MR imaging radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of MammaPrint, oncotype DX, and PAM50 gene assays

Hui Li, Yitan Zhu, Elizabeth S. Burnside, Karen Drukker, Katherine A. Hoadley, Cheng Fan, Suzanne D. Conzen, Gary J. Whitman, Elizabeth J. Sutton, Jose M. Net, Marie Ganott, Erich Huang, Elizabeth A. Morris, Charles M. Perou, Yuan Ji, Maryellen L. Giger

Research output: Contribution to journalReview article

135 Citations (Scopus)

Abstract

Purpose: To investigate relationships between computer-extracted breast magnetic resonance (MR) imaging phenotypes with multigene assays of MammaPrint, Oncotype DX, and PAM50 to assess the role of radiomics in evaluating the risk of breast cancer recurrence. Materials and Methods: Analysis was conducted on an institutional review board-approved retrospective data set of 84 deidentified, multi-institutional breast MR examinations from the National Cancer Institute Cancer Imaging Archive, along with clinical, histopathologic, and genomic data from The Cancer Genome Atlas. The data set of biopsy-proven invasive breast cancers included 74 (88%) ductal, eight (10%) lobular, and two (2%) mixed cancers. Of these, 73 (87%) were estrogen receptor positive, 67 (80%) were progesterone receptor positive, and 19 (23%) were human epidermal growth factor receptor 2 positive. For each case, computerized radiomics of the MR images yielded computer-extracted tumor phenotypes of size, shape, margin morphology, enhancement texture, and kinetic assessment. Regression and receiver operating characteristic analysis were conducted to assess the predictive ability of the MR radiomics features relative to the multigene assay classifications. Results: Multiple linear regression analyses demonstrated significant associations (R2 = 0.25-0.32, r = 0.5-0.56, P<.0001) between radiomics signatures and multigene assay recurrence scores. Important radiomics features included tumor size and enhancement texture, which indicated tumor heterogeneity. Use of radiomics in the task of distinguishing between good and poor prognosis yielded area under the receiver operating characteristic curve values of 0.88 (standard error, 0.05), 0.76 (standard error, 0.06), 0.68 (standard error, 0.08), and 0.55 (standard error, 0.09) for MammaPrint, Oncotype DX, PAM50 risk of relapse based on subtype, and PAM50 risk of relapse based on subtype and proliferation, respectively, with all but the latter showing statistical difference from chance. Conclusion: Quantitative breast MR imaging radiomics shows promise for image-based phenotyping in assessing the risk of breast cancer recurrence.

Original languageEnglish (US)
Pages (from-to)382-391
Number of pages10
JournalRadiology
Volume281
Issue number2
DOIs
StatePublished - Jan 1 2016
Externally publishedYes

Fingerprint

Magnetic Resonance Imaging
Breast Neoplasms
Recurrence
Research
Genes
Neoplasms
Breast
Magnetic Resonance Spectroscopy
ROC Curve
Phenotype
National Cancer Institute (U.S.)
Atlases
Research Ethics Committees
Progesterone Receptors
Estrogen Receptors
Linear Models
Regression Analysis
Genome
Biopsy
Datasets

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

Cite this

MR imaging radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of MammaPrint, oncotype DX, and PAM50 gene assays. / Li, Hui; Zhu, Yitan; Burnside, Elizabeth S.; Drukker, Karen; Hoadley, Katherine A.; Fan, Cheng; Conzen, Suzanne D.; Whitman, Gary J.; Sutton, Elizabeth J.; Net, Jose M.; Ganott, Marie; Huang, Erich; Morris, Elizabeth A.; Perou, Charles M.; Ji, Yuan; Giger, Maryellen L.

In: Radiology, Vol. 281, No. 2, 01.01.2016, p. 382-391.

