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Radiogenomic Models Using Machine Learning Techniques to Predict EGFR Mutations in Non-Small Cell Lung Cancer.

Jay Kumar Raghavan Nair^1,2,3, Umar Abid Saeed^1,3, Connor C McDougall⁴

¹Department of Radiology, 54473McGill University Health Centre, Montreal, Québec, Canada.

Canadian Association of Radiologists Journal = Journal L'Association Canadienne Des Radiologistes

|February 18, 2020

Summary

This summary is machine-generated.

Radiomics models from CT and PET-CT scans can identify epidermal growth factor receptor (EGFR) mutations in non-small cell lung cancer (NSCLC). These imaging signatures aid in pretreatment assessment for precision therapy.

Keywords:

epidermal growth factor receptor (EGFR)machine-learning non-small cell lung cancer ( NSCLC)radiomics

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

Area of Science:

Radiomics and Medical Imaging
Oncology
Genomics

Background:

Non-small cell lung cancer (NSCLC) is a leading cause of cancer mortality.
Epidermal growth factor receptor (EGFR) mutations are key drivers in NSCLC, influencing treatment decisions.
Accurate identification of EGFR mutation status is crucial for effective precision therapy.

Purpose of the Study:

To develop radiogenomic models using texture features from CT and 18F-FDG PET-CT images.
To predict the presence of EGFR mutations in NSCLC patients.
To differentiate between specific EGFR mutations (exon 19 vs. exon 21).

Main Methods:

Retrospective analysis of 50 NSCLC patients with known EGFR mutation status.
Extraction of texture features from pretreatment CT and FDG PET-CT images.
Development of multivariate logistic regression models to predict EGFR mutations.

Main Results:

FDG PET-CT texture features achieved an AUC of 0.87 for differentiating EGFR mutant vs. wild type.
CT texture features achieved an AUC of 0.83 for differentiating EGFR mutant vs. wild type.
FDG PET-CT texture features showed an AUC of 0.86 for discriminating between EGFR exon 19 and 21 mutations.

Conclusions:

Texture analysis of CT and FDG PET-CT images can identify EGFR mutations in NSCLC.
Radiomic signatures show potential for pretreatment assessment and prognosis in precision oncology.
Imaging-based prediction of EGFR mutations may guide personalized treatment strategies.