Evaluation of Deep Learning to Augment Image-Guided Radiotherapy for Head and Neck and Prostate Cancers.

ABSTRACT:

Importance

Personalized radiotherapy planning depends on high-quality delineation of target tumors and surrounding organs at risk (OARs). This process puts additional time burdens on oncologists and introduces variability among both experts and institutions.

Objective

To explore clinically acceptable autocontouring solutions that can be integrated into existing workflows and used in different domains of radiotherapy.

Design, setting, and participants

This quality improvement study used a multicenter imaging data set comprising 519 pelvic and 242 head and neck computed tomography (CT) scans from 8 distinct clinical sites and patients diagnosed either with prostate or head and neck cancer. The scans were acquired as part of treatment dose planning from patients who rece

SUBMITTER: Oktay O 

PROVIDER: S-EPMC7705593 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature