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  • 1
    Online Resource
    Online Resource
    Clinical outcome of proton therapy for patients with chordomas
    Korean Society for Therapeutic Radiology and Oncology ; 2018
    In:  Radiation Oncology Journal Vol. 36, No. 3 ( 2018-09-30), p. 182-191
    Details
    In: Radiation Oncology Journal, Korean Society for Therapeutic Radiology and Oncology, Vol. 36, No. 3 ( 2018-09-30), p. 182-191
    Type of Medium: Online Resource
    ISSN: 2234-1900 , 2234-3156
    URL: Article
    DOI: 10.3857/roj.2018.00164
    Language: English
    Publisher: Korean Society for Therapeutic Radiology and Oncology
    Publication Date: 2018
    detail.hit.zdb_id: 2712565-8
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  • 2
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    Comparative clinical evaluation of atlas and deep-learning-based auto-segmentation of organ structures in liver cancer
    Springer Science and Business Media LLC ; 2019
    In:  Radiation Oncology Vol. 14, No. 1 ( 2019-12)
    Details
    In: Radiation Oncology, Springer Science and Business Media LLC, Vol. 14, No. 1 ( 2019-12)
    Abstract: Accurate and standardized descriptions of organs at risk (OARs) are essential in radiation therapy for treatment planning and evaluation. Traditionally, physicians have contoured patient images manually, which, is time-consuming and subject to inter-observer variability. This study aims to a) investigate whether customized, deep-learning-based auto-segmentation could overcome the limitations of manual contouring and b) compare its performance against a typical, atlas-based auto-segmentation method organ structures in liver cancer. Methods On - contrast computer tomography image sets of 70 liver cancer patients were used, and four OARs (heart, liver, kidney, and stomach) were manually delineated by three experienced physicians as reference structures. Atlas and deep learning auto-segmentations were respectively performed with MIM Maestro 6.5 (MIM Software Inc., Cleveland, OH) and, with a deep convolution neural network (DCNN). The Hausdorff distance (HD) and, dice similarity coefficient (DSC), volume overlap error (VOE), and relative volume difference (RVD) were used to quantitatively evaluate the four different methods in the case of the reference set of the four OAR structures. Results The atlas-based method yielded the following average DSC and standard deviation values (SD) for the heart, liver, right kidney, left kidney, and stomach: 0.92 ± 0.04 (DSC ± SD), 0.93 ± 0.02, 0.86 ± 0.07, 0.85 ± 0.11, and 0.60 ± 0.13 respectively. The deep-learning-based method yielded corresponding values for the OARs of 0.94 ± 0.01, 0.93 ± 0.01, 0.88 ± 0.03, 0.86 ± 0.03, and 0.73 ± 0.09. The segmentation results show that the deep learning framework is superior to the atlas-based framwork except in the case of the liver. Specifically, in the case of the stomach, the DSC, VOE, and RVD showed a maximum difference of 21.67, 25.11, 28.80% respectively. Conclusions In this study, we demonstrated that a deep learning framework could be used more effectively and efficiently compared to atlas-based auto-segmentation for most OARs in human liver cancer. Extended use of the deep-learning-based framework is anticipated for auto-segmentations of other body sites.
    Type of Medium: Online Resource
    ISSN: 1748-717X
    URL: Article
    DOI: 10.1186/s13014-019-1392-z
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2019
    detail.hit.zdb_id: 2224965-5
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  • 3
    Online Resource
    Online Resource
    Dose Profile Modulation of Proton Minibeam for Clinical Application
    MDPI AG ; 2022
    In:  Cancers Vol. 14, No. 12 ( 2022-06-11), p. 2888-
    Details
    In: Cancers, MDPI AG, Vol. 14, No. 12 ( 2022-06-11), p. 2888-
    Abstract: The feasibility of proton minibeam radiation therapy (pMBRT) using a multislit collimator (MSC) and a scattering device was evaluated for clinical use at a clinical proton therapy facility. We fabricated, through Monte Carlo (MC) simulations, not only an MSC with a high peak-to-valley dose ratio (PVDR) at the entrance of the proton beam, to prevent radiation toxicity, but also a scattering device to modulate the PVDR in depth. The slit width and center-to-center distance of the diverging MSC were 2.5 mm and 5.0 mm at the large end, respectively, and its thickness and available field size were 100 mm and 76 × 77.5 mm2, respectively. Spatially fractionated dose distributions were measured at various depths using radiochromic EBT3 films and also tested on bacterial cells. MC simulation showed that the thicker the MSC, the higher the PVDR at the phantom surface. Dosimetric evaluations showed that lateral dose profiles varied according to the scatterer’s thickness, and the depths satisfying PVDR = 1.1 moved toward the surface as their thickness increased. The response of the bacterial cells to the proton minibeams’ depth was also established, in a manner similar to the dosimetric pattern. Conclusively, these results strongly suggest that pMBRT can be implemented in clinical centers by using MSC and scatterers.
