Bayesian Adaptive Randomization Trial of Passive Scattering Proton Therapy and Intensity-Modulated Photon Radiotherapy for Locally Advanced Non–Small-Cell Lung Cancer

Liao, Zhongxing; Lee, John; Komaki, Ritsuko; Gomez, Daniel R.; O’Reilly, Michael S.; Fossella, Frank V.; Blumenschein, George R.; Heymach, John V.; Vaporciyan, Ara A.; Swisher, Stephen G.; Allen, Pamela K.; Choi, Noah C.; DeLaney, Thomas F.; Hahn, Stephen M.; Cox, James D.; Lu, Charles; Mohan, Radhe

doi:10.1200/jco.2017.74.0720

Cited by 249 publications

(213 citation statements)

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“…Depending on the stage and cancer type, 30%–60% of lung cancer patients receive radiation therapy during their treatment . Radiotherapy is also the standard of care for certain lung cancers . The success of radiotherapy depends highly on the control of radiation exposure to organs at risk (OARs), such as the normal lungs, esophagus, spinal cord and heart, etc.…”

Section: Introductionmentioning

confidence: 99%

Automatic multiorgan segmentation in thorax CT images using U‐net‐GAN

et al. 2019

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Purpose Accurate and timely organs‐at‐risk (OARs) segmentation is key to efficient and high‐quality radiation therapy planning. The purpose of this work is to develop a deep learning‐based method to automatically segment multiple thoracic OARs on chest computed tomography (CT) for radiotherapy treatment planning. Methods We propose an adversarial training strategy to train deep neural networks for the segmentation of multiple organs on thoracic CT images. The proposed design of adversarial networks, called U‐Net‐generative adversarial network (U‐Net‐GAN), jointly trains a set of U‐Nets as generators and fully convolutional networks (FCNs) as discriminators. Specifically, the generator, composed of U‐Net, produces an image segmentation map of multiple organs by an end‐to‐end mapping learned from CT image to multiorgan‐segmented OARs. The discriminator, structured as an FCN, discriminates between the ground truth and segmented OARs produced by the generator. The generator and discriminator compete against each other in an adversarial learning process to produce the optimal segmentation map of multiple organs. Our segmentation results were compared with manually segmented OARs (ground truth) for quantitative evaluations in geometric difference, as well as dosimetric performance by investigating the dose‐volume histogram in 20 stereotactic body radiation therapy (SBRT) lung plans. Results This segmentation technique was applied to delineate the left and right lungs, spinal cord, esophagus, and heart using 35 patients’ chest CTs. The averaged dice similarity coefficient for the above five OARs are 0.97, 0.97, 0.90, 0.75, and 0.87, respectively. The mean surface distance of the five OARs obtained with proposed method ranges between 0.4 and 1.5 mm on average among all 35 patients. The mean dose differences on the 20 SBRT lung plans are −0.001 to 0.155 Gy for the five OARs. Conclusion We have investigated a novel deep learning‐based approach with a GAN strategy to segment multiple OARs in the thorax using chest CT images and demonstrated its feasibility and reliability. This is a potentially valuable method for improving the efficiency of chest radiotherapy treatment planning.

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Section: Introductionmentioning

confidence: 99%

Automatic multiorgan segmentation in thorax CT images using U‐net‐GAN

et al. 2019

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“…The same institution performed a randomized phase II trial that compared 3D PBT with IMRT; however, that study did not confirm the earlier results. 12 A phase III CRT trial comparing IMRT with PBT is currently in the accrual stage.…”

Section: Discussionmentioning

confidence: 99%

Toxicity Related to Radiotherapy Dose and Targeting Strategy: A Pooled Analysis of Cooperative Group Trials of Combined Modality Therapy for Locally Advanced Non–Small Cell Lung Cancer

