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  • 1
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    Dosimetric comparison and biological evaluation of fixed-jaw intensity-modulated radiation therapy for T-shaped esophageal cancer
    Springer Science and Business Media LLC ; 2021
    In:  Radiation Oncology Vol. 16, No. 1 ( 2021-12)
    Details
    In: Radiation Oncology, Springer Science and Business Media LLC, Vol. 16, No. 1 ( 2021-12)
    Abstract: 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. The correlation between the length of PTV-C below the upper boundary of lung tissue (PTV-C inferior ) and dosimetric parameters and NTCP of the lung tissue were analyzed. Results The homogeneity and conformity of the target in the four plans were basically equivalent. When compared to the JT IMRT and SJ IMRT plans, FJ IMRT plan led to a statistically significant improvement in the NTCP and low-middle dosimetric parameters of the lung, and the improvement had a moderately positive correlation with the length of PTV-C inferior , with a correlation coefficient ranging from 0.523 to 0.797; the FJ IMRT plan exhibited better lung sparing in low-dose volumes than the JT VMAT plan. The FJ IMRT plan had similar MUs (888 ± 99) and delivery times (516.1 ± 54.7 s) as the JT IMRT plan (937 ± 194, 522 ± 5.6 s) but higher than SJ IMRT (713 ± 137, 488.8 ± 45.2 s) and JT VMAT plan (517 ± 59, 263.7 ± 43.3 s). Conclusions The FJ IMRT technique is superior in reducing the low-dose volumes of lung tissues for patients with T-shaped esophageal cancer.
    Type of Medium: Online Resource
    ISSN: 1748-717X
    URL: Article
    DOI: 10.1186/s13014-021-01882-7
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 2224965-5
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  • 2
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    Radiation pneumonitis prediction after stereotactic body radiation therapy based on 3D dose distribution: dosiomics and/or deep learning-based radiomics features
    Springer Science and Business Media LLC ; 2022
    In:  Radiation Oncology Vol. 17, No. 1 ( 2022-11-17)
    Details
    In: Radiation Oncology, Springer Science and Business Media LLC, Vol. 17, No. 1 ( 2022-11-17)
    Abstract: 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. Besides, 107 dosiomics features were extracted by Pyradiomics, and 1316 DLR features were extracted by ResNet50. Feature visualization was performed based on Spearman’s correlation coefficients, and feature selection was performed based on the least absolute shrinkage and selection operator. Three different models were constructed based on random forest, including (1) a dosiomics model (a model constructed based on dosiomics features), (2) a DLR model (a model constructed based on DLR features), and (3) a hybrid model (a model constructed based on dosiomics and DLR features). Subsequently, the performance of these three models was compared with receiver operating characteristic curves. Finally, these dosiomics and DLR features were analyzed with Spearman’s correlation coefficients. Results In the training set, the area under the curve (AUC) of the dosiomics, DLR, and hybrid models was 0.9986, 0.9992, and 0.9993, respectively; the accuracy of these three models was 0.9643, 0.9464, and 0.9642, respectively. In the test set, the AUC of these three models was 0.8462, 0.8750, and 0.9000, respectively; the accuracy of these three models was 0.8214, 0.7857, and 0.8571, respectively. The hybrid model based on dosiomics and DLR features outperformed other two models. Correlation analysis between dosiomics features and DLR features showed weak correlations. The dosiomics features that correlated DLR features with the Spearman’s rho | ρ | ≥ 0.8 were all first-order features. Conclusion The hybrid features based on dosiomics and DLR features from 3D dose distribution could improve the performance of RP prediction after SBRT.