    Type of Medium: Online Resource
    ISSN: 2072-6694
    URL: Article
    DOI: 10.3390/cancers14122888
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2527080-1
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  • 4
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    Online Resource
    Beam Angle Optimization for Double-Scattering Proton Delivery Technique Using an Eclipse Application Programming Interface and Convolutional Neural Network
    Frontiers Media SA ; 2021
    In:  Frontiers in Oncology Vol. 11 ( 2021-9-14)
    Details
    In: Frontiers in Oncology, Frontiers Media SA, Vol. 11 ( 2021-9-14)
    Abstract: To automatically identify optimal beam angles for proton therapy configured with the double-scattering delivery technique, a beam angle optimization method based on a convolutional neural network (BAODS-Net) is proposed. Fifty liver plans were used for training in BAODS-Net. To generate a sequence of input data, 25 rays on the eye view of the beam were determined per angle. Each ray collects nine features, including the normalized Hounsfield unit and the position information of eight structures per 2° of gantry angle. The outputs are a set of beam angle ranking scores ( S beam ) ranging from 0° to 359°, with a step size of 1°. Based on these input and output designs, BAODS-Net consists of eight convolution layers and four fully connected layers. To evaluate the plan qualities of deep-learning, equi-spaced, and clinical plans, we compared the performances of three types of loss functions and performed K -fold cross-validation ( K = 5). For statistical analysis, the volumes V 27Gy and V 30Gy as well as the mean, minimum, and maximum doses were calculated for organs-at-risk by using a paired-samples t -test. As a result, smooth-L1 loss showed the best optimization performance. At the end of the training procedure, the mean squared errors between the reference and predicted S beam were 0.031, 0.011, and 0.004 for L1, L2, and smooth-L1 loss, respectively. In terms of the plan quality, statistically, Plan BAO has no significant difference from Plan Clinic ( P & gt;.05). In our test, a deep-learning based beam angle optimization method for proton double-scattering treatments was developed and verified. Using Eclipse API and BAODS-Net, a plan with clinically acceptable quality was created within 5 min.
    Type of Medium: Online Resource
    ISSN: 2234-943X
    URL: Article
    DOI: 10.3389/fonc.2021.707464
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2021
    detail.hit.zdb_id: 2649216-7
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  • 5
    Online Resource
    Online Resource
    Active small bowel sparing in intracavitary brachytherapy for cervical cancer
    Oxford University Press (OUP) ; 2022
    In:  Japanese Journal of Clinical Oncology Vol. 52, No. 3 ( 2022-03-03), p. 266-273
    Details
    In: Japanese Journal of Clinical Oncology, Oxford University Press (OUP), Vol. 52, No. 3 ( 2022-03-03), p. 266-273
    Abstract: To propose and evaluate an active method for sparing the small bowel in the treatment field of cervical cancer brachytherapy by prone position procedure. Methods The prone position procedure consists of five steps: making bladder empty, prone-positioning a patient on belly board, making the small bowel move to abdomen, filling the bladder with Foley catheter and finally turning the patient into the supine position. The proposed method was applied for the treatment of seven cervical cancer patients. Its effectiveness was evaluated and a correlation between the patient characteristics and the volumetric dose reduction of small bowel was also investigated. Brachytherapy treatment plans were built before and after the proposed method, and their dose-volume histograms were compared for targets and organs-at-risk. In this comparison, all plans were normalized to satisfy the same D90% for high-risk clinical target volume. Results For the enrolled patients, the average dose of small bowel was significantly reduced from 75.2 ± 4.9 Gy before to 60.2 ± 4.0 Gy after the prone position procedure, while minor dosimetric changes were observed in rectum, sigmoid and bladder. The linear correlation to body mass index, thickness and width of abdominopelvic cavity and bladder volume were 76.2, 69.7, 28.8 and −36.3%, respectively. Conclusions The application of prone position procedure could effectively lower the volumetric dose of the small bowel. The dose reduction in the small bowel had a strong correlation with the patient’s obesity and abdominal thickness. This means the patients for whom the proposed method would be beneficial can be judiciously selected for safe brachytherapy.
