Radiation Oncology最新文献

{"title":"The impact of bolus on clinical outcomes for post-mastectomy breast cancer patients treated with IMRT: data from China.","authors":"Tao Jiang, Jiao Tian, Peijie Lei, Chunliu Meng, Jialei Fu, Lianjing Cao, Jingjing Cheng, Fei Zhou, Hongjun Zhang, Hao Song, Haijun Lu, Xiaojuan Wei","doi":"10.1186/s13014-024-02456-z","DOIUrl":"10.1186/s13014-024-02456-z","url":null,"abstract":"<p><strong>Purpose: </strong>This study aims to investigate the effects of chest wall bolus in intensity-modulated radiotherapy (IMRT) technology on clinical outcomes for post-mastectomy breast cancer patients.</p><p><strong>Materials and methods: </strong>This retrospective study included patients with invasive carcinoma ((y)pT0-4, (y)pN0-3) who received photon IMRT after mastectomy at the Affiliated Hospital of Qingdao University from 2014 to 2019. The patients were divided into two groups based on whether they received daily bolus application or not, and the baseline characteristics were matched using propensity score matching (PSM). Cumulative incidence (CI) of local recurrence (LR), locoregional recurrence (LRR), overall survival (OS) and disease-free survival (DFS) were evaluated with a log-rank test. Acute skin toxicity and late radiation pneumonia was analyzed using chi-square test.</p><p><strong>Results: </strong>A total of 529 patients were included in this study, among whom 254 (48%) patients received bolus application. The median follow-up time was 60 months. After matching, 175 well-paired patients were selected. The adjusted 5-year outcomes (95% confidence interval) in patients treated with and without bolus were, respectively: CI of LR 2.42% (0.04-4.74) versus 2.38% (0.05-4.65), CI of LRR 2.42% (0.04-4.74) versus 3.59% (0.73-6.37), DFS 88.12% (83.35-93.18) versus 84.69% (79.42-90.30), OS 94.21% (90.79-97.76) versus 95.86% (92.91-98.91). No correlation between bolus application and skin toxicity (P = 0.555) and late pneumonia (P = 0.333) was observed.</p><p><strong>Conclusions: </strong>The study revealed a low recurrence rate using IMRT technology. The daily used 5 mm chest wall bolus was not associated with improved clinical outcomes.</p>","PeriodicalId":49639,"journal":{"name":"Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11134933/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141162457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A non-invasive preoperative prediction model for predicting axillary lymph node metastasis in breast cancer based on a machine learning approach: combining ultrasonographic parameters and breast gamma specific imaging features.","authors":"Ranze Cai, Li Deng, Hua Zhang, Hongwei Zhang, Qian Wu","doi":"10.1186/s13014-024-02453-2","DOIUrl":"10.1186/s13014-024-02453-2","url":null,"abstract":"<p><strong>Background: </strong>The most common route of breast cancer metastasis is through the mammary lymphatic network. An accurate assessment of the axillary lymph node (ALN) burden before surgery can avoid unnecessary axillary surgery, consequently preventing surgical complications. In this study, we aimed to develop a non-invasive prediction model incorporating breast specific gamma image (BSGI) features and ultrasonographic parameters to assess axillary lymph node status.</p><p><strong>Materials and methods: </strong>Cohorts of breast cancer patients who underwent surgery between 2012 and 2021 were created (The training set included 1104 ultrasound images and 940 BSGI images from 235 patients, the test set included 568 ultrasound images and 296 BSGI images from 99 patients) for the development of the prediction model. six machine learning (ML) methods and recursive feature elimination were trained in the training set to create a strong prediction model. Based on the best-performing model, we created an online calculator that can make a linear predictor in patients easily accessible to clinicians. The receiver operating characteristic (ROC) and calibration curve are used to verify the model performance respectively and evaluate the clinical effectiveness of the model.