Predicting response before initiation of neoadjuvant chemotherapy in breast cancer using new methods for the analysis of dynamic contrast enhanced MRI (DCE MRI) data

Joseph B. DeGrandchamp, Jennifer G. Whisenant, Lori R. Arlinghaus, V. G. Abramson, Thomas Yankeelov, Julio Cárdenas-Rodríguez

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

The pharmacokinetic parameters derived from dynamic contrast enhanced (DCE) MRI have shown promise as biomarkers for tumor response to therapy. However, standard methods of analyzing DCE MRI data (Tofts model) require high temporal resolution, high signal-to-noise ratio (SNR), and the Arterial Input Function (AIF). Such models produce reliable biomarkers of response only when a therapy has a large effect on the parameters. We recently reported a method that solves the limitations, the Linear Reference Region Model (LRRM). Similar to other reference region models, the LRRM needs no AIF. Additionally, the LRRM is more accurate and precise than standard methods at low SNR and slow temporal resolution, suggesting LRRM-derived biomarkers could be better predictors. Here, the LRRM, Non-linear Reference Region Model (NRRM), Linear Tofts model (LTM), and Non-linear Tofts Model (NLTM) were used to estimate the RKtrans between muscle and tumor (or the Ktrans for Tofts) and the tumor kep,TOI for 39 breast cancer patients who received neoadjuvant chemotherapy (NAC). These parameters and the receptor statuses of each patient were used to construct cross-validated predictive models to classify patients as complete pathological responders (pCR) or non-complete pathological responders (non-pCR) to NAC. Model performance was evaluated using area under the ROC curve (AUC). The AUC for receptor status alone was 0.62, while the best performance using predictors from the LRRM, NRRM, LTM, and NLTM were AUCs of 0.79, 0.55, 0.60, and 0.59 respectively. This suggests that the LRRM can be used to predict response to NAC in breast cancer.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2016
Subtitle of host publicationBiomedical Applications in Molecular, Structural, and Functional Imaging
EditorsBarjor Gimi, Andrzej Krol
PublisherSPIE
ISBN (Electronic)9781510600232
DOIs
StatePublished - Jan 1 2016
EventMedical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging - San Diego, United States
Duration: Mar 1 2016Mar 3 2016

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume9788
ISSN (Print)1605-7422

Other

OtherMedical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging
CountryUnited States
CitySan Diego
Period3/1/163/3/16

Fingerprint

Nonlinear Dynamics
Chemotherapy
chemotherapy
breast
Magnetic resonance imaging
cancer
Breast Neoplasms
Area Under Curve
Drug Therapy
Linear Models
Signal-To-Noise Ratio
ROC Curve
Biomarkers
Tumor Biomarkers
Neoplasms
Pharmacokinetics
biomarkers
Muscles
Tumors
transponders

Keywords

  • DCE
  • MRI
  • Pharmacokinetic
  • Predictive modeling

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

Cite this

DeGrandchamp, J. B., Whisenant, J. G., Arlinghaus, L. R., Abramson, V. G., Yankeelov, T., & Cárdenas-Rodríguez, J. (2016). Predicting response before initiation of neoadjuvant chemotherapy in breast cancer using new methods for the analysis of dynamic contrast enhanced MRI (DCE MRI) data. In B. Gimi, & A. Krol (Eds.), Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging [978811] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 9788). SPIE. https://doi.org/10.1117/12.2217008

Predicting response before initiation of neoadjuvant chemotherapy in breast cancer using new methods for the analysis of dynamic contrast enhanced MRI (DCE MRI) data. / DeGrandchamp, Joseph B.; Whisenant, Jennifer G.; Arlinghaus, Lori R.; Abramson, V. G.; Yankeelov, Thomas; Cárdenas-Rodríguez, Julio.

Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging. ed. / Barjor Gimi; Andrzej Krol. SPIE, 2016. 978811 (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 9788).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

DeGrandchamp, JB, Whisenant, JG, Arlinghaus, LR, Abramson, VG, Yankeelov, T & Cárdenas-Rodríguez, J 2016, Predicting response before initiation of neoadjuvant chemotherapy in breast cancer using new methods for the analysis of dynamic contrast enhanced MRI (DCE MRI) data. in B Gimi & A Krol (eds), Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging., 978811, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 9788, SPIE, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging, San Diego, United States, 3/1/16. https://doi.org/10.1117/12.2217008
DeGrandchamp JB, Whisenant JG, Arlinghaus LR, Abramson VG, Yankeelov T, Cárdenas-Rodríguez J. Predicting response before initiation of neoadjuvant chemotherapy in breast cancer using new methods for the analysis of dynamic contrast enhanced MRI (DCE MRI) data. In Gimi B, Krol A, editors, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging. SPIE. 2016. 978811. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE). https://doi.org/10.1117/12.2217008
DeGrandchamp, Joseph B. ; Whisenant, Jennifer G. ; Arlinghaus, Lori R. ; Abramson, V. G. ; Yankeelov, Thomas ; Cárdenas-Rodríguez, Julio. / Predicting response before initiation of neoadjuvant chemotherapy in breast cancer using new methods for the analysis of dynamic contrast enhanced MRI (DCE MRI) data. Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging. editor / Barjor Gimi ; Andrzej Krol. SPIE, 2016. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE).
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abstract = "The pharmacokinetic parameters derived from dynamic contrast enhanced (DCE) MRI have shown promise as biomarkers for tumor response to therapy. However, standard methods of analyzing DCE MRI data (Tofts model) require high temporal resolution, high signal-to-noise ratio (SNR), and the Arterial Input Function (AIF). Such models produce reliable biomarkers of response only when a therapy has a large effect on the parameters. We recently reported a method that solves the limitations, the Linear Reference Region Model (LRRM). Similar to other reference region models, the LRRM needs no AIF. Additionally, the LRRM is more accurate and precise than standard methods at low SNR and slow temporal resolution, suggesting LRRM-derived biomarkers could be better predictors. Here, the LRRM, Non-linear Reference Region Model (NRRM), Linear Tofts model (LTM), and Non-linear Tofts Model (NLTM) were used to estimate the RKtrans between muscle and tumor (or the Ktrans for Tofts) and the tumor kep,TOI for 39 breast cancer patients who received neoadjuvant chemotherapy (NAC). These parameters and the receptor statuses of each patient were used to construct cross-validated predictive models to classify patients as complete pathological responders (pCR) or non-complete pathological responders (non-pCR) to NAC. Model performance was evaluated using area under the ROC curve (AUC). The AUC for receptor status alone was 0.62, while the best performance using predictors from the LRRM, NRRM, LTM, and NLTM were AUCs of 0.79, 0.55, 0.60, and 0.59 respectively. This suggests that the LRRM can be used to predict response to NAC in breast cancer.",
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