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  • 1
    In: SSRN Electronic Journal, Elsevier BV
    Type of Medium: Online Resource
    ISSN: 1556-5068
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2022
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  • 2
    In: Current Medicinal Chemistry, Bentham Science Publishers Ltd., Vol. 15, No. 23 ( 2008-10-01), p. 2329-2336
    Type of Medium: Online Resource
    ISSN: 0929-8673
    Language: English
    Publisher: Bentham Science Publishers Ltd.
    Publication Date: 2008
    SSG: 15,3
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  • 3
    In: Cancers, MDPI AG, Vol. 13, No. 8 ( 2021-04-19), p. 1959-
    Abstract: To determine whether a single dose of double immune checkpoint blockade (induction chemoimmunotherapy (ICIT)) adds benefit to induction single-cycle platinum doublet (induction chemotherapy (IC)) in locally advanced head and neck squamous cell carcinoma (HNSCC), patients treated with cisplatin 30 mg/m2 d1-3 and docetaxel 75 mg/m2 d1 combined with durvalumab 1500 mg fixed dose d5 and tremelimumab 75 mg fixed dose d5 (ICIT) within the CheckRad-CD8 trial were compared with a retrospective cohort receiving the same chemotherapy (IC) without immunotherapy. The endpoint of this analysis was the complete response rate (CR). A total of 53 patients were treated with ICIT and 104 patients with IC only. CR rates were 60.3% for ICIT and 40.3% for IC (p = 0.018). In the total population (n = 157), the most important predictor to achieve a CR was treatment type (OR: 2.21 for ICIT vs. IC; p = 0.038, multivariate analysis). The most diverse effects in CR rates between ICIT and IC were observed in younger (age ≤ 60) patients with HPV-positive OPSCCs (82% vs. 33%, p = 0.176), while there was no difference in older patients without HPV-positive OPSCCs (53% vs. 48%). The analysis provides initial evidence that ICIT could result in higher CR rates than IC. Young patients with HPV-positive OPSCCs may have the greatest benefit from additional immune checkpoint inhibitors.
    Type of Medium: Online Resource
    ISSN: 2072-6694
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2527080-1
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  • 4
    In: Journal for ImmunoTherapy of Cancer, BMJ, Vol. 9, No. Suppl 2 ( 2021-11), p. A355-A355
    Abstract: Immune checkpoint inhibitor (ICI) therapy is a major breakthrough for non-small cell lung cancer (NSCLC) treatment given its high efficacy and tolerable toxicity. Although pre-treatment PD-L1 expression levels and tumor mutation burden (TMB) may serve as prognostic biomarkers for patient stratification, effective predictive biomarkers are lacking. Blood cell count test (BCT) is a routine, regular blood test conducted before and during treatment to provide a direct overview of the immune landscape based on the counts of various types of immune cells (ICs). For instance, previous studies showed that neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) both indicate poor treatment outcomes of ICI therapy of NSCLC patients. Methods This study analyzed relevant combinations of IC counts from four international, multi-center clinical trials of OAK, BIRCH, POPLAR and FIR to conduct post-hoc analysis of NSCLC patients undergoing atezolizumab (anti-PD-L1) single-agent treatment (n = 1,479), while docetaxel single-agent treatment (n = 707) was used as control. BCT was conducted at three timepoints, T1 to T3, during pre-treatment and on the first day of treatment cycles 3 and 5, which correspond to baseline, 6, and 12 weeks on-treatment, respectively. Univariate and multivariate Cox regression analysis was conducted to identify NLR_T3, PLR_T3 and neutrophil-to-monocyte (NMR) at T2 as early BCT biomarkers that may predict ICI efficacy. Next, univariate and multivariate Cox proportional hazards regression analysis were used to identify any effective combination of BCT biomarkers and their absolute cutoff values that may serve as predictive biomarkers to predict atezolizumab treatment outcomes. Lastly, combinations of these BCT biomarkers were tested to optimize BCTscore model for clinical evaluation. Results The final BCT biomarker combination, comprising of the BCT biomarkers of NLR and PLR at 12 weeks on-treatment (T3) and NMR at 6 weeks on-treatment (T2), was identified to be a strong predictive biomarker for atezolizumab (Ate)-treated