Article Text
PDF
Abstract
Introduction/Background Accurate prediction of patient prognosis can be especially useful for the selection of best treatment protocols. Machine Learning (ML) can serve this purpose by making predictions based upon generalizable clinical patterns embedded within learning datasets. We hypothesised that use of ML algorithms could improve prognosis estimation in advanced high grade serous ovarian (HGSOC) patients. We aimed to compare the performance of two ML prediction methods for HGSOC prognosis, based on Area Under Curve (AU-ROC) performance for a 2-year prognosis period.
Methodology This was a retrospective analysis of 209 FIGO stage III-IV HGSOC women, who were scheduled for cytoreductive surgery in SJUH, Leeds between Jan 2015 to Dec 2018 with curative or life-prolonging intent. Support-Vector-Machine (SVM) and K-Nearest Neigbors (K-NN) were employed to model prognosis. The prognosis estimation problem was formulated as a binary classification problem. For the 2-year prognosis period, two groups were defined using patient survival information; patients who did not relapse or survived beyond two years were labelled in the positive class and patients who relapsed or died before reaching that period were considered in the negative class. The study was restricted to the most common prognostic variables and focused on predictive model comparisons. Dataset was split into training and test cohorts with repeated random sampling until there was no significant difference (p=0.20) between the two cohorts with respect to all variables.
Results 172 out of 209 patients with fully curated data were eligible for 2-year prognosis prediction analysis. 104/172 (60%) and 55/172 (32%) patients had disease recurrence or died of disease within two years, respectively. The variable importance for the 2-year progression free survival (PFS) and overall survival (OS) is shown in figure 1. A combination of good performance status, upfront cytoreduction and increased surgical complexity score best predicted 2-year PFS with an accuracy of 63% and 62.1% for the SVM and K-NN classifiers, respectively. SVM best predicted 2-year OS by a combination of Carboplatin/Taxol chemotherapy, low disease score, no residual disease, increased surgical complexity score, and upfront cytoreduction with an accuracy of 71.6% ( AU-ROC: 0.66) (figure 2).
Conclusion ML appears to be promising for accurate estimation of HGSOC prognosis. We provide evidence as to what combination of prognosticators leads to the largest impact on the HGSOC two-year prognosis. The cohort is currently expanding to further examine the short term vs long term contribution of the clinical variables from the comparative models
Disclosures No disclosures.