RoBERTa-Assisted Outcome Prediction in Ovarian Cancer Cytoreductive Surgery Using Operative Notes

Author:

Laios Alexandros1ORCID,Kalampokis Evangelos2,Mamalis Marios Evangelos2ORCID,Tarabanis Constantine3,Nugent David1,Thangavelu Amudha1,Theophilou Georgios1,De Jong Diederick1ORCID

Affiliation:

1. Department of Gynaecologic Oncology, ESGO Center of Excellence for Ovarian Cancer Surgery, St James’s University Hospital, Leeds, UK

2. Information Systems Lab, Department of Business Administration, University of Macedonia, Thessaloniki, Greece

3. Department of Internal Medicine, School of Medicine, New York University, New York, NY, USA

Abstract

Introduction Contemporary efforts to predict surgical outcomes focus on the associations between traditional discrete surgical risk factors. We aimed to determine whether natural language processing (NLP) of unstructured operative notes improves the prediction of residual disease in women with advanced epithelial ovarian cancer (EOC) following cytoreductive surgery. Methods Electronic Health Records were queried to identify women with advanced EOC including their operative notes. The Term Frequency – Inverse Document Frequency (TF-IDF) score was used to quantify the discrimination capacity of sequences of words (n-grams) regarding the existence of residual disease. We employed the state-of-the-art RoBERTa-based classifier to process unstructured surgical notes. Discrimination was measured using standard performance metrics. An XGBoost model was then trained on the same dataset using both discrete and engineered clinical features along with the probabilities outputted by the RoBERTa classifier. Results The cohort consisted of 555 cases of EOC cytoreduction performed by eight surgeons between January 2014 and December 2019. Discrete word clouds weighted by n-gram TF-IDF score difference between R0 and non-R0 resection were identified. The words ‘adherent’ and ‘miliary disease’ best discriminated between the two groups. The RoBERTa model reached high evaluation metrics (AUROC .86; AUPRC .87, precision, recall, and F1 score of .77 and accuracy of .81). Equally, it outperformed models that used discrete clinical and engineered features and outplayed the performance of other state-of-the-art NLP tools. When the probabilities from the RoBERTa classifier were combined with commonly used predictors in the XGBoost model, a marginal improvement in the overall model’s performance was observed (AUROC and AUPRC of .91, with all other metrics the same). Conclusion/Implications We applied a sui generis approach to extract information from the abundant textual surgical data and demonstrated how it can be effectively used for classification prediction, outperforming models relying on conventional structured data. State-of-art NLP applications in biomedical texts can improve modern EOC care.

Publisher

SAGE Publications

Subject

Oncology,Hematology,General Medicine

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