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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

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Informatics in radiology: improving clinical work flow through an AIM database: a sample web-based lesion tracking

Aaron C Abajian¹, Mia Levy, Daniel L Rubin

¹Departments of Radiology and Medicine, Stanford University School of Medicine, Richard M. Lucas Center, Stanford University, Stanford, CA 94305, USA.

Radiographics : a Review Publication of the Radiological Society of North America, Inc

|June 30, 2012

Summary

This study introduces a web-based application that automates quantitative imaging analysis for cancer patients. It streamlines the process of tracking lesion measurements over time, aiding clinical decision-making and accelerating radiology workflows.

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Area of Science:

Medical Imaging
Radiology
Computational Pathology

Background:

Quantitative imaging assessments are vital for tracking cancer lesion progression and treatment response.
Manual compilation of quantitative imaging data from prior and current studies is time-consuming and complex.
Automating quantitative imaging workflows can significantly enhance clinical decision-making.

Purpose of the Study:

To develop and demonstrate a web-based application for automated quantitative imaging analysis.
To leverage the Annotation and Image Markup standard for accessible temporal imaging data.
To facilitate the calculation and visualization of cancer treatment response metrics.

Main Methods:

Developed a World Wide Web-based application utilizing the Annotation and Image Markup standard.
Implemented automated summarization of prior and current quantitative imaging measurements.
Integrated calculation of Response Evaluation Criteria in Solid Tumors (RECIST) and alternative response indicators.
Enabled overlay of calculated metrics on original images for visual inspection.

Main Results:

The system successfully automates the summarization of quantitative imaging measurements.
It accurately calculates RECIST and other cancer treatment response metrics.
Clinical evaluation confirmed the system's potential to accelerate radiology workflows.
The application facilitates the evaluation of complex response metrics.

Conclusions:

Automating quantitative imaging analysis through standardized data formats enhances clinical practice.
The developed system improves efficiency in tracking cancer lesion measurements and treatment response.
Linking quantitative data to images via a standard format is crucial for advancing quantitative imaging.
This approach supports clinical decision-making without disrupting existing radiology workflows.