</p><p><strong>Results: </strong>Six ultrasonographic parameters (transverse diameter of tumour, longitudinal diameter of tumour, lymphatic echogenicity, transverse diameter of lymph nodes, longitudinal diameter of lymph nodes, lymphatic color Doppler flow imaging grade) and one BSGI features (axillary mass status) were selected based on the best-performing model. In the test set, the support vector machines' model showed the best predictive ability (AUC = 0.794, sensitivity = 0.641, specificity = 0.8, PPV = 0.676, NPV = 0.774 and accuracy = 0.737). An online calculator was established for clinicians to predict patients' risk of ALN metastasis ( https://wuqian.shinyapps.io/shinybsgi/ ). The result in ROC showed the model could benefit from incorporating BSGI feature.</p><p><strong>Conclusion: </strong>This study developed a non-invasive prediction model that incorporates variables using ML method and serves to clinically predict ALN metastasis and help in selection of the appropriate treatment option.</p>","PeriodicalId":49639,"journal":{"name":"Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11131273/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141158530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hypofractionated radiotherapy with simultaneous tumor bed boost (Hi-RISE) in breast cancer patients receiving upfront breast-conserving surgery: study protocol for a phase III randomized controlled trial.","authors":"Kairui Jin, Jurui Luo, Xiaoli Yu, Xiaomao Guo","doi":"10.1186/s13014-024-02449-y","DOIUrl":"10.1186/s13014-024-02449-y","url":null,"abstract":"<p><strong>Background: </strong>The effectiveness and safety of moderately hypofractionated radiotherapy (HFRT) in patients undergoing breast-conserving surgery (BCS) has been demonstrated in several pivotal randomized trials. However, the feasibility of applying simultaneous integrated boost (SIB) to the tumor bed and regional node irradiation (RNI) using modern radiotherapy techniques with HFRT needs further evaluation.</p><p><strong>Methods: </strong>This prospective, multi-center, randomized controlled, non-inferiority phase III trial aims to determine the non-inferiority of HFRT combined with SIB (HFRTsib) compared with conventional fractionated radiotherapy with sequential boost (CFRTseq) in terms of five-year locoregional control rate in breast cancer patients undergoing upfront BCS. A total of 2904 participants will be recruited and randomized in a 1:1 ratio into the HFRTsib and CFRTseq groups. All patients will receive whole breast irradiation, and those with positive axillary nodes will receive additional RNI, including internal mammary irradiation. The prescribed dose for the HFRTsib group will be 40 Gy in 15 fractions, combined with a SIB of 48 Gy in 15 fractions to the tumor bed. The CFRTseq group will receive 50 Gy in 25 fractions, with a sequential boost of 10 Gy in 5 fractions to the tumor bed.</p><p><strong>Discussion: </strong>This trial intends to assess the effectiveness and safety of SIB combined with HFRT in early breast cancer patients following BCS. The primary endpoint is locoregional control, and the results of this trial are expected to offer crucial evidence for utilizing HFRT in breast cancer patients after BCS.</p><p><strong>Trial registration: </strong>This trial was registered at ClincalTrials.gov (NCT04025164) on July 18, 2019.</p>","PeriodicalId":49639,"journal":{"name":"Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11131299/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141158597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deformable registration of magnetic resonance images using unsupervised deep learning in neuro-/radiation oncology.","authors":"Alexander F I Osman, Kholoud S Al-Mugren, Nissren M Tamam, Bilal Shahine","doi":"10.1186/s13014-024-02452-3","DOIUrl":"10.1186/s13014-024-02452-3","url":null,"abstract":"<p><strong>Purpose: </strong>Accurate deformable registration of magnetic resonance imaging (MRI) scans containing pathologies is challenging due to changes in tissue appearance. In this paper, we developed a novel automated three-dimensional (3D) convolutional U-Net based deformable image registration (ConvUNet-DIR) method using unsupervised learning to establish correspondence between baseline pre-operative and follow-up MRI scans of patients with brain glioma.