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Related Concept Videos

Radiological Investigation III: Pulmonary Angiogram and PET Scan01:13

Radiological Investigation III: Pulmonary Angiogram and PET Scan

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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...
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Positron Emission Tomography01:29

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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...
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  11. Deep Learning Radiomics Model Based On Pet/ct Predicts Pd-l1 Expression In Non-small Cell Lung Cancer

Deep learning radiomics model based on PET/CT predicts PD-L1 expression in non-small cell lung cancer

Bo Li1, Jie Su1, Kai Liu1

  • 1Department of Radiology, Affiliated Hospital of Xuzhou Medical University, Xuzhou Medical University, Xuzhou, China.

European Journal of Radiology Open
|February 2, 2024

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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View abstract on PubMed

Summary
This summary is machine-generated.

A novel PET/CT-based deep learning and radiomics model accurately predicts Programmed Cell Death Protein-1 ligand (PD-L1) expression in non-small cell lung cancer (NSCLC). This non-invasive tool aids clinicians in selecting patients for immunotherapy.

Area of Science:

  • Radiology
  • Oncology
  • Artificial Intelligence

Background:

  • Programmed Cell Death Protein-1 ligand (PD-L1) is a critical prognostic indicator for immunotherapy in non-small cell lung cancer (NSCLC).
  • Accurate prediction of PD-L1 expression is crucial for effective patient selection for immunotherapy.

Purpose of the Study:

  • To develop and evaluate a non-invasive deep learning and radiomics model using Positron Emission Tomography and Computed Tomography (PET/CT) for predicting PD-L1 expression in NSCLC.
  • To compare the performance of the developed fusion model against standalone radiomics and deep learning models.

Main Methods:

  • 136 NSCLC patients' PET/CT images were analyzed, with data split into training (70%) and validation (30%) sets.
  • Radiomics and deep learning features were extracted, and significant features were selected using statistical methods (Mann-Whitney U-test, LASSO, Spearman correlation).
Keywords:
Deep learningMachine learningNon-small cell lung cancerPD-L1

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  • A fusion model combining radiomics and deep learning features was developed and its performance evaluated using Area Under the Curve (AUC), sensitivity, specificity, and accuracy.

    • Main Results:

    • The fusion model demonstrated superior performance in both training (AUC: 0.954) and validation (AUC: 0.910) datasets compared to individual radiomics (AUC: 0.829, 0.785) and deep learning models (AUC: 0.935, 0.867).
    • The fusion model achieved high accuracy in predicting PD-L1 expression in NSCLC patients.

    Conclusions:

    • The developed PET/CT-based deep learning radiomics fusion model offers an accurate and non-invasive method for predicting PD-L1 expression in NSCLC.
    • This model can assist clinicians in identifying suitable candidates for immunotherapy, potentially improving treatment outcomes.
    PET/CT
    Radiomics