Technical note: Determining the applicability of a clinical knowledge-based learning model via prospective outlier detection.

ABSTRACT:

Background

Knowledge-based planning (KBP) is increasingly implemented clinically because of its demonstrated ability to improve treatment planning efficiency and reduce plan quality variations. However, cases with large dose-volume histogram (DVH) prediction uncertainties may still need manual adjustments by the planner to achieve high plan quality.

Purpose

The purpose of this study is to develop a data-driven method to detect patients with high prediction uncertainties so that intentional effort is directed to these patients.

Methods

We apply an anomaly detection method known as the local outlier factor (LOF) to a dataset consisting of the training set and each of the prospective patients considered, to evaluate their likelihood of being an anomaly when compared with

SUBMITTER: Zhang J 

PROVIDER: S-EPMC11742179 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature