word count (limit 250): 250 Body word count (limit 5000): 3495 Figures and tables (limit 6): 6 References (limit 50): 40 Disclosure of potential conflicts of interest: Research. on March 6, 2020.
AbstractPurpose: GDC-0084 is an oral, brain-penetrant small molecule inhibitor of phosphoinositide 3kinase (PI3K) and mammalian target of rapamycin (mTOR). A first-in-human, Phase I study was conducted in patients with recurrent high-grade glioma.Experimental design: GDC-0084 was administered orally, once-daily to evaluate safety, pharmacokinetics (PK) and activity. Fluorodeoxyglucose positron emission tomography (FDG-PET) was performed to measure metabolic responses.
BackgroundIdentifying SSc and IPF patients at risk for more rapid FVC decline could improve trial design.PurposeTo explore the prognostic value of quantitative HRCT metrics derived by Imbio LTA tool in predicting FVC slope.MethodsThis retrospective study used data from patients who were not treated with investigational drugs with and without background antifibrotic therapies in tocilizumab Phase 3 SSc, lebrikizumab Phase 2 IPF, and zinpentraxin alfa Phase 2 IPF studies conducted from 2015–2021. Controlled HRCT axial volumetric multi-detector CT scans were evaluated using the Imbio LTA tool. Associations between HRCT metrics and FVC slope were assessed through the Spearman correlation coefficient and adjusted R2in a linear regression model adjusted by demographics and baseline clinical characteristics.ResultsA total 271 of participants consisting of SSc and IPF patients were analysed. Correlation coefficients of highest magnitude were observed in the SSc study between the extent of ground glass, normal volume, qILD, reticular pattern and FVC slope (−0.25, 0.28, −0.28, and −0.33, respectively), while the correlation coefficients observed in IPF studies were in general less than 0.2. The incremental prognostic value of the baseline HRCT metrics was marginal after adjusting baseline characteristics and was inconsistent across study arms.ConclusionData from the SSc and IPF studies suggested weak to no and inconsistent correlation between quantitative HRCT metrics derived by the Imbio LTA tool and FVC slope in the studied SSc and IPF population.
Purpose
Positron emission tomography (PET)/ computed tomography (CT) has been extensively used to quantify metabolically active tumors in various oncology indications. However, FDG-PET/CT often encounters false positives in tumor detection due to 18fluorodeoxyglucose (FDG) accumulation from the heart and bladder that often exhibit similar FDG uptake as tumors. Thus, it is necessary to eliminate this source of physiological noise. Major challenges for this task include: (1) large inter-patient variability in the appearance for the heart and bladder. (2) The size and shape of bladder or heart may appear different on PET and CT. (3) Tumors can be very close or connected to the heart or bladder.
Approach
A deep learning based approach is proposed to segment the heart and bladder on whole body PET/CT automatically. Two 3D U-Nets were developed separately to segment the heart and bladder, where each network receives the PET and CT as a multi-modal input. Data sets were obtained from retrospective clinical trials and include 575 PET/CT for heart segmentation and 538 for bladder segmentation.
Results
The models were evaluated on a test set from an independent trial and achieved a Dice Similarity Coefficient (DSC) of 0.96 for heart segmentation and 0.95 for bladder segmentation, Average Surface Distance (ASD) of 0.44 mm on heart and 0.90 mm on bladder.
Conclusions
This methodology could be a valuable component to the FDG-PET/CT data processing chain by removing FDG physiological noise associated with heart and/or bladder accumulation prior to image analysis by manual, semi- or automated tumor analysis methods.
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