Isopar L Release From Saltstone Cured at 55 C

Cozzi, A Alex; Berry, C Cora; Bronikowski, M Michael; Eibling, R Russell; Zámečník, Jiří

doi:10.2172/914551

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…For example, CT-derived radiomic signatures were shown to enable prediction of local disease control and overall survival in PDAC (11,12) or tumor grading in pancreatic neuroendocrine tumors (13). The large-scale implementation of such tools thus has the potential to become a game changer in medical image interpretation and individualized patient care.…”

Section: Discussionmentioning

confidence: 99%

A prospectively validated machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma

Kaissis

Ziegelmayer

Lohöfer

et al. 2019

Preprint

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Purpose: To develop a supervised machine learning algorithm capable of predicting above vs. below-median overall survival from medical imaging-derived radiomic features in a cohort of patients with pancreatic ductal adenocarcinoma (PDAC). Materials and Methods: 102 patients with histopathologically proven PDAC were retrospectively assessed as the training cohort and 30 prospectively enrolled patients served as the external validation cohort. Tumors were segmented in pre-operative diffusion weighted-(DW)-MRI derived ADC maps and radiomic features were extracted. A Random Forest machine learning algorithm was fit to the training cohort and tested in the external validation cohort. The histopathological subtype of the tumor samples was assessed by immunohistochemistry in 21/30 patients of the external validation cohort. Individual radiomic feature importance was evaluated.Results: The machine learning algorithm achieved a sensitivity of 87% and a specificity of 80% (ROC-AUC 90%) for the prediction of above-vs. below-median survival on the unseen data of the external validation cohort. Heterogeneity-related features were highly ranked by the model.Of the 21 patients for whom the histopathological subtype was determined, 8/9 patients predicted by the model to experience below-median overall survival exhibited the quasimesenchymal subtype, while 11/12 patients predicted to experience above-median survival exhibited a non-quasi-mesenchymal subtype (Fisher's exact test P<0.001).Conclusion: The application of machine-learning to the radiomic analysis of DW-MRI-derived ADC maps allowed the prediction of overall survival with high diagnostic accuracy in a prospectively collected cohort. The high overlap of clinically relevant histopathological subtypes with model predictions underlines the potential of quantitative imaging workflows in pre-operative subtyping and risk assessment in PDAC.

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Section: Discussionmentioning

confidence: 99%

A prospectively validated machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma

Kaissis

Ziegelmayer

Lohöfer

et al. 2019

Preprint

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show abstract

“…One of the methods here is the combination of cultured (living) neurons and robots [9] to investigate the dynamical and adaptive properties of neural systems [10]. This work is also related to understanding of how information is encoded [11] and processed within a living neural network [12]. The hybrid technology can be used for neuro-robotic interfaces, different applications of in vitro neural networks [13] or for bidirectional interaction between the brain and the external environment in both collective and non-collective systems.…”

Section: Current Researchmentioning

confidence: 99%

Collective energy homeostasis in a large-scale microrobotic swarm

Kernbach¹,

Kernbach²

2011

Robotics and Autonomous Systems

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“…6 If the release is not instantaneous, but slower due to a finite diffusion rate of Isopar ® L through saltstone slurry, the presence of other organic components in the solvent such as Cs-7SB modifier and trioctylamine (TOA), or grout interactions with these components, the acceptable limit of entrained solvent could be significantly higher than 4 ppm. As the maximum expected Isopar ® L concentration in the DSS sent to SPF under normal process conditions was determined through small scale testing 8 The release rates were to be determined from saltstone prepared with a simulated DSS solution containing Isopar ® L concentrations ranging from 50 ppm to 200 ppm and the expected ratio of TOA to Isopar ® L to be used in the CSSX process. The rates were to be determined at three temperatures ambient (~25 °C), 75 °C, and 95 °C.…”

Section: List Of Tablesmentioning

confidence: 99%