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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: Jun 13, 2025

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
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Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

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AI-enhanced diagnostic model for pulmonary nodule classification.

Jifei Chen1, Moyu Ming2, Shuangping Huang2

  • 1Department of Biochemistry and Molecular Biology, School of Basic Medicine, Guangxi Medical University, Key Laboratory of Biological Molecular Medicine Research (Guangxi Medical University), Education Department of Guangxi Zhuang Autonomous Region, Nanning, China.

Frontiers in Oncology
|September 16, 2024
PubMed
Summary
This summary is machine-generated.

A new diagnostic model aids in distinguishing benign from malignant pulmonary nodules. This tool improves clinical decisions for ambiguous cases, enabling earlier detection and treatment of lung cancer.

Keywords:
DCAdiagnostic modellung cancernomogrampulmonary nodule

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Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy
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Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy
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Area of Science:

  • Pulmonary Medicine
  • Oncology
  • Biomarker Research

Background:

  • Accurate identification of benign and malignant pulmonary nodules (BPN and MPN) is crucial for reducing mortality.
  • A validated diagnostic model for clinical decision-making in pulmonary nodule assessment is currently lacking.

Purpose of the Study:

  • To develop and validate a diagnostic model for differentiating benign from early-stage malignant pulmonary nodules.
  • To improve clinical decision-making and patient stratification for pulmonary nodules.

Main Methods:

  • Serum concentrations of 7 tumor-associated antigens (7AABs) and 4 tumor markers (4TTMs) were measured in 260 participants.
  • An Artificial Intelligence (AI) pulmonary nodule auxiliary diagnosis system and logistic regression were used for model development with 18 variables.
  • A nomogram and decision curve analysis (DCA) were constructed and validated using training (60%) and validation (40%) cohorts.

Main Results:

  • A regression model incorporating age, CYFRA21_1, AI, PGP9.5, GAGE7, and GBU4_5 was established to identify BPN and early-stage MPN.
  • The nomogram achieved an Area Under the Curve (AUC) of 0.884 in the training cohort and 0.820 in the validation cohort.
  • Calibration curves demonstrated high coherence between predicted and actual probabilities, indicating model reliability.

Conclusions:

  • The developed diagnostic model and DCA provide evidence for refining clinical decisions based on malignancy probability stratification.
  • This model benefits patients with low, moderate, or ambiguous risk by enabling precise stratification, timely detection of malignant nodules, and facilitating early treatment.