ArticleBioMed research international2020
Machine Learning Analysis of Image Data Based on Detailed MR Image Reports for Nasopharyngeal Carcinoma Prognosis.
Article in BioMed research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- A coordinate registration-based structured magnetic resonance imaging reporting method for nasopharyngeal carcinoma: a preliminary study.Quantitative imaging in medicine and surgery · 2025Article
- Artificial Intelligence in Head and Neck Surgery.Otolaryngologic clinics of North America · 2024Review
- Deciphering the Prognostic Efficacy of MRI Radiomics in Nasopharyngeal Carcinoma: A Comprehensive Meta-Analysis.Diagnostics (Basel, Switzerland) · 2024Article
- Predictive function of tumor burden-incorporated machine-learning algorithms for overall survival and their value in guiding management decisions in patients with locally advanced nasopharyngeal carcinoma.Journal of the National Cancer Center · 2023Article
- Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinoma.Visual computing for industry, biomedicine, and art · 2023Article
- Application of Artificial Intelligence to the Diagnosis and Therapy of Nasopharyngeal Carcinoma.Journal of clinical medicine · 2023Review
- Predicting prognosis of nasopharyngeal carcinoma based on deep learning: peritumoral region should be valued.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
- Prognostic potential of a voxelwise invasion risk map of nasopharyngeal carcinoma based on a coordinate system of the nasopharynx.Quantitative imaging in medicine and surgery · 2023Article
- Use of automated artificial intelligence to predict the need for orthodontic extractions.Korean journal of orthodontics · 2022Article
- Application of Artificial Intelligence for Nasopharyngeal Carcinoma Management - A Systematic Review.Cancer management and research · 2022Review
- Radiomics in Nasopharyngeal Carcinoma.Clinical Medicine Insights. Oncology · 2022Review
- A Comprehensive Review on Radiomics and Deep Learning for Nasopharyngeal Carcinoma Imaging.Diagnostics (Basel, Switzerland) · 2021Review
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We aimed to assess the use of automatic machine learning (AutoML) algorithm based on magnetic resonance (MR) image data to assign prediction scores to patients with nasopharyngeal carcinoma (NPC). We also aimed to develop a 4-group classification system for NPC, superior to the current clinical staging system. Between January 2010 and January 2013, 792 patients with recent diagnosis of NPC, who had MR image data, were enrolled in the study. The AutoML algorithm was used and all statistical analyses were based on the 10-fold test. Primary endpoints included the probabilities of overall survival (OS), distant metastasis-free survival (DMFS), and local-region relapse-free survival (LRFS), and their sum was recorded as the final voting score, representative of progression-free survival (PFS) for each patient. The area under the receiver operating characteristic (ROC) curve generated from the MR image data-based model compared with the tumor, node, and metastasis (TNM) system-based model was 0.796 (
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