The Tumor Imaging Metrics Core was approved as a developing core in the last CCSG submission. The Tumor Imaging Metrics Core (TIMC) provides objective assessment of response to treatment for patients enrolled in oncology clinical trials. All major radiological assessment criteria are supported including: RECIST (1.0 and 1.1), WHO, IWRC, Cheson, SUV, Choi, 3D Volume and irRC. For each patient, target and nontarget lesions are selected according to the assessment criteria guidelines and are tracked longitudinally. Scans to be measured are transferred from DF/HCC sites to the central Core lab via DICOM imaging network. Quantitative analysis of CT, MR and PET imaging studies are performed on a variety of modalityspecific workstations. After scans are analyzed, the measurement results are reviewed and finalized by Harvard faculty radiologists and/or nuclear medicine physicians. Measurement results are stored in the TIMC database on a secure website and are viewable online by authorized trial staff. The quantitative measurements are used to determine tumor response to treatment and ultimately guide patient care. Summary statistics for the trial are presented as well as individual patient measurements. Requests for scan analysis can be conveniently ordered on-line by the trial staff. Users are authenticated via their home institution's username and password (single sign-on). Rates for sen/ices are very reasonable compared to other options available from outside DF/HCC. Director(s): Gordon J. Harris, PhD"^"and Annick D. Van den Abbeele, MD Category: 4.03 (Clinical - Radiology and Tumor Imaging) Management: Joint (Cancer Center and Institutional).
The Tumor Imaging Metrics Core (TIMC) provides tumor measurements of radiological scans for oncology clinical trials. Its mission is to provide standardized measurements of CT, MR and PET imaging studies according to protocol for oncology clinical trials.
|Chen, Yi-Bin; Batchelor, Tracy; Li, Shuli et al. (2015) Phase 2 trial of high-dose rituximab with high-dose cytarabine mobilization therapy and high-dose thiotepa, busulfan, and cyclophosphamide autologous stem cell transplantation in patients with central nervous system involvement by non-Hodgkin lymphoma. Cancer 121:226-33|
|Waldron, Levi; Haibe-Kains, Benjamin; Culhane, Aedín C et al. (2014) Comparative meta-analysis of prognostic gene signatures for late-stage ovarian cancer. J Natl Cancer Inst 106:|
|Yilmazel, Bahar; Hu, Yanhui; Sigoillot, Frederic et al. (2014) Online GESS: prediction of miRNA-like off-target effects in large-scale RNAi screen data by seed region analysis. BMC Bioinformatics 15:192|
|Mazzola, Emanuele; Chipman, Jonathan; Cheng, Su-Chun et al. (2014) Recent BRCAPRO upgrades significantly improve calibration. Cancer Epidemiol Biomarkers Prev 23:1689-95|
|Zhao, Sihai Dave; Parmigiani, Giovanni; Huttenhower, Curtis et al. (2014) Más-o-menos: a simple sign averaging method for discrimination in genomic data analysis. Bioinformatics 30:3062-9|
|Parkhitko, Andrey A; Priolo, Carmen; Coloff, Jonathan L et al. (2014) Autophagy-dependent metabolic reprogramming sensitizes TSC2-deficient cells to the antimetabolite 6-aminonicotinamide. Mol Cancer Res 12:48-57|
|Cheng, Long; Desai, Jigar; Miranda, Carlos J et al. (2014) Human CFEOM1 mutations attenuate KIF21A autoinhibition and cause oculomotor axon stalling. Neuron 82:334-49|
|Akbay, Esra A; Moslehi, Javid; Christensen, Camilla L et al. (2014) D-2-hydroxyglutarate produced by mutant IDH2 causes cardiomyopathy and neurodegeneration in mice. Genes Dev 28:479-90|
|Brunner, Andrew M; Blonquist, Traci M; Sadrzadeh, Hossein et al. (2014) Population-based disparities in survival among patients with core-binding factor acute myeloid leukemia: a SEER database analysis. Leuk Res 38:773-80|
|Karamichos, D; Hutcheon, A E K; Rich, C B et al. (2014) In vitro model suggests oxidative stress involved in keratoconus disease. Sci Rep 4:4608|
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