Christiaan Schiepers
· ProfessorUniversity of California, Los Angeles · Nuclear Medicine & Theranostics
Active 1971–2023
About
Christiaan Schiepers is a professor in the Pharmacology Department at the University of California, Los Angeles. His research focuses on metabolic imaging in oncology, specifically on glucose utilization of tumors in vivo. He investigates the role of PET/CT in the staging of cancer and monitoring of therapy, utilizing kinetic modeling for the quantification of tracers such as F-18 FDG, F-18 DOPA, F-18 FLT, O-15 Water, and C-11 Acetate. His work includes studying bone blood flow and fluoride influx rate with F-18 Fluoride in metabolic bone disorders and trauma. Dr. Schiepers holds an M.D. and Ph.D., and his research aims to advance understanding of tumor metabolism and improve diagnostic and therapeutic strategies through metabolic imaging techniques.
Research topics
- Medicine
- Nuclear medicine
- Computer Science
- Cancer research
- Internal medicine
- Artificial Intelligence
- Physics
- Radiology
Selected publications
2023-03-31
preprintOpen access<p>PDF file - 329K</p>
2023-03-31
preprintOpen access<p>PDF file - 329K</p>
2023-03-31
preprintOpen access<p>MOV file - 340KB</p>
2023-03-31
preprintOpen access<p>MOV file - 340KB</p>
2023
- Nuclear medicine
- Medicine
- Internal medicine
<div>Abstract<p><b>Purpose:</b> The primary objective of this study was to investigate whether changes in 3′-deoxy-3′-[<sup>18</sup>F]fluorothymidine (<sup>18</sup>F-FLT) kinetic parameters, taken early after the start of therapy, could predict overall survival (OS) and progression-free survival (PFS) in patients with recurrent malignant glioma undergoing treatment with bevacizumab and irinotecan.</p><p><b>Experimental Design:</b> High-grade recurrent brain tumors were investigated in 18 patients (8 male and 10 female), ages 26 to 76 years. Each had 3 dynamic positron emission tomography (PET) studies as follows: at baseline and after 2 and 6 weeks from the start of treatment, <sup>18</sup>F-FLT (2.0 MBq/kg) was injected intravenously, and dynamic PET images were acquired for 1 hour. Factor analysis generated factor images from which blood and tumor uptake curves were derived. A three-compartment, two-tissue model was applied to estimate tumor <sup>18</sup>F-FLT kinetic rate constants using a metabolite- and partial volume–corrected input function. Different combinations of predictor variables were exhaustively searched in a discriminant function to accurately classify patients into their known OS and PFS groups. A leave-one-out cross-validation technique was used to assess the generalizability of the model predictions.</p><p><b>Results:</b> In this study population, changes in single parameters such as standardized uptake value or influx rate constant did not accurately classify patients into their respective OS groups (<1 and ≥1 year; hit ratios ≤78%). However, changes in a set of <sup>18</sup>F-FLT kinetic parameters could perfectly separate these two groups of patients (hit ratio = 100%) and were also able to correctly classify patients into their respective PFS groups (<100 and ≥100 days; hit ratio = 88%).</p><p><b>Conclusions:</b> Discriminant analysis using changes in <sup>18</sup>F-FLT kinetic parameters early during treatment seems to be a powerful method for evaluating the efficacy of therapeutic regimens. <i>Clin Cancer Res; 17(20); 6553–62. ©2011 AACR</i>.</p></div>
2023-03-31
preprintOpen access<p>MOV file - 330KB</p>
2023
- Nuclear medicine
- Medicine
- Internal medicine