    Type of Medium: Online Resource
    ISSN: 1748-717X
    URL: Article
    DOI: 10.1186/s13014-022-02154-8
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
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  • 3
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    Regression models for predicting physical and EQD2 plan parameters of two methods of hybrid planning for stage III NSCLC
    Springer Science and Business Media LLC ; 2021
    In:  Radiation Oncology Vol. 16, No. 1 ( 2021-12)
    Details
    In: Radiation Oncology, Springer Science and Business Media LLC, Vol. 16, No. 1 ( 2021-12)
    Abstract: To establish regression models of physical and equivalent dose in 2 Gy per fraction (EQD 2 ) 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 LN ) and 12.5 Gy × 4f for planning target volume of primary tumor (PTV PT ), while prescription dose of C & SIB plans was 2 Gy × 26f for PTV LN and sequential 2 Gy × 4f for PTV LN combined with 12.5 Gy × 4f for PTV PT . Regression models of physical and EQD 2 plan parameters were established based on anatomical geometry features for two kinds of hybrid plans. The features were mainly characterized by volume ratio, min distance and overlapping slices thickness of two structures. The possibilities of regression models of EQD 2 plan parameters were verified by spearman’s correlation coefficients between physical and EQD 2 plan parameters, and the influence on the consistence of fitting goodness between physical and EQD 2 models was investigated by the correlations between physical and EQD 2 plan parameters. Finally, physical and EQD 2 models predictions were compared with plan parameters for two new patients. Results Physical and EQD 2 plan parameters of PTV LN CI 60Gy have shown strong positive correlations with PTV LN volume and min distance (PT to LN) , and strong negative correlations with PTV PT volume for two kinds of hybrid plans. PTV (PT+LN) CI 60Gy is not only correlated with above three geometry features, but also negatively correlated with overlapping slices thickness (PT and LN) . When neck lymph node metastasis was excluded from PTV LN volume, physical and EQD 2 total lung V 20 showed a high linear correlation with corrected volume ratio (LN to total lung). Meanwhile, physical total lung mean dose (MLD) had a high linear correlation with corrected volume ratio (LN to total lung) , while EQD 2 total lung MLD was not only affected by corrected volume ratio (LN to total lung) but also volume ratio (PT to total lung). Heart D 5 , D 30 and mean dose (MHD) would be more susceptible to overlapping structure (heart and LN) . Min distance (PT to ESO) may be an important feature for predicting EQD 2 esophageal max dose for hybrid plans. It’s feasible for regression models of EQD 2 plan parameters, and the consistence of the fitting goodness of physical and EQD 2 models had a positive correlation with spearman’s correlation coefficients between physical and EQD 2 plan parameters. For total lung V 20 , ipsilateral lung V 20 , and ipsilateral lung MLD, the models could predict that C & SIB plans were higher than C & S plans for two new patients. Conclusion The regression models of physical and EQD 2 plan parameters were established with at least moderate fitting goodness in this work, and the models have a potential to predict physical and EQD 2 plan parameters for two kinds of hybrid planning.
    Type of Medium: Online Resource
    ISSN: 1748-717X
    URL: Article
    DOI: 10.1186/s13014-021-01848-9
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
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  • 4
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    A 2D–3D hybrid convolutional neural network for lung lobe auto-segmentation on standard slice thickness computed tomography of patients receiving radiotherapy
    Springer Science and Business Media LLC ; 2021
    In:  BioMedical Engineering OnLine Vol. 20, No. 1 ( 2021-12)
    Details
    In: BioMedical Engineering OnLine, Springer Science and Business Media LLC, Vol. 20, No. 1 ( 2021-12)
    Abstract: 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. The CT images were acquired with 5 mm slice thickness. Two CNNs were used for lung lobe segmentation, a 3D CNN for extracting 3D contextual information and a 2D CNN for extracting texture information. Contouring quality was evaluated using six quantitative metrics and visual evaluation was performed to assess the clinical acceptability. Results For the 35 cases in the test group, Dice Similarity Coefficient (DSC) of all lung lobes contours exceeded 0.75, which met the pass criteria of the segmentation result. Our model achieved high performances with DSC as high as 0.9579, 0.9479, 0.9507, 0.9484, and 0.9003 for left upper lobe (LUL), left lower lobe (LLL), right upper lobe (RUL), right lower lobe (RLL), and right middle lobe (RML), respectively. The proposed model resulted in accuracy, sensitivity, and specificity of 99.57, 98.23, 99.65 for LUL; 99.6, 96.14, 99.76 for LLL; 99.67, 96.13, 99.81 for RUL; 99.72, 92.38, 99.83 for RML; 99.58, 96.03, 99.78 for RLL, respectively. Clinician's visual assessment showed that 164/175 lobe contours met the requirements for clinical use, only 11 contours need manual correction. Conclusions Our 2D–3D hybrid CNN model achieved accurate automatic segmentation of lung lobes on conventional slice-thickness CT of locally advanced lung cancer patients, and has good clinical practicability.
