Improvement of Prediction Performance for Radiation Pneumonitis by Using 3-Dimensional Dosiomic Features.

Introduction: Patients with early non-small-cell lung cancer (NSCLC) have a relatively long survival time after stereotactic body radiation therapy (SBRT). Predicting radiation-induced pneumonia (RP) has important clinical and social implications for improving the quality of life of such patients. This study developed an RP prediction model by using 3-dimensional (3D) dosiomic features.

Dosimetry and treatment efficiency of SBRT using TaiChiB radiotherapy system for two-lung lesions with one overlapping organs at risk.

Purpose: This study aims to assess the dosimetry and treatment efficiency of TaiChiB-based Stereotactic Body Radiotherapy (SBRT) plans applying to treat two-lung lesions with one overlapping organs at risk.

Methods: For four retrospective patients diagnosed with two-lung lesions each patient, four treatment plans were designed including Plan Edge, TaiChiB linac-based, RGS-based, and a linac-RGS hybrid (Plan TCLinac, Plan TCRGS, and Plan TCHybrid). Dosimetric metrics and beam-on time were employed to evaluate and compare the TaiChiB-based plans against Plan Edge.

Optimization of isocenter position for multiple brain metastases single-isocenter stereotactic radiosurgery to minimize dosimetric variations due to rotational uncertainty.

Purpose: This paper studied a novel calculation framework that can determine the optimal value isocenter position of single isocenter SRS treatment plan for multiple brain metastases, in order to minimize the dosimetric variations caused by rotational uncertainty.

Materials And Methods: 21 patients with 2-4 GTVswho received SRS treatment for multiple brain metastases in our institution were selected for the retrospective study. The PTVwas obtained by expanding GTV 1 mm isotropic margin.

Image-based features in machine learning to identify delivery errors and predict error magnitude for patient-specific IMRT quality assurance.

Objective: To identify delivery error type and predict associated error magnitude by image-based features using machine learning (ML).

Methods: In this study, a total of 40 thoracic plans (including 208 beams) were selected, and four error types with different magnitudes were introduced into the original plans, including 1) collimator misalignment (COLL), 2) monitor unit (MU) variation, 3) systematic multileaf collimator misalignment (MLCS), and 4) random MLC misalignment (MLCR). These dose distributions of portal dose predictions for the original plans were defined as the reference dose distributions (RDD), while those for the error-introduced plans were defined as the error-introduced dose distributions (EDD).

Improving CBCT image quality to the CT level using RegGAN in esophageal cancer adaptive radiotherapy.

Objective: This study aimed to improve the image quality and CT Hounsfield unit accuracy of daily cone-beam computed tomography (CBCT) using registration generative adversarial networks (RegGAN) and apply synthetic CT (sCT) images to dose calculations in radiotherapy.

Methods: The CBCT/planning CT images of 150 esophageal cancer patients undergoing radiotherapy were used for training (120 patients) and testing (30 patients). An unsupervised deep-learning method, the 2.

Radiation pneumonitis prediction after stereotactic body radiation therapy based on 3D dose distribution: dosiomics and/or deep learning-based radiomics features.

Background: This study was designed to establish radiation pneumonitis (RP) prediction models using dosiomics and/or deep learning-based radiomics (DLR) features based on 3D dose distribution.

Methods: A total of 140 patients with non-small cell lung cancer who received stereotactic body radiation therapy (SBRT) were retrospectively included in this study. These patients were randomly divided into the training (n = 112) and test (n = 28) sets.

Voxel-Level BED Corrected Dosimetric and Radiobiological Assessment of 2 Kinds of Hybrid Radiotherapy Planning Methods for Stage III NSCLC.

To access the comparative dosimetric and radiobiological advantages of two methods of intensity-modulated radiation therapy (IMRT)-based hybrid radiotherapy planning for stage III nonsmall cell lung cancer (NSCLC). Two hybrid planning methods were respectively characterized by conventional fraction radiotherapy (CFRT) and stereotactic body radiotherapy (SBRT) and CFRT and simultaneous integrated boost (SIB) planning. All plans were retrospectively completed using the 2 methods for 20 patients with stage III NSCLC.

