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  • 1
    Online Resource
    Online Resource
    A Phase 0 Microdosing PET/CT Study Using O-[18F]Fluoromethyl-d-Tyrosine in Normal Human Brain and Brain Tumor
    Ovid Technologies (Wolters Kluwer Health) ; 2021
    In:  Clinical Nuclear Medicine Vol. 46, No. 9 ( 2021-9), p. 717-722
    Details
    In: Clinical Nuclear Medicine, Ovid Technologies (Wolters Kluwer Health), Vol. 46, No. 9 ( 2021-9), p. 717-722
    Abstract: The aim of the present study was to obtain information about distribution, radiation dosimetry, toxicity, and pharmacokinetics of O -[ 18 F]fluoromethyl- d -tyrosine ( d - 18 F-FMT), an amino acid PET tracer, in patients with brain tumors. Patients and Methods A total of 6 healthy controls (age = 19–25 years, 3 males and 3 females) with brain PET images and radiation dosimetry and 12 patients (median age = 60 years, 6 males and 6 females) with primary (n = 5) or metastatic brain tumor (n = 7) were enrolled. We acquired 60-minute dynamic brain PET images after injecting 370 MBq of d - 18 F-FMT. Time-activity curves of d - 18 F-FMT uptake in normal brain versus brain tumors and tumor-to-background ratio were analyzed for each PET data set. Results Normal cerebral uptake of d - 18 F-FMT decreased from 0 to 5 minutes after injection, but gradually increased from 10 to 60 minutes. Tumoral uptake of d - 18 F-FMT reached a peak before 30 minutes. Tumor-to-background ratio peaked at less than 15 minutes for 8 patients and more than 15 minutes for 4 patients. The mean effective dose was calculated to be 13.2 μSv/MBq. Conclusions Using d - 18 F-FMT as a PET radiotracer is safe. It can distinguish brain tumor from surrounding normal brain tissues with a high contrast. Early-time PET images of brain tumors should be acquired because the tumor-to-background ratio tended to reach a peak within 15 minutes after injection.
    Type of Medium: Online Resource
    ISSN: 1536-0229 , 0363-9762
    URL: Article
    DOI: 10.1097/RLU.0000000000003735
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2021
    detail.hit.zdb_id: 2045053-9
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  • 2
    Online Resource
    Online Resource
    Radioimmunotherapy with 131 I‐rituximab for patients with relapsed or refractory follicular or mantle cell lymphoma
    Wiley ; 2023
    In:  Asia-Pacific Journal of Clinical Oncology Vol. 19, No. 6 ( 2023-12), p. 690-696
    Details
    In: Asia-Pacific Journal of Clinical Oncology, Wiley, Vol. 19, No. 6 ( 2023-12), p. 690-696
    Abstract: This study aimed to evaluate the safety and efficacy of 131 I‐rituximab in patients with relapsed or refractory follicular or mantle cell lymphoma. Methods Twenty‐four patients with relapsed or refractory follicular or mantle cell lymphoma were administered unlabeled rituximab (70 mg) immediately before receiving a therapeutic dose of 131 I‐rituximab. Contrast‐enhanced 18F‐fluorodeoxyglucose positron emission tomography/computed tomography was used a month later to assess tumor response. Results This study enrolled 24 patients between June 2012 and 2022. Depending on how they responded to radioimmunotherapy (RIT), 131 I‐rituximab was administered one to five times. Of the 24 patients, 9 achieved complete response after RIT and 8 achieved partial response. The median progression‐free and overall survival was 5.9 and 37.9 months, respectively. During the follow‐up period of 64.2 months, three patients were diagnosed with a secondary malignancy. Among treatment‐related adverse events, hematologic toxicities were common, and grade 3–4 thrombocytopenia and neutropenia were reported in 66.6% of cases. Conclusion 131 I‐rituximab has an effective and favorable safety profile in patients with relapsed or refractory follicular lymphoma and mantle cell lymphoma. This suggests that RIT may also be considered a treatment option for patients with relapsed or refractory follicular lymphoma and mantle cell lymphoma.
