ArticleDiagnostics (Basel, Switzerland)2022
A CT-Based Radiomic Signature for the Differentiation of Pulmonary Hamartomas from Carcinoid Tumors.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Role of Chest CT Radiomics in Differentiating Tumorlets and Granulomas: A Preliminary Study.Journal of clinical medicine · 2025Article
- Article
- Computed tomography-based radiomics prediction model for differentiating invasive pulmonary aspergillosis andFrontiers in cellular and infection microbiology · 2025Article
- Observer- and radiomics model-based computed tomography classification of suppurative versus tuberculous lymphadenitis complicated with nodal necrosis of the neck in children.Pediatric radiology · 2023Article
- Coming Back to the Basics. Comment on Cangir et al. A CT-Based Radiomic Signature for the Differentiation of Pulmonary Hamartomas from Carcinoid Tumors.Diagnostics (Basel, Switzerland) · 2023Article
- Reply to Perrella et al. Coming Back to the Basics. Comment on "Cangir et al. A CT-Based Radiomic Signature for the Differentiation of Pulmonary Hamartomas from Carcinoid Tumors.Diagnostics (Basel, Switzerland) · 2023Article
- [A case of pediatric tracheal inflammatory myofibroblastic tumor].Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery · 2023Article
- The clinicopathological challenges of symptomatic and incidental pulmonary hamartomas diagnosis.Romanian journal of morphology and embryology = Revue roumaine de morphologie et embryologieArticle
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Authors and funding
8 authors.
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Abstract
Radiomics is a new image processing technology developed in recent years. In this study, CT radiomic features are evaluated to differentiate pulmonary hamartomas (PHs) from pulmonary carcinoid tumors (PCTs). A total of 138 patients (78 PCTs and 60 PHs) were evaluated. The Radcloud platform (Huiying Medical Technology Co., Ltd., Beijing, China) was used for managing the data, clinical data, and subsequent radiomics analysis. Two hand-crafted radiomics models are prepared in this study: the first model includes the data regarding all of the patients to differentiate between the groups; the second model includes 78 PCTs and 38 PHs without signs of fat tissue. The separation of the training and validation datasets was performed randomly using an (8:2) ratio and 620 random seeds. The results revealed that the MLP method (RF) was best for PH (AUC = 0.999) and PCT (AUC = 0.999) for the first model (AUC = 0.836), and PC (AUC = 0.836) in the test set for the second model. Radiomics tumor features derived from CT images are useful to differentiate the carcinoid tumors from hamartomas with high accuracy. Radiomics features may be used to differentiate PHs from PCTs with high levels of accuracy, even without the presence of fat on the CT. Advances in knowledge: CT-based radiomic holds great promise for a more accurate preoperative diagnosis of solitary pulmonary nodules (SPNs).
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