Schild

Fan

Stinchcombe

et al. 2019

Journal of Thoracic Oncology

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Objective: Concurrent chemoradiotherapy (CRT) was the standard treatment for locally advanced NSCLC (LA-NSCLC). This study was performed to examine thoracic radiotherapy (TRT) parameters and their impact on adverse events (AEs). Methods:We collected individual patient data from 3600 patients with LA-NSCLC who participated in 16 cooperative group trials of concurrent CRT. The TRT parameters examined included field design strategy (elective nodal irradiation [ENI] versus involved-field [IF] TRT [IF-TRT]) and TRT dose (60 Gy versus 60 Gy). The primary end point of this analysis was the occurrence of AEs. ORs for AEs were calculated with univariable and multivariable logistic models.Results: TRT doses ranged from 60 to 74 Gy. ENI was not associated with more grade 3 or higher AEs than IF-TRT was (multivariable OR ¼ 0.77, 95% confidence interval [CI]: 0.543-1.102, p ¼ 0.1545). Doses higher than 60 Gy (high-dose TRT) were associated with significantly more grade 3 or higher AEs (multivariable OR ¼ 1.82, 95% CI: 1.501-2.203, p < 0.0001). In contrast, ENI was associated with significantly more grade 4 or higher AEs (multivariable OR ¼ 1.33, 95% CI: 1.035-1.709, p ¼ 0.0258). Doses higher than 60 Gy were also associated with more grade 4 or higher AEs (multivariate OR ¼ 1.42, 95% CI: 1.191-1.700, p ¼ 0.0001). Grade 5 AEs plus treatment-related deaths were more frequent with higher-dose TRT (p ¼ 0.0012) but not ENI (p ¼ 0.099). Conclusions:For patients with LA-NSCLC treated with concurrent CRT, IF-TRT was not associated with the overall risk of grade 3 or higher AEs but was associated with ; grants, personal fees, and reimbursement for travel from and service as an advisory board member of ViewRay, Inc.; and personal fees and reimbursement for travel from and service as an advisory board member of Varian Medical Systems outside the submitted work. The remaining authors declare no conflict of interest.

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“…19 (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24) 15 (11)(12)(13)(14)(15)(16)(17)(18)(19)(20) . ning, it is necessary to account for range uncertainty as well as setup uncertainty.…”

Section: Appendicesmentioning

confidence: 99%

Proton vs photon: A model-based approach to patient selection for reduction of cardiac toxicity in locally advanced lung cancer

Teoh

Fiorini

George

et al. 2020

Radiotherapy and Oncology

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To use a model-based approach to identify a subgroup of patients with locally advanced lung cancer who would benefit from proton therapy compared to photon therapy for reduction of cardiac toxicity. Material/methods: Volumetric modulated arc photon therapy (VMAT) and robust-optimised intensity modulated proton therapy (IMPT) plans were generated for twenty patients with locally advanced lung cancer to give a dose of 70 Gy (relative biological effectiveness (RBE)) in 35 fractions. Cases were selected to represent a range of anatomical locations of disease. Contouring, treatment planning and organs-atrisk constraints followed RTOG-1308 protocol. Whole heart and ub-structure doses were compared. Risk estimates of gradeP3 cardiac toxicity were calculated based on normal tissue complication probability (NTCP) models which incorporated dose metrics and patients baseline risk-factors (pre-existing heart disease (HD)). Results: There was no statistically significant difference in target coverage between VMAT and IMPT. IMPT delivered lower doses to the heart and cardiac substructures (mean, heart V5 and V30, P < .05). In VMAT plans, there were statistically significant positive correlations between heart dose and the thoracic vertebral level that corresponded to the most inferior limit of the disease. The median level at which the superior aspect of the heart contour began was the T7 vertebrae. There was a statistically significant difference in dose (mean, V5 and V30) to the heart and all substructures (except mean dose to left coronary artery and V30 to sino-atrial node) when disease overlapped with or was inferior to the T7 vertebrae. In the presence of pre-existing HD and disease overlapping with or inferior to the T7 vertebrae, the mean estimated relative risk reduction of gradeP3 toxicities was 24-59%. Conclusion: IMPT is expected to reduce cardiac toxicity compared to VMAT by reducing dose to the heart and substructures. Patients with both pre-existing heart disease and tumour and nodal spread overlapping with or inferior to the T7 vertebrae are likely to benefit most from proton over photon therapy.

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Bayesian Adaptive Randomization Trial of Passive Scattering Proton Therapy and Intensity-Modulated Photon Radiotherapy for Locally Advanced Non–Small-Cell Lung Cancer

Cited by 249 publications

References 20 publications

Automatic multiorgan segmentation in thorax CT images using U‐net‐GAN

Automatic multiorgan segmentation in thorax CT images using U‐net‐GAN

Toxicity Related to Radiotherapy Dose and Targeting Strategy: A Pooled Analysis of Cooperative Group Trials of Combined Modality Therapy for Locally Advanced Non–Small Cell Lung Cancer

Proton vs photon: A model-based approach to patient selection for reduction of cardiac toxicity in locally advanced lung cancer

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