ArticleFrontiers in oncology2022
Tumor Prognostic Prediction of Nasopharyngeal Carcinoma Using CT-Based Radiomics in Non-Chinese Patients.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Radiomics of the Paranasal Sinuses: A Systematic Review of Computer-Assisted Techniques to Assess Computed Tomography Radiological Data.American journal of rhinology & allergy · 2025Pooled it
- Integrating computed tomography image features improves clinical prediction models for outcomes in nasopharyngeal carcinoma patients treated with (chemo)radiation.Physics and imaging in radiation oncology · 2026Article
- Predictive value of CT target scan-based radiomics and clinical features for patients with chronic obstructive pulmonary disease combined with malignant pulmonary nodules.Biomedical engineering online · 2026Article
- Prediction of local recurrence in cohorts of head and neck cancer patients after intensity-modulated radiation therapy based on CT radiomics: A double-center observation study.Translational oncology · 2026Article
- Integrating Radiomics and Deep-Learning for Prognostic Evaluation in Nasopharyngeal Carcinoma.Medicina (Kaunas, Lithuania) · 2025Review
- Predicting the efficacy of chemoradiotherapy in advanced nasopharyngeal carcinoma patients: an MRI radiomics and machine learning approach.Frontiers in oncology · 2025Article
- Enhancing Nasopharyngeal Carcinoma Survival Prediction: Integrating Pre- and Post-Treatment MRI Radiomics with Clinical Data.Journal of imaging informatics in medicine · 2024Article
- Precision Medicine for Nasopharyngeal Cancer-A Review of Current Prognostic Strategies.Cancers · 2024Review
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- Integrating CT-based radiomic model with clinical features improves long-term prognostication in high-risk prostate cancer.Frontiers in oncology · 2023Article
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Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeWe aimed to construct predictive models for the overall survival (OS), progression-free survival (PFS), and distant metastasis-free survival (DMFS) for nasopharyngeal carcinoma (NPC) patients by using CT-based radiomics. MATERIALS AND
methodsWe collected data from 197 NPC patients. For each patient, radiomic features were extracted from the CT image acquired at pretreatment
resultsThe combined model that used both radiomics and clinical features yielded significantly higher AUC and Harrell's C-index than models using either feature set alone for all outcomes (
conclusionOur results demonstrated that using CT-based radiomic features together with clinical features provided superior NPC prognostic prediction than using either clinical or radiomic features alone.
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Registered trials
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 Socratic graph.