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Automated Interstitial Lung Abnormality Probability Prediction at CT: A Stepwise Machine Learning Approach in the

Akinori Hata¹, Kota Aoyagi¹, Takuya Hino¹

¹From the Center for Pulmonary Functional Imaging, Department of Radiology (A.H., T.H., N.W., V.I.V., M. Nishino, H.H.), and Pulmonary and Critical Care Division (G.M.H.), Brigham and Women's Hospital and Harvard Medical School, 75 Francis St, Boston, MA 02115; Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Osaka, Japan (A.H., N.T.); Canon Medical Systems, Tochigi, Japan (K.A., Y.M., M. Nakatsugawa, A.K., N.S., M.O.); Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan (T.H., N.W.); R&D Headquarters, Canon, Tokyo, Japan (M.K.); Department of Biostatistics, University of Michigan, Ann Arbor, Mich (J.S., Y.L.); Departments of Biostatistics (X.W., D.C.C.) and Environmental Health (D.C.C.), Harvard T.H. Chan School of Public Health, Boston, Mass; and Department of Imaging, Dana Farber Cancer Institute, Boston, Mass (M. Nishino).

|September 3, 2024

Summary

This summary is machine-generated.

Automated models can now predict interstitial lung abnormalities (ILAs) probability from CT scans. Machine learning achieved a high accuracy (AUC 0.87), showing potential for clinical use in identifying ILAs.

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Area of Science:

Radiology
Medical Imaging
Artificial Intelligence

Background:

Interstitial lung abnormalities (ILAs) have clinical significance, but automated detection on CT scans is not yet established.
Accurate identification of ILAs is crucial for patient management and further research.

Purpose of the Study:

To develop and validate machine learning models for automated prediction of ILA probability using CT images.
To assess the performance of different machine learning classifiers in ILA detection.

Main Methods:

A retrospective analysis of 1382 CT scans from the Boston Lung Cancer Study was performed.
Automated ILA probability prediction models were built using a stepwise approach with section and case inference models.
Machine learning classifiers including Support Vector Machine (SVM), Random Forest (RF), and Convolutional Neural Network (CNN) were evaluated, with ground truth established by expert radiologists.

Main Results:

The best-performing model, utilizing a three-label method for section inference and a two-label method with RF for case inference, achieved an Area Under the Curve (AUC) of 0.87.
Out of 1382 scans, 8% were confirmed positive for ILA, 36% were indeterminate, and 57% were negative.
The model demonstrated substantial performance in estimating ILA probability.

Conclusions:

The developed automated model shows significant potential for clinical application in identifying interstitial lung abnormalities.
This AI-driven approach can aid radiologists in the detection and assessment of ILAs on CT scans.