ArticleEuropean radiology2024
Deep convolutional neural network for differentiating between sarcoidosis and lymphoma based on [
Article in European radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
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Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it, 11 citations in OpenAlex.
- Artificial intelligence performance in detecting lymphoma from medical imaging: a systematic review and meta-analysis.BMC medical informatics and decision making · 2024Pooled it
- Diagnostic Value ofTechnology in cancer research & treatmentPooled it
- Enhancing interpretability of AI with radiomics-based deep neural network: proof of concept in the classification of Parkinsonian syndromes withEuropean journal of nuclear medicine and molecular imaging · 2026Article
- Article
- CT Imaging Features of Pulmonary Sarcoidosis: Typical and Atypical Radiological Features and Their Differential Diagnosis.Medicina (Kaunas, Lithuania) · 2025Review
- Review
- Review
- Sarcoidosis and Cancer: The Role of the Granulomatous Reaction as a Double-Edged Sword.Journal of clinical medicine · 2024Article
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Authors and funding
18 authors at 1 institution in 1 country.
Funding
Abstract
objectivesTo compare the [
methodsWe retrospectively collected data on consecutive patients newly diagnosed with sarcoidosis and ML who underwent [
resultsA total of 56 patients with sarcoidosis and 62 patients with ML were included. Patients with sarcoidosis had more prominent FDG accumulation in the mediastinal lymph nodes and lung lesions, while those with ML had more prominent accumulation in the cervical lymph nodes (all p < 0.001). For the mediastinal lymph nodes, sarcoidosis patients had significant FDG accumulation in the level 2, 4, 7, and 10 lymph nodes (all p < 0.01). Otherwise, the accumulation in ML patients tended to be in the level 1 lymph nodes (p = 0.08). The CNN model using frontal and lateral MIP images achieved an average accuracy of 0.890 (95% CI: 0.804-0.977), a sensitivity of 0.898 (95% CI: 0.782-1.000), a specificity of 0.907 (95% CI: 0.799-1.000), and an area under the curve of 0.963 (95% CI: 0.899-1.000). Grad-CAM showed that the model focused on the sites of abnormal FDG accumulation.
conclusionsCNN models based on differences in FDG accumulation sites archive high performance in differentiating between sarcoidosis and ML. CLINICAL RELEVANCE STATEMENT: We developed a CNN model using MIP images of [ KEY POINTS: • There are differences in FDG distribution when comparing whole-body [
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