Automatic prediction model for online diaphragm motion tracking based on optical surface monitoring by machine learning.

Background: The aim of this study was to establish a correlation model between external surface motion and internal diaphragm apex movement using machine learning and to realize online automatic prediction of the diaphragm motion trajectory based on optical surface monitoring.

Methods: The optical body surface parameters and kilovoltage (kV) X-ray fluoroscopic images of 7 liver tumor patients were captured synchronously for 50 seconds. The location of the diaphragm apex was manually delineated by a radiation oncologist and automatically detected with a convolutional network model in fluoroscopic images.

Geometric and Dosimetric Evaluation of Deep Learning-Based Automatic Delineation on CBCT-Synthesized CT and Planning CT for Breast Cancer Adaptive Radiotherapy: A Multi-Institutional Study.

Purpose: We developed a deep learning model to achieve automatic multitarget delineation on planning CT (pCT) and synthetic CT (sCT) images generated from cone-beam CT (CBCT) images. The geometric and dosimetric impact of the model was evaluated for breast cancer adaptive radiation therapy.

Methods: We retrospectively analyzed 1,127 patients treated with radiotherapy after breast-conserving surgery from two medical institutions.