ArticleCancers2022
Early Prediction of Planning Adaptation Requirement Indication Due to Volumetric Alterations in Head and Neck Cancer Radiotherapy: A Machine Learning Approach.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Patient outcome prognosis for external beam radiation therapy using CBCT-based radiomics: a systematic review.Biomedical physics & engineering express · 2026Pooled it
- Artificial intelligence applied to image-guided radiation therapy (IGRT): a systematic review by the Young Group of the Italian Association of Radiotherapy and Clinical Oncology (yAIRO).La Radiologia medica · 2024Pooled it
- The complementary roles of oversight, education, and collaboration in the responsible integration of artificial intelligence in radiation medicine: White paper of CADRA.Journal of applied clinical medical physics · 2026Review
- Artificial intelligence applications in adaptive radiotherapy-a narrative review.Translational cancer research · 2026Review
- Early toxicity prediction using longitudinal cone-beam CT delta-radiomics in HPV-positive oropharyngeal cancer.Frontiers in oncology · 2026Article
- Beyond Standard Radiotherapy: an AI-Driven Framework for Personalized Radiotherapy with a Four-Step Classification in Head and Neck Squamous Cell Carcinoma (HNSCC) Patients.Current oncology reports · 2025Review
- Interfractional body surface monitoring using daily cone-beam computed tomography imaging for pediatric adaptive proton therapy.Physics and imaging in radiation oncology · 2025Article
- A systematic review of the role of artificial intelligence in automating computed tomography-based adaptive radiotherapy for head and neck cancer.Physics and imaging in radiation oncology · 2025Review
- Cone beam computed tomography (CBCT) and megavoltage computed tomography (MVCT)-based radiomics in head and neck cancers: a systematic review and radiomics quality score assessment.Quantitative imaging in medicine and surgery · 2024Review
- Towards Automation in Radiotherapy Planning: A Deep Learning Approach for the Delineation of Parotid Glands in Head and Neck Cancer.Bioengineering (Basel, Switzerland) · 2024Article
- Image Guided Radiotherapy (IGRT) and Delta (Δ) Radiomics-An Urgent Alliance for the Front Line of the War against Head and Neck Cancers.Diagnostics (Basel, Switzerland) · 2023Review
- Current Role of Delta Radiomics in Head and Neck Oncology.International journal of molecular sciences · 2023Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundDuring RT cycles, the tumor response pattern could affect tumor coverage and may lead to organs at risk of overdose. As such, early prediction of significant volumetric changes could therefore reduce potential radiation-related adverse effects. Nevertheless, effective machine learning approaches based on the radiomic features of the clinically used CBCT images to determine the tumor volume variations due to RT not having been implemented so far.
methodsCBCT images from 40 HN cancer patients were collected weekly during RT treatment. From the obtained images, the Clinical Target Volume (CTV) and Parotid Glands (PG) regions of interest were utilized to calculate 104 delta-radiomics features. These features were fed on a feature selection and classification procedure for the early prediction of significant volumetric alterations.
resultsThe proposed framework was able to achieve 0.90 classification performance accuracy while detecting a small subset of discriminative characteristics from the 1st week of RT. The selected features were further analyzed regarding their effects on temporal changes in anatomy and tumor response modeling.
conclusionThe use of machine learning algorithms offers promising perspectives for fast and reliable early prediction of large volumetric deviations as a result of RT treatment, exploiting hidden patterns in the overall anatomical characteristics.
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