    Type of Medium: Online Resource
    ISSN: 1743-7555 , 1743-7563
    URL: Issue
    URL: Article
    DOI: 10.1111/ajco.v19.6
    DOI: 10.1111/ajco.13932
    Language: English
    Publisher: Wiley
    Publication Date: 2023
    detail.hit.zdb_id: 2187409-8
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  • 3
    Online Resource
    Online Resource
    Machine Learning Model for Lymph Node Metastasis Prediction in Breast Cancer Using Random Forest Algorithm and Mitochondrial Metabolism Hub Genes
    MDPI AG ; 2021
    In:  Applied Sciences Vol. 11, No. 7 ( 2021-03-24), p. 2897-
    Details
    In: Applied Sciences, MDPI AG, Vol. 11, No. 7 ( 2021-03-24), p. 2897-
    Abstract: Breast cancer metastasis can have a fatal outcome, with the prediction of metastasis being critical for establishing effective treatment strategies. RNA-sequencing (RNA-seq) is a good tool for identifying genes that promote and support metastasis development. The hub gene analysis method is a bioinformatics method that can effectively analyze RNA sequencing results. This can be used to specify the set of genes most relevant to the function of the cell involved in metastasis. Herein, a new machine learning model based on RNA-seq data using the random forest algorithm and hub genes to estimate the accuracy of breast cancer metastasis prediction. Single-cell breast cancer samples (56 metastatic and 38 non-metastatic samples) were obtained from the Gene Expression Omnibus database, and the Weighted Gene Correlation Network Analysis package was used for the selection of gene modules and hub genes (function in mitochondrial metabolism). A machine learning prediction model using the hub gene set was devised and its accuracy was evaluated. A prediction model comprising 54-functional-gene modules and the hub gene set (NDUFA9, NDUFB5, and NDUFB3) showed an accuracy of 0.769 ± 0.02, 0.782 ± 0.012, and 0.945 ± 0.016, respectively. The test accuracy of the hub gene set was over 93% and that of the prediction model with random forest and hub genes was over 91%. A breast cancer metastasis dataset from The Cancer Genome Atlas was used for external validation, showing an accuracy of over 91%. The hub gene assay can be used to predict breast cancer metastasis by machine learning.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    URL: Article
    DOI: 10.3390/app11072897
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2704225-X
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  • 4
    Online Resource
    Online Resource
    The Feasibility of 64 Cu-PSMA I&T PET for Prostate Cancer
    Mary Ann Liebert Inc ; 2022
    In:  Cancer Biotherapy and Radiopharmaceuticals Vol. 37, No. 6 ( 2022-08-01), p. 417-423
    Details
    In: Cancer Biotherapy and Radiopharmaceuticals, Mary Ann Liebert Inc, Vol. 37, No. 6 ( 2022-08-01), p. 417-423
    Type of Medium: Online Resource
    ISSN: 1084-9785 , 1557-8852
    URL: Article
    DOI: 10.1089/cbr.2020.4189
    Language: English
    Publisher: Mary Ann Liebert Inc
    Publication Date: 2022
    detail.hit.zdb_id: 2029859-6
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  • 5
    Online Resource
    Online Resource
    Targeted alpha immunotherapy of CD20-positive B-cell lymphoma model: dosimetry estimate of 225Ac-DOTA-rituximab using 64Cu-DOTA-rituximab
    Springer Science and Business Media LLC ; 2021
    In:  Annals of Nuclear Medicine Vol. 35, No. 5 ( 2021-05), p. 639-647
    Details
    In: Annals of Nuclear Medicine, Springer Science and Business Media LLC, Vol. 35, No. 5 ( 2021-05), p. 639-647
    Type of Medium: Online Resource
    ISSN: 0914-7187 , 1864-6433
    URL: Article
    DOI: 10.1007/s12149-021-01607-6
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 2039738-0
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  • 6
    Online Resource
    Online Resource
    Early prediction of neoadjuvant chemotherapy response for advanced breast cancer using PET/MRI image deep learning
    Springer Science and Business Media LLC ; 2020
    In:  Scientific Reports Vol. 10, No. 1 ( 2020-12-03)
    Details
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 10, No. 1 ( 2020-12-03)
