Cancers are heterogeneous in biology among patients, tumors in the same patient, and within tumors. As a result, they respond differently to therapy per patient, per tumor and within tumors. Different radiotracers and imaging modalities provide information about different aspects of biology and the physio-metabolic environments of the cancer. As a result, a single modality or radiotracer may not provide sufficient information to predict or assess response to therapy. We hypothesize that improved prediction and assessment of response can thus be obtained by combining quantitative image-derived parameters obtained from multiple imaging modalities or radiotracers. We propose to develop, optimize, and validate approaches for combining multiple image-derived parameters obtained from quantitative imaging procedures in order to optimally predict and assess treatment response. In particular, we propose to combine quantitative metrics from PET/CT, SPECT/CT, and MRI. We will first individually optimize the protocols, acquisition parameters, and imaging methods in order to get the most accurate and reliable parameters to combine. Optimally combining the parameters from different modalities requires knowledge of the reproducibility (precision) of the individual quantitative imaging parameters. We will thus use literature search, phantom studies, realistic simulations, and repeated patient studies to characterize the accuracy and precision of the individual quantitative imaging methods. We will then develop methods to combine the metrics to predict or assess treatment response per patient, per tumor and intra-tumor. We will apply and evaluate these methods in three clinical trials: dynamic and static FDG and FIT PET/CT to assess lung cancer response to cytotoxic chemotherapy;PET/CT and DCE- and DW-MRI in breast cancer response;and SPECT/CT, PET/CT and DCE- and DW-MRI to predict response of brain tumors to anti-angiogenic therapy. In these trials imaging parameters and their signatures will be linked to histology or survival outcomes to provide validation of the combined imaging parameter metrics.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01CA140204-02
Application #
8334502
Study Section
Special Emphasis Panel (ZCA1-SRLB-Y (M1))
Program Officer
Nordstrom, Robert J
Project Start
2011-09-19
Project End
2016-08-31
Budget Start
2012-09-01
Budget End
2013-08-31
Support Year
2
Fiscal Year
2012
Total Cost
$668,129
Indirect Cost
$329,420
Name
Johns Hopkins University
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
001910777
City
Baltimore
State
MD
Country
United States
Zip Code
21218
Vicente, Esther M; Lodge, Martin A; Rowe, Steven P et al. (2017) Simplifying volumes-of-interest (VOIs) definition in quantitative SPECT: Beyond manual definition of 3D whole-organ VOIs. Med Phys 44:1707-1717
Mena, Esther; Taghipour, Mehdi; Sheikhbahaei, Sara et al. (2017) Value of Intratumoral Metabolic Heterogeneity and Quantitative 18F-FDG PET/CT Parameters to Predict Prognosis in Patients With HPV-Positive Primary Oropharyngeal Squamous Cell Carcinoma. Clin Nucl Med 42:e227-e234
Lodge, Martin A; Holdhoff, Matthias; Leal, Jeffrey P et al. (2017) Repeatability of (18)F-FLT PET in a Multicenter Study of Patients with High-Grade Glioma. J Nucl Med 58:393-398
Parekh, Vishwa S; Jacobs, Michael A (2017) Integrated radiomic framework for breast cancer and tumor biology using advanced machine learning and multiparametric MRI. NPJ Breast Cancer 3:43
Crandall, John P; Tahari, Abdel K; Juergens, Rosalyn A et al. (2017) A comparison of FLT to FDG PET/CT in the early assessment of chemotherapy response in stages IB-IIIA resectable NSCLC. EJNMMI Res 7:8
Lodge, Martin A (2017) Repeatability of SUV in Oncologic 18F-FDG PET. J Nucl Med 58:523-532
Jha, Abhinav K; Frey, Eric (2017) No-gold-standard evaluation of image-acquisition methods using patient data. Proc SPIE Int Soc Opt Eng 10136:
Mena, Esther; Sheikhbahaei, Sara; Taghipour, Mehdi et al. (2017) 18F-FDG PET/CT Metabolic Tumor Volume and Intratumoral Heterogeneity in Pancreatic Adenocarcinomas: Impact of Dual-Time Point and Segmentation Methods. Clin Nucl Med 42:e16-e21
Zimmerman, Brian E; Grošev, Darko; Buvat, Irène et al. (2017) Multi-centre evaluation of accuracy and reproducibility of planar and SPECT image quantification: An IAEA phantom study. Z Med Phys 27:98-112
Jha, Abhinav K; Caffo, Brian; Frey, Eric C (2016) A no-gold-standard technique for objective assessment of quantitative nuclear-medicine imaging methods. Phys Med Biol 61:2780-800

Showing the most recent 10 out of 55 publications