NSCLC patients in comparison to docetaxel (Dtx)-treated patients regarding overall survival (OS) (BCTscore low-risk: HR Ate vs Dtx = 1.54 (95% CI: 1.04–2.27), P = 0.036; high-risk: HR Ate vs Dtx = 0.84 (95% CI: 0.62–1.12), P = 0.236). Our BCTscore model consistently exhibited better OS AUC in the OAK (AUC12month=0.696), BIRCH (AUC12month=0.672) and POPLAR+FIR studies (AUC12month=0.727) than that of each of the three BCT biomarkers in these three studies. Conclusions The BCTscore of NLR at 12 weeks, PLR at 12 weeks and NMR at 6 weeks is a strong efficacy predictive biomarker for atezolizumab-treated NSCLC patients. Acknowledgements The authors declare no conflict of interest. This publication is based on research using data from Genentech, Inc. (one of subsidiaries of Roche Group) that has been made available through Vivli, Inc (Data Request ID: 5935; Lead Investigator: Dr. Jian-Guo Zhou). Vivli has not contributed to or approved, and is not in any way responsible for, the contents of this publication. Trial Registration Deidentified individual participant data from the single-arm phase II studies of FIR study ( NCT01846416 ; as of Janurary 7, 2015) [Spigel2018] and BIRCH ( NCT02031458 ; as of May 28, 2015) [Peters2017], and the two-arm randomized controlled trials (RCT) of the POPLAR phase II study ( NCT01903993 ; as of May 8, 2015) [Fehrenbacher2016] and the OAK phase III study ( NCT02008227 ; as of July 7, 2016) [Rittmeyer2017] were made available by Genentech Inc. and accessed through the secure Vivli online platform. Ethics Approval Both studies were approved by the respective national ethics committees and institutional review boards and written informed consent was obtained from all patients.
    Type of Medium: Online Resource
    ISSN: 2051-1426
    Language: English
    Publisher: BMJ
    Publication Date: 2021
    detail.hit.zdb_id: 2719863-7
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  • 5
    In: Journal for ImmunoTherapy of Cancer, BMJ, Vol. 8, No. 2 ( 2020-11), p. e001429-
    Abstract: Combining radiotherapy (RT) with immuno-oncology (IO) therapy (IORT) may enhance IO-induced antitumor response. Quantitative imaging biomarkers can be used to provide prognosis, predict tumor response in a non-invasive fashion and improve patient selection for IORT. A biologically inspired CD8 T-cells-associated radiomics signature has been developed on previous cohorts. We evaluated here whether this CD8 radiomic signature is associated with lesion response, whether it may help to assess disease spatial heterogeneity for predicting outcomes of patients treated with IORT. We also evaluated differences between irradiated and non-irradiated lesions. Methods Clinical data from patients with advanced solid tumors in six independent clinical studies of IORT were investigated. Immunotherapy consisted of 4 different drugs (antiprogrammed death-ligand 1 or anticytotoxic T-lymphocyte-associated protein 4 in monotherapy). Most patients received stereotactic RT to one lesion. Irradiated and non-irradiated lesions were delineated from baseline and the first evaluation CT scans. Radiomic features were extracted from contrast-enhanced CT images and the CD8 radiomics signature was applied. A responding lesion was defined by a decrease in lesion size of at least 30%. Dispersion metrices of the radiomics signature were estimated to evaluate the impact of tumor heterogeneity in patient’s response. Results A total of 94 patients involving multiple lesions (100 irradiated and 189 non-irradiated lesions) were considered for a statistical interpretation. Lesions with high CD8 radiomics score at baseline were associated with significantly higher tumor response (area under the receiving operating characteristic curve (AUC)=0.63, p=0.0020). Entropy of the radiomics scores distribution on all lesions was shown to be associated with progression-free survival (HR=1.67, p=0.040), out-of-field abscopal response (AUC=0.70, p=0.014) and overall survival (HR=2.08, p=0.023), which remained significant in a multivariate analysis including clinical and biological variables. Conclusions These results enhance the predictive value of the biologically inspired CD8 radiomics score and suggests that tumor heterogeneity should be systematically considered in patients treated with IORT. This CD8 radiomics signature may help select patients who are most likely to benefit from IORT.
