ArticleEJNMMI physics2021
AI-based detection of lung lesions in [
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 16 papers.
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Who cites it
16 citing papers in PubMed.
- Examining explainable artificial intelligence in TNM staging with PET-CT: a user-centred observation study.La Radiologia medica · 2026Observational
- Cascaded deep learning for automated lung cancer tumour burden quantification on [Quantitative imaging in medicine and surgery · 2026Article
- Single-cell impedance sensing on integrated circuit chip for fast tumor diagnosis.Microsystems & nanoengineering · 2026Article
- Comparison of organ volumes and standardized uptake values in [18F]FDG-PET/CT images using MOOSE and TotalSegmentator to segment CT images.Medical physics · 2025Article
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- A Holistic Approach to Implementing Artificial Intelligence in Lung Cancer.Indian journal of surgical oncology · 2025Review
- Recent Breakthroughs in PET-CT Multimodality Imaging: Innovations and Clinical Impact.Bioengineering (Basel, Switzerland) · 2024Review
- Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT.Cancer management and research · 2024Review
- A Narrative Review of the Use of Artificial Intelligence in Breast, Lung, and Prostate Cancer.Life (Basel, Switzerland) · 2023Review
- Automated lung cancer assessment on 18F-PET/CT using Retina U-Net and anatomical region segmentation.European radiology · 2023Article
- Acoustic-Based Deep Learning Architectures for Lung Disease Diagnosis: A Comprehensive Overview.Diagnostics (Basel, Switzerland) · 2023Review
- Automatic Lung Cancer Segmentation in [Nuclear medicine and molecular imaging · 2023Article
- A review on lung disease recognition by acoustic signal analysis with deep learning networks.Journal of big data · 2023Article
- Automated classification of PET-CT lesions in lung cancer: An independent validation study.Clinical physiology and functional imaging · 2022Article
- Early molecular imaging response assessment based on determination of total viable tumor burden in [European journal of nuclear medicine and molecular imaging · 2022Article
- Freely available convolutional neural network-based quantification of PET/CT lesions is associated with survival in patients with lung cancer.EJNMMI physics · 2022Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background[
methodsOne hundred twelve patients (59 females and 53 males) who underwent FDG PET-CT due to suspected or for the management of known lung cancer were studied retrospectively. These patients were divided into a training group (59%; n = 66), a validation group (20.5%; n = 23) and a test group (20.5%; n = 23). A nuclear medicine physician manually segmented abnormal lung lesions with increased FDG-uptake in all PET-CT studies. The AI-based method was trained to segment the lesions based on the manual segmentations. TLG was then calculated from manual and AI-based measurements, respectively and analysed with Bland-Altman plots.
resultsThe AI-tool's performance in detecting lesions had a sensitivity of 90%. One small lesion was missed in two patients, respectively, where both had a larger lesion which was correctly detected. The positive and negative predictive values were 88% and 100%, respectively. The correlation between manual and AI TLG measurements was strong (R
conclusionsThe AI-based method is suitable for the detection of lung lesions and automatic calculation of TLG in small- to medium-sized tumours. In a clinical setting, it will have an added value due to its capability to sort out negative examinations resulting in prioritised and focused care on patients with potentially malignant lesions.
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