    Type of Medium: Online Resource
    ISSN: 1475-925X
    URL: Article
    DOI: 10.1186/s12938-021-00932-1
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 2084374-4
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  • 5
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    Account for the Full Extent of Esophagus Motion in Radiation Therapy Planning: A Preliminary Study of the IRV of the Esophagus
    Frontiers Media SA ; 2021
    In:  Frontiers in Oncology Vol. 11 ( 2021-11-25)
    Details
    In: Frontiers in Oncology, Frontiers Media SA, Vol. 11 ( 2021-11-25)
    Abstract: 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. Radiation oncologists contoured the esophagus on the 3DCT, AVG, MIP and 25% exhale scans, and the combination of the esophagus in every phase of 4DCT. The union of all 4D phase delineations (U4D) represented the full extent of esophagus motion during imaging acquisition. Surface distances from U4D to 3D, AVG, and MIP volumes were calculated. Distances in the most extreme surface points (1.5 cm most superoinferior, 10% most right/left/anteroposterior) were used to derive margins accounting only for systematic (delineation) errors. Results Esophagus delineations on the MIP were the closest to the full extent of motion, requiring only 6.9 mm margins. Delineations on the AVG and 3D scans required margins up to 7.97 and 7.90 mm, respectively. The largest margins were for the inferior, right, and anterior aspects for the delineations on the 3D, AVG, and MIP scans, respectively. Conclusion Delineations on 3D, AVG, or MIP scans required extensions for representing the esophagus’s full extent of motion, with the MIP requiring the smallest margins. Research including daily imaging to determine the random components for the margins and dosimetric measurements to determine the relevance of creating a planning organ at risk volume (PRV) of the esophagus is required.
    Type of Medium: Online Resource
    ISSN: 2234-943X
    URL: Article
    DOI: 10.3389/fonc.2021.734552
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2021
    detail.hit.zdb_id: 2649216-7
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  • 6
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    On the optimal number of dose‐limiting shells in the SBRT auto‐planning design for peripheral lung cancer
    Wiley ; 2020
    In:  Journal of Applied Clinical Medical Physics Vol. 21, No. 9 ( 2020-09), p. 134-142
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    In: Journal of Applied Clinical Medical Physics, Wiley, Vol. 21, No. 9 ( 2020-09), p. 134-142
    Abstract: 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. For each of the patients, auto‐plans were optimized based on two shells, three shells, four shells, five shells, six shells, seven shells, eight shells, respectively. The optimal number of shells for the SBRT auto‐planning was derived through the evaluations and comparisons of various dosimetric parameters of planning target volume (PTV) and organs at risk (OARs), monitor units (MU), and optimization time of the plans. Results The conformity index (CI) and the gradient index (GI) of PTV, the maximum dose outside the 2 cm of PTV (D 2cm ), D max of spinal cord (SC max ), the percentage of volume of total lung excluding ITV receiving 20 Gy (V20) and 10 Gy (V10), and the mean lung dose (MLD) were improved when the number of shell increased, but the improvement became not significant as the number of shell reached six. The monitor units (MUs) varied little among different plans where no statistical differences were found. However, as the number of shell increased, the auto‐plan optimization time increased significantly. Conclusions It appears that for peripheral lung SBRT plan using six shells can yield satisfactory plan quality with acceptable beam MUs and optimization time in the Pinnacle TPS.
