AIMC Topic: Lymphoma
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Multicenter investigation of preoperative distinction between primary central nervous system lymphomas and glioblastomas through interpretable artificial intelligence models.
Neuroradiology
Sep 3, 2024
OBJECTIVE: Research into the effectiveness and applicability of deep learning, radiomics, and their integrated models based on Magnetic Resonance Imaging (MRI) for preoperative differentiation between Primary Central Nervous System Lymphoma (PCNSL) a...
Fully automated segmentation and volumetric measurement of ocular adnexal lymphoma by deep learning-based self-configuring nnU-net on multi-sequence MRI: a multi-center study.
Neuroradiology
Jul 17, 2024
PURPOSE: To evaluate nnU-net's performance in automatically segmenting and volumetrically measuring ocular adnexal lymphoma (OAL) on multi-sequence MRI.
A position-enhanced sequential feature encoding model for lung infections and lymphoma classification on CT images.
International journal of computer assisted radiology and surgery
Jul 14, 2024
PURPOSE: Differentiating pulmonary lymphoma from lung infections using CT images is challenging. Existing deep neural network-based lung CT classification models rely on 2D slices, lacking comprehensive information and requiring manual selection. 3D ...
Machine Learning for Automated Interpretation of Fluorodeoxyglucose-Positron Emission Tomography Scans in Lymphoma.
Journal of clinical oncology : official journal of the American Society of Clinical Oncology
Jun 21, 2024
Clinical evaluation of deep learning-enhanced lymphoma pet imaging with accelerated acquisition.
Journal of applied clinical medical physics
May 29, 2024
PURPOSE: This study aims to evaluate the clinical performance of a deep learning (DL)-enhanced two-fold accelerated PET imaging method in patients with lymphoma.
Humans
Prognosis
Female
Aged
Positron Emission Tomography Computed Tomography
Signal-To-Noise Ratio
Middle Aged
Aged, 80 and over
Radiopharmaceuticals
Deep Learning
Whole Body Imaging
Young Adult
Image Processing, Computer-Assisted
Adult
Image Interpretation, Computer-Assisted
Lymphoma
Male
Fluorodeoxyglucose F18
Preliminary study on the ability of the machine learning models based on F-FDG PET/CT to differentiate between mass-forming pancreatic lymphoma and pancreatic carcinoma.
European journal of radiology
May 25, 2024
PURPOSE: The objective of this study was to preliminarily assess the ability of metabolic parameters and radiomics derived from F-fluorodeoxyglucose positron emission tomography/computed tomography (F-FDG PET/CT) to distinguish mass-forming pancreati...
A multicenter proof-of-concept study on deep learning-based intraoperative discrimination of primary central nervous system lymphoma.
Nature communications
May 4, 2024
Accurate intraoperative differentiation of primary central nervous system lymphoma (PCNSL) remains pivotal in guiding neurosurgical decisions. However, distinguishing PCNSL from other lesions, notably glioma, through frozen sections challenges pathol...
Machine learning-based pathomics signature of histology slides as a novel prognostic indicator in primary central nervous system lymphoma.
Journal of neuro-oncology
Apr 1, 2024
PURPOSE: To develop and validate a pathomics signature for predicting the outcomes of Primary Central Nervous System Lymphoma (PCNSL).
Semi-supervised learning towards automated segmentation of PET images with limited annotations: application to lymphoma patients.
Physical and engineering sciences in medicine
Mar 21, 2024
Manual segmentation poses a time-consuming challenge for disease quantification, therapy evaluation, treatment planning, and outcome prediction. Convolutional neural networks (CNNs) hold promise in accurately identifying tumor locations and boundarie...
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