739 research outputs found

    Beyond the Echoes: Extending the Framework for Biblical Intertextuality

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    Although the framework for biblical intertextuality currently used by R. B. Hays and his followers has contributed much to our understanding of the role of the OT in the Pauline letters, it does not account fully for the ways in which the OT writings are used. In addition to the explicit citation and the more implicit allusion and echo, this dissertation argues that the framework should be extended to include the use of Scripture as an ideational resource, as well as the use of the Narrative Summary as a literary device. By revisiting the idea of intertextuality expounded by Kristeva, the hermeneutical framework devised by Schleiermacher, and to a lesser extent borrowing from the ideas of de Saussure, Boyarin and others, a broader model of biblical intertextuality that includes the use of Scripture as an ideational resource is developed. While the analysis of biblical intertextuality under Hays' framework relies on the presence of verbal correspondences in the texts, the proposed approach includes analysing Paul's texts in the light of the ideational resources that his readers who are ingrained in the cultural codes of Scripture would have understood. The method is then demonstrated using Rom 9:1-3, where the wider signification of the OT in Paul's writing has not been sufficiently analysed thus far. Next, a framework for analysing Paul's use of the Narrative Summary is developed. Comparison is made with a group of writings known as the rewritten Bible, which are found mainly among the Dead Sea Scrolls. Despite certain similarities, there are fundamental differences as well. Applying the developed framework on the analysis of seven specimen texts (Rom 4:1-25; Gal 4:21-31; Rom 9:6-13; 1 Cor 10:1-13; 2 Cor 3:7-18; Rom 9:4-5; Rom 11:1-6), the study reveals that they share substantially the same features, and departures from these are largely accounted for by Paul’s use of the Narrative Summary as a literary device. This shows that the Narrative Summary is a specific intertextual category that deserves to be treated separately

    Privacy-Preserving Dashboard for F.A.I.R Head and Neck Cancer data supporting multi-centered collaborations

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    Research in modern healthcare requires vast volumes of data from various healthcare centers across the globe. It is not always feasible to centralize clinical data without compromising privacy. A tool addressing these issues and facilitating reuse of clinical data is the need of the hour. The Federated Learning approach, governed in a set of agreements such as the Personal Health Train (PHT) manages to tackle these concerns by distributing models to the data centers instead of the traditional approach of centralizing datasets. One of the prerequisites of PHT is using semantically interoperable datasets for the models to be able to find them. FAIR (Findable, Accessible, Interoperable, Reusable) principles help in building interoperable and reusable data by adding knowledge representation and providing descriptive metadata. However, the process of making data FAIR is not always easy and straight-forward. Our main objective is to disentangle this process by using domain and technical expertise and get data prepared for federated learning. This paper introduces applications that are easily deployable as Docker containers, which will automate parts of the aforementioned process and significantly simplify the task of creating FAIR clinical data. Our method bypasses the need for clinical researchers to have a high degree of technical skills. We demonstrate the FAIR-ification process by applying it to five Head and Neck cancer datasets (four public and one private). The PHT paradigm is explored by building a distributed visualization dashboard from the aggregated summaries of the FAIR-ified datasets. Using the PHT infrastructure for exchanging only statistical summaries or model coefficients allows researchers to explore data from multiple centers without breaching privacy

    Intensity-based dual model method for generation of synthetic CT images from standard T2-weighted MR images - Generalized technique for four different MR scanners