    Type of Medium: Online Resource
    ISSN: 1526-9914 , 1526-9914
    URL: Issue
    URL: Article
    DOI: 10.1002/acm2.v21.9
    DOI: 10.1002/acm2.12983
    Language: English
    Publisher: Wiley
    Publication Date: 2020
    detail.hit.zdb_id: 2010347-5
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  • 7
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    Improving CBCT image quality to the CT level using RegGAN in esophageal cancer adaptive radiotherapy
    Springer Science and Business Media LLC ; 2023
    In:  Strahlentherapie und Onkologie Vol. 199, No. 5 ( 2023-05), p. 485-497
    Details
    In: Strahlentherapie und Onkologie, Springer Science and Business Media LLC, Vol. 199, No. 5 ( 2023-05), p. 485-497
    Abstract: 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 2.5D RegGAN model with an adaptively trained registration network, was proposed, through which sCT images were generated. The quality of deep-learning-generated sCT images was quantitatively compared to the reference deformed CT (dCT) image using mean absolute error (MAE), root mean square error (RMSE) of Hounsfield units (HU), and peak signal-to-noise ratio (PSNR). The dose calculation accuracy was further evaluated for esophageal cancer radiotherapy plans, and the same plans were calculated on dCT, CBCT, and sCT images. Results The quality of sCT images produced by RegGAN was significantly improved compared to the original CBCT images. ReGAN achieved image quality in the testing patients with MAE sCT vs. CBCT: 43.7 ± 4.8 vs. 80.1 ± 9.1; RMSE sCT vs. CBCT: 67.2 ± 12.4 vs. 124.2 ± 21.8; and PSNR sCT vs. CBCT: 27.9 ± 5.6 vs. 21.3 ± 4.2. The sCT images generated by the RegGAN model showed superior accuracy on dose calculation, with higher gamma passing rates (93.3 ± 4.4, 90.4 ± 5.2, and 84.3 ± 6.6) compared to original CBCT images (89.6 ± 5.7, 85.7 ± 6.9, and 72.5 ± 12.5) under the criteria of 3 mm/3%, 2 mm/2%, and 1 mm/1%, respectively. Conclusion The proposed deep-learning RegGAN model seems promising for generation of high-quality sCT images from stand-alone thoracic CBCT images in an efficient way and thus has the potential to support CBCT-based esophageal cancer adaptive radiotherapy.
    Type of Medium: Online Resource
    ISSN: 0179-7158 , 1439-099X
    URL: Article
    DOI: 10.1007/s00066-022-02039-5
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
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  • 8
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    A novel specific grading standard study of auto-segmentation of organs at risk in thorax: subjective–objective-combined grading standard
    Springer Science and Business Media LLC ; 2021
    In:  BioMedical Engineering OnLine Vol. 20, No. 1 ( 2021-12)
    Details
    In: BioMedical Engineering OnLine, Springer Science and Business Media LLC, Vol. 20, No. 1 ( 2021-12)
    Abstract: 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). The grading results were compared with those of the corresponding geometric indexes obtained by geometric objective methods in the other two centers. OAR auto-segmentation accuracy was also graded by our subjective evaluation standard. These grading results were compared with those of DSC. Based on the subjective evaluation standard and the five geometric indexes, the correspondence between the subjective evaluation level and the geometric index range was established for each OAR. Results For ΔCMD, ΔV, and MHD, the grading results of the geometric objective evaluation methods at our center and the other two centers were inconsistent. For DSC and AHD, the grading results of three centers were consistent. Seven OARs’ grading results in the subjective evaluation standard were inconsistent with those of DSC. Six OARs’ grading results in the subjective evaluation standard were consistent with those of DSC. Finally, we proposed a new evaluation method that combined the subjective evaluation level of those OARs with the range of corresponding DSC to determine the grading standard. If the DSC ranges between the adjacent levels did not overlap, the DSC range was used as the grading standard. Otherwise, the mean value of DSC was used as the grading standard. Conclusions A novel OAR-specific SOC grading standard in thorax was developed. The SOC grading standard provides a possible alternative for evaluation of the auto-segmentation accuracy for thoracic OARs.
