Can natural language processing be effectively applied for audit data analysis in gynaecological oncology at a UK cancer centre?

BACKGROUND: The British Gynaecological Cancer Society (BGCS) has highlighted the disparity of ovarian cancer outcomes in the UK compared to other European countries. Therefore, cancer quality assurance audits and subspecialty training are important in improving the UK standard of care for these patients. The current workforce crisis afflicting the NHS creates difficulty in dedicating teams of clinicians to these audits. We present a single institution study to evaluate if NLP-generated code can improve the efficiency of ovarian cancer and subspeciality reaccreditations audits. We used the chat bot Google Bard to write Visual Basic Applications algorithms that utilise Excel files from electronic health records. METHODS: Primary ovarian cancer data from 2019 to 2022 was retrospectively collected from the Cambridge University Hospital electronic health records. The surgical subspecialty reaccreditation audit analysed the 2022 surgical database. A modular coding approach with Google Bard was applied to generate audit algorithms. The time to complete these current audits was compared against the 2016 ovarian cancer and 2020 subspeciality reaccreditation audits. RESULTS: The previous ovarian cancer audit conducted in 2016 required 3 clinicians for the 135 cases and data collection required 1800 min. Data analysis was completed in 300 min. The current ovarian cancer audit allocated 2 clinicians to the 600 surgical cases. Data collection was completed in 3120 min, 3360 min for code development and 720 min for testing. The 2020 subspecialty reaccreditation audit was completed in 360 min. The 2022 subspecialty reaccreditation audit was completed in 1680 min, with 960 min for code development, 240 for debugging and 480 min for testing. CONCLUSION: We have demonstrated that NLP-generated code can significantly increase the efficiency of surgical quality assurance audits by eliminating the need for manual data analysis. With the current trajectory of NLP development, increasingly complex algorithms can be developed with minimal programming knowledge.

High-grade serous ovarian carcinoma organoids as models of chromosomal instability.

High-grade serous ovarian carcinoma (HGSOC) is the most genomically complex cancer, characterized by ubiquitous TP53 mutation, profound chromosomal instability, and heterogeneity. The mutational processes driving chromosomal instability in HGSOC can be distinguished by specific copy number signatures. To develop clinically relevant models of these mutational processes we derived 15 continuous HGSOC patient-derived organoids (PDOs) and characterized them using bulk transcriptomic, bulk genomic, single-cell genomic, and drug sensitivity assays. We show that HGSOC PDOs comprise communities of different clonal populations and represent models of different causes of chromosomal instability including homologous recombination deficiency, chromothripsis, tandem-duplicator phenotype, and whole genome duplication. We also show that these PDOs can be used as exploratory tools to study transcriptional effects of copy number alterations as well as compound-sensitivity tests. In summary, HGSOC PDO cultures provide validated genomic models for studies of specific mutational processes and precision therapeutics.