ArticleMedical oncology (Northwood, London, England)2020
Delta-radiomics increases multicentre reproducibility: a phantom study.
Article in Medical oncology (Northwood, London, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 1 of them a synthesis that pooled it.
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Who cites it
48 citing papers in PubMed, 1 synthesis or guideline pooled it, 74 citations in OpenAlex.
- Delta radiomics: a systematic review.La Radiologia medica · 2021Pooled it
- Radiomic Parameters for the Evaluation of Response to Treatment in Metastatic Colorectal Cancer Patients with Liver Metastasis: Findings from the CAVE-GOIM mCRC Phase 2 Trial.Clinical drug investigation · 2024Trial
- Development of multiphasic CT-based delta-radiomics model for predicting postoperative recurrence risk in bladder cancer.BMC medical imaging · 2026Article
- Dynamic Vascular Spatiotemporal Heterogeneity on Multiphase CT for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2026Article
- Predictive value of 18F-FDG PET/CT delta radiomics model for prognosis after neoadjuvant therapy for locally advanced pancreatic cancer.Frontiers in oncology · 2026Article
- Deep learning image enhancement algorithms in PET/CT imaging: a phantom and sarcoma patient radiomic evaluation.European journal of nuclear medicine and molecular imaging · 2025Article
- MRI-based artificial intelligence models for post-neoadjuvant surgery personalization in breast cancer: a narrative review of evidence from Western Pacific.The Lancet regional health. Western Pacific · 2025Review
- Predictive Potential of Contrast-Enhanced MRI-Based Delta-Radiomics for Chemoradiation Responsiveness in Muscle-Invasive Bladder Cancer.Diagnostics (Basel, Switzerland) · 2025Article
- Photon-Counting CT Scan Phantom Study: Stability of Radiomics Features.Diagnostics (Basel, Switzerland) · 2025Article
- Predicting the immune therapy response of advanced non-small cell lung cancer based on primary tumor and lymph node radiomics features.Frontiers in medicine · 2025Article
- Investigating the causes and pattern of peritoneal involvement in CT scan and comparison with ultrasound findings in peritoneal conditions: A two-center, cross-sectional study.Health science reports · 2024Article
- Article
- Rethinking the potential role of dose painting in personalized ultra-fractionated stereotactic adaptive radiotherapy.Frontiers in oncology · 2024Article
- Delta-radiomics in cancer immunotherapy response prediction: A systematic review.European journal of radiology open · 2023Article
- Reproducibility and location-stability of radiomic features derived from cone-beam computed tomography: a phantom study.Dento maxillo facial radiology · 2023Article
- Article
- Is It Really Gone? Assessing Response to Neoadjuvant Therapy in Rectal Cancer.Journal of gastrointestinal cancer · 2023Review
- Peritoneal Carcinosis: What the Radiologist Needs to Know.Diagnostics (Basel, Switzerland) · 2023Review
- Article
- Article
Corrections and comments
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Authors and funding
15 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Texture analysis (TA) can provide quantitative features from medical imaging that can be correlated to clinical endpoints. The challenges relevant to robustness of radiomics features have been analyzed by many researchers, as it seems to be influenced by acquisition and reconstruction protocols. Delta-texture analysis (D-TA), conversely, consist in the analysis of TA feature variations at different acquisition times, usually before and after a therapy. Aim of this study was to investigate the influence of different CT scanners and acquisition parameters in the robustness of TA and D-TA. We scanned a commercial phantom (CIRS model 467, Gammex, Middleton, WI, USA), that is used for the calibration of electron density, two times by varying the disposition of plugs, using three different scanners. After the segmentation, we extracted TA features with LifeX and calculated TA features and D-TA features, defined as the variation of each TA parameters extracted from the same position by varying the plugs with the formula (Y-X)/X. The robustness of TA and D-TA features were then tested with intraclass coefficient correlation (ICC) analysis. The reliability of TA parameters across different scans, with different acquisition parameters and ROI positions has shown poor reliability in 12/37 and moderate reliability in the remaining 25/37, with no parameters showing good reliability. The reliability of D-TA, conversely, showed poor reliability in 10/37 parameters, moderate reliability in 10/37 parameters, and good reliability in 17/37 parameters. The comparison between TA and D-TA ICCs showed a significant difference for the whole group of parameters (p:0.004) and for the subclasses of GLCM parameters (p:0.033), whereas for the other subclasses of matrices (GLRLM, NGLDM, GLZLM, Histogram), the difference was not significant. D-TA features seem to be more robust than TA features. These findings reinforce the potentiality for using D-TA features for early assessment of treatment response and for developing tailored therapies. More work is needed in a clinical setting to confirm the results of the present study.
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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.