Home ›
Entities
› academia
› Evaluation of the performance of both machine learning models using PET and CT radiomics for predicting recurrence following lung stereotactic body radiation therapy: A single‐institutional study
Evaluation of the performance of both machine learning models using PET and CT radiomics for predicting recurrence following lung stereotactic body radiation therapy: A single‐institutional study
Research article (Journal of Applied Clinical Medical Physics, 2024) · cited 12× · AI/ML
Evaluation of the performance of both machine learning models using PET and CT radiomics for predicting recurrence following lung stereotactic body radiation therapy: A single‐institutional study
Summary
Evaluation of the performance of both machine learning models using PET and CT radiomics for predicting recurrence following lung stereotactic body radiation therapy: A single‐institutional study is a scholarly article[1].
Key Facts
Evaluation of the performance of both machine learning models using PET and CT radiomics for predicting recurrence following lung stereotactic body radiation therapy: A single‐institutional study's instance of is recorded as scholarly article[2].
References
Programmatic citations — every numbered marker resolves to a verifiable graph row below.
Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). Evaluation of the performance of both machine learning models using PET and CT radiomics for predicting recurrence following lung stereotactic body radiation therapy: A single‐institutional study. Retrieved May 24, 2026, from https://4ort.xyz/entity/evaluation-of-the-performance-of-both-machine-learning-models-using-pet-and-ct-radiomics-for-predicting-recurrence-follo
MLA“Evaluation of the performance of both machine learning models using PET and CT radiomics for predicting recurrence following lung stereotactic body radiation therapy: A single‐institutional study.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/evaluation-of-the-performance-of-both-machine-learning-models-using-pet-and-ct-radiomics-for-predicting-recurrence-follo.
BibTeX@misc{4ortxyz_evaluation-of-the-performance-of-both-machine-learning-models-using-pet-and-ct-radiomics-for-predicting-recurrence-follo_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Evaluation of the performance of both machine learning models using PET and CT radiomics for predicting recurrence following lung stereotactic body radiation therapy: A single‐institutional study}}, year = {2026}, url = {https://4ort.xyz/entity/evaluation-of-the-performance-of-both-machine-learning-models-using-pet-and-ct-radiomics-for-predicting-recurrence-follo}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Evaluation of the performance of both machine learning models using PET and CT radiomics for predicting recurrence following lung stereotactic body radiation therapy: A single‐institutional study — https://4ort.xyz/entity/evaluation-of-the-performance-of-both-machine-learning-models-using-pet-and-ct-radiomics-for-predicting-recurrence-follo (retrieved 2026-05-24)