<div>Abstract<p><b>Purpose:</b> The primary objective of this study was to investigate whether changes in 3′-deoxy-3′-[<sup>18</sup>F]fluorothymidine (<sup>18</sup>F-FLT) kinetic parameters, taken early after the start of therapy, could predict overall survival (OS) and progression-free survival (PFS) in patients with recurrent malignant glioma undergoing treatment with bevacizumab and irinotecan.</p><p><b>Experimental Design:</b> High-grade recurrent brain tumors were investigated in 18 patients (8 male and 10 female), ages 26 to 76 years. Each had 3 dynamic positron emission tomography (PET) studies as follows: at baseline and after 2 and 6 weeks from the start of treatment, <sup>18</sup>F-FLT (2.0 MBq/kg) was injected intravenously, and dynamic PET images were acquired for 1 hour. Factor analysis generated factor images from which blood and tumor uptake curves were derived. A three-compartment, two-tissue model was applied to estimate tumor <sup>18</sup>F-FLT kinetic rate constants using a metabolite- and partial volume–corrected input function. Different combinations of predictor variables were exhaustively searched in a discriminant function to accurately classify patients into their known OS and PFS groups. A leave-one-out cross-validation technique was used to assess the generalizability of the model predictions.</p><p><b>Results:</b> In this study population, changes in single parameters such as standardized uptake value or influx rate constant did not accurately classify patients into their respective OS groups (<1 and ≥1 year; hit ratios ≤78%). However, changes in a set of <sup>18</sup>F-FLT kinetic parameters could perfectly separate these two groups of patients (hit ratio = 100%) and were also able to correctly classify patients into their respective PFS groups (<100 and ≥100 days; hit ratio = 88%).</p><p><b>Conclusions:</b> Discriminant analysis using changes in <sup>18</sup>F-FLT kinetic parameters early during treatment seems to be a powerful method for evaluating the efficacy of therapeutic regimens. <i>Clin Cancer Res; 17(20); 6553–62. ©2011 AACR</i>.</p></div>
2023-03-31
preprintOpen access<p>MOV file - 330KB</p>
Multi-tracer PET Imaging Using Deep Learning: Applications in Patients with High-Grade Gliomas
Lecture notes in computer science · 2022 · 1 citations
- Artificial Intelligence
- Computer Science
- Artificial Intelligence
Journal of Medical Imaging · 2018-01-11 · 12 citations
articleOpen accessA clinical validation of the bone scan lesion area (BSLA) as a quantitative imaging biomarker was performed in metastatic castration-resistant prostate cancer (mCRPC). BSLA was computed from whole-body bone scintigraphy at baseline and week 12 posttreatment in a cohort of 198 mCRPC subjects (127 treated and 71 placebo) from a clinical trial involving a different drug from the initial biomarker development. BSLA computation involved automated image normalization, lesion segmentation, and summation of the total area of segmented lesions on bone scan AP and PA views as a measure of tumor burden. As a predictive biomarker, treated subjects with baseline BSLA [Formula: see text] had longer survival than those with higher BSLA ([Formula: see text] and [Formula: see text]). As a surrogate outcome biomarker, subjects were categorized as progressive disease (PD) if the BSLA increased by a prespecified 30% or more from baseline to week 12 and non-PD otherwise. Overall survival rates between PD and non-PD groups were statistically different ([Formula: see text] and [Formula: see text]). Subjects without PD at week 12 had longer survival than subjects with PD: median 398 days versus 280 days. BSLA has now been demonstrated to be an early surrogate outcome for overall survival in different prostate cancer drug treatments.
Frequent coauthors
- 51 shared
Johannes Czernin
- 45 shared
Magnus Dahlbom
University of California, Los Angeles
- 27 shared
Michael E. Phelps
- 19 shared
Carl K. Hoh
University of California, San Diego
- 17 shared
Timothy F. Cloughesy
University of California, Los Angeles
- 16 shared
Sung‐Cheng Huang
- 15 shared
Nagichettiar Satyamurthy
- 13 shared
Sanjiv S. Gambhir
Stanford University
Labs
- Resume-aware match score
- Save to shortlist
- AI-drafted outreach
See your match with Christiaan Schiepers
PhdFit ranks faculty by your research interests, methods, and publications — grounded in their actual work, not templates.
- Free to start
- No credit card
- 30-second signup