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    Background and purpose: Recent studies have shown that it is possible to conduct entire radiotherapy treatment planning (RTP) workflow using only MR images. This study aims to develop a generalized intensity-based method to generate synthetic CT (sCT) images from standard T2-weighted (T2(W)) MR images of the pelvis. Materials and methods: This study developed a generalized dual model HU conversion method to convert standard T2(W) MR image intensity values to synthetic HU values, separately inside and outside of atlas-segmented bone volume contour. The method was developed and evaluated with 20 and 35 prostate cancer patients, respectively. MR images with scanning sequences in clinical use were acquired with four different MR scanners of three vendors. Results: For the generated synthetic CT (sCT) images of the 35 prostate patients, the mean (and maximal) HU differences in soft and bony tissue volumes were 16 +/- 6 HUs (34 HUs) and -46 +/- 56 HUs (181 HUs), respectively, against the true CT images. The average of the PTV mean dose difference in sCTs compared to those in true CTs was -0.6 +/- 0.4% (-1.3%). Conclusions: The study provides a generalized method for sCT creation from standard T2(W) images of the pelvis. The method produced clinically acceptable dose calculation results for all the included scanners and MR sequences. (c) 2017 Elsevier B.V. All rights reserved.Peer reviewe

    Emerging role of quantitative imaging (radiomics) and artificial intelligence in precision oncology

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    Cancer is a fatal disease and the second most cause of death worldwide. Treatment of cancer is a complex process and requires a multi-modality-based approach. Cancer detection and treatment starts with screening/diagnosis and continues till the patient is alive. Screening/diagnosis of the disease is the beginning of cancer management and continued with the staging of the disease, planning and delivery of treatment, treatment monitoring, and ongoing monitoring and follow-up. Imaging plays an important role in all stages of cancer management. Conventional oncology practice considers that all patients are similar in a disease type, whereas biomarkers subgroup the patients in a disease type which leads to the development of precision oncology. The utilization of the radiomic process has facilitated the advancement of diverse imaging biomarkers that find application in precision oncology. The role of imaging biomarkers and artificial intelligence (AI) in oncology has been investigated by many researchers in the past. The existing literature is suggestive of the increasing role of imaging biomarkers and AI in oncology. However, the stability of radiomic features has also been questioned. The radiomic community has recognized that the instability of radiomic features poses a danger to the global generalization of radiomic-based prediction models. In order to establish radiomic-based imaging biomarkers in oncology, the robustness of radiomic features needs to be established on a priority basis. This is because radiomic models developed in one institution frequently perform poorly in other institutions, most likely due to radiomic feature instability. To generalize radiomic-based prediction models in oncology, a number of initiatives, including Quantitative Imaging Network (QIN), Quantitative Imaging Biomarkers Alliance (QIBA), and Image Biomarker Standardisation Initiative (IBSI), have been launched to stabilize the radiomic features

    External validation of a prognostic model incorporating quantitative PET image features in esophageal cancer

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    Aim Enhanced prognostic models are required to improve risk stratification of patients with oesophageal cancer so treatment decisions can be optimised. The primary aim was to externally validate a published prognostic model incorporating PET image features. Transferability of the model was compared using only clinical variables. Methods This was a Transparent Reporting of a multivariate prediction model for Individual Prognosis Or Diagnosis (TRIPOD) type 3 study. The model was validated against patients treated with neoadjuvant chemoradiotherapy according to the Neoadjuvant chemoradiotherapy plus surgery versus surgery alone for oesophageal or junctional cancer (CROSS) trial regimen using pre- and post-harmonised image features. The Kaplan–Meier method with log-rank significance tests assessed risk strata discrimination. A Cox proportional hazards model assessed model calibration. Primary outcome was overall survival (OS). Results Between 2010 and 2015, 449 patients were included in the development (n = 302), internal validation (n = 101) and external validation (n = 46) cohorts. No statistically significant difference in OS between patient quartiles was demonstrated in prognostic models incorporating PET image features (X2 = 1.42, df = 3, p = 0.70) or exclusively clinical variables (age, disease stage and treatment; X2 = 1.19, df = 3, p = 0.75). The calibration slope β of both models was not significantly different from unity (p = 0.29 and 0.29, respectively). Risk groups defined using only clinical variables suggested differences in OS, although these were not statistically significant (X2 = 0.71, df = 2, p = 0.70). Conclusion The prognostic model did not enable significant discrimination between the validation risk groups, but a second model with exclusively clinical variables suggested some transferable prognostic ability. PET harmonisation did not significantly change the results of model validation
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