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. 2022 Jul 20;4(5):e220055. doi: 10.1148/ryai.220055

Figure 3:

Receiver operating characteristic curves for the deep natural language processing model bidirectional encoder representations from transformers (BERT) and symbols for each annotator group. The data show (A) the performance on free-text oncology reports (FTOR) of the cancer research center (FTOR1) and (B) the hospital specializing in chest diseases (FTOR2) in predicting the tumor response categories (TRCs) of progressive disease (PD), stable disease (SD), partial response (PR), and complete response (CR). (C) Performance of BERT on the held-out test subset of the structured oncology reports from the tertiary care center (SORTEST). AUC = area under the receiver operating characteristic curve, RT = radiology technologist.

Receiver operating characteristic curves for the deep natural language processing model bidirectional encoder representations from transformers (BERT) and symbols for each annotator group. The data show (A) the performance on free-text oncology reports (FTOR) of the cancer research center (FTOR1) and (B) the hospital specializing in chest diseases (FTOR2) in predicting the tumor response categories (TRCs) of progressive disease (PD), stable disease (SD), partial response (PR), and complete response (CR). (C) Performance of BERT on the held-out test subset of the structured oncology reports from the tertiary care center (SORTEST). AUC = area under the receiver operating characteristic curve, RT = radiology technologist.