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Multimodal radiomics-based methods using deep learning for prediction of brain metastasis in non-small cell lung cancer withF-FDG PET/CT images. | LitMetric

Multimodal radiomics-based methods using deep learning for prediction of brain metastasis in non-small cell lung cancer withF-FDG PET/CT images.

Biomed Phys Eng Express

The Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China.

Published: September 2024

AI Article Synopsis

  • - Approximately 57% of non-small cell lung cancer (NSCLC) patients have a 20% risk of developing brain metastases, making early detection and treatment crucial for better outcomes.
  • - This study explored a new method using 3D neural networks on F-FDG PET/CT images to predict brain metastases in 226 NSCLC patients, combining shallow and deep lung-brain imaging features for improved accuracy.
  • - The results showed strong predictive performance (AUC = 0.96) and statistical significance, indicating that this non-invasive technique could help identify features related to brain metastases and assist in creating targeted treatments for NSCLC patients.

Article Abstract

. Approximately 57% of non-small cell lung cancer (NSCLC) patients face a 20% risk of brain metastases (BMs). The delivery of drugs to the central nervous system is challenging because of the blood-brain barrier, leading to a relatively poor prognosis for patients with BMs. Therefore, early detection and treatment of BMs are highly important for improving patient prognosis. This study aimed to investigate the feasibility of a multimodal radiomics-based method using 3D neural networks trained onF-FDG PET/CT images to predict BMs in NSCLC patients.. We included 226 NSCLC patients who underwentF-FDG PET/CT scans of areas, including the lung and brain, prior to EGFR-TKI therapy. Moreover, clinical data (age, sex, stage, etc) were collected and analyzed. Shallow lung features and deep lung-brain features were extracted using PyRadiomics and 3D neural networks, respectively. A support vector machine (SVM) was used to predict BMs. The receiver operating characteristic (ROC) curve and F1 score were used to assess BM prediction performance.. The combination of shallow lung and shallow-deep lung-brain features demonstrated superior predictive performance (AUC = 0.96 ± 0.01). Shallow-deep lung-brain features exhibited strong significance (P < 0.001) and potential predictive performance (coefficient > 0.8). Moreover, BM prediction by age was significant (P < 0.05).. Our approach enables the quantitative assessment of medical images and a deeper understanding of both superficial and deep tumor characteristics. This noninvasive method has the potential to identify BM-related features with statistical significance, thereby aiding in the development of targeted treatment plans for NSCLC patients.

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Source
http://dx.doi.org/10.1088/2057-1976/ad7595DOI Listing

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