Using Auto-Segmentation to Reduce Contouring and Dose Inconsistency in Clinical Trials: The Simulated Impact on RTOG 0617.

Journal: International journal of radiation oncology, biology, physics

PMID: 33197531

Abstract

PURPOSE: Contouring inconsistencies are known but understudied in clinical radiation therapy trials. We applied auto-contouring to the Radiation Therapy Oncology Group (RTOG) 0617 dose escalation trial data. We hypothesized that the trial heart doses were higher than reported due to inconsistent and insufficient heart segmentation. We tested our hypothesis by comparing doses between deep-learning (DL) segmented hearts and trial hearts.

Authors

Maria Thor

Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Aditya Apte

Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Rabia Haq

Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
Aditi Iyer

Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
Eve LoCastro

Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
Joseph O Deasy

Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York.

Keywords

Adult Aged Aged, 80 and over Algorithms Carcinoma, Non-Small-Cell Lung Clinical Trials, Phase III as Topic Confidence Intervals Deep Learning Female Heart Humans Linear Models Lung Neoplasms Male Middle Aged Organs at Risk Proportional Hazards Models Radiotherapy Dosage Statistics, Nonparametric Tomography, X-Ray Computed

External Resources

View on PubMed Access via DOI PubMed (33197531)

Using Auto-Segmentation to Reduce Contouring and Dose Inconsistency in Clinical Trials: The Simulated Impact on RTOG 0617.

Abstract

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Keywords

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