Feature Selection is Critical for 2-Year Prognosis in Advanced Stage High Grade Serous Ovarian Cancer by Using Machine Learning

Abstract

Introduction Accurate prediction of patient prognosis can be especially useful for the selection of best treatment protocols. Machine Learning can serve this purpose by making predictions based upon generalizable clinical patterns embedded within learning datasets. We designed a study to support the feature selection for the 2-year prognostic period and compared the performance of several Machine Learning prediction algorithms for accurate 2-year prognosis estimation in advanced-stage high grade serous ovarian cancer (HGSOC) patients. Methods The prognosis estimation was formulated as a binary classification problem. 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. A ten-fold cross-validation was applied. Various state-of-the-art supervised classifiers were used. For feature selection, in addition to the exhaustive search for the best combination of features, we used the-chi square test of independence and the MRMR method. Results Two hundred nine patients were identified. The model's mean prediction accuracy reached 73%. We demonstrated that Support-Vector-Machine and Ensemble Subspace Discriminant algorithms outperformed Logistic Regression in accuracy indices. The probability of achieving a cancer-free state was maximised with a combination of primary cytoreduction, good performance status and maximal surgical effort (AUC 0.63). Standard chemotherapy, performance status, tumour load and residual disease were consistently predictive of the mid-term overall survival (AUC 0.63–0.66). The model recall and precision were greater than 80%. Conclusion Machine Learning appears to be promising for accurate prognosis estimation. Appropriate feature selection is required when building an HGSOC model for 2-year prognosis prediction. We provide evidence as to what combination of prognosticators leads to the largest impact on the HGSOC 2-year prognosis.

Systematic Analysis of Ovarian Cancer Empowered with Machine and Deep Learning: A Taxonomy and Future Challenges

“…However, 104 patients out of 172 had to suffer from the disease recurrence and remaining 55 of which died within two years. The results of the study confirm the (support vector machine) SVM method as the appropriate predictor for two-year prognosis analysis with 63% of accuracy rate while KNN performs 62.1% of accuracy [66].…”

“…A study in [66], Hypothesized novel prediction of high-grade serous ovarian cancer at advanced stage with prognosis estimation of two years by combining two different machine learning methods which includes support vector machine along with (Knn) k-nearest neighbor and also compare their performances. Analysis performed on 209 of total patients at III-IV stage of high-grade serous ovarian cancer who ready for cytoreductive surgery with life prologue intent and curative.…”

Machine Learning-Based Risk Prediction of Critical Care Unit Admission for Advanced Stage High Grade Serous Ovarian Cancer Patients Undergoing Cytoreductive Surgery: The Leeds-Natal Score

“…The mean age and BMI of the patients were 64 ± 10.5 yrs, respectively. The mean SCS was 3 ± 2 (2)(3)(4)(5)(6)(7)(8)(9)(10)(11). Of these patients, 69/291 (23.7%) and 222/291 (76.3%) underwent PDS and IDS, respectively.…”

“…We previously developed ML algorithms to improve the prediction accuracy of complete cytoreduction in advanced stage HGSOC patients [ 8 ]. In addition, we highlighted the importance of feature selection for the ML-based two-year prognosis estimation in the same population [ 9 ]. The usefulness of ML as a prognostic toll in the ovarian cancer environment has been increasingly demonstrated [ 10 ].…”

Factors Predicting Surgical Effort Using Explainable Artificial Intelligence in Advanced Stage Epithelial Ovarian Cancer

“…We previously investigated survival predictions in advanced stage EOC, using clinical variables. We applied ML-based feature selection to build models for two-year prognosis prediction, with satisfactory accuracy [ 13 ]. The end-point of such discoveries is their effective translation into patient care workflows.…”