Literature DB >> 31757841

Preclinical PERCIST and 25% of SUVmax Threshold: Precision Imaging of Response to Therapy in Co-clinical 18F-FDG PET Imaging of Triple-Negative Breast Cancer Patient-Derived Tumor Xenografts.

Madhusudan A Savaikar1, Timothy Whitehead1, Sudipta Roy1, Lori Strong1, Nicole Fettig1, Tina Prmeau2, Jingqin Luo3, Shunqiang Li2, Richard L Wahl1, Kooresh I Shoghi4,5.   

Abstract

Numerous recent works highlight the limited utility of established tumor cell lines in recapitulating the heterogeneity of tumors in patients. More realistic preclinical cancer models are thought to be provided by transplantable, patient-derived xenografts (PDXs). The inter- and intratumor heterogeneity of PDXs, however, presents several challenges in developing optimal quantitative pipelines to assess response to therapy. The objective of this work was to develop and optimize image metrics for 18F-FDG PET to assess response to combination docetaxel and carboplatin therapy in a co-clinical trial involving triple-negative breast cancer PDXs. We characterized the reproducibility of standardized uptake value (SUV) metrics to assess response to therapy, and we optimized a preclinical PERCIST paradigm to complement clinical standards. Considerations in this effort included variability in tumor growth rate and tumor size, solid tumors versus tumor heterogeneity and a necrotic phenotype, and optimal selection of tumor slices versus whole tumor.
Methods: A test-retest protocol was implemented to optimize the reproducibility of 18F-FDG PET SUV thresholds, SUVpeak metrics, and preclinical PERCIST parameters. In assessing response to therapy, 18F-FDG PET imaging was performed at baseline and 4 d after therapy. The reproducibility, accuracy, variability, and performance of imaging metrics to assess response to therapy were determined. We defined an index called the Quantitative Response Assessment Score to integrate parameters of prediction and precision and thus aid in selecting the optimal image metric to assess response to therapy.
Results: Our data suggest that a threshold of 25% of SUVmax (SUV25) was highly reproducible (<9% variability). The concordance and reproducibility of preclinical PERCIST were maximized at α = 0.7 and β = 2.8 and exhibited a high correlation with SUV25 measures of tumor uptake, which in turn correlated with the SUV of metabolic tumor.
Conclusion: The Quantitative Response Assessment Score favors SUV25 followed by SUVpeak for a sphere with a volume of 14 mm3 (SUVP14) as optimal metrics of response to therapy. Additional studies are warranted to fully characterize the utility of SUV25 and preclinical PERCIST SUVP14 as image metrics for response to therapy across a wide range of therapeutic regimens and PDX models.
© 2020 by the Society of Nuclear Medicine and Molecular Imaging.

Entities:  

Keywords:  co-clinical trials; patient-derived xenografts; quantitative imaging; reproducibility; response to therapy; triple-negative breast cancer

Mesh:

Substances:

Year:  2019        PMID: 31757841      PMCID: PMC7262224          DOI: 10.2967/jnumed.119.234286

Source DB:  PubMed          Journal:  J Nucl Med        ISSN: 0161-5505            Impact factor:   10.057


  26 in total

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Journal:  Pharmacogenomics       Date:  2015-09-24       Impact factor: 2.533

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4.  Impact of animal handling on the results of 18F-FDG PET studies in mice.

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Journal:  Clin Cancer Res       Date:  2013-12-10       Impact factor: 12.531

Review 6.  From RECIST to PERCIST: Evolving Considerations for PET response criteria in solid tumors.

Authors:  Richard L Wahl; Heather Jacene; Yvette Kasamon; Martin A Lodge
Journal:  J Nucl Med       Date:  2009-05       Impact factor: 10.057

Review 7.  Quantitative imaging biomarkers: a review of statistical methods for technical performance assessment.

Authors:  David L Raunig; Lisa M McShane; Gene Pennello; Constantine Gatsonis; Paul L Carson; James T Voyvodic; Richard L Wahl; Brenda F Kurland; Adam J Schwarz; Mithat Gönen; Gudrun Zahlmann; Marina V Kondratovich; Kevin O'Donnell; Nicholas Petrick; Patricia E Cole; Brian Garra; Daniel C Sullivan
Journal:  Stat Methods Med Res       Date:  2014-06-11       Impact factor: 3.021

8.  Co-clinical trials demonstrate predictive biomarkers for dovitinib, an FGFR inhibitor, in lung squamous cell carcinoma.

Authors:  H R Kim; H N Kang; H S Shim; E Y Kim; J Kim; D J Kim; J G Lee; C Y Lee; M H Hong; S-M Kim; H Kim; K-H Pyo; M R Yun; H J Park; J Y Han; H A Youn; M-J Ahn; S Paik; T-M Kim; B C Cho
Journal:  Ann Oncol       Date:  2017-06-01       Impact factor: 32.976

9.  Optimal definition of biological tumor volume using positron emission tomography in an animal model.

Authors:  Ingrid Wu; Hao Wang; David Huso; Richard L Wahl
Journal:  EJNMMI Res       Date:  2015-10-21       Impact factor: 3.138

10.  Development of sodium fluoride PET response criteria for solid tumours (NAFCIST) in a clinical trial of radium-223 in osteosarcoma: from RECIST to PERCIST to NAFCIST.

Authors:  Kalevi Kairemo; Eric M Rohren; Pete M Anderson; Gregory Ravizzini; Arvind Rao; Homer A Macapinlac; Vivek Subbiah
Journal:  ESMO Open       Date:  2019-02-28
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2.  Deep Learning and Transfer Learning for Malaria Detection.

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3.  Co-Clinical Imaging Resource Program (CIRP): Bridging the Translational Divide to Advance Precision Medicine.

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4.  Optimal co-clinical radiomics: Sensitivity of radiomic features to tumour volume, image noise and resolution in co-clinical T1-weighted and T2-weighted magnetic resonance imaging.

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Journal:  EBioMedicine       Date:  2020-09-02       Impact factor: 8.143

5.  Co-clinical FDG-PET radiomic signature in predicting response to neoadjuvant chemotherapy in triple-negative breast cancer.

Authors:  Sudipta Roy; Timothy D Whitehead; Shunqiang Li; Foluso O Ademuyiwa; Richard L Wahl; Farrokh Dehdashti; Kooresh I Shoghi
Journal:  Eur J Nucl Med Mol Imaging       Date:  2021-07-30       Impact factor: 10.057

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