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Last Updated: 09/21/17
Quantitative Imaging Network (QIN)

University of Chicago

Quantatitive Image Analysis for Assessing Response to Breast Cancer Therapy

Maryellen L. Giger, Ph.D.

Grant Number: U01 CA195564

The goal of this research is to develop quantitative image-based phenotypes for use in predicting response to therapy and ultimately aiding in patient management. There is a large variation in the clinical presentation of breast cancer in women, and it has been shown that in many instances, biological characteristics of the primary tumor correlates with outcome. Methods to assess such biological features for the prediction of outcome, however, may be invasive, expensive or not widely available. In addition, breast cancer tumors are heterogeneous, and current MRI biomarkers include only size and SER (signal enhancement ratio). Our hypothesis is that MRI phenotypes obtained through quantitative image analysis will prove useful as non-invasive biomarkers for the assessment of, and prediction of, the response of breast cancer to neoadjuvant therapy. The team proposes to validate such image-based biomarkers (phenotypic tumor signatures) using magnetic resonance (MR) images of breast tumors from the ACRIN 6657 clinical trial, which includes pathological response data. Specifically the team will (1) investigate the relationship of breast cancer therapy outcome and MR image-based tumor characteristics (phenotypes), and changes in these phenotypes over time, using two University of Chicago databases and the ACRIN 6657 I-SPY clinical trial dataset of breast cancer tumors from patients who have undergone neoadjuvant treatment, (2) and will develop and evaluate the MRI-derived ‘signatures’ of breast cancer tumors for the prediction of, and assessment of, response to therapy, and (3) will conduct preliminary, initial stratification and association of the MRI phenotypes with cancer subtype and other clinical/histopathological data. The team will build on their 25-year history of taking innovation to the clinical setting by extending our prior development, validation, and translation of quantitative image analyses for computer-aided diagnosis to the post-diagnosis, predictive component in order to assess response to therapy. The team’s research addresses the development & validation of algorithms using the existing ACRIN 6657 dataset with the goal of “improving the ability to measure the response of targeted tumors to therapy quantitatively”, and is aligned with the QIN U01 goals of including robustness investigations and multi-site trial data. This research is expected to yield a set of validated tumor phenotypes/signatures that will serve as quantitative tools for use in clinical studies/trials to predict and/or assess tumor response.

Reference listing:

  1. Schacht D, Drukker K, Pak I, Abe H, Giger ML. Using quantitative image analysis to classify axillary lymph nodes on breast MRI: A new application for the Z 0011 Era. European Journal of Radiology 84: 392-397, 2015. PMID: 25547328 PMCID: PMC4628184

  2. Weiss W, Medved M, Karczmar G, Giger ML: Preliminary assessment of dispersion versus absorption analysis of high spectral and spatial resolution magnetic resonance images in the diagnosis of breast cancer. J Medical Imaging 2(2), 024502 (Apr-Jun 2015). PMID: 26158106 PMCID: PMC4479021

  3. Guo W, Li H, Zhu Y, Lan L, Yang S, Drukker K, Morris E, Burnside E, Whitman G, Giger ML*, Ji Y*: Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data. J Medical Imaging 2(4), 041007 (Oct-Dec 2015). PMID: 26835491 PMCID: PMC4718467

  4. Burnside E, Drukker K, Li H, Bonaccio E, Zuley M, Ganott M, Net JM, Sutton E, Brandt K, Whitman G, Conzen S, Lan L, Ji Y, Zhu Y, Jaffe C, Huang E, Freymann J, Kirby J, Morris EA*, Giger ML*: Using computer-extracted image phenotypes from tumors on breast MRI to predict breast cancer pathologic stage. Cancer doi: 10.1002/cncr.29791, 2015. PMID: 26619259 PMCID: PMC4764425

  5. Zhu Y, Li H, Guo W, Drukker K, Lan L, Giger ML*, Ji Y*: Deciphering genomic underpinnings of quantitative MRI-based radiomic phenotypes of invasive breast carcinoma. Nature — Scientific Reports 5:17787. doi: 10.1038/srep17787, 2015. PMID: 26639025 PMCID: PMC4671006

  6. Li H, Zhu Y, Burnside ES, …. Perou CM, Ji Y*, Giger ML*: MRI radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of gene assays of MammaPrint, Oncotype DX, and PAM50. Radiology DOI:, 2016. PMID: 27144536 PMCID: PMC5069147

  7. Li H, Zhu Y, Burnside ES, …. Perou CM, Ji Y, Giger ML: Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA Dataset. npj Breast Cancer (2016) 2, 16012; doi:10.1038/npjbcancer.2016.12; published online 11 May 2016. PMID: 27853751 PMCID: PMC5108580

  8. Huynh B, Li H, Giger ML: Digital mammographic tumor classification using transfer learning from deep convolutional neural networks. J Medical Imaging 3(3), 034501, 2016. PMID: 27610399 PMCID: PMC4992049

  9. Li H, Weiss WA, Medved M, Abe H, Newstead GM, Karczmar GS, Giger ML: Breast density estimation from high spectral and spatial resolution MRI. J Med Imaging 2016 Oct;3(4):044507. doi: 10.1117/1.JMI.3.4.044507. Epub 2016 Dec 28, 2016. PMID: 28042590 PMCID: PMC5193119

  10. Medved M, Li H, Abe H, Sheth D, Newstead GM, Olopade OI, Giger ML, Karczmar GS: Fast bilateral breast coverage with high spectral and spatial resolution (HiSS) MRI at 3T. J Magn Reson Imaging, 2017 Mar 6. doi: 10.1002/jmri.25658. [Epub ahead of print], 2017. PMID: 28263425

  11. Antropova N, Huynh BQ, Giger ML: A deep fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets. Medical Physics (in press), 2017. PMID: 28681390

  12. Mendel K, Li H, Lan L, Cahill C, Rael V, Abe H, Giger ML: Quantitative texture analysis: robustness of radiomics across two digital mammography manufacturers' systems. J Med Imaging (in press), 2017.