</p><p><strong>Methods: </strong>This study involved multi-parametric brain MRI scans (T1, T1-contrast enhanced, T2, FLAIR) acquired at pre-operative and follow-up time for 160 patients diagnosed with glioma, representing the BraTS-Reg 2022 challenge dataset. ConvUNet-DIR, a deep learning-based deformable registration workflow using 3D U-Net style architecture as a core, was developed to establish correspondence between the MRI scans. The workflow consists of three components: (1) the U-Net learns features from pairs of MRI scans and estimates a mapping between them, (2) the grid generator computes the sampling grid based on the derived transformation parameters, and (3) the spatial transformation layer generates a warped image by applying the sampling operation using interpolation. A similarity measure was used as a loss function for the network with a regularization parameter limiting the deformation. The model was trained via unsupervised learning using pairs of MRI scans on a training data set (n = 102) and validated on a validation data set (n = 26) to assess its generalizability. Its performance was evaluated on a test set (n = 32) by computing the Dice score and structural similarity index (SSIM) quantitative metrics. The model's performance also was compared with the baseline state-of-the-art VoxelMorph (VM1 and VM2) learning-based algorithms.</p><p><strong>Results: </strong>The ConvUNet-DIR model showed promising competency in performing accurate 3D deformable registration. It achieved a mean Dice score of 0.975 ± 0.003 and SSIM of 0.908 ± 0.011 on the test set (n = 32). Experimental results also demonstrated that ConvUNet-DIR outperformed the VoxelMorph algorithms concerning Dice (VM1: 0.969 ± 0.006 and VM2: 0.957 ± 0.008) and SSIM (VM1: 0.893 ± 0.012 and VM2: 0.857 ± 0.017) metrics. The time required to perform a registration for a pair of MRI scans is about 1 s on the CPU.</p><p><strong>Conclusions: </strong>The developed deep learning-based model can perform an end-to-end deformable registration of a pair of 3D MRI scans for glioma patients without human intervention. The model could provide accurate, efficient, and robust deformable registration without needing pre-alignment and labeling. It outperformed the state-of-the-art VoxelMorph learning-based deformable registration algorithms and other supervised/unsupervised deep learning-based methods reported in the literature.</p>","PeriodicalId":49639,"journal":{"name":"Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11110381/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141077199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A survey on brachytherapy training of gynecological cancer focusing on the competence of residents in China.","authors":"Mohan Dong, Changhao Liu, Junfang Yan, Yong Zhu, Yutian Yin, Jia Wang, Ying Zhang, Lichun Wei, Lina Zhao","doi":"10.1186/s13014-024-02433-6","DOIUrl":"10.1186/s13014-024-02433-6","url":null,"abstract":"<p><strong>Background: </strong>The brachytherapy is an indispensable treatment for gynecological tumors, but the quality and efficiency of brachytherapy training for residents is still unclear.</p><p><strong>Methods: </strong>An anonymous questionnaire was designed to collect information on gynecological brachytherapy (GBT) training for radiation oncology residents from 28 training bases in China. The questionnaire content was designed based on the principle of competency based medical education (CBME). The Likert scale was employed to evaluate self-reported competence and comprehension regarding GBT. A total of 132 senior residents were included in the final analysis.</p><p><strong>Results: </strong>53.79% (71/132) of senior residents had experience in performing image-guided GBT, whereas 76.52% (101/132) had observed the procedure during their standardized residency training. The proportion of senior residents who reported having the self-reported competence to independently complete the GBT was 78.03% for intracavity GBT, 75.00% for vaginal stump GBT, and 50.03% for interstitial GBT, respectively. The number of successful completion of Interstitial, intracavity and vaginal GBT was correlated with the self- confidence of trainees after standardized training. In particular, the independent completion of interstitial GBT for more than 20 cases was an independent factor for the self-reported competence of senior residents. During the training period, 50.76% and 56.82% of the residents had not participated in the specialized examinations and professional GBT courses.</p><p><strong>Conclusions: </strong>The study revealed that the self-confidence of residents to independently complete brachytherapy was relatively high, and the specialized curriculum setting and training process assessment for brachytherapy training still need to be strengthened in the future.