ArticleEJNMMI physics2021
Application of dual-stream 3D convolutional neural network based on
Article in EJNMMI physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The trial behind it
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Who cites it
9 citing papers in PubMed, 9 citations in OpenAlex.
- A Meta-Analysis of Deep-Learning-Based Fusion of Positron Emission Tomography and Computed Tomography in Oncologic Imaging.Journal of imaging informatics in medicine · 2026Review
- Automated diagnosis of pulmonary nodules in 3D PET/CT images using dual-path densely connected networks with cross-modal fusion.Quantitative imaging in medicine and surgery · 2026Article
- Prognostic role of subsolid ground-glass opacity, pure ground-glass opacity, and solid nodules of the lung: a retrospective observational study.Journal of thoracic disease · 2025Article
- Assessments of lung nodules by an artificial intelligence chatbot using longitudinal CT images.Cell reports. Medicine · 2025Article
- Predicting malignant risk of ground-glass nodules using convolutional neural networks based on dual-time-pointCancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Fully automated classification of pulmonary nodules in positron emission tomography-computed tomography imaging using a two-stage multimodal learning approach.Quantitative imaging in medicine and surgery · 2024Article
- Prognostic value of combining clinical factors,Annals of nuclear medicine · 2024Article
- PET/CT radiomics and deep learning in the diagnosis of benign and malignant pulmonary nodules: progress and challenges.Frontiers in oncology · 2024Review
- Radial-EBUS: CryoBiopsy Versus Conventional Biopsy: Time-Sample and C-Arm.International journal of environmental research and public health · 2022Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
purposeThis work aims to train, validate, and test a dual-stream three-dimensional convolutional neural network (3D-CNN) based on fluorine 18 (
methodsWe retrospectively analyzed patients with suspicious GGNs who underwent
resultsA total of 23 benign nodules and 92 IAC nodules from 106 patients were included in this study. In the training set, the performance of PET network (accuracy, sensitivity, and specificity of 0.92 ± 0.02, 0.97 ± 0.03, and 0.76 ± 0.15) was better than the CT network (accuracy, sensitivity, and specificity of 0.84 ± 0.03, 0.90 ± 0.07, and 0.62 ± 0.16) (especially accuracy was significant, P-value was 0.001); in the testing set, the performance of both networks declined. However, the accuracy and sensitivity of PET network were still higher than that of CT network (0.76 vs. 0.67; 0.85 vs. 0.70). For dual-stream PET/CT network, its performance was almost the same as PET network in the training set (P-value was 0.372-1.000), while in the testing set, although its performance decreased, the accuracy and sensitivity (0.85 and 0.96) were still higher than both CT and PET networks. Moreover, the accuracy of PET/CT network was higher than two nuclear medicine physicians [physician 1 (3-year experience): 0.70 and physician 2 (10-year experience): 0.73].
conclusionThe 3D-CNN based on
Indexed as
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.