    Abstract: This study aimed to investigate the predictive efficacy of positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (MRI) for the pathological response of advanced breast cancer to neoadjuvant chemotherapy (NAC). The breast PET/MRI image deep learning model was introduced and compared with the conventional methods. PET/CT and MRI parameters were evaluated before and after the first NAC cycle in patients with advanced breast cancer [n = 56; all women; median age, 49 (range 26–66) years]. The maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were obtained with the corresponding baseline values (SUV0, MTV0, and TLG0, respectively) and interim PET images (SUV1, MTV1, and TLG1, respectively). Mean apparent diffusion coefficients were obtained from baseline and interim diffusion MR images (ADC0 and ADC1, respectively). The differences between the baseline and interim parameters were measured (ΔSUV, ΔMTV, ΔTLG, and ΔADC). Subgroup analysis was performed for the HER2-negative and triple-negative groups. Datasets for convolutional neural network (CNN), assigned as training (80%) and test datasets (20%), were cropped from the baseline (PET0, MRI0) and interim (PET1, MRI1) images. Histopathologic responses were assessed using the Miller and Payne system, after three cycles of chemotherapy. Receiver operating characteristic curve analysis was used to assess the performance of the differentiating responders and non-responders. There were six responders (11%) and 50 non-responders (89%). The area under the curve (AUC) was the highest for ΔSUV at 0.805 (95% CI 0.677–0.899). The AUC was the highest for ΔSUV at 0.879 (95% CI 0.722–0.965) for the HER2-negative subtype. AUC improved following CNN application (SUV0:PET0 = 0.652:0.886, SUV1:PET1 = 0.687:0.980, and ADC1:MRI1 = 0.537:0.701), except for ADC0 (ADC0:MRI0 = 0.703:0.602). PET/MRI image deep learning model can predict pathological responses to NAC in patients with advanced breast cancer.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    URL: Article
    DOI: 10.1038/s41598-020-77875-5
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2020
    detail.hit.zdb_id: 2615211-3
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  • 7
    Online Resource
    Online Resource
    Prediction of Chemotherapy Response of Osteosarcoma Using Baseline 18 F-FDG Textural Features Machine Learning Approaches with PCA
    Hindawi Limited ; 2019
    In:  Contrast Media & Molecular Imaging Vol. 2019 ( 2019-07-24), p. 1-7
    Details
    In: Contrast Media & Molecular Imaging, Hindawi Limited, Vol. 2019 ( 2019-07-24), p. 1-7
    Abstract: Purpose . Patients with high-grade osteosarcoma undergo several chemotherapy cycles before surgical intervention. Response to chemotherapy, however, is affected by intratumor heterogeneity. In this study, we assessed the ability of a machine learning approach using baseline 18 F-fluorodeoxyglucose ( 18 F-FDG) positron emitted tomography (PET) textural features to predict response to chemotherapy in osteosarcoma patients. Materials and Methods . This study included 70 osteosarcoma patients who received neoadjuvant chemotherapy. Quantitative characteristics of the tumors were evaluated by standard uptake value (SUV), total lesion glycolysis (TLG), and metabolic tumor volume (MTV). Tumor heterogeneity was evaluated using textural analysis of 18 F-FDG PET scan images. Assessments were performed at baseline and after chemotherapy using 18 F-FDG PET; 18 F-FDG textural features were evaluated using the Chang-Gung Image Texture Analysis toolbox. To predict the chemotherapy response, several features were chosen using the principal component analysis (PCA) feature selection method. Machine learning was performed using linear support vector machine (SVM), random forest, and gradient boost methods. The ability to predict chemotherapy response was evaluated using the area under the receiver operating characteristic curve (AUC). Results . AUCs of the baseline 18 F-FDG features SUVmax, TLG, MTV, 1st entropy, and gray level co-occurrence matrix entropy were 0.553, 0538, 0.536, 0.538, and 0.543, respectively. However, AUCs of the machine learning features linear SVM, random forest, and gradient boost were 0.72, 0.78, and 0.82, respectively. Conclusion . We found that a machine learning approach based on 18 F-FDG textural features could predict the chemotherapy response using baseline PET images. This early prediction of the chemotherapy response may aid in determining treatment plans for osteosarcoma patients.
