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A Pulmonary Angiogram is an invasive procedure involving injecting a contrast medium through a catheter threaded into the pulmonary artery or the right side of the heart to visualize the pulmonary vasculature. Computed Tomography (CT) scans have mainly replaced this...

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Updated: Dec 9, 2025

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

Multiview framework using a 3D residual network for pulmonary micronodule malignancy risk classification.

Yujie Yang^1,2, Qianqian Zhang^1,2

¹Institute of Computer and Information Engineering, Henan Normal University, Henan Province, Xinxiang, China.

Bio-Medical Materials and Engineering

|September 7, 2020

Summary

This summary is machine-generated.

This study developed a 3D-ResNet framework to classify pulmonary micronodules from CT scans, achieving high accuracy. Early detection of these nodules can significantly improve patient survival rates.

Keywords:

Pulmonary micronodule computed tomography (CT)multiview three-dimensional residual network (3D-ResNet)

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Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Pulmonary micronodules constitute 80% of lung nodules.
Early detection via thoracic computed tomography (CT) scans is vital for survival.

Purpose of the Study:

To estimate the malignancy risk of pulmonary micronodules.
To improve patient survival rates through accurate classification.

Main Methods:

Extracted 3D features from CT images for enhanced characterization.
Employed a 3D residual network (3D-ResNet) for pulmonary micronodule classification.
Utilized a multi-view framework with three parallel ResNets for robust analysis.

Main Results:

Achieved an area under the receiver operating characteristic curve (AUC) of 0.9681 by fusing multi-view image characteristics.
Demonstrated superior performance of 3D-ResNet over 3D-VGG and 3D-Inception in precision, AUC, and accuracy.
Reported recall performance comparable to 3D-Inception.

Conclusions:

The proposed framework is applicable and feasible for pulmonary micronodule classification.
This approach holds potential for improving early diagnosis and patient outcomes.