Observational studyLa Radiologia medica2020
Computed tomography (CT)-derived radiomic features differentiate prevascular mediastinum masses as thymic neoplasms versus lymphomas.
Observational study in La Radiologia medica, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 1 of them a synthesis that pooled it.
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49 citing papers in PubMed, 1 synthesis or guideline pooled it, 58 citations in OpenAlex.
- Delta radiomics: a systematic review.La Radiologia medica · 2021Pooled it
- A multiparameter diagnostic model based on MRI volumetric ADC histogram and clinical variables accurately differentiates thymic epithelial tumors from mediastinal lymphomas.BMC medical imaging · 2026Article
- Artificial intelligence for diagnosis and prognosis of thymic epithelial tumors: a systematic review.Mediastinum (Hong Kong, China) · 2025Review
- CT radiomics and human-machine hybrid system for differentiating mediastinal lymphomas from thymic epithelial tumors.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Multimodal modeling with low-dose CT and clinical information for diagnostic artificial intelligence on mediastinal tumors: a preliminary study.BMJ open respiratory research · 2024Article
- Computed tomography radiomic feature analysis of thymic epithelial tumors: Differentiation of thymic epithelial tumors from thymic cysts and prediction of histological subtypes.Japanese journal of radiology · 2024Article
- Imaging of human papilloma virus (HPV) related oropharynx tumour: what we know to date.Infectious agents and cancer · 2023Review
- Development and validation of a deep learning radiomics nomogram for preoperatively differentiating thymic epithelial tumor histologic subtypes.European radiology · 2023Article
- Radiomics in colorectal cancer patients.World journal of gastroenterology · 2023Review
- Conventional and radiomic features to predict pathology in the preoperative assessment of anterior mediastinal masses.Lung cancer (Amsterdam, Netherlands) · 2023Article
- Reproducibility of CT radiomic features in lung neuroendocrine tumours (NETs) patients: analysis in a heterogeneous population.La Radiologia medica · 2023Article
- Clinical radiomics-based machine learning versus three-dimension convolutional neural network analysis for differentiation of thymic epithelial tumors from other prevascular mediastinal tumors on chest computed tomography scan.Frontiers in oncology · 2023Article
- Imaging biomarkers to stratify lymph node metastases in abdominal CT - Is radiomics superior to dual-energy material decomposition?European journal of radiology open · 2023Article
- Integration between Novel Imaging Technologies and Modern Radiotherapy Techniques: How the Eye Drove the Chisel.Cancers · 2022Review
- Heart Failure and Cardiomyopathies: CT and MR from Basics to Advanced Imaging.Diagnostics (Basel, Switzerland) · 2022Review
- Radiomics in medical imaging: pitfalls and challenges in clinical management.Japanese journal of radiology · 2022Review
- Structured Reporting in Radiological Settings: Pitfalls and Perspectives.Journal of personalized medicine · 2022Review
- A Narrative Review on LI-RADS Algorithm in Liver Tumors: Prospects and Pitfalls.Diagnostics (Basel, Switzerland) · 2022Review
- Radiomics and machine learning analysis based on magnetic resonance imaging in the assessment of liver mucinous colorectal metastases.La Radiologia medica · 2022Article
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12 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesWe aimed to assess the ability of radiomics, applied to not-enhanced computed tomography (CT), to differentiate mediastinal masses as thymic neoplasms vs lymphomas.
methodsThe present study was an observational retrospective trial. Inclusion criteria were pathology-proven thymic neoplasia or lymphoma with mediastinal localization, availability of CT. Exclusion criteria were age < 16 years and mediastinal lymphoma lesion < 4 cm. We selected 108 patients (M:F = 47:61, median age 48 years, range 17-79) and divided them into a training and a validation group. Radiomic features were used as predictors in linear discriminant analysis. We built different radiomic models considering segmentation software and resampling setting. Clinical variables were used as predictors to build a clinical model. Scoring metrics included sensitivity, specificity, accuracy and area under the curve (AUC). Wilcoxon paired test was used to compare the AUCs.
resultsFifty-five patients were affected by thymic neoplasia and 53 by lymphoma. In the validation analysis, the best radiomics model sensitivity, specificity, accuracy and AUC resulted 76.2 ± 7.0, 77.8 ± 5.5, 76.9 ± 6.0 and 0.84 ± 0.06, respectively. In the validation analysis of the clinical model, the same metrics resulted 95.2 ± 7.0, 88.9 ± 8.9, 92.3 ± 8.5 and 0.98 ± 0.07, respectively. The AUCs of the best radiomic and the clinical model not differed.
conclusionsWe developed and validated a CT-based radiomic model able to differentiate mediastinal masses on non-contrast-enhanced images, as thymic neoplasms or lymphoma. The proposed method was not affected by image postprocessing. Therefore, the present image-derived method has the potential to noninvasively support diagnosis in patients with prevascular mediastinal masses with major impact on management of asymptomatic cases.
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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.