for [18F]FDG PET/CT outcome prediction of Diffuse large B-cell lymphoma patients to image reconstruction protocols.

2023-07-11

Abstract excerpt

<h4>Background: </h4> Convolutional Neural Networks (CNN), applied to baseline [ 18 F]-FDG PET/CT maximum intensity projections (MIPs) show potential for treatment outcome prediction in diffuse large B-cell lymphoma (DLBCL). The aim of this study is to investigate the robustness of CNN predictions to different image reconstruction protocols, such as the European Association of Nuclear Medicine Research Ltd. (EARL)...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: aa4f5359-a8ce-5117-b51f-5d88f23eb34a
DOI: 10.21203/rs.3.rs-3128710/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Sensitivity of an AI method for [18F]FDG PET/CT outcome prediction of Diffuse large B-cell lymphoma patients to image reconstruction protocols.DOI 10.21203/rs.3.rs-3128710/v1Read

An artificial intelligence method using 18F-FDG PET maximum intensity projections to predict 2-year time-to-progression in diffuse large B-cell lymphoma patientsSemantic proximity Read

Deriving Imaging Biomarkers for Primary Central Nervous System Lymphoma Using Deep LearningSemantic proximity Read

Testing the Ability of Convolutional Neural Networks to Learn Radiomic FeaturesSemantic proximity Read

2D and 2.5D Deep Learning Models for Neoadjuvant Chemotherapy Response Prediction in Breast CancerSemantic proximity Read

Uncertainty-Aware Deep Learning for Segmentation of Primary Tumour and Pathologic Lymph Nodes in Oropharyngeal Cancer: Insights from a Multi-Centre CohortSemantic proximity Read

18F-FDG PET/CT-Based Deep Radiomic Models for Enhancing Chemotherapy Response Prediction in Breast CancerSemantic proximity Read

[18F]FDG PET radiomics score generated by cross-combination approach for treatment response and prognosis prediction in primary gastrointestinal diffuse large B-cell lymphoma patientsSemantic proximity Read

Prognostic value of combining clinical factors, 18F-FDG PET-based intensity, volumetric features, and deep learning predictor in patients with EGFR-mutated lung adenocarcinoma undergoing targeted therapies: a cross-scanner and temporal validation study.Semantic proximity Read

Development of a High-Performance Multiparametric MRI Oropharyngeal Primary Tumor Auto-Segmentation Deep Learning Model and Investigation of Input Channel Effects: Results from a Prospective Imaging RegistrySemantic proximity Read

Comparison of radiomic pre-processing steps in the reproducible prediction of disease free survival across multi-scanners/centersSemantic proximity Read

Can a Deep Progressive Learning Method Help to Achieve High Image Quality as Total-body PET Imaging?Semantic proximity Read

Interpreting convolutional neural network explainability for head-and-neck cancer radiotherapy organ-at-risk segmentationSemantic proximity Read