    Type of Medium: Online Resource
    ISSN: 2051-1426
    Language: English
    Publisher: BMJ
    Publication Date: 2020
    detail.hit.zdb_id: 2719863-7
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  • 6
    In: Journal for ImmunoTherapy of Cancer, BMJ, Vol. 9, No. 2 ( 2021-02), p. e001845-
    Abstract: The predictive power of novel biological markers for treatment response to immune checkpoint inhibitors (ICI) is still not satisfactory for the majority of patients with cancer. One should identify valid predictive markers in the peripheral blood, as this is easily available before and during treatment. The current interim analysis of patients of the ST-ICI cohort therefore focuses on the development and validation of a liquid immune profile-based signature (LIPS) to predict response of patients with metastatic cancer to ICI targeting the programmed cell death protein 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) axis. Methods A total of 104 patients were prospectively enrolled. 54 immune cell subsets were prospectively analyzed in patients’ peripheral blood by multicolor flow cytometry before treatment with ICI (pre-ICI; n=89), and after the first application of ICI (n=65). Pre-ICI, patients were randomly allocated to a training (n=56) and a validation cohort (n=33). Univariate Cox proportional hazards regression analysis and least absolute shrinkage and selection operator Cox model were used to create a predictive immune signature, which was also checked after the first ICI, to consider the dynamics of changes in the immune status. Results Whole blood samples were provided by 89 patients pre-ICI and by 65 patients after the first ICI. We identified a LIPS which is based on five immune cell subtypes: CD14 high monocytes, CD8+/PD-1 + T cells, plasmacytoid dendritic cells, neutrophils, and CD3 + /CD56 + /CD16 + natural killer (NK)T cells. The signature achieved a high accuracy (C-index 0.74 vs 0.71) for predicting overall survival (OS) benefit in both the training and the validation cohort. In both cohorts, the low-risk group had significantly longer OS than the high-risk group (HR 0.26, 95% CI 0.12 to 0.56, p=0.00025; HR 0.30, 95% CI 0.10 to 0.91, p=0.024, respectively). Regarding the whole cohort, LIPS also predicted progression-free survival (PFS). The identified LIPS was not affected by clinicopathological features with the exception of brain metastases. NKT cells and neutrophils of the LIPS can be used as dynamic predictive biomarkers for OS and PFS after first administration of the ICI. Conclusion Our study identified a predictive LIPS for survival of patients with cancer treated with PD-1/PD-L1 ICI, which is based on immune cell subsets in the peripheral whole blood. Trial registration number NCT03453892 .
    Type of Medium: Online Resource
    ISSN: 2051-1426
    Language: English
    Publisher: BMJ
    Publication Date: 2021
    detail.hit.zdb_id: 2719863-7
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  • 7
    In: Medical Physics, Wiley, Vol. 49, No. 9 ( 2022-09), p. 5773-5786
    Abstract: Brain metastases (BM) occur frequently in patients with metastatic cancer. Early and accurate detection of BM is essential for treatment planning and prognosis in radiation therapy. Due to their tiny sizes and relatively low contrast, small BM are very difficult to detect manually. With the recent development of deep learning technologies, several res earchers have reported promising results in automated brain metastasis detection. However, the detection sensitivity is still not high enough for tiny BM, and integration into clinical practice in regard to differentiating true metastases from false positives (FPs) is challenging. Methods The DeepMedic network with the binary cross‐entropy (BCE) loss is used as our baseline method. To improve brain metastasis detection performance, a custom detection loss called volume‐level sensitivity–specificity (VSS) is proposed, which rates metastasis detection sensitivity and specificity at a (sub)volume level. As sensitivity and precision are always a trade‐off, either a high sensitivity or a high precision can be achieved for brain metastasis detection by adjusting the weights in the VSS loss without decline in dice score coefficient for segmented metastases. To reduce metastasis‐like structures being detected as FP metastases, a temporal prior volume is proposed as an additional input of DeepMedic. The modified network is called DeepMedic+ for distinction. Combining a high‐sensitivity VSS loss and a high specificity loss for DeepMedic+, the majority of true positive metastases are confirmed with high specificity, while additional metastases candidates in each patient are marked with high sensitivity for detailed expert evaluation. Results Our proposed VSS loss improves the sensitivity of brain metastasis detection, increasing the sensitivity from 85.3% for DeepMedic with BCE to 97.5% for DeepMedic with VSS. Alternatively, the precision is improved from 69.1% for DeepMedic with BCE to 98.7% for DeepMedic with VSS. Comparing DeepMedic+ with DeepMedic with the same VSS loss, 44.4% of the FP metastases are reduced in the high‐sensitivity model and the precision reaches 99.6% for the high‐specificity model. The mean dice coefficient for all metastases is about 0.81. With the ensemble of the high‐sensitivity and high‐specificity models, on average only 1.5 FP metastases per patient need further check, while the majority of true positive metastases are confirmed. Conclusions Our proposed VSS loss and temporal prior improve brain metastasis detection sensitivity and precision. The ensemble learning is able to distinguish high confidence true positive metastases from metastases candidates that require special expert review or further follow‐up, being particularly well‐fit to the requirements of expert support in real clinical practice. This facilitates metastasis detection and segmentation for neuroradiologists in diagnostic and radiation oncologists in therapeutic clinical applications.
