ArticleLa Radiologia medica2018
Texture analysis as a predictor of radiation-induced xerostomia in head and neck patients undergoing IMRT.
Article in La Radiologia medica, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
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Who cites it
37 citing papers in PubMed, 1 synthesis or guideline pooled it, 62 citations in OpenAlex.
- Prognostic models for radiation-induced complications after radiotherapy in head and neck cancer patients.The Cochrane database of systematic reviews · 2025Pooled it
- A Deep Learning Model for Predicting Xerostomia Due to Radiation Therapy for Head and Neck Squamous Cell Carcinoma in the RTOG 0522 Clinical Trial.International journal of radiation oncology, biology, physics · 2019Trial
- Dose-volume predictors of post-radiation xerostomia in head and neck cancer: A systematic review.Clinical and translational radiation oncology · 2027Review
- Normal tissue complication probability modeling of radiation-induced neck fibrosis in head and neck Cancer survivors: a patient-reported outcome-based analysis.Clinical and translational radiation oncology · 2026Article
- Impact of radiation dose on patient-reported acute taste alteration in a prospective observational study cohort in head and neck squamous cell cancer (HNSCC).La Radiologia medica · 2023Observational
- Machine learning-based CT radiomics model to discriminate the primary and secondary intracranial hemorrhage.Scientific reports · 2023Article
- Integrated radiomics, dose-volume histogram criteria and clinical features for early prediction of saliva amount reduction after radiotherapy in nasopharyngeal cancer patients.Discover oncology · 2022Article
- Identification of texture MRI brain abnormalities on first-episode psychosis and clinical high-risk subjects using explainable artificial intelligence.Translational psychiatry · 2022Article
- Prognosis prediction of extremity and trunk wall soft-tissue sarcomas treated with surgical resection with radiomic analysis based on random survival forest.Updates in surgery · 2022Article
- Radiomics-based nomogram as predictive model for prognosis of hepatocellular carcinoma with portal vein tumor thrombosis receiving radiotherapy.Frontiers in oncology · 2022Article
- Current Omics Trends in Personalised Head and Neck Cancer Chemoradiotherapy.Journal of personalized medicine · 2021Review
- Benchmarking Various Radiomic Toolkit Features While Applying the Image Biomarker Standardization Initiative toward Clinical Translation of Radiomic Analysis.Journal of digital imaging · 2021Article
- Delta-radiomics and response to neoadjuvant treatment in locally advanced gastric cancer-a multicenter study of GIRCG (Italian Research Group for Gastric Cancer).Quantitative imaging in medicine and surgery · 2021Article
- Article
- The Role of Preoperative Computed Tomography Radiomics in Distinguishing Benign and Malignant Tumors of the Parotid Gland.Frontiers in oncology · 2021Article
- Dual energy CT in gland tumors: a comprehensive narrative review and differential diagnosis.Gland surgery · 2020Review
- Review
- Narrative review of multiparametric ultrasound in parotid gland evaluation.Gland surgery · 2020Review
- Discrimination between pituitary adenoma and craniopharyngioma using MRI-based image features and texture features.Japanese journal of radiology · 2020Article
- Evaluation of CT-based radiomics signature and nomogram as prognostic markers in patients with laryngeal squamous cell carcinoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2020Article
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Authors and funding
10 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeImage texture analysis (TA) is a heterogeneity quantifying approach that cannot be appreciated by the naked eye, and early evidence suggests that TA has great potential in the field of oncology. The aim of this study is to evaluate parotid gland texture analysis (TA) combined with formal dosimetry as a factor for predicting severe late xerostomia in patients undergoing radiation therapy for head and neck cancers.
methodsWe performed a retrospective analysis of patients treated at our Radiation Oncology Unit between January 2010 and December 2015, and selected the patients whose normal dose constraints for the parotid gland (mean dose < 26 Gy for the bilateral gland) could not be satisfied due to the presence of positive nodes close to the parotid glands. The parotid gland that showed the higher V30 was contoured on CT simulation and analysed with LifeX Software©. TA parameters included features of grey-level co-occurrence matrix (GLCM), neighbourhood grey-level dependence matrix (NGLDM), grey-level run length matrix (GLRLM), grey-level zone length matrix (GLZLM), sphericity, and indices from the grey-level histogram. We performed a univariate and multivariate analysis between all the texture parameters, the volume of the gland, the normal dose parameters (V30 and Mean Dose), and the development of severe chronic xerostomia.
resultsSeventy-eight patients were included and 25 (31%) developed chronic xerostomia. The TA parameters correlated with severe chronic xerostomia included V30 (OR 5.63), Dmean (OR 5.71), Kurtosis (OR 0.78), GLCM Correlation (OR 1.34), and RLNU (OR 2.12). The multivariate logistic regression showed a significant correlation between V30 (0.001), GLCM correlation (p: 0.026), RLNU (p: 0.011), and chronic xerostomia (p < 0.001, R2:0.664).
conclusionsXerostomia represents an important cause of morbidity for head and neck cancer survivors after radiation therapy, and in certain cases normal dose constraints cannot be satisfied. Our results seem promising as texture analysis could enhance the normal dose constraints for the prediction of xerostomia.
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