Related Experiment Video

Updated: Jan 29, 2026

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
10:17

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

Published on: January 8, 2018

13.6K

Identifying EGFR mutations in lung adenocarcinoma by noninvasive imaging using radiomics features and random forest

Tian-Ying Jia1, Jun-Feng Xiong2,3, Xiao-Yang Li1

  • 1Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, No.241 Huaihai Road, Shanghai, 200030, China.

European Radiology
|February 20, 2019
PubMed
Abstract

Insights

Radiomics analysis of CT scans offers a noninvasive method to predict epidermal growth factor receptor (EGFR) gene mutations in lung adenocarcinoma. This approach aids treatment decisions when biopsies are risky, though further accuracy improvements are needed.

Area of Science:

  • Medical Imaging
  • Oncology
  • Genetics

Background:

  • Epidermal growth factor receptor (EGFR) mutations are crucial for lung adenocarcinoma treatment.
  • Obtaining tissue samples for EGFR testing can be challenging and risky.
  • Noninvasive methods are needed to assess EGFR mutation status.

Purpose of the Study:

  • To evaluate the utility of radiomics features from CT scans for predicting EGFR mutations in lung adenocarcinoma.
  • To develop and validate a noninvasive model for identifying EGFR mutation status.

Main Methods:

  • A retrospective study included 503 lung adenocarcinoma patients.
  • Radiomics features were extracted from pre-operative CT scans.
  • Random forest models were trained to predict EGFR mutation status, with and without clinical data.

Main Results:

  • The radiomics model achieved an Area Under the Curve (AUC) of 0.802.
  • Adding clinical features (sex, smoking history) improved the AUC to 0.828.
  • The model demonstrated a sensitivity of 60.6% and specificity of 85.1%.

Conclusions:

  • Radiomics can reflect genetic differences and has diagnostic value for EGFR mutation status.
  • CT image-based radiomics models offer a potential noninvasive tool for treatment decisions.
  • Further improvements in accuracy are required for clinical application.
Keywords:
Epidermal growth factor receptor (EGFR)Non-small cell lung cancer (NSCLC)RadiomicsRandom forest

More Related Videos

Deciphering the Structural Effects of Activating EGFR Somatic Mutations with Molecular Dynamics Simulation15:05

Deciphering the Structural Effects of Activating EGFR Somatic Mutations with Molecular Dynamics Simulation

Published on: May 20, 2020

9.3K
A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds13:34

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

Published on: April 6, 2016

10.6K

Related Concept Videos

Mutations01:39

Mutations

Overview
94.4K
Mutations01:35

Mutations

Mutations are changes in the sequence of DNA. These changes can occur spontaneously or they can be induced by exposure to environmental factors. Mutations can be characterized in a number of different ways: whether and how they alter the amino acid sequence of the protein, whether they occur over a small or large area of DNA, and whether they occur in somatic cells or germline cells.
Chromosomal Alterations Are Large-Scale Mutations
While point mutations are changes in a single nucleotide in...
44.5K
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
64.0K
Viral Mutations00:36

Viral Mutations

A mutation is a change in the sequence of bases of DNA or RNA in a genome. Some mutations occur during replication of the genome due to errors made by the polymerase enzymes that replicate DNA or RNA. Unlike DNA polymerase, RNA polymerase is prone to errors because it is not capable of “proofreading” its work. Viruses with RNA-based genomes, like HIV, therefore accrue mutations faster than viruses with DNA-based genomes. Because mutation and recombination provide the raw material...
39.9K
Random Error01:04

Random Error

Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
9.8K
Random Variables01:09

Random Variables

A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
17.8K