</p>","PeriodicalId":49639,"journal":{"name":"Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11110279/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141077198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of adjuvant radiotherapy on overall survival and breast cancer-specific survival of patients with malignant phyllodes tumor of the breast in different age groups: a retrospective observational study based on SEER.","authors":"Ping Yang, Gongyin Zhang, Yu Zhang, Wanying Zhao, Jinhai Tang, Siyuan Zeng, Xiupeng Lv, Li Lv","doi":"10.1186/s13014-024-02442-5","DOIUrl":"10.1186/s13014-024-02442-5","url":null,"abstract":"<p><strong>Purpose: </strong>Malignant phyllodes tumor of the breast (MPTB) is a rare type of breast cancer, with an incidence of less than 1%. The value of adjuvant radiotherapy (RT) for MPTB has been controversial. The aim of the study was to explore the effect of radiotherapy on the long-term survival of female patients with MPTB at different ages.</p><p><strong>Methods: </strong>Female MPTB patients were selected from the Surveillance, Epidemiology, and End Results (SEER) database between 2000 and 2020. A Kaplan-Meier survival analysis was conducted to investigate the value of RT for the long-term survival of MPTB patients in different age groups. Additionally, univariate and multivariate Cox regression analyses were performed for overall survival (OS) and breast cancer-specific survival (BCSS) of MPTB patients. Furthermore, propensity score matching (PSM) was also performed to balance the differences in baseline characteristics.</p><p><strong>Results: </strong>2261 MPTB patients were included in this study, including 455 patients (20.12%) with RT and 1806 patients (79.88%) without RT. These patients were divided into four cohorts based on their ages: 18-45, 46-55, 56-65, and 65-80. Before adjustment, there was a statistically significant difference in long-term survival between RT-treated and non-RT-treated patients in the younger age groups (age group of 18-45 years: OS P = 0.019, BCSS P = 0.016; age group of 46-55 years: OS P < 0.001, BCSS P < 0.001). After PSM, no difference was found in long-term survival of patients in both younger and older groups regardless of whether they received RT (age group of 18-45 years: OS P = 0.473, BCSS P = 0.750; age group of 46-55 years: OS P = 0.380, BCSS P = 0.816, age group of 56-65 years: OS P = 0.484, BCSS P = 0.290; age group of 66-80 years: OS P = 0.997, BCSS P = 0.763). In multivariate COX regression analysis, RT did not affect long-term survival in patients with MPTB.</p><p><strong>Conclusion: </strong>There is no evidence that long-term survival of MPTB patients in specific age groups can benefit from RT.</p>","PeriodicalId":49639,"journal":{"name":"Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11107058/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141077200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Systematic review and pooled analysis of the impact of treatment-induced lymphopenia on survival of glioblastoma patients.","authors":"Ali M Saeed, Søren M Bentzen, Haroon Ahmad, Lily Pham, Graeme F Woodworth, Mark V Mishra","doi":"10.1186/s13014-024-02443-4","DOIUrl":"10.1186/s13014-024-02443-4","url":null,"abstract":"","PeriodicalId":49639,"journal":{"name":"Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11097475/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140946048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Real world clinical experience using daily intelligence-assisted online adaptive radiotherapy for head and neck cancer","authors":"Philip Blumenfeld, Eduard Arbit, Robert Den, Ayman Salhab, Tal Falick Michaeli, Marc Wygoda, Yair Hillman, Raphael M. Pfeffer, Marcel Fang, Yael Misrati, Noam Weizman, Jon Feldman, Aron Popovtzer","doi":"10.1186/s13014-024-02441-6","DOIUrl":"https://doi.org/10.1186/s13014-024-02441-6","url":null,"abstract":"<p><b>Correction: Radiat Oncol 19, 43 (2024)</b></p><p>https://doi.org/10.1186/s13014-024-02436-3</p><p>In this article [1] the abstract was omitted due to a typesetting error and should have appeared as below:</p><p><b>Abstract</b></p><p>Background</p><p>Adaptive radiation therapy (ART) offers a dynamic approach to address structural and spatial changes that occur during radiotherapy (RT) for locally advanced head and neck cancers. The integration of daily ART with Cone-Beam CT (CBCT) imaging presents a solution to enhance the therapeutic ratio by addressing inter-fractional changes.