Deep Learning–based Assessment of Oncologic Outcomes from Natural Language Processing of Structured Radiology Reports

作者：Matthias A. Fink, Klaus Kades, Arved Bischoff, Martin Moll, Merle Schnell, Maike Küchler, Gregor Köhler, Jan Sellner, Claus Peter Heußel, Hans‐Ulrich Kauczor, Heinz‐Peter Schlemmer, Klaus Maier‐Hein, Tim Frederik Weber, Jens Kleesiek · 发表于：Radiology Artificial Intelligence · 年份：2022 · DOI：10.1148/ryai.220055 · 被引用次数：54 · 研究领域：Artificial Intelligence in Healthcare and Education、Topic Modeling、Radiomics and Machine Learning in Medical Imaging

Purpose To train a deep natural language processing (NLP) model, using data mined structured oncology reports (SOR), for rapid tumor response category (TRC) classification from free-text oncology reports (FTOR) and to compare its performance with human readers and conventional NLP algorithms. Materials and Methods In this retrospective study, databases of three independent radiology departments were queried for SOR and FTOR dated from March 2018 to August 2021. An automated data mining and curation pipeline was developed to extract Response Evaluation Criteria in Solid Tumors–related TRCs for SOR for ground truth definition. The deep NLP bidirectional encoder representations from transformers (BERT) model and three feature-rich algorithms were trained on SOR to predict TRCs in FTOR. Models’ F1 scores were compared against scores of radiologists, medical students, and radiology technologist students. Lexical and semantic analyses were conducted to investigate human and model performance on FTOR. Results Oncologic findings and TRCs were accurately mined from 9653 of 12 833 (75.2%) queried SOR, yielding oncology reports from 10 455 patients (mean age, 60 years ± 14 [SD]; 5303 women) who met inclusion criteria. On 802 FTOR in the test set, BERT achieved better TRC classification results (F1, 0.70; 95% CI: 0.68, 0.73) than the best-performing reference linear support vector classifier (F1, 0.63; 95% CI: 0.61, 0.66) and technologist students (F1, 0.65; 95% CI: 0.63, 0.67), had simi...