Harnessing Automation in Data Mining: A Review on the Impact of PyESAPI in Radiation Oncology Data Extraction and Management

TL;DR

This review discusses how PyESAPI automation improves data extraction in Radiation Oncology, highlighting its benefits, challenges, and future prospects for clinical and research workflows.

Contribution

It provides a comprehensive analysis of PyESAPI's role, efficiency, and challenges in automating data extraction in Radiation Oncology, comparing it with manual methods.

Findings

PyESAPI significantly increases data extraction efficiency.

Automation reduces errors compared to manual extraction.

Challenges include system compatibility and scripting complexity.

Abstract

Data extraction and management are crucial components of research and clinical workflows in Radiation Oncology (RO), where accurate and comprehensive data are imperative to inform treatment planning and delivery. The advent of automated data mining scripts, particularly using the Python Environment for Scripting APIs (PyESAPI), has been a promising stride towards enhancing efficiency, accuracy, and reliability in extracting data from RO Information Systems (ROIS) and Treatment Planning Systems (TPS). This review dissects the role, efficiency, and challenges of implementing PyESAPI in RO data extraction and management, juxtaposing manual data extraction techniques and explicating future avenues