Figure 2:

Structured oncologic assessment in clinical routine and natural language processing (NLP) model building. An exemplary structured oncology report (SOR) for a 32-year-old woman with a history of breast cancer (left side) was interpreted as progressive disease (PD). The oncologic data were automatically processed and then fed into the NLP development pipeline (right side, A–E). (A) The deep NLP architecture used was based on the bidirectional encoder representations from transformers (BERT) language model pretrained on unlabeled general domain data and adapted to the German vocabulary. (B) Automatic extraction of the Response Evaluation Criteria in Solid Tumors (RECIST)–related categories PD, stable disease (SD), partial response (PR), and complete response (CR) from the SOR “impression” section by using a rule-based pattern-matching command called regular expressions (RegEx). (C) Fine-tuning of BERT and three feature-rich NLP methods (linear support vector classifier [SVC], k-nearest neighbors [KNN], multinomial naive Bayes [MNB]) on the extracted SOR oncologic findings section. The output of (B) was used as ground truth classifier for (D) NLP model training and validation, followed by (E) performance evaluation on the free-text oncology reports (FTOR) test sets in comparison with human baseline scores. A live demo of the SOR template can be accessed for review at http://www.targetedreporting.com/sor/. For demonstration purposes, the presented exemplary SOR and the online template have been translated from German to English. TF-IDF = term frequency–inverse document frequency.