    Type of Medium: Online Resource
    ISSN: 1475-925X
    URL: Article
    DOI: 10.1186/s12938-021-00890-8
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
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  • 9
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    Voxel-Level BED Corrected Dosimetric and Radiobiological Assessment of 2 Kinds of Hybrid Radiotherapy Planning Methods for Stage III NSCLC
    SAGE Publications ; 2022
    In:  Technology in Cancer Research & Treatment Vol. 21 ( 2022-01), p. 153303382211079-
    Details
    In: Technology in Cancer Research & Treatment, SAGE Publications, Vol. 21 ( 2022-01), p. 153303382211079-
    Abstract: Background/purpose: 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). Methods: 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. CFRT and SBRT dose regimes 2 Gy  ×  30 f and 12.5 Gy  ×  4 f were, respectively, used for planning target volume of lymph node (PTV LN ) and planning target volume of the primary tumor (PTV PT ), while dose regimes 2 Gy  ×  26 f for PTV LN and sequential 2 Gy  ×  4 f for PTV LN combined with 12.5 Gy  ×  4 f for PTV PT were adopted for CFRT and SIB plans. SBRT and SIB EQD 2 dose were calculated voxel by voxel, and then, respectively, superimposed with 30-fraction and 26-fraction CFRT plan dose to achieve biological equivalent dose (BED) dosimetric parameters of CFRT and SBRT and CFRT and SIB plans. Tumor control probability (TCP)/normal tissue complication probability (NTCP) was, respectively, calculated by equivalent uniform dose/Lyman–Kutcher–Burman models. BED plan parameters and TCP/NTCP were analyzed between 2 methods of hybrid planning. Primary tumor/lymph node (LN)/total TCP values were, respectively, evaluated as a function of the radiation dose needed to control 50% of tumor (TCD 50 ) for 20 patients. Dosimetric errors were analyzed by nontransit electronic portal imaging device dosimetry measurement during hybrid plan delivery. Results: Statistically lower BED plan parameters of PTV LN D 2 and homogeneity index resulted in slightly lower averaged LN/total TCP curves by CFRT and SIB planning. The gaps between Max and Min LN/total TCP curves were significantly closer for CFRT and SIB planning, which indicated better robustness of LN/total TCPs. A lower esophagus dose resulted in a lower esophagus NTCP by CFRT and SIB planning, which may be compromised by 1 week shorter overall treatment time by CFRT and SIB irradiation. Spinal cord D max was significantly reduced by CFRT and SIB plans. The dose verification results of the subplans involved in hybrid plans were acceptable, which showed that the 2 methods of hybrid planning could be delivered accurately in our center. Conclusion: CFRT and SIB plannings have more advantages on BED plan parameters and TCP/NTCP than CFRT and SBRT planning, and both methods of IMRT-based hybrid planning could be executed accurately for stage III NSCLC. The effectiveness of the results needs to be validated in the hybrid trial.
    Type of Medium: Online Resource
    ISSN: 1533-0346 , 1533-0338
    URL: Article
    DOI: 10.1177/15330338221107966
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2022
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  • 10
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    AS-NeSt: A Novel 3D Deep Learning Model for Radiation Therapy Dose Distribution Prediction in Esophageal Cancer Treatment With Multiple Prescriptions
    Elsevier BV ; 2024
    In:  International Journal of Radiation Oncology*Biology*Physics Vol. 119, No. 3 ( 2024-07), p. 978-989
    Details
    In: International Journal of Radiation Oncology*Biology*Physics, Elsevier BV, Vol. 119, No. 3 ( 2024-07), p. 978-989
    Type of Medium: Online Resource
    ISSN: 0360-3016
    URL: Article
    DOI: 10.1016/j.ijrobp.2023.12.001
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2024
    detail.hit.zdb_id: 1500486-7
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