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Automated, High-Throughput Platform to Generate a High-Reliability, Comprehensive Rectal Cancer Database
Neal Bhutiani1, Mahmoud M G Yousef2, Abdelrahman Yousef2
1Department of Colon and Rectal Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX.
JCO Clinical Cancer Informatics
|May 17, 2024
Summary
Automated platforms efficiently extract and integrate oncology data from electronic health records for rectal cancer patients. This improves data accuracy, aiding clinical research and patient care.
Area of Science:
- Oncology
- Health Informatics
- Clinical Research
Background:
- Dynamic operations platforms facilitate data extraction and analysis across systems.
- Their application in large-scale oncology settings, particularly for rectal cancer, remains underexplored.
Purpose of the Study:
- To present a pipeline for automated, high-fidelity extraction, integration, and validation of cross-platform oncology data.
- To assess the application of dynamic operations platforms in a high-volume rectal cancer patient cohort.
Main Methods:
- Utilized a dynamic operations platform to identify rectal cancer patients (2016-2022) with available MRI and preoperative treatment data in the EHR.
- Extracted demographic, clinicopathologic, mutation, radiographic, and treatment data, assessing accuracy via manual review.
- Compared data extraction accuracy before and after implementing synoptic reporting for MRI data.
Main Results:
- Included 516 patients with localized rectal cancer.
- Post-synoptic reporting, T-category extraction accuracy increased from 87% to 95%, N-category from 58% to 88%.
- Accuracy for pelvic sidewall adenopathy improved from 78% to 94%, extramural vascular invasion from 89% to 99%.
Conclusions:
- Dynamic operations platforms enable high-fidelity, automated integration of multiparameter oncology data.
- Pipelines are adaptable to other solid tumors and, with standardized reporting, enhance clinical research efficiency.
- This approach aids in translating findings to optimize patient outcomes.