Research output: Contribution to journalReview article

Li, H, Zhu, Y, Burnside, ES, Drukker, K, Hoadley, KA, Fan, C, Conzen, SD, Whitman, GJ, Sutton, EJ, Net, JM, Ganott, M, Huang, E, Morris, EA, Perou, CM, Ji, Y & Giger, ML 2016, 'MR imaging radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of MammaPrint, oncotype DX, and PAM50 gene assays', Radiology, vol. 281, no. 2, pp. 382-391. https://doi.org/10.1148/radiol.2016152110
Li, Hui ; Zhu, Yitan ; Burnside, Elizabeth S. ; Drukker, Karen ; Hoadley, Katherine A. ; Fan, Cheng ; Conzen, Suzanne D. ; Whitman, Gary J. ; Sutton, Elizabeth J. ; Net, Jose M. ; Ganott, Marie ; Huang, Erich ; Morris, Elizabeth A. ; Perou, Charles M. ; Ji, Yuan ; Giger, Maryellen L. / MR imaging radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of MammaPrint, oncotype DX, and PAM50 gene assays. In: Radiology. 2016 ; Vol. 281, No. 2. pp. 382-391.
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title = "MR imaging radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of MammaPrint, oncotype DX, and PAM50 gene assays",
abstract = "Purpose: To investigate relationships between computer-extracted breast magnetic resonance (MR) imaging phenotypes with multigene assays of MammaPrint, Oncotype DX, and PAM50 to assess the role of radiomics in evaluating the risk of breast cancer recurrence. Materials and Methods: Analysis was conducted on an institutional review board-approved retrospective data set of 84 deidentified, multi-institutional breast MR examinations from the National Cancer Institute Cancer Imaging Archive, along with clinical, histopathologic, and genomic data from The Cancer Genome Atlas. The data set of biopsy-proven invasive breast cancers included 74 (88{\%}) ductal, eight (10{\%}) lobular, and two (2{\%}) mixed cancers. Of these, 73 (87{\%}) were estrogen receptor positive, 67 (80{\%}) were progesterone receptor positive, and 19 (23{\%}) were human epidermal growth factor receptor 2 positive. For each case, computerized radiomics of the MR images yielded computer-extracted tumor phenotypes of size, shape, margin morphology, enhancement texture, and kinetic assessment. Regression and receiver operating characteristic analysis were conducted to assess the predictive ability of the MR radiomics features relative to the multigene assay classifications. Results: Multiple linear regression analyses demonstrated significant associations (R2 = 0.25-0.32, r = 0.5-0.56, P<.0001) between radiomics signatures and multigene assay recurrence scores. Important radiomics features included tumor size and enhancement texture, which indicated tumor heterogeneity. Use of radiomics in the task of distinguishing between good and poor prognosis yielded area under the receiver operating characteristic curve values of 0.88 (standard error, 0.05), 0.76 (standard error, 0.06), 0.68 (standard error, 0.08), and 0.55 (standard error, 0.09) for MammaPrint, Oncotype DX, PAM50 risk of relapse based on subtype, and PAM50 risk of relapse based on subtype and proliferation, respectively, with all but the latter showing statistical difference from chance. Conclusion: Quantitative breast MR imaging radiomics shows promise for image-based phenotyping in assessing the risk of breast cancer recurrence.",
author = "Hui Li and Yitan Zhu and Burnside, {Elizabeth S.} and Karen Drukker and Hoadley, {Katherine A.} and Cheng Fan and Conzen, {Suzanne D.} and Whitman, {Gary J.} and Sutton, {Elizabeth J.} and Net, {Jose M.} and Marie Ganott and Erich Huang and Morris, {Elizabeth A.} and Perou, {Charles M.} and Yuan Ji and Giger, {Maryellen L.}",
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T1 - MR imaging radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of MammaPrint, oncotype DX, and PAM50 gene assays

AU - Li, Hui

AU - Zhu, Yitan

AU - Burnside, Elizabeth S.

AU - Drukker, Karen

AU - Hoadley, Katherine A.

AU - Fan, Cheng

AU - Conzen, Suzanne D.

AU - Whitman, Gary J.

AU - Sutton, Elizabeth J.

AU - Net, Jose M.

AU - Ganott, Marie

AU - Huang, Erich

AU - Morris, Elizabeth A.

AU - Perou, Charles M.

