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Related Concept Videos

Computed Tomography01:10

Computed Tomography

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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...
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Related Experiment Video

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Explainable Machine Learning for Predicting Invasiveness of Pulmonary Adenocarcinoma Presenting as Ground-Glass

Huairong Zhang1, Lina Miao2, Li Ma1

  • 1Deparment of Radiology, the General Hospital of Ningxia Medical University, Yinchuan 750003, China (H.Z., L.M., X.S., L.N.O., Y.W., P.W., L.Z.).

Academic Radiology
|October 23, 2025
PubMed
Summary

This study developed a comprehensive model integrating radiomics, deep learning, and intratumoral habitat analysis to accurately predict invasiveness in early-stage lung adenocarcinoma ground-glass nodules (GGNs). This approach supports personalized surgical decisions for lung cancer.

Keywords:
Deep learningGround-glass noduleIntratumoral habitat analysisLung adenocarcinomaRadiomics

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Area of Science:

  • Pulmonary Medicine
  • Radiology
  • Oncology
  • Artificial Intelligence in Medicine

Background:

  • Accurate prediction of invasiveness in early-stage pulmonary adenocarcinoma presenting as ground-glass nodules (GGNs) is challenging.
  • Distinguishing pre-invasive lesions from invasive adenocarcinoma is crucial for appropriate treatment and personalized surgical decision-making.

Purpose of the Study:

  • To integrate radiomics, deep learning, and intratumoral habitat features for improved prediction of GGN invasiveness.
  • To develop a robust model supporting personalized surgical decisions in early-stage lung adenocarcinoma.

Main Methods:

  • A dual-center retrospective study of 516 patients with pathologically confirmed GGNs.
  • Integration of radiomics from non-contrast CT (NECT) and contrast-enhanced CT (CECT), deep learning features (ResNet50), and intratumoral habitat analysis (ITH) using K-means clustering.
  • Development and validation of eight predictive models, including a comprehensive dynamic nomogram, assessed using AUC, DCA, and calibration curves.

Main Results:

  • The comprehensive model demonstrated high accuracy in distinguishing invasive adenocarcinoma from pre-invasive lesions, with AUCs of 0.92 (training), 0.90 (internal validation), and 0.85 (external validation).
  • The model achieved high sensitivity and specificity across validation sets, with decision curve analysis indicating significant clinical utility.
  • SHAP analysis identified wavelet-based texture features, deep learning features, and ITH features as key contributors to predictive performance.

Conclusions:

  • The comprehensive model reliably predicts GGN invasiveness in lung adenocarcinoma.
  • This multi-modal approach bridges imaging and pathology, offering potential advancements in personalized surgical decision-making for early-stage lung cancer.