Dosimetric effect of intensity-modulated radiation therapy for postoperative non-small cell lung cancer with and without air cavity in the planning target volume.

To evaluate the dosimetric effect of intensity-modulated radiation therapy (IMRT) for postoperative non-small cell lung cancer (NSCLC), with and without the air cavity in the planning target volume (PTV). Two kinds of IMRT plans were made for 21 postoperative NSCLC patients. In Plan-0: PTV included the tracheal air cavity, and in Plan-1: the air cavity was subtracted from the PTV.

Automatic segmentation of lung tumors on CT images based on a 2D & 3D hybrid convolutional neural network.

Objective: A stable and accurate automatic tumor delineation method has been developed to facilitate the intelligent design of lung cancer radiotherapy process. The purpose of this paper is to introduce an automatic tumor segmentation network for lung cancer on CT images based on deep learning.

Methods: In this paper, a hybrid convolution neural network (CNN) combining 2D CNN and 3D CNN was implemented for the automatic lung tumor delineation using CT images.

Evaluation of IGRT-Induced Imaging Doses and Secondary Cancer Risk for SBRT Early Lung Cancer Patients In Silico Study.

Objectives: This study performed dosimetry studies and secondary cancer risk assessments on using electronic portal imaging device (EPID) and cone beam computed tomography (CBCT) as image guided tools for the early lung cancer patients treated with SBRT.

Methods: The imaging doses from MV-EPID and kV-CBCT of the Edge accelerator were retrospectively added to sixty-one SBRT treatment plans of early lung cancer patients. The MV-EPID imaging dose (6MV Photon beam) was calculated in Pinnacle TPS, and the kV-CBCT imaging dose was simulated and calculated by modeling of the kV energy beam in TPS using Pinnacle automatic modeling program.

A novel specific grading standard study of auto-segmentation of organs at risk in thorax: subjective-objective-combined grading standard.

Background: To develop a novel subjective-objective-combined (SOC) grading standard for auto-segmentation for each organ at risk (OAR) in the thorax.

Methods: A radiation oncologist manually delineated 13 thoracic OARs from computed tomography (CT) images of 40 patients. OAR auto-segmentation accuracy was graded by five geometric objective indexes, including the Dice similarity coefficient (DSC), the difference of the Euclidean distance between centers of mass (ΔCMD), the difference of volume (ΔV), maximum Hausdorff distance (MHD), and average Hausdorff distance (AHD).

Prediction of Three-Dimensional Radiotherapy Optimal Dose Distributions for Lung Cancer Patients With Asymmetric Network.

The iterative design of radiotherapy treatment plans is time-consuming and labor-intensive. In order to provide a guidance to treatment planning, Asymmetric network (A-Net) is proposed to predict the optimal 3D dose distribution for lung cancer patients. A-Net was trained and tested in 392 lung cancer cases with the prescription doses of 50Gy and 60Gy.

On the optimal number of dose-limiting shells in the SBRT auto-planning design for peripheral lung cancer.

Purpose: The number of dose-limiting shells in the optimization process is one of the key factors determining the quality of stereotactic body radiotherapy (SBRT) auto-planning in the Pinnacle treatment planning system (TPS). This study attempted to derive the optimal number of shells by evaluating the auto-plans designed with different number of shells for peripheral lung cancer patients treated with SBRT.

Methods: Identical treatment technique, optimization process, constraints, and dose calculation algorithm in the Pinnacle TPS were retrospectively applied to 50 peripheral lung cancer patients who underwent SBRT in our center.