ArticleMedical & biological engineering & computing2024
Automatic text classification of prostate cancer malignancy scores in radiology reports using NLP models.
Article in Medical & biological engineering & computing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Artificial intelligence for detection, grading, and prognostication in prostate cancer pathology: A scoping review.Histology and histopathology · 2026Article
- A cross-sectional evaluation of Large Language Model answers to dental questions.BMC oral health · 2026Article
- Next-Generation Advances in Prostate Cancer Imaging and Artificial Intelligence Applications.Journal of imaging · 2025Review
- The added value of including thyroid nodule features into large language models for automatic ACR TI-RADS classification based on ultrasound reports.Japanese journal of radiology · 2025Article
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Authors and funding
8 authors.
Funding
Abstract
This paper presents the implementation of two automated text classification systems for prostate cancer findings based on the PI-RADS criteria. Specifically, a traditional machine learning model using XGBoost and a language model-based approach using RoBERTa were employed. The study focused on Spanish-language radiological MRI prostate reports, which has not been explored before. The results demonstrate that the RoBERTa model outperforms the XGBoost model, although both achieve promising results. Furthermore, the best-performing system was integrated into the radiological company's information systems as an API, operating in a real-world environment.
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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 OpenQuestion graph.