    Type of Medium: Online Resource
    ISSN: 1465-3621
    URL: Article
    DOI: 10.1093/jjco/hyab197
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 1494610-5
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  • 6
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    Online Resource
    Feature Importance Analysis of a Deep Learning Model for Predicting Late Bladder Toxicity Occurrence in Uterine Cervical Cancer Patients
    MDPI AG ; 2023
    In:  Cancers Vol. 15, No. 13 ( 2023-07-02), p. 3463-
    Details
    In: Cancers, MDPI AG, Vol. 15, No. 13 ( 2023-07-02), p. 3463-
    Abstract: (1) In this study, we developed a deep learning (DL) model that can be used to predict late bladder toxicity. (2) We collected data obtained from 281 uterine cervical cancer patients who underwent definitive radiation therapy. The DL model was trained using 16 features, including patient, tumor, treatment, and dose parameters, and its performance was compared with that of a multivariable logistic regression model using the following metrics: accuracy, prediction, recall, F1-score, and area under the receiver operating characteristic curve (AUROC). In addition, permutation feature importance was calculated to interpret the DL model for each feature, and the lightweight DL model was designed to focus on the top five important features. (3) The DL model outperformed the multivariable logistic regression model on our dataset. It achieved an F1-score of 0.76 and an AUROC of 0.81, while the corresponding values for the multivariable logistic regression were 0.14 and 0.43, respectively. The DL model identified the doses for the most exposed 2 cc volume of the bladder (BD2cc) as the most important feature, followed by BD5cc and the ICRU bladder point. In the case of the lightweight DL model, the F-score and AUROC were 0.90 and 0.91, respectively. (4) The DL models exhibited superior performance in predicting late bladder toxicity compared with the statistical method. Through the interpretation of the model, it further emphasized its potential for improving patient outcomes and minimizing treatment-related complications with a high level of reliability.
    Type of Medium: Online Resource
    ISSN: 2072-6694
    URL: Article
    DOI: 10.3390/cancers15133463
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2527080-1
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  • 7
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    Investigation of the Dependency of the Output Factor on the Field Size in Passive Scattering Proton Therapy at the National Cancer Center in Korea
    Korean Physical Society ; 2020
    In:  Journal of the Korean Physical Society Vol. 77, No. 5 ( 2020-09), p. 351-353
    Details
    In: Journal of the Korean Physical Society, Korean Physical Society, Vol. 77, No. 5 ( 2020-09), p. 351-353
    Type of Medium: Online Resource
    ISSN: 0374-4884 , 1976-8524
    URL: Article
    DOI: 10.3938/jkps.77.351
    RVK:
    UA 4880
    Language: English
    Publisher: Korean Physical Society
    Publication Date: 2020
    detail.hit.zdb_id: 2046361-3
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  • 8
    Online Resource
    Online Resource
    Deep learning method for prediction of patient-specific dose distribution in breast 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: Patient-specific dose prediction improves the efficiency and quality of radiation treatment planning and reduces the time required to find the optimal plan. In this study, a patient-specific dose prediction model was developed for a left-sided breast clinical case using deep learning, and its performance was compared with that of conventional knowledge-based planning using RapidPlan™. Methods Patient-specific dose prediction was performed using a contour image of the planning target volume (PTV) and organs at risk (OARs) with a U-net-based modified dose prediction neural network. A database of 50 volumetric modulated arc therapy (VMAT) plans for left-sided breast cancer patients was utilized to produce training and validation datasets. The dose prediction deep neural network (DpNet) feature weights of the previously learned convolution layers were applied to the test on a cohort of 10 test sets. With the same patient data set, dose prediction was performed for the 10 test sets after training in RapidPlan. The 3D dose distribution, absolute dose difference error, dose-volume histogram, 2D gamma index, and iso-dose dice similarity coefficient were used for quantitative evaluation of the dose prediction. Results The mean absolute error (MAE) and one standard deviation (SD) between the clinical and deep learning dose prediction models were 0.02 ± 0.04%, 0.01 ± 0.83%, 0.16 ± 0.82%, 0.52 ± 0.97, − 0.88 ± 1.83%, − 1.16 ± 2.58%, and − 0.97 ± 1.73% for D 95% , D mean in the PTV, and the OARs of the body, left breast, heart, left lung, and right lung, respectively, and those measured between the clinical and RapidPlan dose prediction models were 0.02 ± 0.14%, 0.87 ± 0.63%, − 0.29 ± 0.98%, 1.30 ± 0.86%, − 0.32 ± 1.10%, 0.12 ± 2.13%, and − 1.74 ± 1.79, respectively. Conclusions In this study, a deep learning method for dose prediction was developed and was demonstrated to accurately predict patient-specific doses for left-sided breast cancer. Using the deep learning framework, the efficiency and accuracy of the dose prediction were compared to those of RapidPlan. The doses predicted by deep learning were superior to the results of the RapidPlan-generated VMAT plan.