</p><p>Methods</p><p>We evaluated the initial clinical experience of daily ART for patients with head and neck cancer using an online adaptive platform with intelligence-assisted workflows on daily CBCT. Treatment included auto-contour and structure deformation of Organs at Risk (OARs) and target structures, with adjustments by the treating physician. Two plans were generated: one based on the initial CT simulation with the edited structures (scheduled) and a re-optimized plan (adaptive). Both plans were evaluated and the superior one approved and delivered. Clinical and dosimetric outcomes were reviewed.</p><p>Results</p><p>Twenty two patients with head and neck cancers (7 Nasopharynx, 6 Oropharynx, 1 oral cavity, 8 larynx) stages I-IVA were treated with daily ART. 770 adaptive and scheduled radiotherapy plans were generated. 703 (91.3%) adaptive plans were chosen. Median time to deliver ART was 20 min (range: 18–23). Adaptive compared to scheduled plans demonstrated improved mean V95 values for the PTV70, PTV59.5, and PTV56 by 1.2%, 7.2%, and 6.0% respectively and a mean 1.4% lower maximum dose in PTV70. Fourteen of 17 OARs demonstrated improved dosimetry with adaptation, with select OARs reaching statistical significance. At a median follow up of 14.1 months, local control was 95.5%, two patients developed metastatic disease and four patients died. 9.1% of patients had acute grade 3 dysphagia and 13.6% had grade 2 chronic xerostomia.</p><p>Discussion</p><p>These findings provide real world evidence of the feasibility and dosimetric benefit of incorporating daily ART on CBCT in the treatment of head and neck cancer. Prospective study is needed to determine if these dosimetric improvements translate into improved outcomes.</p><p>The original article has been updated.</p><ol data-track-component=\"outbound reference\"><li data-counter=\"1.\"><p>Blumenfeld P, Arbit E, Den R, et al. Real world clinical experience using daily intelligence-assisted online adaptive radiotherapy for head and neck cancer. Radiat Oncol. 2024;19:43. https://doi.org/10.1186/s13014-024-02436-3</p><p>Article PubMed PubMed Central Google Scholar </p></li></ol><p>Download references<svg aria-hidden=\"true\" focusable=\"false\" height=\"16\" role=\"img\" width=\"16\"><use xlink:href=\"#icon-eds-i-download-medium\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"></use></svg></p><span>Author notes</span><ol><li><p>Jon Fe","PeriodicalId":49639,"journal":{"name":"Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140939803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prospective phase II trial of preoperative hypofractionated proton therapy for extremity and truncal soft tissue sarcoma: the PRONTO study rationale and design.","authors":"Emile Gogineni, Hao Chen, Chen Hu, Karim Boudadi, Jessica Engle, Adam Levine, Curtiland Deville","doi":"10.1186/s13014-024-02447-0","DOIUrl":"10.1186/s13014-024-02447-0","url":null,"abstract":"<p><strong>Background: </strong>Oncologic surgical resection is the standard of care for extremity and truncal soft tissue sarcoma (STS), often accompanied by the addition of pre- or postoperative radiation therapy (RT). Preoperative RT may decrease the risk of joint stiffness and fibrosis at the cost of higher rates of wound complications. Hypofractionated, preoperative RT has been shown to provide acceptable outcomes in prospective trials. Proton beam therapy (PBT) provides the means to decrease dose to surrounding organs at risk, such as the skin, bone, soft tissues, and adjacent joint(s), and has not yet been studied in patients with extremity and truncal sarcoma.</p><p><strong>Methods: </strong>Our study titled \"PROspective phase II trial of preoperative hypofractionated protoN therapy for extremity and Truncal soft tissue sarcOma (PRONTO)\" is a non-randomized, prospective phase II trial evaluating the safety and efficacy of preoperative, hypofractionated PBT for patients with STS of the extremity and trunk planned for surgical resection. Adult patients with Eastern Cooperative Group Performance Status ≤ 2 with resectable extremity and truncal STS will be included, with the aim to accrue 40 patients. Treatment will consist of 30 Gy radiobiological equivalent of PBT in 5 fractions delivered every other day, followed by surgical resection 2-12 weeks later. The primary outcome is rate of major wound complications as defined according to the National Cancer Institute of Canada Sarcoma2 (NCIC-SR2) Multicenter Trial. Secondary objectives include rate of late grade ≥ 2 toxicity, local recurrence-free survival and distant metastasis-free survival at 1- and 2-years, functional outcomes, quality of life, and pathologic response.