AU - Ji, Yuan

AU - Giger, Maryellen L.

PY - 2016/1/1

Y1 - 2016/1/1

N2 - Purpose: To investigate relationships between computer-extracted breast magnetic resonance (MR) imaging phenotypes with multigene assays of MammaPrint, Oncotype DX, and PAM50 to assess the role of radiomics in evaluating the risk of breast cancer recurrence. Materials and Methods: Analysis was conducted on an institutional review board-approved retrospective data set of 84 deidentified, multi-institutional breast MR examinations from the National Cancer Institute Cancer Imaging Archive, along with clinical, histopathologic, and genomic data from The Cancer Genome Atlas. The data set of biopsy-proven invasive breast cancers included 74 (88%) ductal, eight (10%) lobular, and two (2%) mixed cancers. Of these, 73 (87%) were estrogen receptor positive, 67 (80%) were progesterone receptor positive, and 19 (23%) were human epidermal growth factor receptor 2 positive. For each case, computerized radiomics of the MR images yielded computer-extracted tumor phenotypes of size, shape, margin morphology, enhancement texture, and kinetic assessment. Regression and receiver operating characteristic analysis were conducted to assess the predictive ability of the MR radiomics features relative to the multigene assay classifications. Results: Multiple linear regression analyses demonstrated significant associations (R2 = 0.25-0.32, r = 0.5-0.56, P<.0001) between radiomics signatures and multigene assay recurrence scores. Important radiomics features included tumor size and enhancement texture, which indicated tumor heterogeneity. Use of radiomics in the task of distinguishing between good and poor prognosis yielded area under the receiver operating characteristic curve values of 0.88 (standard error, 0.05), 0.76 (standard error, 0.06), 0.68 (standard error, 0.08), and 0.55 (standard error, 0.09) for MammaPrint, Oncotype DX, PAM50 risk of relapse based on subtype, and PAM50 risk of relapse based on subtype and proliferation, respectively, with all but the latter showing statistical difference from chance. Conclusion: Quantitative breast MR imaging radiomics shows promise for image-based phenotyping in assessing the risk of breast cancer recurrence.

AB - Purpose: To investigate relationships between computer-extracted breast magnetic resonance (MR) imaging phenotypes with multigene assays of MammaPrint, Oncotype DX, and PAM50 to assess the role of radiomics in evaluating the risk of breast cancer recurrence. Materials and Methods: Analysis was conducted on an institutional review board-approved retrospective data set of 84 deidentified, multi-institutional breast MR examinations from the National Cancer Institute Cancer Imaging Archive, along with clinical, histopathologic, and genomic data from The Cancer Genome Atlas. The data set of biopsy-proven invasive breast cancers included 74 (88%) ductal, eight (10%) lobular, and two (2%) mixed cancers. Of these, 73 (87%) were estrogen receptor positive, 67 (80%) were progesterone receptor positive, and 19 (23%) were human epidermal growth factor receptor 2 positive. For each case, computerized radiomics of the MR images yielded computer-extracted tumor phenotypes of size, shape, margin morphology, enhancement texture, and kinetic assessment. Regression and receiver operating characteristic analysis were conducted to assess the predictive ability of the MR radiomics features relative to the multigene assay classifications. Results: Multiple linear regression analyses demonstrated significant associations (R2 = 0.25-0.32, r = 0.5-0.56, P<.0001) between radiomics signatures and multigene assay recurrence scores. Important radiomics features included tumor size and enhancement texture, which indicated tumor heterogeneity. Use of radiomics in the task of distinguishing between good and poor prognosis yielded area under the receiver operating characteristic curve values of 0.88 (standard error, 0.05), 0.76 (standard error, 0.06), 0.68 (standard error, 0.08), and 0.55 (standard error, 0.09) for MammaPrint, Oncotype DX, PAM50 risk of relapse based on subtype, and PAM50 risk of relapse based on subtype and proliferation, respectively, with all but the latter showing statistical difference from chance. Conclusion: Quantitative breast MR imaging radiomics shows promise for image-based phenotyping in assessing the risk of breast cancer recurrence.

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