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Malignancy in Ground-Glass Opacity Using Multivariate Regression and Deep Learning Models: A Proof-of-Concept Study.

Abed Agbarya1,2, Edmond Sabo3, Mohammad Sheikh-Ahmad2,4

  • 1Department of Oncology, Bnai-Zion Medical Center, 47 Eliyahu Golomb Avenue, Haifa 3339419, Israel.

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|November 27, 2025
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Summary

Artificial intelligence (AI) deep learning models show superior performance in predicting ground-glass opacity (GGO) malignancy compared to statistical regression. The AI model achieved higher sensitivity and specificity in distinguishing malignant from benign lung lesions on CT scans.

Keywords:
artificial intelligence (AI) deep learningcomputed tomography (CT)ground-glass opacity (GGO)lung cancertexture features: pixels

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Medicine
  • Oncology

Background:

  • Ground-glass opacities (GGOs) on CT scans present a diagnostic challenge in differentiating malignant from benign lung lesions.
  • Accurate prediction of GGO malignancy is crucial for timely and appropriate patient management.

Purpose of the Study:

  • To compare the diagnostic performance of a linear multivariate statistical regression model against an AI deep learning method for predicting GGO malignancy.
  • To evaluate the ability of both methods to distinguish between benign and malignant GGO lesions based on CT scan pixel features.

Main Methods:

  • Retrospective analysis of lung CT scans from 47 patients with pure or part-solid GGO lesions.
  • Manual segmentation of GGOs and extraction of six image texture features using MaZda software.
  • Application of a linear multivariate statistical regression model and a custom-developed AI deep learning model to the extracted features and CT images.

Main Results:

  • The multivariate regression model identified two key variables (S(4,4)AngScMom and WavEnLH_s-2) with significant differences between benign and malignant GGOs, achieving 91% sensitivity and 67% specificity (AUC: 0.8).
  • The AI deep learning model demonstrated superior performance with 100% sensitivity and 80% specificity (AUC: 0.96).
  • Pathology confirmed 32 malignant and 15 benign lesions among the 47 patients.

Conclusions:

  • The AI deep learning model significantly outperformed the multivariate statistical regression in predicting GGO malignancy, particularly in sensitivity and specificity.
  • These findings suggest the potential of AI in improving the accuracy of GGO lesion characterization.
  • Further validation with larger patient cohorts is recommended due to the study's proof-of-concept nature and small sample size.