    Type of Medium: Online Resource
    ISSN: 0094-2405 , 2473-4209
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2022
    detail.hit.zdb_id: 1466421-5
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  • 8
    In: Frontiers in Oncology, Frontiers Media SA, Vol. 13 ( 2023-9-14)
    Abstract: The potential of large language models in medicine for education and decision-making purposes has been demonstrated as they have achieved decent scores on medical exams such as the United States Medical Licensing Exam (USMLE) and the MedQA exam. This work aims to evaluate the performance of ChatGPT-4 in the specialized field of radiation oncology. Methods The 38th American College of Radiology (ACR) radiation oncology in-training (TXIT) exam and the 2022 Red Journal Gray Zone cases are used to benchmark the performance of ChatGPT-4. The TXIT exam contains 300 questions covering various topics of radiation oncology. The 2022 Gray Zone collection contains 15 complex clinical cases. Results For the TXIT exam, ChatGPT-3.5 and ChatGPT-4 have achieved the scores of 62.05% and 78.77%, respectively, highlighting the advantage of the latest ChatGPT-4 model. Based on the TXIT exam, ChatGPT-4’s strong and weak areas in radiation oncology are identified to some extent. Specifically, ChatGPT-4 demonstrates better knowledge of statistics, CNS & amp; eye, pediatrics, biology, and physics than knowledge of bone & amp; soft tissue and gynecology, as per the ACR knowledge domain. Regarding clinical care paths, ChatGPT-4 performs better in diagnosis, prognosis, and toxicity than brachytherapy and dosimetry. It lacks proficiency in in-depth details of clinical trials. For the Gray Zone cases, ChatGPT-4 is able to suggest a personalized treatment approach to each case with high correctness and comprehensiveness. Importantly, it provides novel treatment aspects for many cases, which are not suggested by any human experts. Conclusion Both evaluations demonstrate the potential of ChatGPT-4 in medical education for the general public and cancer patients, as well as the potential to aid clinical decision-making, while acknowledging its limitations in certain domains. Owing to the risk of hallucinations, it is essential to verify the content generated by models such as ChatGPT for accuracy.
    Type of Medium: Online Resource
    ISSN: 2234-943X
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2023
    detail.hit.zdb_id: 2649216-7
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  • 9
    Online Resource
    Online Resource
    MDPI AG ; 2016
    In:  International Journal of Molecular Sciences Vol. 17, No. 8 ( 2016-08-11), p. 1316-
    In: International Journal of Molecular Sciences, MDPI AG, Vol. 17, No. 8 ( 2016-08-11), p. 1316-
    Type of Medium: Online Resource
    ISSN: 1422-0067
    Language: English
    Publisher: MDPI AG
    Publication Date: 2016
    detail.hit.zdb_id: 2019364-6
    SSG: 12
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  • 10
    Online Resource
    Online Resource
    MDPI AG ; 2018
    In:  International Journal of Molecular Sciences Vol. 19, No. 10 ( 2018-10-16), p. 3197-
    In: International Journal of Molecular Sciences, MDPI AG, Vol. 19, No. 10 ( 2018-10-16), p. 3197-
    Abstract: Low-dose radiotherapy (LD-RT) for benign inflammatory and/or bone destructive diseases has been used long. Therefore, mechanistic investigations on cells being present in joints are mostly made in an inflammatory setting. This raises the question whether similar effects of LD-RT are also seen in healthy tissue and thus might cause possible harmful effects. We performed examinations on the functionality and phenotype of key cells within the joint, namely on fibroblast-like synoviocytes (FLS), osteoclasts and osteoblasts, as well as on immune cells. Low doses of ionizing radiation showed only a minor impact on cytokine release by healthy FLS as well as on molecules involved in cartilage and bone destruction and had no significant impact on cell death and migration properties. The bone resorbing abilities of healthy osteoclasts was slightly reduced following LD-RT and a positive impact on bone formation of healthy osteoblasts was observed after in particular exposure to 0.5 Gray (Gy). Cell death rates of bone-marrow cells were only marginally increased and immune cell composition of the bone marrow showed a slight shift from CD8+ to CD4+ T cell subsets. Taken together, our results indicate that LD-RT with particularly a single dose of 0.5 Gy has no harmful effects on cells of healthy joints.
    Type of Medium: Online Resource
    ISSN: 1422-0067
    Language: English
    Publisher: MDPI AG
    Publication Date: 2018
    detail.hit.zdb_id: 2019364-6
    SSG: 12
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