Deep Learning for Patient-Specific Quality Assurance: Predicting Gamma Passing Rates for IMRT Based on Delivery Fluence Informed by log Files.

In this study, we propose a deep learning-based approach to predict Intensity-modulated radiation therapy (IMRT) quality assurance (QA) gamma passing rates using delivery fluence informed by log files. A total of 112 IMRT plans for chest cancers were planned and measured by portal dosimetry equipped on TrueBeam linac. The convolutional neural network (CNN) based learning model was trained using delivery fluence as inputs and gamma passing rates (GPRs) of 4 different criteria (3%/3 mm, 2%/3 mm, 3%/2 mm, and 2%/2 mm) as outputs.

Account for the Full Extent of Esophagus Motion in Radiation Therapy Planning: A Preliminary Study of the IRV of the Esophagus.

Objective: Accounting for esophagus motion in radiotherapy planning is an important basis for accurate assessment of toxicity. In this study, we calculated how much the delineations of the esophagus should be expanded based on three-dimensional (3D) computed tomography (CT), four-dimensional (4D) average projection (AVG), and maximum intensity projection (MIP) scans to account for the full extent of esophagus motion during 4D imaging acquisition.

Methods And Materials: The 3D and 4D CT scans of 20 lung cancer patients treated with conventional radiotherapy and 20 patients treated with stereotactic ablative radiation therapy (SBRT) were used.

How Does the Gradient Measure of the Lung SBRT Treatment Plan Depend on the Tumor Volume and Shape?

Purpose: Gradient measure (GM) is a critical index related to normal tissue sparing in radiosurgery. This study aims to describe the dependence of GM on target volume and target shape for lung stereotactic body radiation therapy (SBRT) treatment plans.

Methods: A total of 307 peripheral and 119 central lung SBRT treatment plans were enrolled for this study.

Influence of Clinical and Tumor Factors on Interfraction Setup Errors With Rotation Correction for Vacuum Cushion in Lung Stereotactic Body Radiation Therapy.

Purpose: To explore the influence of clinical and tumor factors over interfraction setup errors with rotation correction for non-small cell lung cancer (NSCLC) stereotactic body radiation therapy (SBRT) patients immobilized in vacuum cushion (VC) to better understand whether patient re-setup could further be optimized with these parameters.

Materials And Methods: This retrospective study was conducted on 142 NSCLC patients treated with SBRT between November 2017 to July 2019 in the local institute. Translation and rotation setup errors were analyzed in 732 cone-beam computed tomography (CBCT) scans before treatment.

A novel CRT-IMRT-combined (Co-CRIM) planning technique for peripheral lung stereotactic body radiotherapy in pinnacle treatment planning system.

Objectives: This study attempts to explore a novel peripheral lung stereotactic body radiotherapy (SBRT) planning technique that can balance the pros and cons of three-dimensional conformal radiotherapy (CRT) and intensity-modulated radiation therapy (IMRT) / volumetric modulated arc therapy (VMAT).

Methods: Treatment plans were retrospectively designed based on CRT, IMRT, VMAT, and the proposed CRT-IMRT-combined (Co-CRIM) techniques using Pinnacle treatment planning system (TPS) for 20 peripheral lung cancer patients. Co-CRIM used an inverse optimization algorithm available in Pinnacle TPS.

Investigation of Predictors to Achieve Acceptable Lung Dose in T-Shaped Upper and Middle Esophageal Cancer With IMRT and VMAT.

Purpose: The purpose of this study is to investigate whether there are predictors and cutoff points that can predict the acceptable lung dose using intensity-modulated radiation therapy (IMRT) and volume-modulated arc therapy (VMAT) in radiotherapy for upper ang middle esophageal cancer.

Material And Methods: Eighty-two patients with T-shaped upper-middle esophageal cancer (UMEC) were enrolled in this retrospective study. Jaw-tracking IMRT plan (JT-IMRT), full-arc VMAT plan (F-VMAT), and pactial-arc VMAT plan (P-VMAT) were generated for each patient.

A 2D-3D hybrid convolutional neural network for lung lobe auto-segmentation on standard slice thickness computed tomography of patients receiving radiotherapy.