</p><p><strong>Discussion: </strong>PRONTO represents the first trial evaluating the use of hypofractionated PBT for STS. We aim to prove the safety and efficacy of this approach and to compare our results to historical outcomes established by previous trials. Given the low number of proton centers and limited availability, the short course of PBT may provide the opportunity to treat patients who would otherwise be limited when treating with daily RT over several weeks. We hope that this trial will lead to increased referral patterns, offer benefits towards patient convenience and clinic workflow efficiency, and provide evidence supporting the use of PBT in this setting.</p><p><strong>Trial registration: </strong>NCT05917301 (registered 23/6/2023).</p>","PeriodicalId":49639,"journal":{"name":"Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11095023/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140923605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accurate object localization facilitates automatic esophagus segmentation in deep learning.","authors":"Zhibin Li, Guanghui Gan, Jian Guo, Wei Zhan, Long Chen","doi":"10.1186/s13014-024-02448-z","DOIUrl":"10.1186/s13014-024-02448-z","url":null,"abstract":"<p><strong>Background: </strong>Currently, automatic esophagus segmentation remains a challenging task due to its small size, low contrast, and large shape variation. We aimed to improve the performance of esophagus segmentation in deep learning by applying a strategy that involves locating the object first and then performing the segmentation task.</p><p><strong>Methods: </strong>A total of 100 cases with thoracic computed tomography scans from two publicly available datasets were used in this study. A modified CenterNet, an object location network, was employed to locate the center of the esophagus for each slice. Subsequently, the 3D U-net and 2D U-net_coarse models were trained to segment the esophagus based on the predicted object center. A 2D U-net_fine model was trained based on the updated object center according to the 3D U-net model. The dice similarity coefficient and the 95% Hausdorff distance were used as quantitative evaluation indexes for the delineation performance. The characteristics of the automatically delineated esophageal contours by the 2D U-net and 3D U-net models were summarized. Additionally, the impact of the accuracy of object localization on the delineation performance was analyzed. Finally, the delineation performance in different segments of the esophagus was also summarized.</p><p><strong>Results: </strong>The mean dice coefficient of the 3D U-net, 2D U-net_coarse, and 2D U-net_fine models were 0.77, 0.81, and 0.82, respectively. The 95% Hausdorff distance for the above models was 6.55, 3.57, and 3.76, respectively. Compared with the 2D U-net, the 3D U-net has a lower incidence of delineating wrong objects and a higher incidence of missing objects. After using the fine object center, the average dice coefficient was improved by 5.5% in the cases with a dice coefficient less than 0.75, while that value was only 0.3% in the cases with a dice coefficient greater than 0.75. The dice coefficients were lower for the esophagus between the orifice of the inferior and the pulmonary bifurcation compared with the other regions.</p><p><strong>Conclusion: </strong>The 3D U-net model tended to delineate fewer incorrect objects but also miss more objects. Two-stage strategy with accurate object location could enhance the robustness of the segmentation model and significantly improve the esophageal delineation performance, especially for cases with poor delineation results.</p>","PeriodicalId":49639,"journal":{"name":"Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11088757/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140913042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}