Using meta-analysis and CNN-NLP to review and classify the medical literature for normal tissue complication probability in head and neck cancer.
Radiat Oncol
; 19(1): 5, 2024 Jan 09.
Article
en En
| MEDLINE
| ID: mdl-38195582
ABSTRACT
PURPOSE:
The study aims to enhance the efficiency and accuracy of literature reviews on normal tissue complication probability (NTCP) in head and neck cancer patients using radiation therapy. It employs meta-analysis (MA) and natural language processing (NLP). MATERIAL ANDMETHODS:
The study consists of two parts. First, it employs MA to assess NTCP models for xerostomia, dysphagia, and mucositis after radiation therapy, using Python 3.10.5 for statistical analysis. Second, it integrates NLP with convolutional neural networks (CNN) to optimize literature search, reducing 3256 articles to 12. CNN settings include a batch size of 50, 50-200 epoch range and a 0.001 learning rate.RESULTS:
The study's CNN-NLP model achieved a notable accuracy of 0.94 after 200 epochs with Adamax optimization. MA showed an AUC of 0.67 for early-effect xerostomia and 0.74 for late-effect, indicating moderate to high predictive accuracy but with high variability across studies. Initial CNN accuracy of 66.70% improved to 94.87% post-tuning by optimizer and hyperparameters.CONCLUSION:
The study successfully merges MA and NLP, confirming high predictive accuracy for specific model-feature combinations. It introduces a time-based metric, words per minute (WPM), for efficiency and highlights the utility of MA and NLP in clinical research.Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Xerostomía
/
Neoplasias de Cabeza y Cuello
Tipo de estudio:
Etiology_studies
/
Prognostic_studies
/
Systematic_reviews
Límite:
Humans
Idioma:
En
Revista:
Radiat Oncol
Asunto de la revista:
NEOPLASIAS
/
RADIOTERAPIA
Año:
2024
Tipo del documento:
Article