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Related Concept Videos

Radiological Investigation III: Pulmonary Angiogram and PET Scan01:13

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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 30, 2026

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
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AI-assisted clinico-quantitative imaging nomogram for preoperative malignancy risk in solid and part-solid pulmonary

Yingding Ruan1, Chuan Long1, Wenjun Cao2

  • 1Department of Thoracic Surgery, the First People's Hospital of Jiande, Jiande, China.

Frontiers in Oncology
|April 29, 2026
PubMed
Summary

An AI tool aids in assessing lung nodule cancer risk before surgery. This clinico-quantitative imaging model combines AI-extracted features and clinical data for personalized preoperative risk stratification.

Keywords:
artificial intelligencenomogrampulmonary nodulequantitative imagingrisk prediction

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Pulmonary nodules (PNs) require accurate preoperative risk assessment for malignancy.
  • Distinguishing benign from malignant PNs is crucial for appropriate patient management.

Purpose of the Study:

  • To develop and validate an AI-assisted prediction model for preoperative malignancy risk in solid and part-solid pulmonary nodules (PNs) ≤ 3 cm.
  • To integrate automatically extracted quantitative imaging features with clinical data for individualized risk assessment.

Main Methods:

  • Retrospective analysis of 951 patients with PNs ≤ 3 cm who underwent surgical resection.
  • Utilized AI software (InferRead CT Lung AI) for automatic measurement of quantitative CT features.
  • Developed a multivariable logistic regression model incorporating AI-derived features, clinical variables, and inflammatory markers, followed by internal bootstrap validation.

Main Results:

  • The AI-assisted model achieved strong discrimination with an AUC of 0.836 and excellent calibration (MAE 0.015).
  • Decision curve analysis showed meaningful clinical utility across a range of threshold probabilities.
  • Risk stratification identified distinct malignancy rates (43.3% to 95.0%) across predicted risk strata in the surgically managed cohort.

Conclusions:

  • An AI-assisted clinico-quantitative imaging nomogram provides a validated tool for preoperative malignancy risk assessment in indeterminate PNs.
  • The model is best suited for preoperative surgical decision support in malignancy-enriched cohorts.
  • External validation in unselected cohorts is necessary for broader implementation.