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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...
Radiological Investigation III: Pulmonary Angiogram and PET Scan01:13

Radiological Investigation III: Pulmonary Angiogram and PET Scan

Radiological investigations are paramount in the diagnosis and management of various pulmonary diseases. Two essential investigations are the Pulmonary Angiogram and the Positron Emission Tomography (PET) Scan.
Pulmonary Angiogram
A Pulmonary Angiogram is an invasive procedure involving injecting a contrast medium through a catheter threaded into the pulmonary artery or the right side of the heart to visualize the pulmonary vasculature. Computed Tomography (CT) scans have mainly replaced this...
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

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Related Experiment Video

Updated: Jun 21, 2026

Multicolor 3D Printing of Complex Intracranial Tumors in Neurosurgery
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Deep learning-based 3D classification of head and neck cancer PET/MRI: Radiologist comparison and Grad-CAM

Joonas Liedes1, Jussi Hirvonen1,2, Oona Rainio1

  • 1Turku PET Centre, University of Turku and Turku University Hospital, Turku, Finland.

Clinical Physiology and Functional Imaging
|September 25, 2025
PubMed
Summary
This summary is machine-generated.

A new AI model accurately detects head and neck cancer (HNC) using PET scans, showing high sensitivity for pre-screening. Further improvements are needed to enhance specificity and reduce false positives before clinical use.

Keywords:
Grad‐CAMPET/MRIdeep learninghead and neck cancerinterpretable AI

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Area of Science:

  • Artificial Intelligence in Medical Imaging
  • Oncology
  • Radiology

Background:

  • Head and neck cancer (HNC) diagnosis relies on imaging, but interpretation can be challenging.
  • Automated classification using deep learning offers potential for improved diagnostic accuracy.
  • Convolutional neural networks (CNNs) are effective for image analysis.

Purpose of the Study:

  • To develop and evaluate a 3D CNN for automated classification of PET/MRI images in HNC patients.
  • To assess the AI model's performance against radiologist interpretation.
  • To explore the model's potential as a diagnostic aid for HNC.

Main Methods:

  • Trained and validated PET-, MRI-, and PET/MRI-based 3D CNN models using data from 202 HNC patients.
  • Utilized 18F-FDG PET/MRI scans for model development.
  • Compared model performance (sensitivity, specificity, accuracy, AUC) with radiologist interpretation on a test set, using Grad-CAM for interpretability.

Main Results:

  • The PET-based model achieved high performance on the test set: AUC 0.92, accuracy 90%, sensitivity 100%, specificity 80%.
  • PET/MRI and MRI-based models underperformed compared to the PET-based model.
  • Grad-CAM analysis confirmed model classifications were based on relevant areas and provided insights into false positives.

Conclusions:

  • The PET-based CNN shows promise as a sensitive pre-screening tool for HNC.
  • Specificity needs enhancement to minimize false positives before clinical integration.
  • Further research with larger datasets and refined model architectures, alongside interpretability tools like Grad-CAM, is essential for clinical adoption.