BI-RADS BERT & Using Section Segmentation to Understand Radiology Reports

Radiology reports are one of the main forms of communication between radiologists and other clinicians and contain important information for patient care. In order to use this information for research and automated patient care programs, it is necessary to convert the raw text into structured data suitable for analysis. State-of-the-art natural language processing (NLP) domain-specific contextual word embeddings have been shown to achieve impressive accuracy for these tasks in medicine, but have yet to be utilized for section structure segmentation. In this work, we pre-trained a contextual embedding BERT model using breast radiology reports and developed a classifier that incorporated the embedding with auxiliary global textual features in order to perform section segmentation. This model achieved a 98% accuracy at segregating free text reports sentence by sentence into sections of information outlined in the Breast Imaging Reporting and Data System (BI-RADS) lexicon, a significant improvement over the Classic BERT model without auxiliary information. We then evaluated whether using section segmentation improved the downstream extraction of clinically relevant information such as modality/procedure, previous cancer, menopausal status, the purpose of the exam, breast density, and breast MRI background parenchymal enhancement. Using the BERT model pre-trained on breast radiology reports combined with section segmentation resulted in an overall accuracy of 95.9% in the field extraction tasks. This is a 17% improvement compared to an overall accuracy of 78.9% for field extraction with models using Classic BERT embeddings and not using section segmentation. Our work shows the strength of using BERT in radiology report analysis and the advantages of section segmentation in identifying key features of patient factors recorded in breast radiology reports

Solution structure of the cytochrome P450 reductase–cytochrome c complex determined by neutron scattering

Electron transfer in all living organisms critically relies on formation of complexes between the proteins involved. The function of these complexes requires specificity of the interaction to allow for selective electron transfer but also a fast turnover of the complex, and they are therefore often transient in nature, making them challenging to study. Here, using small-angle neutron scattering with contrast matching with deuterated protein, we report the solution structure of the electron transfer complex between cytochrome P450 reductase (CPR) and its electron transfer partner cytochrome c This is the first reported solution structure of a complex between CPR and an electron transfer partner. The structure shows that the interprotein interface includes residues from both the FMN- and FAD-binding domains of CPR. In addition, the FMN is close to the heme of cytochrome c but distant from the FAD, indicating that domain movement is required between the electron transfer steps in the catalytic cycle of CPR. In summary, our results reveal key details of the CPR catalytic mechanism, including interactions of two domains of the reductase with cytochrome c and motions of these domains relative to one another. These findings shed light on interprotein electron transfer in this system and illustrate a powerful approach for studying solution structures of protein-protein complexes