Deep-Learning-Based Predictive Imaging Biomarker Model for EGFR Mutation Status in Non-Small Cell Lung Cancer from CT Imaging.

Autor:	Mahajan, Abhishek, Kania, Vatsal, Agarwal, Ujjwal, Ashtekar, Renuka, Shukla, Shreya, Patil, Vijay Maruti, Noronha, Vanita, Joshi, Amit, Menon, Nandini, Kaushal, Rajiv Kumar, Rane, Swapnil, Chougule, Anuradha, Vaidya, Suthirth, Kaluva, Krishna, Prabhash, Kumar
Předmět:	PREDICTION models THREE-dimensional imaging RESEARCH funding COMPUTED tomography RADIOMICS ARTIFICIAL intelligence TUMOR markers DESCRIPTIVE statistics DEEP learning ARTIFICIAL neural networks LUNG tumors GENETIC mutation LUNG cancer EPIDERMAL growth factor receptors
Zdroj:	Cancers; Mar2024, Vol. 16 Issue 6, p1130, 17p
Abstrakt:	Simple Summary: Deep-learning-based radiogenomic (DLR) models show promising performance in assisting with lung cancer care. The primary aim of our study was to develop and validate a DLR model to predict EGFR mutation status in non-small-cell lung cancer (NSCLC) patients. Using 990 patients from two clinical trials, the study employed a machine learning pipeline that analysed CT images with manually selected tumour regions. Two deep convolutional neural networks segmented lung masses and nodules from 3D regions of the CT image. The combined radiomics and DLR model achieved 88% accuracy in predicting EGFR mutations, outperforming individual models. The semantic features extracted from CT images also contributed to accurate predictions. The study suggests that this AI-based model in combination with CT semantic features could serve as a non-invasive biomarker that aids in predicting EGFR mutation status with significant accuracy. Purpose: The authors aimed to develop and validate deep-learning-based radiogenomic (DLR) models and radiomic signatures to predict the EGFR mutation in patients with NSCLC, and to assess the semantic and clinical features that can contribute to detecting EGFR mutations. Methods: Using 990 patients from two NSCLC trials, we employed an end-to-end pipeline analyzing CT images without precise segmentation. Two 3D convolutional neural networks segmented lung masses and nodules. Results: The combined radiomics and DLR model achieved an AUC of 0.88 ± 0.03 in predicting EGFR mutation status, outperforming individual models. Semantic features further improved the model's accuracy, with an AUC of 0.88 ± 0.05. CT semantic features that were found to be significantly associated with EGFR mutations were pure solid tumours with no associated ground glass component (p < 0.03), the absence of peripheral emphysema (p < 0.03), the presence of pleural retraction (p = 0.004), the presence of fissure attachment (p = 0.001), the presence of metastatic nodules in both the tumour-containing lobe (p = 0.001) and the non-tumour-containing lobe (p = 0.001), the presence of ipsilateral pleural effusion (p = 0.04), and average enhancement of the tumour mass above 54 HU (p < 0.001). Conclusions: This AI-based radiomics and DLR model demonstrated high accuracy in predicting EGFR mutation, serving as a non-invasive and user-friendly imaging biomarker for EGFR mutation status prediction. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
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