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Valdes, Gilmer

Publications and source records attributed to Valdes, Gilmer.

5 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

A Deep Look Into the Future of Quantitative Imaging in Oncology: A Statement of Working Principles and Proposal for Change

The adoption of enterprise digital imaging, along with the development of quantitative imaging methods and the re-emergence of statistical learning, has opened the opportunity for more personalized cancer treatments through transformative data science research. In the last 5 years, accumulating evidence has indicated that noninvasive advanced imaging analytics (i.e., radiomics) can reveal key components of tumor phenotype for multiple lesions at multiple time points over the course of treatment. Many groups using homegrown software have extracted engineered and deep quantitative features on 3-dimensional medical images for better spatial and longitudinal understanding of tumor biology and for the prediction of diverse outcomes. These developments could augment patient stratification and prognostication, buttressing emerging targeted therapeutic approaches. Unfortunately, the rapid growth in popularity of this immature scientific discipline has resulted in many early publications that miss key information or use underpowered patient data sets, without production of generalizable results. Quantitative imaging research is complex, and key principles should be followed to realize its full potential. The fields of quantitative imaging and radiomics in particular require a renewed focus on optimal study design and reporting practices, standardization, interpretability, data sharing, and clinical trials. Standardization of image acquisition, feature calculation, and statistical analysis (i.e., machine learning) are required for the field to move forward. A new data-sharing paradigm enacted among open and diverse participants (medical institutions, vendors and associations) should be embraced for faster development and comprehensive clinical validation of imaging biomarkers. In this review and critique of the field, we propose working principles and fundamental changes to the current scientific approach, with the goal of high-impact research and development of actionable prediction models that will yield more meaningful applications of precision cancer medicine.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Salvage HDR Brachytherapy: Multiple Hypothesis Testing Versus Machine Learning Analysis

Salvage high-dose-rate brachytherapy (sHDRB) is a treatment option for recurrences after prior radiation therapy. However, only approximately 50% of patients benefit, with the majority of second recurrences after salvage brachytherapy occurring distantly. Therefore, identification of characteristics that can help select patients who may benefit most from sHDRB is critical. Machine learning may be used to identify characteristics that predict outcome following sHDRB. We aimed to use machine learning to identify patient characteristics associated with biochemical failure (BF) following prostate sHDRB.

62 RADIOLOGY AND NUCLEAR MEDICINE↗