Guyu Dai

TL;DR: In this paper, machine learning methods were applied to predict external respiratory motion signals and predict internal liver motion in this therapeutic context. And the LSTM-based integrated model performs well at predicting liver motion from external respiratory signals with system latencies of up to 450mm.

TL;DR: The proposed dose convolution-fitting method can accurately measure the time delay of respiratory-gating radiotherapy and provides high accuracy for respiratory motion tracking.

TL;DR: The 3D-print silica bolus provided patients with higher individuation, and improved the conformity and uniformity of the planning target volume (PTV) compared to other kinds of boluses.

TL;DR: In this article , the impact of possible interobserver variability in CBCT-based soft-tissue matching for prostate cancer radiotherapy was analyzed, and the impact was evaluated using tumor control probabilities (TCPs) and normal tissue complication probabilities (NTCPs).

TL;DR: In this article, three types of machine learning (ML) models that utilize radiomics features of the DD maps (ML 1 models), features of structural similarity (SSIM maps), and features of both DD and SSIM maps were used to perform three different types of position error classification, namely a binary classification of the isocenter error (type 1), three binary classifications of LR, SI, and AP direction errors (type 2), and an eight-element classification (type 3).