Home ›
Entities
› academia
› Baseline whole-lung CT features deriving from deep learning and radiomics: prediction of benign and malignant pulmonary ground-glass nodules
Baseline whole-lung CT features deriving from deep learning and radiomics: prediction of benign and malignant pulmonary ground-glass nodules
Research article (Frontiers in Oncology, 2023) · cited 15× · AI/ML
Baseline whole-lung CT features deriving from deep learning and radiomics: prediction of benign and malignant pulmonary ground-glass nodules
Summary
Baseline whole-lung CT features deriving from deep learning and radiomics: prediction of benign and malignant pulmonary ground-glass nodules is a scholarly article[1].
Key Facts
Baseline whole-lung CT features deriving from deep learning and radiomics: prediction of benign and malignant pulmonary ground-glass nodules'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). Baseline whole-lung CT features deriving from deep learning and radiomics: prediction of benign and malignant pulmonary ground-glass nodules. Retrieved May 24, 2026, from https://4ort.xyz/entity/baseline-whole-lung-ct-features-deriving-from-deep-learning-and-radiomics-prediction-of-benign-and-malignant-pulmonary-g
MLA“Baseline whole-lung CT features deriving from deep learning and radiomics: prediction of benign and malignant pulmonary ground-glass nodules.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/baseline-whole-lung-ct-features-deriving-from-deep-learning-and-radiomics-prediction-of-benign-and-malignant-pulmonary-g.
BibTeX@misc{4ortxyz_baseline-whole-lung-ct-features-deriving-from-deep-learning-and-radiomics-prediction-of-benign-and-malignant-pulmonary-g_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Baseline whole-lung CT features deriving from deep learning and radiomics: prediction of benign and malignant pulmonary ground-glass nodules}}, year = {2026}, url = {https://4ort.xyz/entity/baseline-whole-lung-ct-features-deriving-from-deep-learning-and-radiomics-prediction-of-benign-and-malignant-pulmonary-g}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Baseline whole-lung CT features deriving from deep learning and radiomics: prediction of benign and malignant pulmonary ground-glass nodules — https://4ort.xyz/entity/baseline-whole-lung-ct-features-deriving-from-deep-learning-and-radiomics-prediction-of-benign-and-malignant-pulmonary-g (retrieved 2026-05-24)
