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Intratumoral metabolic heterogeneity predicts invasive components in breast ductal carcinoma in situ

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Abstract

Objectives

This study investigated whether texture-based imaging parameters could identify invasive components of ductal carcinoma in situ (DCIS).

Methods

We enrolled 65 biopsy-confirmed DCIS patients (62 unilateral, 3 bilateral) who underwent 18 F-FDG PET, diffusion-weighted imaging (DWI), or breast-specific gamma imaging (BSGI). We measured SUV max and intratumoral metabolic heterogeneity by the area under the curve (AUC) of cumulative SUV histograms (CSH) on PET, tumour-to-normal ratio (TNR) and coefficient of variation (COV) as an index of heterogeneity on BSGI, minimum ADC (ADC min ) and ADC difference (ADC diff ) as an index of heterogeneity on DWI. After surgery, final pathology was categorized as pure-DCIS (DCIS-P), DCIS with microinvasion (DCIS-MI), or invasive ductal carcinoma (IDC). Clinicopathologic features of DCIS were correlated with final classification.

Results

Final pathology confirmed 44 DCIS-P, 14 DCIS-MI, and 10 IDC. The invasive component of DCIS was significantly correlated with higher SUV max (p = 0.017) and lower AUC-CSH (p < 0.001) on PET, higher TNR (p = 0.008) and COV (p = 0.035) on BSGI, lower ADC min (p = 0.016) and higher ADC diff (p = 0.009) on DWI, and larger pathologic size (p = 0.018). On multiple regression analysis, AUC-CSH was the only significant predictor of invasive components (p = 0.044).

Conclusions

The intratumoral metabolic heterogeneity of 18 F-FDG PET was the most important predictor of invasive components of DCIS.

Key Points

• Preoperative identification of invasion in DCIS is important for axillary nodal management

• Higher SUV max and lower AUC-CSH from FDG PET may indicate invasive components of DCIS

• Higher TNR and COV from BSGI may indicate invasive components of DCIS

• Lower ADC min and higher ADC diff from DWI may indicate invasive components of DCIS

• AUC-CSH, an index of metabolic heterogeneity, is an independent predictor for invasive components

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Abbreviations

AUC-CSH:

Area under curve of cumulative SUV histogram

BSGI:

Breast-specific gamma imaging

TNR:

Tumor-to-normal count ratio

COV:

Coefficient of variation

DCIS-P:

Pure-DCIS

DCIS-MI:

DCIS with microinvasion

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Acknowledgments

The scientific guarantor of this publication is Bom Sahn Kim. The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article. This research was supported by grants of National Research Foundation (2012R1A1A1012913 and 2012M3A9B6055379) of South Korea. No complex statistical methods were necessary for this paper. Institutional Review Board approval was obtained. Written informed consent was waived by the Institutional Review Board. Methodology: retrospective, observational, performed at one institution.

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Authors and Affiliations

  1. Department of Nuclear Medicine, Ewha Womans University School of Medicine, 911-1 Mok-Dong, Yangchun-Ku, Seoul, 158-710, Republic of Korea

    Hai-Jeon Yoon & Bom Sahn Kim

  2. Clinical Research Institute, Ewha Womans University, Seoul, Republic of Korea

    Yemi Kim & Bom Sahn Kim

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  1. Hai-Jeon Yoon
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  2. Yemi Kim
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Correspondence to Bom Sahn Kim.

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Yoon, HJ., Kim, Y. & Kim, B.S. Intratumoral metabolic heterogeneity predicts invasive components in breast ductal carcinoma in situ. Eur Radiol 25, 3648–3658 (2015). https://doi.org/10.1007/s00330-015-3761-9

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  • DOI: https://doi.org/10.1007/s00330-015-3761-9

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