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Radiomics-Based Features for Prediction of Histological Subtypes in Central Lung Cancer.

Huanhuan Li¹, Long Gao², He Ma³

¹Department of Radiology, The First Hospital of China Medical University, Shenyang, China.

Frontiers in Oncology

|May 17, 2021

Summary

This summary is machine-generated.

Radiomic features from contrast-enhanced CT scans show promise in noninvasively predicting central lung cancer subtypes. A neural network model effectively classified adenocarcinoma, squamous cell carcinoma, and small cell lung cancer using these features.

Keywords:

central lung cancer computed tomography histological subtype neural network radiomics

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Area of Science:

Radiology
Medical Imaging
Artificial Intelligence

Background:

Accurate histological subtyping of central lung cancer is crucial for treatment selection.
Current methods often rely on invasive biopsies, necessitating non-invasive alternatives.

Purpose of the Study:

To assess the efficacy of radiomic features derived from contrast-enhanced CT (CECT) images for classifying central lung cancer histological subtypes.
To compare the performance of machine learning models using radiomic features alone versus combined clinical and radiomic features.

Main Methods:

A cohort of 200 patients with central lung cancer underwent dual-phase chest CECT.
107 radiomic features were extracted and analyzed using five machine learning classifiers.
Models were trained and validated, with performance evaluated using the area under the receiver operating characteristic curve (AUC).

Main Results:

Radiomic features alone, particularly in a feedforward neural network, achieved high AUCs for subtype classification.
The highest AUCs were 0.879 for adenocarcinoma vs. squamous cell carcinoma, 0.836 for adenocarcinoma vs. small cell lung cancer, and 0.783 for squamous cell carcinoma vs. small cell lung cancer.

Conclusions:

Radiomic features from CECT images offer a potential non-invasive method for predicting central lung cancer histological subtypes.
Neural network classifiers demonstrate suitability for this classification task.