Dataset Information

Deep learning for predicting epidermal growth factor receptor mutations of non-small cell lung cancer on PET/CT images.

ABSTRACT:

Background

Predicting the mutation status of the epidermal growth factor receptor (EGFR) gene based on an integrated positron emission tomography/computed tomography (PET/CT) image of non-small cell lung cancer (NSCLC) is a noninvasive, low-cost method which is valuable for targeted therapy. Although deep learning has been very successful in robotic vision, it is still challenging to predict gene mutations in PET/CT-derived studies because of the small amount of medical data and the different parameters of PET/CT devices.

Methods

We used the advanced EfficientNet-V2 model to predict the EGFR mutation based on fused PET/CT images. First, we extracted 3-dimensional (3D) pulmonary nodules from PET and CT as regions of interest (ROIs). We then fused each single PET and CT image. The network model was used to predict the mutation status of lung nodules by the new data after fusion, and the model was weighted adaptively. The EfficientNet-V2 model used multiple channels to represent nodules comprehensively.

Results

We trained the EfficientNet-V2 model through our PET/CT fusion algorithm using a dataset of 150 patients. The prediction accuracy of EGFR and non-EGFR mutations was 86.25% in the training dataset, and the accuracy rate was 81.92% in the validation set.

Conclusions

Combined with experiments, the demonstrated PET/CT fusion algorithm outperformed radiomics methods in predicting EGFR and non-EGFR mutations in NSCLC.

SUBMITTER: Xiao Z

PROVIDER: S-EPMC10006109 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Publications

Deep learning for predicting epidermal growth factor receptor mutations of non-small cell lung cancer on PET/CT images.

Xiao Zhenghui Z Cai Haihua H Wang Yue Y Cui Ruixue R Huo Li L Lee Elaine Yuen-Phin EY Liang Ying Y Li Xiaomeng X Hu Zhanli Z Chen Long L Zhang Na N

Quantitative imaging in medicine and surgery 20230206 3

<h4>Background</h4>Predicting the mutation status of the epidermal growth factor receptor (EGFR) gene based on an integrated positron emission tomography/computed tomography (PET/CT) image of non-small cell lung cancer (NSCLC) is a noninvasive, low-cost method which is valuable for targeted therapy. Although deep learning has been very successful in robotic vision, it is still challenging to predict gene mutations in PET/CT-derived studies because of the small amount of medical data and the diff ...[more]

PMID: 36915325

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Project description:Background18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is valuable for determining presence of viable tumor, but is limited by geographical restrictions, radiation exposure, and high cost.PurposeTo generate diagnostic-quality PET equivalent imaging for patients with brain neoplasms by deep learning with multi-contrast MRI.Study typeRetrospective.SubjectsPatients (59 studies from 51 subjects; age 56 ± 13 years; 29 males) who underwent 18 F-FDG PET and MRI for determining recurrent brain tumor.Field strength/sequence3T; 3D GRE T1, 3D GRE T1c, 3D FSE T2-FLAIR, and 3D FSE ASL, 18 F-FDG PET imaging.AssessmentConvolutional neural networks were trained using four MRIs as inputs and acquired FDG PET images as output. The agreement between the acquired and synthesized PET was evaluated by quality metrics and Bland-Altman plots for standardized uptake value ratio. Three physicians scored image quality on a 5-point scale, with score ≥3 as high-quality. They assessed the lesions on a 5-point scale, which was binarized to analyze diagnostic consistency of the synthesized PET compared to the acquired PET.Statistical testsThe agreement in ratings between the acquired and synthesized PET were tested with Gwet's AC and exact Bowker test of symmetry. Agreement of the readers was assessed by Gwet's AC. P = 0.05 was used as the cutoff for statistical significance.ResultsThe synthesized PET visually resembled the acquired PET and showed significant improvement in quality metrics (+21.7% on PSNR, +22.2% on SSIM, -31.8% on RSME) compared with ASL. A total of 49.7% of the synthesized PET were considered as high-quality compared to 73.4% of the acquired PET which was statistically significant, but with distinct variability between readers. For the positive/negative lesion assessment, the synthesized PET had an accuracy of 87% but had a tendency to overcall.ConclusionThe proposed deep learning model has the potential of synthesizing diagnostic quality FDG PET images without the use of radiotracers.Evidence level3 TECHNICAL EFFICACY: Stage 2.

Dataset Information

Deep learning for predicting epidermal growth factor receptor mutations of non-small cell lung cancer on PET/CT images.

Background

Methods

Results

Conclusions

Publications

Deep learning for predicting epidermal growth factor receptor mutations of non-small cell lung cancer on PET/CT images.

Similar Datasets

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