ArticleRadiology2025
Two-Stage Deep Learning Model for Adrenal Nodule Detection on CT Images: A Retrospective Study.
Article in Radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- The application of artificial intelligence in adrenal imaging: current state of knowledge, challenges, and future directions.Endocrine connections · 2026Article
- Unveiling the Future: A Deep Learning Model for Accurate Detection of Adrenal Nodules.Radiology · 2025Article
- Multi-model radiomics and machine learning for differentiating lipid-poor adrenal adenomas from metastases using automatic segmentation.Frontiers in oncology · 2025Article
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10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background The detection and classification of adrenal nodules are crucial for their management. Purpose To develop and test a deep learning model to automatically depict adrenal nodules on abdominal CT images and to simulate triaging performance in combination with human interpretation. Materials and Methods This retrospective study (January 2000-December 2020) used an internal dataset enriched with adrenal nodules for model training and testing and an external dataset reflecting real-world practice for further simulated testing in combination with human interpretation. The deep learning model had a two-stage architecture, a sequential detection and segmentation model, trained separately for the right and left adrenal glands. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) for nodule detection and intersection over union for nodule segmentation. Results Of a total of 995 patients in the internal dataset, the AUCs for detecting right and left adrenal nodules in internal test set 1 (
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