ArticleiScience2023
Radiomics and dosiomics for predicting radiation-induced hypothyroidism and guiding intensity-modulated radiotherapy.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prognostic models for radiation-induced complications after radiotherapy in head and neck cancer patients.The Cochrane database of systematic reviews · 2025Pooled it
- Artificial intelligence in thyroid ultrasound: clinical applications and perspectives.Frontiers in endocrinology · 2026Review
- A deep learning model for predicting radiation-induced xerostomia in patients with head and neck cancer based on multi-channel fusion.BMC medical imaging · 2025Article
- Predicting radiation-induced hypothyroidism in nasopharyngeal carcinoma patients using a deep learning model.Clinical and translational radiation oncology · 2025Article
- A subregional prediction model for radiation-induced hypothyroidism.BMC medical imaging · 2025Article
- Region-specific Multi-Omics Modeling for Predicting Acute Radiation-Induced Proctitis in Cervical Cancer Radiotherapy: A Retrospective Analysis.Technology in cancer research & treatmentArticle
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Authors and funding
9 authors.
Funding
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Abstract
To guide individualized intensity-modulated radiotherapy (IMRT), we developed and prospectively validated a multiview radiomics risk model for predicting radiation-induced hypothyroidism in patients with nasopharyngeal carcinoma. And simulated radiotherapy plans with same dose-volume-histogram (DVH) but different dose distributions were redesigned to explore the clinical application of the multiview radiomics risk model. The radiomics and dosiomics were built based on selected radiomics and dosiomics features from planning computed tomography and dose distribution, respectively. The multiview radiomics risk model that integrated radiomics, dosiomics, DVH parameters, and clinical factors had better performance than traditional normal tissue complication probability models. And multiview radiomics risk model could identify differences of patient hypothyroidism-free survival that cannot be stratified by traditional models. Besides, two redesigned simulated plans further verified the clinical application and advantage of the multiview radiomics risk model. The multiview radiomics risk model was a promising method to predict radiation-induced hypothyroidism and guide individualized IMRT.
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