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Related Concept Videos

Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
Positron Emission Tomography01:29

Positron Emission Tomography

Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body being...
Imaging Studies II: Positron Emission Tomography and Scintigraphy01:25

Imaging Studies II: Positron Emission Tomography and Scintigraphy

Positron Emission Tomography (PET) is a medical imaging technique that provides crucial insights into the body's physiological functions at a molecular level. It is an indispensable resource for diagnosing, staging, and monitoring various illnesses, notably cancer, neurological disorders, and cardiovascular conditions.
Fundamental Principles of PET
Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...

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Erratum to Wu J, Bae J, Chen C, Ryu S, Lozeau D, Stessin A, Prasanna P. Magnetic resonance imaging radiomic analysis of radiation-induced morphea of the breast: a proof-of-concept study. Adv Radiat Oncol 2025;10:101881.

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PET and MRI Guided Irradiation of a Glioblastoma Rat Model Using a Micro-irradiator
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Spatial Radiomic Graphs for Outcome Prediction in Radiation Therapy-treated Head and Neck Squamous Cell Carcinoma

Joseph Bae1, Kartik Mani2, Lukasz Czerwonka3

  • 1Department of Biomedical Informatics, 100 Nicolls Rd, Health Science Center Level 3, Rm 043, Stony Brook, NY 11794.

Radiology. Imaging Cancer
|February 21, 2025
PubMed
Summary

A new radiomic graph framework, RadGraph, accurately predicts recurrence and metastasis in head and neck cancer using CT scans. This deep learning approach improves upon existing methods for predicting local-regional recurrence and distant metastasis.

Keywords:
CTComputer Applications–General (Informatics)Deep LearningHead and Neck Squamous Cell CarcinomaHead/NeckInformaticsLocoregional RecurrenceNeural NetworksRadiation TherapyRadiomicsRadiotherapyTumor Response

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Area of Science:

  • Radiomics and Medical Informatics
  • Deep Learning in Medical Imaging
  • Oncology and Radiation Therapy

Background:

  • Head and neck squamous cell carcinoma (HNSCC) poses significant challenges for predicting treatment outcomes.
  • Accurate prediction of local-regional recurrence (LR) and distant metastasis (DM) is crucial for optimizing radiation therapy in HNSCC.
  • Existing prediction models often lack the ability to comprehensively analyze spatial information from medical images.

Purpose of the Study:

  • To develop and validate RadGraph, a novel radiomic graph framework utilizing deep learning for spatial analysis of pretreatment CT images.
  • To enhance the prediction accuracy of local-regional recurrence (LR) and distant metastasis (DM) in patients with HNSCC.
  • To investigate the utility of graph attention mechanisms for interpreting model predictions and identifying key anatomical regions.

Main Methods:

  • A retrospective study using four public CT datasets of HNSCC patients treated with radiotherapy.
  • Development of a computational graph framework (RadGraph) employing graph attention deep learning to model head and neck anatomy.
  • Integration of clinical features (age, sex, HPV status) and evaluation of model performance using AUC for LR and DM prediction.

Main Results:

  • RadGraph achieved high predictive performance with AUCs up to 0.83 for LR and 0.90 for DM.
  • The framework significantly outperformed the clinical baseline (AUCs up to 0.73 for LR, 0.83 for DM) and prior approaches (AUCs up to 0.81 for LR, 0.87 for DM).
  • Graph attention atlases highlighted cervical lymph node chains as critical regions for outcome prediction, aiding interpretability.

Conclusions:

  • RadGraph effectively leverages radiomic information from both tumor and nontumor regions to predict LR and DM in HNSCC.
  • The framework demonstrates superior predictive capabilities on a large, multi-institutional dataset.
  • Graph attention atlases provide valuable insights into model predictions, enhancing clinical understanding and trust.