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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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Related Experiment Video

Updated: Apr 21, 2026

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
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An Interpretable Radiomics Model Based on Multi-DLCT Images for Differentiating Benign and Malignant Solid Solitary

Ze Lin1, Pei Huang2, Shiyin Xiao3

  • 1Department of Radiology, Wuhan Third Hospital (Tongren Hospital of WuHan University), Wuhan, China.

Medical Physics
|April 20, 2026
PubMed
Summary

This study developed a dual-layer CT radiomics model to differentiate benign and malignant solid pulmonary nodules. The interpretable SVM-based model accurately distinguished between nodule types, improving early lung cancer diagnosis.

Keywords:
diagnosis (differential)dual‐layer spectral detector CTlung cancerradiomicssolitary pulmonary nodule

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Oncology

Background:

  • Early-stage lung cancer often presents as solitary pulmonary nodules.
  • Solid pulmonary nodules (SPNs) indicate higher malignancy risk than subsolid nodules.
  • Accurate differentiation is crucial for timely treatment and improved patient outcomes.

Purpose of the Study:

  • To create an interpretable radiomics model using multi-parametric dual-layer CT (DLCT) images.
  • To non-invasively differentiate between benign and malignant solid solitary pulmonary nodules (SSPNs).

Main Methods:

  • Radiomic features were extracted from seven DLCT image series (CI, ID, Zeff, ED, VMI, VNC).
  • Feature selection used Mann-Whitney U test, Spearman correlation, and LASSO.
  • Support Vector Machine (SVM) algorithm was employed to build the radiomics model.

Main Results:

  • The SVM-based combined model showed robust performance across training, internal, and external test cohorts (AUCs: 0.909, 0.852, 0.793).
  • The model achieved the highest AUC in the external test cohort.
  • The combined model outperformed conventional imaging (CI) in differentiating SSPNs.

Conclusions:

  • An interpretable radiomics model using multi-parametric DLCT images and SVM can accurately differentiate benign and malignant SSPNs.
  • This approach offers a non-invasive tool for improved lung nodule characterization.
  • The findings support the clinical utility of DLCT-based radiomics in lung cancer diagnosis.