Multiclass risk models for ovarian malignancy: an illustration of prediction uncertainty due to the choice of algorithm.

ABSTRACT:

Background

Assessing malignancy risk is important to choose appropriate management of ovarian tumors. We compared six algorithms to estimate the probabilities that an ovarian tumor is benign, borderline malignant, stage I primary invasive, stage II-IV primary invasive, or secondary metastatic.

Methods

This retrospective cohort study used 5909 patients recruited from 1999 to 2012 for model development, and 3199 patients recruited from 2012 to 2015 for model validation. Patients were recruited at oncology referral or general centers and underwent an ultrasound examination and surgery ≤ 120 days later. We developed models using standard multinomial logistic regression (MLR), Ridge MLR, random forest (RF), XGBoost, neural networks (NN), and support vector machines (SVM). We used

SUBMITTER: Ledger A 

PROVIDER: S-EPMC10668424 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature