文章：

应用自然语言处理技术基于OR-RADS词典对单次报告预测转移性疾病反应

Applying Natural Language Processing to Single-Report Prediction of Metastatic Disease Response Using the OR-RADS Lexicon

原文发布日期：10 October 2023

DOI: 10.3390/cancers15204909

类型: Article

开放获取: 是

英文摘要：

Generating Real World Evidence (RWE) on disease responses from radiological reports is important for understanding cancer treatment effectiveness and developing personalized treatment. A lack of standardization in reporting among radiologists impacts the feasibility of large-scale interpretation of disease response. This study examines the utility of applying natural language processing (NLP) to the large-scale interpretation of disease responses using a standardized oncologic response lexicon (OR-RADS) to facilitate RWE collection. Radiologists annotated 3503 retrospectively collected clinical impressions from radiological reports across several cancer types with one of seven OR-RADS categories. A Bidirectional Encoder Representations from Transformers (BERT) model was trained on this dataset with an 80–20% train/test split to perform multiclass and single-class classification tasks using the OR-RADS. Radiologists also performed the classification to compare human and model performance. The model achieved accuracies from 95 to 99% across all classification tasks, performing better in single-class tasks compared to the multiclass task and producing minimal misclassifications, which pertained mostly to overpredicting the equivocal and mixed OR-RADS labels. Human accuracy ranged from 74 to 93% across all classification tasks, performing better on single-class tasks. This study demonstrates the feasibility of the BERT NLP model in predicting disease response in cancer patients, exceeding human performance, and encourages the use of the standardized OR-RADS lexicon to improve large-scale prediction accuracy.

摘要翻译： 

从放射学报告中生成关于疾病反应的现实世界证据对于理解癌症治疗效果和制定个体化治疗方案具有重要意义。放射科医师报告缺乏标准化，影响了大规模解读疾病反应的可行性。本研究探讨了应用自然语言处理技术，结合标准化肿瘤反应词典，以促进现实世界证据收集的大规模疾病反应解读方法。放射科医师使用七类OR-RADS标准对3503份回顾性收集的多种癌症类型放射报告临床印象进行了标注。基于该数据集，采用80-20%的训练/测试分割方式训练了双向编码器表示转换模型，以执行基于OR-RADS的多类别和单类别分类任务。放射科医师也进行了相同分类以比较人工与模型性能。该模型在所有分类任务中达到95%至99%的准确率，在单类别任务中表现优于多类别任务，且误分类率极低，主要涉及对不确定性和混合性OR-RADS标签的过度预测。人工分类准确率在74%至93%之间，在单类别任务中表现更佳。本研究证明了BERT自然语言处理模型在预测癌症患者疾病反应方面的可行性，其性能超越人工分类，并支持使用标准化OR-RADS词典来提高大规模预测的准确性。

原文链接：

Applying Natural Language Processing to Single-Report Prediction of Metastatic Disease Response Using the OR-RADS Lexicon