Application of Artificial Intelligence for Preoperative Diagnostic and Prognostic Prediction in Epithelial Ovarian Cancer Based on Blood Biomarkers

Abstract

Purpose: We aimed to develop an ovarian cancer-specific predictive framework for clinical stage, histotype, residual tumor burden, and prognosis using machine learning methods based on multiple biomarkers.Experimental Design: Overall, 334 patients with epithelial ovarian cancer (EOC) and 101 patients with benign ovarian tumors were randomly assigned to "training" and "test" cohorts. Seven supervised machine learning classifiers, including Gradient Boosting Machine (GBM), Support Vector Machine, Random Forest (RF), Conditional RF (CRF), Na€ ve Bayes, Neural Network, and Elastic Net, were used to derive diagnostic and prognostic information from 32 parameters commonly available from pretreatment peripheral blood tests and age.Results: Machine learning techniques were superior to conventional regression-based analyses in predicting multiple clinical parameters pertaining to EOC. Ensemble meth-ods combining weak decision trees, such as GBM, RF, and CRF, showed the best performance in EOC prediction. The values for the highest accuracy and area under the ROC curve (AUC) for segregating EOC from benign ovarian tumors with RF were 92.4% and 0.968, respectively. The highest accuracy and AUC for predicting clinical stages with RF were 69.0% and 0.760, respectively. High-grade serous and mucinous histotypes of EOC could be preoperatively predicted with RF. An ordinal RF classifier could distinguish complete resection from others. Unsupervised clustering analysis identified subgroups among early-stage EOC patients with significantly worse survival.Conclusions: Machine learning systems can provide critical diagnostic and prognostic prediction for patients with EOC before initial intervention, and the use of predictive algorithms may facilitate personalized treatment options through pretreatment stratification of patients.NOTE: There were too few early-stage EOC patients with residual tumor. A definition for the significance of bold is P value of < 0.05.

Predicting complete cytoreduction for advanced ovarian cancer patients using nearest-neighbor models

“…Other studies used an ordinal classification method to predict surgical outcomes in aEOC patients with a 64.9% accuracy and AUC of 0.697 (R0 vs non-R0) based solely on preoperative information [19]. Another AI model predicted the outcome of surgery and again showed that ANN could predict outcome (optimal cytoreduction vs. suboptimal cytoreduction) with 77% accuracy and an AUC of 0.73.…”

<p>EMP1 Promotes the Proliferation and Invasion of Ovarian Cancer Cells Through Activating the MAPK Pathway</p>

“…1,2 Although a great deal of effort has been devoted to the study of new cytotoxic and targeted drugs and surgery, the overall survival rate of patients is still around 40%. 3,4 Standard treatment is still surgery, paclitaxel, carboplatin and combined chemotherapy, but recurrence and metastasis were happened in about 80% of patients. 5,6 As a result, it is crucial to discover significant biomarkers and to know their cellular and molecular mechanism in regulating the invasion and metastasis of ovarian cancer.…”

<p>Progress of Artificial Intelligence in Gynecological Malignant Tumors</p>

“…Serological diagnosis has always been a common method for malignant tumor patients, especially for ovarian cancer (OC) patients. Kawakami et al 34 have established a specific predictive framework for pretreatment estimation of histotypes, clinical stage, residual tumor burden and prognosis of epithelial ovarian cancer (EOC) patients using machine learning algorithms based on multiple biomarkers and clinical variables. They randomly assigned 334 patients with EOC and 101 patients with benign ovarian tumor into training group and testing group.…”