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Related Experiment Video

Updated: Aug 23, 2025

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Deep learning-based growth prediction for sub-solid pulmonary nodules on CT images.

Ri-Qiang Liao1, An-Wei Li2, Hong-Hong Yan1

  • 1Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.

Frontiers in Oncology
|October 31, 2022
PubMed
Summary
This summary is machine-generated.

Mass increase rate is a sensitive indicator for pulmonary sub-solid nodule (SSN) growth, particularly for lung cancer detection. A deep learning model effectively predicts SSN growth, improving patient management.

Keywords:
deep learninggrowthmassradiomicssub solid pulmonary nodules

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Pulmonology

Background:

  • Estimating pulmonary sub-solid nodule (SSN) growth is critical for effective patient follow-up.
  • Current methods for assessing SSN growth require refinement for optimal clinical utility.

Purpose of the Study:

  • To evaluate the measurement sensitivity of SSN diameter, volume, and mass for identifying growth.
  • To develop and validate a deep learning-based model for predicting SSN growth.

Main Methods:

  • Retrospective analysis of 2,523 patients with at least two years of examination records for SSNs.
  • Utilized LUNA16 and Lndb19 datasets to train models for automatic SSN measurement (diameter, volume, mass).
  • Developed and verified a deep learning model (SiamModel) and a radiomics model to predict SSN growth.

Main Results:

  • Mass increase rate showed higher sensitivity for SSN growth associated with lung cancer compared to diameter and volume.
  • The SiamModel demonstrated superior performance over the radiomics model in both validation and external test sets (AUCs ranging from 0.760 to 0.862).
  • The SiamModel successfully predicted SSN growth using initial CT data with high AUC values (0.855-0.821).

Conclusions:

  • Mass increase rate is a more sensitive biomarker for SSN growth related to lung cancer than diameter or volume.
  • A deep learning-based model shows significant potential for accurately predicting SSN growth, aiding in clinical management.