The Critical Components for Effective Adaptive Radiotherapy in Patients with Unresectable Non-Small-Cell Lung Cancer: Who, When and How

Zhou, Suna; Meng, Yinnan; Sun, Xuefeng; Jin, Zhicheng; Feng, Wei; Yang, Haihua

doi:10.2217/fon-2022-0291

Cited by 2 publications

(1 citation statement)

References 72 publications

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“…These tasks may be repetitive and time-consuming. Furthermore, some questions, such as which kind of patients can benefit more from radiotherapy and when is the optimal time to receive radiotherapy, are still in dispute ( 13 ). Therefore, it is necessary to reduce the burden of clinicians and improve the efficacy of radiotherapy.…”

Section: Introductionmentioning

confidence: 99%

Machine learning in non-small cell lung cancer radiotherapy: A bibliometric analysis

et al. 2023

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BackgroundMachine learning is now well-developed in non-small cell lung cancer (NSCLC) radiotherapy. But the research trend and hotspots are still unclear. To investigate the progress in machine learning in radiotherapy NSCLC, we performed a bibliometric analysis of associated research and discuss the current research hotspots and potential hot areas in the future.MethodsThe involved researches were obtained from the Web of Science Core Collection database (WoSCC). We used R-studio software, the Bibliometrix package and VOSviewer (Version 1.6.18) software to perform bibliometric analysis.ResultsWe found 197 publications about machine learning in radiotherapy for NSCLC in the WoSCC, and the journal Medical Physics contributed the most articles. The University of Texas MD Anderson Cancer Center was the most frequent publishing institution, and the United States contributed most of the publications. In our bibliometric analysis, “radiomics” was the most frequent keyword, and we found that machine learning is mainly applied to analyze medical images in the radiotherapy of NSCLC.ResultsThe research we identified about machine learning in NSCLC radiotherapy was mainly related to the radiotherapy planning of NSCLC and the prediction of treatment effects and adverse events in NSCLC patients who were under radiotherapy. Our research has added new insights into machine learning in NSCLC radiotherapy and could help researchers better identify hot research areas in the future.

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Section: Introductionmentioning

confidence: 99%

Machine learning in non-small cell lung cancer radiotherapy: A bibliometric analysis

et al. 2023

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Assessment of clinical feasibility:offline adaptive radiotherapy for lung cancer utilizing kV iCBCT and UNet++ based deep learning model

Zeng,

Chen,

et al. 2024

J Applied Clin Med Phys

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BackgroundLung cancer poses a significant global health challenge. Adaptive radiotherapy (ART) addresses uncertainties due to lung tumor dynamics. We aimed to investigate a comprehensively and systematically validated offline ART regimen with high clinical feasibility for lung cancer.MethodsThis study enrolled 102 lung cancer patients, who underwent kV iterative cone‐beam computed tomography (iCBCT). Data collection included iCBCT and planning CT (pCT) scans. Among these, data from 70 patients were employed for training the UNet++ based deep learning model, while 15 patients were allocated for testing the model. The model transformed iCBCT into adaptive CT (aCT). Clinical radiotherapy feasibility was verified in 17 patients. The dosimetric evaluation encompassed GTV, organs at risk (OARs), and monitor units (MU), while delivery accuracy was validated using ArcCHECK and thermoluminescent dosimeter (TLD) detectors.ResultsThe UNet++ based deep learning model substantially improved image quality, reducing mean absolute error (MAE) by 70.05%, increasing peak signal‐to‐noise ratio (PSNR) by 17.97%, structural similarity (SSIM) by 7.41%, and the Hounsfield Units (HU) of aCT approaching a closer proximity to pCT compared to kV iCBCT. There were no significant differences observed in the dosimetric parameters of GTV and OARs between the aCT and pCT plans, confirming the accuracy of the dose maps in ART plans. Similarly, MU manifested no notable disparities, underscoring the consistency in treatment efficiency. Gamma passing rates for intensity‐modulated radiation therapy (IMRT) and volumetric‐modulated arc therapy (VMAT) plans derived from aCT and pCT exceeded 98%, while the deviations in TLD measurements (within 2% to 7%) also exhibited no significant differences, thus corroborating the precision of dose delivery.ConclusionAn offline ART regimen utilizing kV iCBCT and UNet++ based deep learning model is clinically feasible for lung cancer treatment. This approach provides enhanced image quality, comparable treatment plans to pCT, and precise dose delivery.

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The Critical Components for Effective Adaptive Radiotherapy in Patients with Unresectable Non-Small-Cell Lung Cancer: Who, When and How

Cited by 2 publications

References 72 publications

Machine learning in non-small cell lung cancer radiotherapy: A bibliometric analysis

Machine learning in non-small cell lung cancer radiotherapy: A bibliometric analysis

Assessment of clinical feasibility:offline adaptive radiotherapy for lung cancer utilizing kV iCBCT and UNet++ based deep learning model

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