ArticleScientific reports2019
The complexity of tumor shape, spiculatedness, correlates with tumor radiomic shape features.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.
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Who cites it
54 citing papers in PubMed.
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- Gaussian Function Model for Task-Specific Evaluation in Medical Imaging: A Theoretical Investigation.Journal of imaging informatics in medicine · 2026Article
- A novel interpretable machine learning framework integrating clinicopathological and radiomic features for early recurrence prediction in mass-forming intrahepatic cholangiocarcinoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Investigation of clinical applicability of deep learning-based virtual contrast-enhanced MRI for nasopharyngeal carcinoma patients.Frontiers in oncology · 2026Article
- Morphometry-based detection of deep learning faults in glomerular segmentation.bioRxiv : the preprint server for biology · 2025Article
- Development and validation of a diagnostic radiomics model for the differential diagnosis of coexistent tuberculosis and lung cancer versus isolated lung cancer.Journal of thoracic disease · 2025Article
- Application of Fractal Radiomics and Machine Learning for Differentiation of Non-Small Cell Lung Cancer Subtypes on PET/MR Images.Journal of clinical medicine · 2025Article
- Predicting PD-L1 status in NSCLC patients using deep learning radiomics based on CT images.Scientific reports · 2025Article
- Radiomics in thymic epithelial tumors: a scoping review of current status and advances.BMC cancer · 2025Article
- Early and hereditary breast cancer: advances in risk stratification and imaging approaches.Therapeutic advances in medical oncology · 2025Review
- Application of Tuning-ensemble N-Best in Auto-Sklearn for Mammographic Radiomic Analysis for Breast Cancer Prediction.Current medical imaging · 2025Article
- Finite Element Simulation of Interstitial-Lymphatic Fluid Flow and Nanodrug Transport in a Solid Tumor: An Intratumoral Injection Approach.BME frontiers · 2025Article
- Robust Automated Tumour Segmentation Network Using 3D Direction-Wise Convolution and Transformer.Journal of imaging informatics in medicine · 2024Article
- Hybridization of Acoustic and Visual Features of Polish Sibilants Produced by Children for Computer Speech Diagnosis.Sensors (Basel, Switzerland) · 2024Article
- Machine learning approach usingJapanese journal of radiology · 2024Article
- Peri-hematomal edema shape features related to 3-month outcome in acute supratentorial intracerebral hemorrhage.European stroke journal · 2024Article
- Radiomics approach to distinguish between benign and malignant soft tissue tumors on magnetic resonance imaging.European journal of radiology open · 2024Article
- Tumor margin irregularity degree is an important preoperative predictor of adverse pathology for clinical T1/2 renal cell carcinoma and the construction of predictive model.World journal of urology · 2024Article
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9 authors.
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Abstract
Radiomics extracts high-throughput quantitative data from medical images to contribute to precision medicine. Radiomic shape features have been shown to correlate with patient outcomes. However, how radiomic shape features vary in function of tumor complexity and tumor volume, as well as with method used for meshing and voxel resampling, remains unknown. The aims of this study are to create tumor models with varying degrees of complexity, or spiculatedness, and evaluate their relationship with quantitatively extracted shape features. Twenty-eight tumor models were mathematically created using spherical harmonics with the spiculatedness degree d being increased by increments of 3 (d = 11 to d = 92). Models were 3D printed with identical bases of 5 cm, imaged with a CT scanner with two different slice thicknesses, and semi-automatically delineated. Resampling of the resulting masks on a 1 × 1 × 1 mm
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