Predicting High-risk Lung Adenocarcinoma in Solid and Part-solid Nodules on Low-dose CT: A Multicenter Study

Jieke Liu ¹, Yong Li ¹, Yu Long ¹

⁰Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China (J.L., Y.L., Y.L., L.G., H.Q., P.Z.).

Academic Radiology +

|

December 13, 2024

View abstract on PubMed

Summary

A new radiomic model using low-dose CT (LDCT) accurately identifies high-risk lung adenocarcinomas (LUADs) in solid and part-solid nodules. This non-invasive approach aids in early detection and decision-making for lung cancer screening.

Area Of Science

Radiology and Oncology
Medical Imaging Analysis
Artificial Intelligence in Medicine

Background

High-risk features in lung adenocarcinomas (LUADs) like visceral pleural invasion and lymph node metastasis are linked to poor prognosis.
Accurate identification of these high-risk LUADs is crucial for timely and effective patient management.

Purpose Of The Study

To develop and validate a radiomic model and a radiographic model using low-dose CT (LDCT) for predicting high-risk LUADs.
To compare the diagnostic performance of the radiomic and radiographic models in identifying high-risk LUADs within solid and part-solid nodules.

Main Methods

Retrospective analysis of 658 pathologically confirmed LUADs across four centers.
Construction of radiomic and radiographic models using multivariable logistic regression based on extracted features.
Validation of model performance using internal and external datasets, assessed by Area Under the Receiver Operating Characteristic Curve (AUC).

Main Results

The radiomic model incorporated three key features: GLCM_Correlation, GLSZM_SmallAreaEmphasis, and GLDM_LargeDependenceHighGrayLevelEmphasis.
The radiographic model included maximal diameter, consolidation/tumor ratio (CTR), spiculation, and pleural indentation.
The radiomic model demonstrated superior diagnostic performance with AUCs of 0.916 (internal) and 0.938 (external), significantly outperforming the radiographic model (0.868 and 0.880, respectively).

Conclusions

The developed LDCT-based radiomic model effectively identifies high-risk LUADs in solid and part-solid nodules.
This non-invasive radiomic model shows promising diagnostic performance.
The model may support personalized decision-making within lung cancer screening programs.

Keywords:

Adenocarcinoma of lung Low-dose CT Lung cancer Radiomics

Related Concept Videos

Radiological Investigation I: X-ray and CT

Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...