    Type of Medium: Online Resource
    ISSN: 1555-4309 , 1555-4317
    URL: Article
    DOI: 10.1155/2019/3515080
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2019
    detail.hit.zdb_id: 2222967-X
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  • 8
    Online Resource
    Online Resource
    A preliminary clinical trial to evaluate 64Cu-NOTA-Trastuzumab as a positron emission tomography imaging agent in patients with breast cancer
    Springer Science and Business Media LLC ; 2021
    In:  EJNMMI Research Vol. 11, No. 1 ( 2021-12)
    Details
    In: EJNMMI Research, Springer Science and Business Media LLC, Vol. 11, No. 1 ( 2021-12)
    Abstract: The purpose of this study was to evaluate both the biodistribution and safety of 64 Cu-1,4,7-triazacyclononane-1,4,7-triacetic acid (NOTA)-Trastuzumab, a novel  64 Cu-labeled positron emission tomography (PET) tracer for human epidermal growth factor receptor 2 (HER2) in patients with breast cancer. Methods PET images at 1, 24, and 48 h after 296 MBq of 64 Cu-NOTA-Trastuzumab injection were obtained from seven patients with breast cancer. Both the primary tumors’ and metastatic lesions’ maximum standardized uptake value (SUV max ) was evaluated. The mean SUV max (SUV mean ) was evaluated in the other organs, including the blood pool, liver, kidney, muscle, spleen, bladder, and the lungs, as well as the bones. Moreover, the internal radiation dosimetry was calculated using the OLINDA/EXM software. Safety was assessed based on feedback regarding adverse reactions and safety-related issues within 1 month after 64 Cu-NOTA-Trastuzumab administration. Results 64 Cu-NOTA-Trastuzumab PET images showed that the overall SUV mean values in each organ negatively correlated with time. The liver’s average SUV mean values were measured at 5.3 ± 0.7, 4.8 ± 0.6, and 4.4 ± 0.5 on 1 h, 24 h, and 48 h after injection, respectively. The average SUV mean blood values were measured at 13.1 ± 0.9, 9.1 ± 1.2, and 7.1 ± 1.9 on 1 h, 24 h, and 48 h after injection, respectively. The SUV max of HER2-positive tumors was relatively higher than HER2-negative tumors (8.6 ± 5.1 and 5.2 ± 2.8 on 48 h after injection, respectively). Tumor-to-background ratios were higher in the HER2-positive tumors than in the HER2-negative tumors. No adverse events related to 64 Cu-NOTA-Trastuzumab were reported. The calculated effective dose with a 296 MBq injection of 64 Cu-NOTA-Trastuzumab was 2.96 mSv. The highest absorbed dose was observed in the liver (0.076 mGy/MBq), followed by the spleen (0.063 mGy/MBq), kidney (0.044 mGy/MBq), and heart wall (0.044 mGy/MBq). Conclusions 64 Cu-NOTA-Trastuzumab showed a specific uptake at the HER2-expressing tumors, thus making it a feasible and safe monitoring tool of HER2 tumor status in patients with breast cancer. Trial registration CRIS, KCT0002790. Registered 02 February 2018, https://cris.nih.go.kr
    Type of Medium: Online Resource
    ISSN: 2191-219X
    URL: Article
    DOI: 10.1186/s13550-021-00746-1
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 2619892-7
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  • 9
    Online Resource
    Online Resource
    Deep Learning-Based Delayed PET Image Synthesis from Corresponding Early Scanned PET for Dosimetry Uptake Estimation
    MDPI AG ; 2023
    In:  Diagnostics Vol. 13, No. 19 ( 2023-09-25), p. 3045-
    Details
    In: Diagnostics, MDPI AG, Vol. 13, No. 19 ( 2023-09-25), p. 3045-
    Abstract: The acquisition of in vivo radiopharmaceutical distribution through imaging is time-consuming due to dosimetry, which requires the subject to be scanned at several time points post-injection. This study aimed to generate delayed positron emission tomography images from early images using a deep-learning-based image generation model to mitigate the time cost and inconvenience. Eighteen healthy participants were recruited and injected with [18F] Fluorodeoxyglucose. A paired image-to-image translation model, based on a generative adversarial network (GAN), was used as the generation model. The standardized uptake value (SUV) mean of the generated image of each organ was compared with that of the ground-truth. The least square GAN and perceptual loss combinations displayed the best performance. As the uptake time of the early image became closer to that of the ground-truth image, the translation performance improved. The SUV mean values of the nominated organs were estimated reasonably accurately for the muscle, heart, liver, and spleen. The results demonstrate that the image-to-image translation deep learning model is applicable for the generation of a functional image from another functional image acquired from normal subjects, including predictions of organ-wise activity for specific normal organs.
    Type of Medium: Online Resource
    ISSN: 2075-4418
    URL: Article
    DOI: 10.3390/diagnostics13193045
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2662336-5
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  • 10
    Online Resource
    Online Resource
    A microdose clinical trial to evaluate [18F]Florastamin as a positron emission tomography imaging agent in patients with prostate cancer
    Springer Science and Business Media LLC ; 2021
    In:  European Journal of Nuclear Medicine and Molecular Imaging Vol. 48, No. 1 ( 2021-01), p. 95-102
    Details
    In: European Journal of Nuclear Medicine and Molecular Imaging, Springer Science and Business Media LLC, Vol. 48, No. 1 ( 2021-01), p. 95-102
    Type of Medium: Online Resource
    ISSN: 1619-7070 , 1619-7089
    URL: Article
    DOI: 10.1007/s00259-020-04883-y
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 2098375-X
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