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• W2007809020 abstract "Purpose: To develop a framework for automatic extraction of clinically meaningful dosimetric-outcome relationships from an in-house, analytic oncology database. Methods: Dose-volume histograms (DVH) and clinical outcome-related structured data elements have been routinely stored to our database for 513 HN cancer patients treated from 2007 to 2014. SQL queries were developed to extract outcomes that had been assessed for at least 100 patients, as well as DVH curves for organs-at-risk (OAR) that were contoured for at least 100 patients. DVH curves for paired OAR (e.g., left and right parotids) were automatically combined and included as additional structures for analysis. For each OAR-outcome combination, DVH dose points, D(Vt), at a series of normalized volume thresholds, Vt=[0.01,0.99], were stratified into two groups based on outcomes after treatment completion. The probability, P[D(Vt)], of an outcome was modeled at each Vt by logistic regression. Notable combinations, defined as having P[D(Vt)] increase by at least 5% per Gy (p<0.05), were further evaluated for clinical relevance using a custom graphical interface. Results: A total of 57 individual and combined structures and 115 outcomes were queried, resulting in over 6,500 combinations for analysis. Of these, 528 combinations met the 5%/Gy requirement, with further manual inspection revealing a number of reasonable models based on either reported literature or proximity between neighboring OAR. The data mining algorithm confirmed the following well-known toxicity/outcome relationships: dysphagia/larynx, voice changes/larynx, esophagitis/esophagus, xerostomia/combined parotids, and mucositis/oral mucosa. Other notable relationships included dysphagia/pharyngeal constrictors, nausea/brainstem, nausea/spinal cord, weight-loss/mandible, and weight-loss/combined parotids. Conclusion: Our database platform has enabled large-scale analysis of dose-outcome relationships. The current data-mining framework revealed both known and novel dosimetric and clinical relationships, underscoring the potential utility of this analytic approach. Multivariate models may be necessary to further evaluate the complex relationship between neighboring OARs and observed outcomes. This research was supported through collaborations with Elekta, Philips, and Toshiba." @default.
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• W2007809020 date "2014-05-29" @default.
• W2007809020 modified "2023-09-26" @default.
• W2007809020 title "MO-A-BRD-09: A Data-Mining Algorithm for Large Scale Analysis of Dose-Outcome Relationships in a Database of Irradiated Head-And-Neck (HN) Cancer Patients" @default.
• W2007809020 doi "https://doi.org/10.1118/1.4889112" @default.
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