Background: Accurate segmentation of lung lobe on routine computed tomography (CT) images of locally advanced stage lung cancer patients undergoing radiotherapy can help radiation oncologists to implement lobar-level treatment planning, dose assessment and efficacy prediction. We aim to establish a novel 2D-3D hybrid convolutional neural network (CNN) to provide reliable lung lobe auto-segmentation results in the clinical setting.

Methods: We retrospectively collected and evaluated thorax CT scans of 105 locally advanced non-small-cell lung cancer (NSCLC) patients treated at our institution from June 2019 to August 2020.

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.

Dosimetric Comparison, Treatment Efficiency Estimation, and Biological Evaluation of Popular Stereotactic Radiosurgery Options in Treating Single Small Brain Metastasis.

Objectives: This study aimed to show the advantages of each stereotactic radiosurgery (SRS) treatment option for single small brain metastasis among Gamma Knife (GK), Cone-based VMAT (Cone-VMAT), and MLC-based CRT (MLC-CRT) plans.

Materials And Methods: GK, Cone-VMAT, and MLC-CRT SRS plans were retrospectively generated for 11 patients with single small brain metastasis whose volume of gross tumor volume (GTV) ranged from 0.18 to 0.

Dosimetric comparison and biological evaluation of fixed-jaw intensity-modulated radiation therapy for T-shaped esophageal cancer.

Background: To evaluate the dosimetric and biological benefits of the fixed-jaw (FJ) intensity-modulated radiation therapy (IMRT) technique for patients with T-shaped esophageal cancer.

Methods: FJ IMRT plans were generated for thirty-five patients and compared with jaw tracking (JT) IMRT, static jaw (SJ) IMRT and JT volumetric modulated arc therapy (VMAT). Dosimetric parameters, tumor control probability (TCP) and normal tissue complication probability (NTCP), monitor units (MUs), delivery time and gamma passing rate, as a measure of dosimetric verification, were compared.

Geometric and Dosimetric Changes in Tumor and Lung Tissue During Radiotherapy for Lung Cancer With Atelectasis.

Background And Purpose: This article retrospectively characterized the geometric and dosimetric changes in target and normal tissues during radiotherapy for lung cancer patients with atelectasis.

Materials And Methods: A total of 270 cone beam computed tomography (CBCT) scans of 18 lung patients with atelectasis were collected. The degree and time of resolution or expansion of the atelectasis were recorded.

Virtual Patient-Specific Quality Assurance of IMRT Using UNet++: Classification, Gamma Passing Rates Prediction, and Dose Difference Prediction.

The dose verification in radiotherapy quality assurance (QA) is time-consuming and places a heavy workload on medical physicists. To provide a clinical tool to perform patient specific QA accurately, the UNet++ is investigated to classify failed or pass fields (the GPR lower than 85% is considered "failed" while the GPR higher than 85% is considered "pass"), predict gamma passing rates (GPR) for different gamma criteria, and predict dose difference from virtual patient-specific quality assurance in radiotherapy. UNet++ was trained and validated with 473 fields and tested with 95 fields.

A novel lung-avoidance planning strategy based on 4DCT ventilation imaging and CT density characteristics for stage III non-small-cell lung cancer patients.

Background: Functional planning based merely on 4DCT ventilation imaging has limitations. In this study, we proposed a radiotherapy planning strategy based on 4DCT ventilation imaging and CT density characteristics.

Materials And Methods: For 20 stage III non-small-cell lung cancer (NSCLC) patients, clinical plans and lung-avoidance plans were generated.

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.

Regression models for predicting physical and EQD plan parameters of two methods of hybrid planning for stage III NSCLC.

Background/purpose: To establish regression models of physical and equivalent dose in 2 Gy per fraction (EQD) plan parameters of two kinds of hybrid planning for stage III NSCLC.

Methods: Two kinds of hybrid plans named conventional fraction radiotherapy & stereotactic body radiotherapy (C&S) and conventional fraction radiotherapy & simultaneous integrated boost (C&SIB) were retrospectively made for 20 patients with stage III NSCLC. Prescription dose of C&S plans was 2 Gy × 30f for planning target volume of lymph node (PTV) and 12.