    Type of Medium: Online Resource
    ISSN: 1748-717X
    URL: Article
    DOI: 10.1186/s13014-021-01864-9
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
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  • 9
    Online Resource
    Online Resource
    Two-dimensional in vivo rectal dosimetry using an endorectal balloon with unfoldable radiochromic film during prostate cancer radiotherapy
    Elsevier BV ; 2016
    In:  Radiotherapy and Oncology Vol. 120, No. 2 ( 2016-08), p. 327-332
    Details
    In: Radiotherapy and Oncology, Elsevier BV, Vol. 120, No. 2 ( 2016-08), p. 327-332
    Type of Medium: Online Resource
    ISSN: 0167-8140
    URL: Article
    DOI: 10.1016/j.radonc.2016.05.003
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2016
    detail.hit.zdb_id: 1500707-8
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  • 10
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    Facial expression monitoring system for predicting patient’s sudden movement during radiotherapy using deep learning
    Wiley ; 2020
    In:  Journal of Applied Clinical Medical Physics Vol. 21, No. 8 ( 2020-08), p. 191-199
    Details
    In: Journal of Applied Clinical Medical Physics, Wiley, Vol. 21, No. 8 ( 2020-08), p. 191-199
    Abstract: Imaging, breath‐holding/gating, and fixation devices have been developed to minimize setup errors so that the prescribed dose can be exactly delivered to the target volume in radiotherapy. Despite these efforts, additional patient monitoring devices have been installed in the treatment room to view patients’ whole‐body movement. We developed a facial expression recognition system using deep learning with a convolutional neural network (CNN) to predict patients’ advanced movement, enhancing the stability of the radiation treatment by giving warning signs to radiation therapists. Materials and methods Convolutional neural network model and extended Cohn‐Kanade datasets with 447 facial expressions of source images for training were used. Additionally, a user interface that can be used in the treatment control room was developed to monitor real‐time patient's facial expression in the treatment room, and the entire system was constructed by installing a camera in the treatment room. To predict the possibility of patients' sudden movement, we categorized facial expressions into two groups: (a) uncomfortable expressions and (b) comfortable expressions. We assumed that the warning sign about the sudden movement was given when the uncomfortable expression was recognized. Results We have constructed the facial expression monitoring system, and the training and test accuracy were 100% and 85.6%, respectively. In 10 patients, their emotions were recognized based on their comfortable and uncomfortable expressions with 100% detection rate. The detected various emotions were represented by a heatmap and motion prediction accuracy was analyzed for each patient. Conclusion We developed a system that monitors the patient's facial expressions and predicts patient's advanced movement during the treatment. It was confirmed that our patient monitoring system can be complementarily used with the existing monitoring system. This system will help in maintaining the initial setup and improving the accuracy of radiotherapy for the patients using deep learning in radiotherapy.
    Type of Medium: Online Resource
    ISSN: 1526-9914 , 1526-9914
    URL: Issue
    URL: Article
    DOI: 10.1002/acm2.v21.8
    DOI: 10.1002/acm2.12945
    Language: English
    Publisher: Wiley
    Publication Date: 2020
    detail.hit.zdb_id: 2010347-5
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