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Related Experiment Video

Updated: Jan 13, 2026

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

Lung Nodule Malignancy Classification Integrating Deep and Radiomic Features in a Three-Way Attention-Based Fusion

Sadaf Khademi¹, Shahin Heidarian², Parnian Afshar¹

¹Concordia Institute for Information Systems Engineering, Montreal, QC H3G 1M8, Canada.

Journal of Imaging

|October 28, 2025

Summary

This summary is machine-generated.

A new hybrid framework, I-VISTA, accurately classifies lung adenocarcinoma invasiveness using integrated visual, spatial, and temporal features. This deep learning and radiomic approach improves differentiation of early-stage from invasive lung cancers.

Keywords:

attention fusion auto-encoder deep learning lung cancer malignancy classification vision transformer

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Electromagnetic Navigation Transthoracic Nodule Localization for Minimally Invasive Thoracic Surgery

Electromagnetic Navigation Transthoracic Nodule Localization for Minimally Invasive Thoracic Surgery

Published on: May 4, 2022

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Lung adenocarcinomas presenting as subsolid nodules require accurate invasiveness assessment for optimal treatment.
Differentiating between minimally invasive and invasive adenocarcinoma is critical for patient management.
Current diagnostic methods can be limited in fully characterizing nodule invasiveness.

Purpose of the Study:

To develop and evaluate a novel hybrid framework, I-VISTA, for assessing lung adenocarcinoma invasiveness.
To integrate visual, spatial, and temporal features using deep learning and radiomic models.
To improve the classification accuracy of subsolid nodules into different invasiveness groups.

Main Methods:

A hybrid framework (I-VISTA) was developed, integrating three parallel processing paths: Shifted Window (SWin) Transformer for spatial features, Convolutional Auto-Encoder (CAE) Transformer for inter-slice relations, and a 3D Radiomic path for texture analysis.
Features from these paths were fused using a Criss-Cross attention module for nodule classification.
The framework was evaluated on 114 pathologically proven lung adenocarcinomas using a ten-fold cross-validation scheme.

Main Results:

The I-VISTA framework achieved high performance metrics: 93.93% overall accuracy, 92.66% sensitivity, and 94.99% specificity.
The Area Under the ROC Curve (AUC) was 0.93 ± 0.08.
The hybrid approach integrating deep learning and radiomic models significantly outperformed standalone models in differentiating G1 (less invasive) from G2 (invasive) subsolid nodules.

Conclusions:

The proposed I-VISTA framework demonstrates superior performance in classifying the invasiveness of lung adenocarcinomas presenting as subsolid nodules.
Integrating multi-modal features through deep learning and radiomics offers a comprehensive approach for improved diagnostic accuracy.
This hybrid model holds promise for enhancing clinical decision-making in lung nodule management.