ArticleJapanese journal of radiology2020
A multi-scanner study of MRI radiomics in uterine cervical cancer: prediction of in-field tumor control after definitive radiotherapy based on a machine learning method including peritumoral regions.
Article in Japanese journal of radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.
- Cervical cancer survival prediction by machine learning algorithms: a systematic review.BMC cancer · 2023Pooled it
- Machine Learning-Based Prognostic Modelling Using MRI Radiomic Data in Cervical Cancer Treated with Definitive Chemoradiotherapy and Brachytherapy.Current oncology (Toronto, Ont.) · 2025Article
- Habitat-based transformer model in pretreatmentBMC cancer · 2025Article
- Machine and Deep Learning for the Diagnosis, Prognosis, and Treatment of Cervical Cancer: A Scoping Review.Diagnostics (Basel, Switzerland) · 2025Review
- Artificial intelligence radiomics in the diagnosis, treatment, and prognosis of gynecological cancer: a literature review.Translational cancer research · 2025Review
- Machine learning in image-based outcome prediction after radiotherapy: A review.Journal of applied clinical medical physics · 2025Review
- Pre-treatment T2-weighted magnetic resonance radiomics for prediction of loco-regional recurrence after image-guided adaptive brachytherapy for locally advanced cervical cancer.Journal of contemporary brachytherapy · 2024Article
- Radiomic profiles improve prognostication and reveal targets for therapy in cervical cancer.Scientific reports · 2024Article
- Article
- Predictive value of magnetic resonance imaging radiomics-based machine learning for disease progression in patients with high-grade glioma.Quantitative imaging in medicine and surgery · 2023Article
- Prediction of out-of-field recurrence after chemoradiotherapy for cervical cancer using a combination model of clinical parameters and magnetic resonance imaging radiomics: a multi-institutional study of the Japanese Radiation Oncology Study Group.Journal of radiation research · 2022Article
- Radiomics in cervical and endometrial cancer.The British journal of radiology · 2021Review
- Radiogenomics of gastroenterological cancer: The dawn of personalized medicine with artificial intelligence-based image analysis.Annals of gastroenterological surgery · 2021Review
- Radiomics in Oncology, Part 2: Thoracic, Genito-Urinary, Breast, Neurological, Hematologic and Musculoskeletal Applications.Cancers · 2021Review
- Can peritumoral regions increase the efficiency of machine-learning prediction of pathological invasiveness in lung adenocarcinoma manifesting as ground-glass nodules?Journal of thoracic disease · 2021Article
- Quantitative PET Imaging and Clinical Parameters as Predictive Factors for Patients With Cervical Carcinoma: Implications of a Prediction Model Generated Using Multi-Objective Support Vector Machine Learning.Technology in cancer research & treatmentArticle
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis study aimed to identify the most appropriate volume of interest (VOI) setting in prognostic prediction using pretreatment magnetic resonance imaging (MRI) radiomic analysis for cervical cancer (CC) treated with definitive radiotherapy. MATERIALS AND
methodsThe study participants were 87 patients who had undergone pretreatment MRI and definitive radiotherapy for CC. VOI
resultsVOI expansion improved AUC-ROCs compared with the predictive models of VOI
conclusionRecurrence could be predicted with high accuracy using expanded VOI for CC treated with definitive radiotherapy, suggesting that including the pathological characteristics of invasive margins in radiomics may improve predictive ability.
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Registered trials
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